text:
\r\n\tThis book is intended to provide a series of peer reviewed chapters that the guest editor believe will aid in increasing the quality of the research focus across the growing field of grain and seeds compound functionality research. Overall, the objective of this project is to serve as a reference book and as an excellent resource for students, researchers, and scientists interested and working in different functional aspects of grain and seed compounds, and particularly for the scientific community to encourage it to continue publishing their research findings on grain and seed and to provide basis for new research, and the area of sustainable crop production.
The development and implementation of vibrational spectroscopic methods such as near infrared (NIR) or Raman spectroscopy has increased significantly as the use of computer technology and chemometric methods has become more available. Considering the pharmaceutical domain, these methods have been extensively applied to quantify active pharmaceutical ingredients, excipients, or physical properties either as offline method for intermediate/final product characterization [1] or as real-time-monitoring methods implemented within blending [2], granulation [3], extrusion [4], tableting [5], coating [6], or freeze-drying processes [7].
The high-throughput analysis associated with vibrational spectroscopy favored its application to gain better process understanding, sustaining the pharmaceutical product development from a Quality by Design and Process Analytical Technology point of view [8], thus enhancing the opportunity to develop well-understood, well-controlled, and continuously optimized manufacturing processes and products [5]. The nondestructive nature of vibrational spectroscopic methods is of great importance in the quality evaluation of production batches, as they allow the testing of a high number of samples or the entire process, depending on the type of method. Using classical methods, such as chromatography the quality of a 1–3 million tablet batch is certified on 20–30 tablets, and many functional excipients that directly influence product performance are not quantified. These limitations are exceeded by implementing process analytical instruments, such as NIR or Raman [9].
Near infrared spectra are generated by molecular vibrations that imply a change of the dipole moment (─CH, ─NH, ─OH, ─SH) and are further complicated by overtones and combination bands that reduce the specificity of spectra. In case of Raman spectroscopy, the spectra are generated by inelastic scattering, caused by chemical groups that undergo a change in polarizability when excited with an incident light beam. These differences in molecular contribution to the generation of spectral data make the two methods complementary [10].
NIR and Raman spectra are considered a source of multivariate data, as they contain information related to physical and chemical properties of the analyzed sample. Thus, the application of chemometric methods for extracting predictive spectral variability and reducing orthogonal sources of variation is indispensable [11]. The sensitivity to both physical and chemical properties of the sample can be considered an advantage, if the analyst wants to predict several quality attributes of a drug product, such as content uniformity and crystalline structure. However, if only active content characterization is desired and polymorphism is not considered to be a critical attribute, but it is present, the calibration phase still has to include both aspects to ensure the accuracy of prediction for active content. The main disadvantage of vibrational spectroscopic methods relates to the need of an extensive calibration set that needs to include chemical, physical, instrumental, and environmental variability that is expected in future prediction sets and analysis conditions.
Vibrational spectroscopy is well suited to the means of multivariate calibration, as each observation is characterized by analytical signal/absorbance recorded at multiple wavelengths. Using multiple predictor variables instead of one wavelength overcomes some univariate calibration problems related to selectivity, precision, and diagnosis, resulting in a more robust calibration model [12].
The milestone in the development of a vibrational spectroscopic method is the chemometric model that is able to accurately predict the sample properties considered in calibration phase. Before building a model, there are several key steps that need to be considered, as they directly influence its quality and predictive performance. The first step would be the specification of responses along with variation ranges, followed by the selection of instrumental method and configuration, building a representative calibration set, recording of spectral data, data pre-processing, and developing the multivariate regression model that is further tested using external prediction sets. Each step plays an important role; however, a well-built calibration set is the best starting point to a well-performing model, as it is the source of spectral data that is used for further processing and model development.
In the calibration set development phase, the analyst has to incorporate the expected variability of future prediction sets, to ensure the representativeness of the samples. This expected variability is given first by the quality attributes that are to be predicted, for example, the concentration ranges of important formulation constituents. Frequently, this is not enough for a robust model, and other type of variability has to be included in the calibration process, such as process-induced variability or environmental variability. Production samples contain process-induced variability; however, constructing a calibration set solely on production samples is not appropriate as the factor ranges do not cover the required interval. A first option would be to prepare pilot-plant samples reproducing full-scale conditions. As the number of responses increases, the calibration set becomes larger and quickly becomes unfeasible due to the high costs of production. The second option would be to prepare laboratory samples in which the concentration ranges of desired components are varied simultaneously within appropriate ranges to avoid correlations [13].
The calibration set development strategy applied for the development of quantitative spectroscopic methods depends on the sample complexity (the number of responses and the number of interfering factors included in the calibration) and on the type of method that is developed, here considering off-line or real-time-monitoring methods. In the following section, a description of calibration opportunities will be provided starting from the simplest cases and heading toward more complex situations.
The most simple calibration situations include a low number of responses, one or two, here considering a chemical and a physical property of a sample. In this case, the calibration set development strategy simply resumes to the preparation of a sample with different levels of the investigated property. Mbinze et al. developed quantitative NIR and Raman methods for the assay of antimalarial oral drops and prepared a calibration set by diluting a stock solution of quinine to obtain three concentration levels. For each level, three series with three replicates were prepared resulting in a calibration set with 27 samples [14]. Tomuta et al. used NIR to characterize meloxicam tablets by evaluating content uniformity, tablet hardness, disintegration, and friability. For content uniformity assay, the calibration set included active ingredient concentration range (five levels), days (three), and batches (three) as a source of variation, whereas in the case of physical properties assay the middle formulation was compressed on seven levels of compression force, ranging from 5 to 42 kN. Compressing the powder mixture with different forces yielded tablets with different hardness, disintegration, and friability. Different settings of a one-process factor were enough to induce variability in physical properties of the samples [15]. In a similar study, Tomuta et al. developed NIR method for physico-chemical characterization of low active content indapamide tablets (2%, w/w) [1]. Virtanen et al. evaluated the crushing strength of theophylline tablets through Raman spectroscopy by considering both a process factor and a formulation factor to generate variability in tablet surface roughness. In this case, the tablets were prepared considering two particle sizes of theophylline, as raw material for the granulation phase, followed by mixing with lubricants and by compressing each granulate on five different compression forces [16].
The impact of polymorphism is a well-recognized phenomenon in the pharmaceutical industry, as the differences in crystalline structure of the same active ingredient generate different physical properties that get reflected in the quality of the final medicinal product. Croker et al. developed NIR and Raman methods to quantify FII and FIII of nootropic drug-piracetam from binary mixtures using a calibration set of 15 formulations with FII ranging from 0 to 100% [17].
Gómez et al. calibrated a Raman method for the content uniformity control of low-dosebreak-scored acenocumarol tablets by under and overdosing the powdered commercial medicinal product, by adding either lactose or the active pharmaceutical ingredient to the mixture. Two commercial products with different content uniformity were considered and the two calibration sets included 7 samples in the range of 1–3% (w/w) and 12 samples in the range of 0.35–1.50% [18]. Creating calibration sets by under-overdosing samples can result in correlated concentrations between API and excipients [19]. Collinearity between concentrations leads to spurious predictions by attributing changes to the correlated formulation component instead of the real contributor [20].
Changing the production scale generates samples that incorporate different types of variability from the primary conditions through which the calibration set was prepared. As laboratory-prepared samples lack manufacturing variability, the accuracy of prediction may be affected for production prediction sets. This limitation has been exceeded by extending the calibration set with production samples [13], adjusting the sampling strategy, pre-conditioning the calibration set to future expected environmental conditions [21], or by mathematically adding process variability to laboratory samples [20].
Blanco et al. developed NIR methods to control individual steps of paracetamol tablet manufacturing, resuming to an intermediate granulation step and tableting. Prior to building a calibration model, both laboratory-prepared samples and industrial production samples were taken into account to evaluate the eventual spectral differences. In case of the granule-active content assay, the calibration set was built solely on laboratory-prepared samples, whereas in the case of tablet assay the differences between laboratory and production samples made the calibration set include both, in order to ensure representativeness. For granule particle size characterization, samples collected over a period of 2 years ensured the presence of future expected variability in prediction set [22].
Blanco et al. used NIR to characterize mirtazapine tablets in terms of content uniformity and tablet hardness. For active ingredient content, the calibration set included production tablets from 20 batches and 34 laboratory-prepared samples, whereas for tablet hardness the laboratory samples were compacted in the range of 300–740 MPa. Including production samples for both responses reduced the systematic errors and gave better predictions [13]. By adding spectra from different manufacturing scales to the calibration set, the spectral variability becomes more representative, an important aspect for prediction accuracy. As the number of manufacturing samples is lower compared to the initial calibration set, proper weighting is necessary to avoid the dominating tendency of the larger dataset. To this regard, Farrel et al. applied Tikhonov regularization as a multi-criterion-based weighting selection method to augment the performance of NIR models regarding their ability to predict production scale products [23].
Blanco et al. proposed a method to incorporate physical variability that originates from production into the calibration. The concept relies on calculating a process spectrum, which added to the laboratory sample spectra incorporates process-related physical changes. The process spectrum represents the difference between the laboratory sample spectrum and the intermediate/final product spectra of an identical composition prepared on a different scale. The variability given by the process spectra can be further increased by multiplying the data with different coefficients [20, 24].
In situations where solid-state transformations occur within the manufacturing process, it is frequently desired to construct the calibration set with components obtained through the same method to have more representative formulations. Netchacovitch et al. used Raman spectroscopy to determine crystalline itraconazole in amorphous solid dispersions prepared by hot-melt extrusion. Calibration set included three levels of concentration and was built by using crystalline API powder, six batches of grinded extrudates with amorphous API, and placebo-grinded extrudate [25].
Pan et al. calibrated NIR method for the quantification of low-level Irbesartan Form B from pharmaceutical tablets. Form B is known to have a limited solubility and is formed from Form A via a solution-mediated process. To incorporate physical variability into the calibration set, the sample preparation procedure supposed the use of specifications similar to the manufacturing process. The robustness of the method to process induced physical variability, the effect of tablet hardness, granule size, and atmospheric humidity was evaluated. It was demonstrated that the prediction accuracy was influenced only by relative humidity, generating a positive bias in samples stored at 50%RH. Therefore, the entire calibration and validation was reconsidered by pre-conditioning the samples at 25°C and 50%RH for 20 h, prior to recording the spectra and building the model. This way, the robustness of the method was increased to future expected variations in environmental conditions [21].
As the number of factors increases, the calibration set becomes more complex and different strategies have to be applied to avoid correlated responses. If two formulation components C1 and C2 are correlated, a change in the concentration of C1 can be spuriously predicted as a change in C2. In DoE, factors are varied simultaneously in a systematic manner, providing orthogonality, an essential condition for estimating regression coefficients [26]. There are several design types that can be used for calibration purposes, starting from the classic full factorials down to central composite, mixture, or D-optimal designs. Considering more complex formulations, NIR spectroscopy has been applied to determine the amount of amoxicillin in the presence of seven other excipients. By applying a three-factor (API, saccharose, and other excipients) experimental design, the concentration of factors was varied orthogonally [27]. Ferreira et al. used a calibration set prepared according to a DoE with three factors: hydrochlorothyazide, cellulose, and other excipients to train a NIR method for the quantification of the active ingredient in pharmaceutical samples [28].
Li et al. calibrated Raman method to quantify active ingredient content considering the presence of different sources of variability: degradation compound, relative humidity, change of scales, and compression force. Laboratory samples were prepared based on a 32 full-factorial design where the active ingredient ranged between 80 and 120%, from which a subset of samples were spiked with the degradation product, added in two molar ratios. Each powder mixture was compacted at 8 and 30 kN in laboratory scale and three design points were compacted at manufacturing scale [29]. Casian et al. developed NIR and Raman methods for the quantification of two APIs found in significantly different concentrations from immediate release tablets. The calibration set was built on a full-factorial design with two factors and five levels with a total of 25 formulations [10]. The use of full-factorial designs is feasible with two factors if five levels of variation are used [52]. Adding one more factor will generate 125 experimental runs that are impractical [26, 53].
Netchacovitch et al. calibrated a Raman method to quantify low-level polymorphic impurities in a pharmaceutical formulation through a 12-run central-composite experimental design [25]. Central composite designs are extensions of the two-level full-factorial designs that are built by adding symmetrically axial points. Dependent on the position of axial points, factors can be varied on three levels (central composite face-centered design) or five levels (central composite circumscribed) [26].
Short et al. used NIR to evaluate relative density and crushing strength of four component tablets. Compared to other studies, where only the compaction pressure was considered as a factor to induce variability in the investigated response, in this case formulation composition was varied also. The calibration set consisted of 29 formulations (mixture design) with each formulation being compressed at different pressures [30]. Lyndgaard et al. developed a Raman method to quantify paracetamol content from tablets through blisters. The calibration set included 18 formulations, selected on the basis of a ternary mixture design (paracetamol, starch, and sucrose) with each factor being varied on six levels [31]. Igne et al. evaluated the effect of API physical form, excipient particle size, different manufacturer, and changes in environmental conditions on the performance of a NIR model. The calibration samples were prepared according to a 29-run quaternary mixture design with every formulation being compressed at two of five different forces. Only changes in the particle size of lactose produced biased predictions in both ambient and chamber conditions. The authors tested variable-selection methods to increase method robustness to raw material variability [32].
Griffen et al. used Raman spectroscopy to quantify all tablet constituents, three active ingredients and two excipients. In this case, the calibration set was built on a first-order (linear) five-level, five-factor mixture design that uniformly covered the concentration ranges of the components. The concentration of individual components ranged from 1 to 85% (w/w) [33]. Mixture designs are well suited for formulation application, where the sum of all ingredients adds up to 100% and where factors cannot be manipulated independently one from another. Porfire et al. used a D-optimal design with three variables and five levels to build a calibration set with 63 formulations with the purpose of quantifying encapsulated simvastatin and two functional excipients
A D-optimal design is frequently applied for a high number of factors as it gives a lower number of runs compared to factorial designs. The D-letter originates from its criterion of selecting the best subset of factor combinations from a pool of theoretically possible combinations, which relies on maximizing the X'X matrix Determinant [26]. In another study, Heinz et al. trained NIR and Raman to quantify ternary mixtures of alpha, gamma, and amorphous forms of indomethacin from ternary mixtures using a 13-sample calibration set built on a cubic model experimental design [35]. Lin et al. developed an at-line blend uniformity NIR method for simultaneous quantification of four active ingredients with structural similarity, found in different concentrations. Calibration was built on six formulations, where five factors (four APIs and one diluent) were varied on six levels while avoiding correlations. The performance of the model was improved by adding a set of spectral data from a different production scale [43].
When DoE is used, correlations are significantly reduced dependent on the type of design, number of factors, and experimental runs. However, an increased number of factors will require a high number of experimental runs to avoid collinearity, which rapidly increases the costs. Several papers have addressed the question of how many samples are needed to ensure a robust calibration [19]. The fact that models with similar performance were developed on a reduced design compared to its full-factorial counterpart suggests the presence of redundant information in full-factorial designs [36].
Saraguca proposed a method that relies on building the model on a limited number of samples and uses the remaining formulations to test the predictive performance in terms of RMSECV and RMSEP. In the following steps, the calibration set was extended by transferring one formulation at a time from the test set until the calculated cross-validation and prediction errors stabilized. The sample selection procedure focused on maximizing the concentration variability of all components [19].
Alam et al. proposed a method for calibration set development in spectral space instead of concentration space. Orthogonality in spectral response will yield a better estimation of coefficients with a minimum number of samples, while orthogonality in concentration space will not necessarily translate into spectral orthogonality, as the contribution of each component to the sample spectrum is different. The method is based on decomposing the pure component spectra of a formulation into orthogonal directions (scores), which will be varied around a model tablet score through DoE. The model tablet score represents the score of the spectra recorded on a target formulation projected onto the orthonormal basis vector of the pure components spectra. After designing the spectral space calibration set, the composition of each spectra is retrieved by mathematical means [37].
The application of vibrational spectroscopy for in-line monitoring implies the use of fiber optic probes mounted at the interface of the process itself to acquire spectral data with a defined rate. The simplest way to calibrate an in-line method is to acquire real-time spectra through the entire process length along with collecting samples at regular intervals. The response values obtained through reference methods are correlated with the spectral data, considering the process time as a link between the two [38, 39, 40]. More extensive calibrations also evaluate the effect of sample presentation, changing process, and formulation parameters, to challenge the robustness of the methods.
For coating application, the calibration strategy relies on the linear variation of spectral response as the contribution of the coating material increases and the tablet core contribution decreases [41]. Moes et al. developed quantitative NIR method using three batches of tablets by varying the tablet core weight (240–200–160 g) and the amount of coating suspension resulting in different coating thicknesses [42]. Möltgen et al. used five full-scale experimental runs to develop a quantitative NIR method (one run) and to evaluate the effect of changing exhaust air temperature and spray rate (two runs) and the effect of tablet density and flow motion in the coater (two runs). For quantitative calibration, samples were collected through the entire process and analyzed using reference methods [6]. For the quantification of coating thickness by means of Raman spectroscopy, Kauffman et al. calibrated the method by considering film thickness and film composition variables. Tablets were coated on five levels ranging 0.5–6% weight gain by varying their residence time in the coater. As for film composition, three different TiO2 levels were evaluated due to the strong Raman signal of this component offering the potential for an indirect measure [41]. In the case of thin coatings, the generation of a calibration set can become a difficult task and can become limited due to the lack of reference methods. In this situation, an alternative to classical regression methods would be the Science-Based Calibration (SBC) approach, which allows the calibration without a reference method by separating spectral variability into orthogonal (covariance matrix) and predictive parts (related to the coating). Möltgen et al. applied SBC to develop quantitative NIR method for in-line evaluation of thin hydroxypropyl methylcellulose (HPMC) coatings through four experimental runs. For calibration, the pure HPMC spectrum was used as the coating response spectrum and the covariance matrix included hardware, core, water, and process-related noise. The method developed without reference samples predicted accurately coating thickness values in the range of 8–28 μm demonstrating the value of SBC [43].
In order to predict granule moisture content in a six-segmented fluid bed dryer through NIR spectroscopy, a calibration set of 20 experiments was applied. Granules were prepared with five moisture levels by varying the drying air temperature and drying time. Each moisture level had four replicates prepared on two different days [3].
Clavaud et al. developed a global regression model for moisture content estimation from freeze-dried medicine. As expected, the calibration set was extensive, including three types of active ingredient with different concentrations, different vial diameters, and excipient amounts. To include intra- and inter-product variability, 5 batches and 100 samples were used for each product [44]. Martinez et al. calibrated NIR method for in-line quantification of two active ingredients in a batch-blending process by investigating the influence of sample presentation. With regard to this, the high-loading API was used either in the form of a cohesive powder or in a granular form prepared by melt-extrusion. The observed spectral differences were resumed to the polymer wavelength absorption band that coincided with the water region. The offline calibration of the method was built on 13 samples which included both forms of the high-loading API [2].
Wahl et al. evaluated in-line the content uniformity of ternary mixtures with an NIR mounted on the feed frame of a tablet press. For calibration, the active ingredient and two excipients concentrations were varied through eight experiments selected by means of a D-optimal design and two extra runs added to ensure equidistant steps in the content of each component. Spectral data were recorded in a dynamic acquisition mode, simulating real conditions [5].
Karande et al. developed NIR method for real-time monitoring of tableting based on a 105-sample calibration set generated through a simplex lattice design with four factors (chlorpheniramine maleate, lactose, microcrystalline cellulose, and magnesium stearate). Prior to building the calibration, the effect of sampling was evaluated by recording NIR spectra in both static and dynamic conditions. The differences between measurements revealed the importance of ensuring similar sampling conditions for calibration as for actual real-time monitoring [9]. For another application, Karande et al. evaluated the effect of different spectral-sampling strategies on the performance of an NIR model, to accurately predict blend components in quaternary mixtures. Calibration samples (24 formulations-D-optimal mixture design) were recorded in three ways: laboratory mixing and static spectral acquisition; IBC (intermediate bulk container) mixing and static spectral acquisition; IBC mixing and dynamic spectral acquisition. Dynamic sampling yielded the best calibration model with highest accuracy, demonstrating the importance of selecting similar sampling conditions to the actual testing [45].
Based on the presented examples found in literature, the most frequently applied methods to design a calibration set were as follows:
One chemical/physical property: formulations with three to five levels of variation for the response that span the desired range of concentration/physical property.
One chemical and one physical property: formulations with three to five levels of variation for the chemical response and for the physical property calibration are considered only for target formulation (five levels).
Two chemical/physical properties: any type of DoE (full-factorial, central composite, mixture design, D-optimal) to avoid collinearity and spurious predictions.
Three chemical/physical properties: simple lattice mixture designs or D-optimal designs.
In-line methods: models built by correlating sampled product properties with in-line collected spectra. Most rigorous studies also investigated the effect of process parameters on the NIR spectra.
The dependence of the NIR spectra on the sample's chemical and physical properties caused by absorption and scatter effects can be an advantage of this type of spectroscopy, but at the same time, the scatter effects caused by sample variations or even by environmental phenomena can create a series of analytical problems. In such cases, each type of interferences has to be considered in the calibration model development. In the following section, the importance of chemical, physical, and environmental interferences will be described, providing insights on specific spectral variations produced by each category and highlighting how to handle them in order to increase model robustness [1, 2].
Generally, a quality NIR analysis should provide a model that manages a correct interconnection of the spectral variables with the samples properties of interest. At the same time, an ideal calibration model will not react to instrument variation, environmental changes, background interferences, and will be mostly focused on the information of interest. Chemometrics is the science that enables the extraction of relevant information, as well as the reduction of unrelated information as well as interfering parameters.
Spectral interferences resulting from variable physico-chemical sample properties (e.g., particle size variation and moisture content) or instrumental effects (e.g., path-length variation, light scattering, and random noise) can be reduced, eliminated, or standardized by using spectral pretreatments, prior to the multivariate data analysis [3]. Since the correct selection of spectral pretreatment can significantly improve the reliability of the model, this topic will be discussed in the following paragraphs. The most common pre-processing techniques can be divided into two groups: pretreatments for spectral normalization and for smoothing/differentiation. The first group achieves spectral normalization through scatter-correction methods. Scatter effects are common for all spectroscopic techniques and the phenomenon appears mostly because of the physical variabilities between samples or path-length variations. Two of those pre-processing concepts are standard normal variate (SNV) and multiplicative scatter correction (MSC) which also normalize the baseline shifts of different samples [4, 5]. The second set of pre-processing methods has the capacity to reduce or remove the noise by smoothing and differentiating the spectral values. The most common spectral derivatives are based on the Savitzky-Golay (SG) [6] and the Norris-Williams algorithms [7].
In most cases, in order to obtain best results, there is the need to apply both types of pretreatment techniques one after the other. Peeters et al. tested both types of pre-processings not only to reduce light scattering effects but also to minimize peak shifts of Raman and NIR spectra. They applied SNV, MSC, and first and second derivatives obtained by calculating 15-point quadratic Savitzky-Golay filters, in order to develop a method for the off-line prediction of tablet properties [8]. Sylvester et al. developed an in-line NIR-monitoring method for a freeze-drying process using the SNV pre-processing in order to remove multiplicative interferences caused by scatter and particle size variations and the first Savitzky-Golay derivative to reduce baseline shifts and to improve the spectral resolution [9]. The successful development of a real-time method for monitoring continuous powder flow from a tableting machine feeder was described by Alam et al. Savitzky-Golay derivatization was first applied for smoothing, followed by SNV for scatter correction [10]. Environmental interferences can be caused by sample, instrument, or even laboratory variations; this type of interferences causes misalignments or shifts of the spectra and is commonly overcome by applying alignment/warping techniques to the data [3]. Those methods stretch or compress the signal in order to match it in the best way possible with a given reference spectra [11, 12].
All pre-processing methods have the purpose to reduce the undesirable variability and interferences from the data, but there is always a risk of choosing an inappropriate type or applying a severe pre-processing that would also remove valuable information. Because of this, choosing the correct technique is one of the most important steps in data pre-processing and model development.
A last useful solution to deal with problems caused by interferences is wavelength selection method. The model development can be done based on the specific spectral domain that contains the information of interest. In order to select the domain of interest or to eliminate irrelevant wavelength domains, principal component analysis (PCA) can be performed. Prior to the PCA, the collected spectra should be pre-processed and column centered, then the analysis can be performed on the data matrix. Finally, the variables should be selected according to high peak loadings obtained for all relevant principal components (PCs), and the position of the resulting features should be compared with the original spectrum to validate the selection.
During the development of a multivariate calibration model, systematic variation such as baseline shifts and scatter effects, not relevant for the prediction of the response variables (Y), is present in predictor variables (X). Pre-processing methods are used in order to remove the systematic variation not related to the Y-matrix, which might impair the interpretation or predictive ability of the developed model.
The main goals of data-pre-processing are the following:
improvement of the robustness and accuracy of subsequent analyses;
improved interpretability: raw data are transformed into a format that will be better understandable by both humans and machines;
detection and removal of outliers and trends; and
reduction of the dimensionality of the data mining task and removal of irrelevant and redundant information [46].
The methods generally used for data pre-processing are divided into two categories. The first consists of classical pre-processing methods, used for normalization, smoothing, and differentiation. The second is represented by methods for variable selection and dimensionality reduction [47]. Among these methods, the most appropriate has to be chosen, such as to only remove unwanted variation, without excluding or altering chemically relevant information [48].
When used in an inappropriate way, pre-processing may introduce artifacts or cause loss of information. Thus, the purpose of the analysis is important for the selection of the most appropriate pre-processing method, because scattering is disruptive for compound identification and quantitation, but is useful to study the physical properties of the sample. As a consequence, the best pre-processing method, ensuring a correct data analysis and robust results, has to be chosen by testing and comparing the results of different methods [48].
In many analytical methods, the variables measured for a given sample are increased or decreased from their true value by a multiplicative factor, which is called the scaling or gain effect. In spectroscopic methods, the scaling effect arises from path-length effects, scattering effects, source or detector variations, so the relative value of variables should be used during multivariate modeling rather than the absolute measured value. The sample normalization is one of the most important pre-processing methods, which is applied in an attempt to correct for multiplicative scaling effects, the shifts and the trends in baseline and curvilinearity, by identifying some aspect for each sample which should be essentially constant from one sample to the next, and correcting the scaling of all variables based on this characteristic [48].
Normalization methods can be subdivided into two main groups: simple normalization methods (min-max normalization, one-norm, vector normalization, standard normal variate), requiring only the information from the spectrum to be normalized, and normalization methods requiring the presence of collective spectral data matrices or of reference spectra (multiplicative scatter correction and extended multiplicative signal correction (EMSC) [46]. Among these, the most used scattering correction algorithms include the SNV and MSC. The two pretreatments give similar results, being considered exchangeable, but the results obtained through both algorithms are compared usually, since they may be different [49]. SNV was proposed to reduce multiplicative effects of scattering, particle size, and multicollinearity changes over the NIR spectra. This approach starts with mean centering and consists of dividing mean-centered spectra by the standard deviation over the spectral intensities [50]. SNV normalizes each spectrum returning a mean of 0 and a variance of 1 spectra dataset [48]. The disadvantage is the assumption that multiplicative effects are uniform over the whole spectral range, so artifacts may be introduced by this transformation.
The de-trend method is another approach to correct for baseline shift, which removes the baseline curvature by expressing it as a quadratic function of the wavelengths. The modeled baseline is subtracted from the spectrum, so de-trend can be used after SNV to circumvent any curvilinear trend, where the baseline drift is a function of wavelength [50]. The MSC pretreatment performs a linear regression of each spectrum on a reference spectrum, which is usually the mean of all available spectra, for example, the average spectra of the calibration set, or a generic reference spectrum can also be applied [49].
The smoothing algorithms are used in order to correct the spectral noise, while differentiation is used to enhance spectral resolution and to eliminate background absorption. The most common ways to achieve smoothing are the use of noise filters for de-noising and smoothing and Savitzky-Golay smoothing/derivative filters for smoothing/resolution enhancement. Noise filters are specific low-pass filters which can be used to reduce random noise. Their drawback is that the signal-to-noise ratio is increased at the expenses of distorting the signal. The most popular smoothing filters are the zeroth-order SG-smoothing/derivative filter, the binomial filter, and the moving average filter [46].
Derivatives are used for their capability to remove both additive and multiplicative effects in the spectra. The first derivative removes only the baseline; the second derivative removes both baseline and linear trend. The first derivative is estimated as the difference between two subsequent spectral measurement points, while the second-order derivative is estimated as the difference between two successive points of the first-order derivative spectra [51]. The most popular derivation method is SG algorithm, proposed by Golay and Savitzky in 1962 [52]. The method has the advantage that computation of the derivatives and smoothing are carried out in a single step. The algorithm used in this method is based on fitting a polynomial in a symmetric window on the raw data, in order to find the derivative at the center point. The parameters of the polynomial are calculated and the derivative of this function is found, this value being used as the derivative estimate for this center point. The same operation is subsequently applied to all points in the spectra. Two decisions are important to be made in this algorithm, i.e., the window width (width of the subset of the data) and the fitted polynomial order. The highest derivative that can be determined depends on the degree of the polynomial used during the fitting [51].
These methods rely on reducing the dimension of the predictor space spanned by a number of variables or wavelengths, in order to find the subspace mainly containing variations related to the response matrix. The orthogonal projection and the variable-selection methods are in this group. Orthogonal signal correction (OSC) and its modified version direct orthogonal signal correction ((D)OSC) are the most common among this group, developed to remove systematic variation in the descriptor matrix, that is not correlated to the response matrix. In other words, the pre-processing is performed in such a way that the removed parts are orthogonal (not linearly related) to the response matrix [53, 54]. The method has the advantage of correcting at once multiple artifacts.
An alternative OSC algorithm was developed by Trygg and Wold and is called orthogonal projection to latent structures (OPLS). The objective of OPLS is the same as of OSC, but the approach is different, i.e., the OPLS method analyzes the variation explained in each PLS component. The non-correlated systematic variation in descriptor matrix is removed, making interpretation of the resulting PLS model easier, and the non-correlated variation can be analyzed further [55].
Variable-selection techniques consist of selecting particular variables related to the response, instead of removing the interference modeled as a spectrum, the aim being to identify a subset of wavelengths that produces the smallest possible error [56]. Selecting the most correlated wavelengths may lead to better performance in PLS and PCR, but, on the other hand, selection of the most correlated wavelengths may eliminate those that correct for the influence of interfering compounds or factors [56].
In practical applications, combinations of pre-processing methods are usually employed in search for the best algorithm, involving more than one pre-processing step. According to Rinnan et al., several rules may serve as guidelines: scatter correction (except of normalization) should always be performed prior to differentiation; normalization can be used at both ends of the correction, but usually is easier to be assessed if it is done prior to any other strategy; MSC gives a smaller baseline correction than SNV with subsequent de-trending; it is not recommended to perform de-trending followed by SNV [51].
The ideal pre-processing strategy should only remove artifacts present in the data, without introducing any unwanted artifacts or variability in the data. When physical properties, that is, tablets' crushing strength, are evaluated through vibrational spectroscopy, typical pre-processing methods such as SNV, MSC, and the derivatives cannot be used, because they lose the baseline-shifting information, which is relevant for the physical properties. The data in this case should be modeled as such or after normalization [16]. Three approaches are described in the literature, for the selection of the most appropriate strategy: the trial-and-error approach; visual inspection and the use of data-quality parameters [57].
In the trial-and-error approach, all pre-processing methods are applied to the data and the pre-processed data are used as an input to a calibration model, which is further used to assess the quality of the pre-processing strategy by an internal measure, such as RMSEP or RMSECV [57]. For example, Karande et al. chose among various pre-processing methods through comparing the figures of merit (explained variance, R2, RMSEC, and RMSECV) of the developed partial least-squares (PLS1) regression models, for the quantification of micronized drug and excipients in tablets by NIR spectroscopy. The raw calibration spectra were pretreated with SNV followed by first derivative and SNV followed by second derivative pre-processing. All models were developed using the entire spectral range or narrow spectral ranges. The best performance of the calibration method (highest explained variance, lowest RMSEC and RMSECV) was obtained using the whole spectral range, pretreated with SNV followed by first derivative spectral pre-processing [9] . The same approach has been used by Porfire et al. in the attempt to select the best pretreatment method in the development of calibration models for prediction of chemical composition and crushing strength of sustained-release tablets with indapamide. PLS regression was performed for non-processed spectra as well as for spectra treated by various pre-processing methods (i.e., FD, SD, SNV, MSC, FD + SNV, FD + MSC), and the most suitable pretreatment algorithm was chosen based on the results obtained for PLS model validation through cross-validation, i.e., based on its RMSECV and bias [58].
In visual inspection method, the effect of pre-processing is assessed before a model is constructed. Thus, because artifacts have been removed during pre-processing, samples should show more spectral overlap after pre-processing in visual inspection, and differences between groups of samples should be more pronounced. However, as visual inspection may be very difficult and not objective, the data are not usually inspected in "spectral mode" but in a lower dimensional space, obtained usually through principal component analysis [57]. PCA reduces the dimensionality of the problem by generating linear combinations of the original variables returning new "latent" variables. Each original variable is weighted by a loading representing the importance of the considered variable on the variance of the data. The variability of the data is expressed by new dimensions called principal components, and the projection of a pixel onto the PCs is called its score. The result of PCA is the decomposition of the pre-processed matrix in a score matrix and a loading matrix [48]. PCA is used for data overview, for example, for detecting outliers, groups, and trends among observations, for evaluating relationships among variables, and between observations and variables. In PCA, data in the matrix X are transferred into a new coordinate system defined by principal components. The direction in variable space occupied by the most varying data points will define the location of the first PC, and the second PC will be given by the largest variation orthogonal to the first component. PCs are extracted until only minor variation is left unexplained by the PC model, each component consisting of a score vector and a loading vector. Observations close to each other in a score plot have similar properties, and variables close to each other in a loading plot are correlated. Thus, the score plot is useful for the detection of strong outliers, clustering, and time trends [59] .
The detection of strong outliers through PCA is done by analyzing the score plot. The strong outliers are removed, as they may have a degrading impact on model quality. A statistic tool called Hotteling's T2 may be used in conjunction with the score plot for the detection of strong outliers. This tool is a multivariate generalization of Student's t-test, defining the normal area corresponding to 95 or 99% confidence. Subsequently, for a better understanding of the properties of grouped data, a splitting of data into smaller groups according to the nature of the clustering is done, and separate PCA models may be fitted. For the detection of weakly deviating observations (moderate outliers), which are not strong enough to show up as outliers in score plots, the residuals of each observation are used. The detection tool is called DmodX (a notation for distance to the model in X-space). A value of Dmodx is calculated for each observation, and the values are plotted in a control chart where the maximum tolerable distance (Dcrit) for the dataset is given. The plot of DmodX enables an overview of the unsystematic process variation, as moderate outliers have DmodX values higher than Dcrit [59].
Before PCA, scaling of data is usually performed, because variables have different numerical ranges so they will have different variance and they will weight differently in the data analysis. The most common approach is the unit variance (UV) scaling, consisting in dividing each variable by its standard deviation. The result is that each variable has equal variance, meaning that the "length" of each variable is identical, although the mean values still remain different [59].
Tôrres et al. used Hotelling's T2 chart to analyze the NIR spectra of a training (calibration) set for the development of a monitoring method for the stability of captopril in tablets. Before being analyzed by PCA, NIR spectra were smoothed as described by Savitzky-Golay with a 21-point window and second-order polynomial and were processed by MSC for the correction of baseline variation due to non-homogeneity of particle's distribution [60]. The Hotelling's T2 chart measures the distance from an observation to the center of the samples under normal operating conditions and evaluates whether a particular sample has a systematic deviation from the samples considered to be under statistical control [61]. As all samples from the training set were assumed to be normal, the training chart was not expected to identify systematic deviations in these samples in the training phase, so the number of PC retained in the model was selected to minimize the number of false alarms (false positives and false negatives) during the training phase of the control charts [60].
Regression analysis is a modeling technique used to investigate the relationship between dependent variables (responses or y's variables) and independent variables (predictor, factors or x's variables). According to the number of variable, three cases can be distinguished:
Simple linear regression—one y and one x variable.
Univariate linear regression—one y and several x's variables.
Multivariate linear regression—several y's and several x's variables [62].
The objective of a regression method can be achieved by means of a model where the observed result (dependent variables, response, y's variables) is described as a function of independent variables (x's variables) and the noise is left as residual.
In a regression analysis, the relationship between two data matrix X (BxK) and Y (NxM) are related to each other. A regression model can be written as in a matrix form as
where is the matrix of x's variables; X is the matrix of y's variables; B is the matrix of regression coefficient, B(KxM).
A good estimate of regression coefficient (B matrix) provides a good fit to Y and good prediction of future unknown parameters yT. More, the regression coefficient vector should be of mechanistic understanding and interpretable [59, 63, 64].
A large number of regression methods were developed, all with the goal of finding the best estimation of B. In the calibration of spectroscopic methods, only multivariate regression techniques can be applied, and the most used are (1) multiple linear regression (MLR), (2) principal component regression, (3) partial least-squares regression, and (4) orthogonal partial least-squares regression (O-PLS). In the last years, some advanced regression methods as (5) Bayesian ridge regression (Bayes-RR) (6) support vector regression (SVR) or (7) decision tree regression (DTR) have started to be used.
Multiple linear regression is an extension of simple linear regression model. In the case of MLR determination, the relationship between x's—variables and y's—variables is achieved by means of a model where the responses (y's—variables) are described as a function of analyzed factors (x's—variables) and the noise is left in the residual (ε) (Eq. (1)) [65]
The function f is approximated by a polynomial equation (Eq. (3)),
where bi (i = 1, 2, 3, …,n) are the regression coefficients and describe the effect of each term on the response y.
The polynomial equation (Eq. (3)) can be written in matrix way as follows:
where X are the matrix of x's variables and b the vector, and the multiple linear regression is used to determinate vector b.
If there are orthogonalities between x's variables, Eq. (4) can be written as
In this equation, matrix XTX become a diagonal matrix and b is easily calculated.
If not all the x's variables can be controlled, the number of x's variables extends the number of experimental runs or the number of experimental runs is larger than the number of x's variables, the co-linearity between x's variables arises and the orthogonality no longer exists, so the inverse of XTX cannot be applied.
Except the cases when the calibration of spectroscopic methods is performed following the design of experiment strategy in the other multivariate calibration, the orthogonalities do not exist and the MLR cannot be applied. That is the reason why other regression methods based on latent variables as partial last squares are preferred and become popular. When the calibrations are performed based on latent variables, inside of using the original variables in the regression, a new set of orthogonal (latent) variables is calculated and leads to reduction of the original dimension of x's variables matrix and performs the least-square estimation.
Principal component regression is a regression method based on principal component analysis and it is used when datasets are highly collinear. In a PCA regression, the original set of collinear variables is transformed to a new set of correlated variables. So, the principal component analysis is used to decompose the x's variables into a principal component (orthogonal basis) and a subset of components in order to predict y's variables. The basic idea of the principal component regression is to calculate the principal components and then use some of these components as predictors in a linear regression model fitted using the typical least-squares procedure [66, 67].
In the case of PCR determination, the relationship between x's variables and y's variables is achieved by means of a matrix of lower dimension (TPT), called principal components, and a matrix of residuals (E).
where contains X average; T is a matrix of scores that summarizes the X variables; P is a matrix of loadings showing the influence of the X variables; E is a matrix of residuals (the deviations between the original and the predicted values) [66].
The main idea of principal regression is to replace X matrix of row date to a smaller orthogonal score—loading matrix (TPT matrix) that summarized the original X matrix, and then to relate the T-scores to y's variables.
The core of PCR is that a small number of principal components is enough to explain the variability into the data. In most of the cases, it might be found out that four to six principal components are enough to explain more that 90% of the variance into the data.
The partial least-squares regression is the most popular method for the creation of models used in the development of NIR and Raman spectrometric methods and is used to develop a linear link between two matrices, the NIR/Raman spectral data and the reference values.
The PLS approach was first proposed by Herman Wold around 1975 for the modeling of complicated datasets in terms of chains of matrices, the so-called path models. PLS regression is preferable to develop calibration models because unlike MLR, it can analyze data with strongly collinear, noisy, and numerous X-variables, and also simultaneously model several response variables [68]. PLS was developed for situation in which the data have more independent variables than observations (the "small n, large p") or/and where collinearity is present among dataset [69].
The PLS finds a multivariate model (linear or polynomial) that describes the relationship between Y matrix (dependent variables) and X matrix (predictor variables) expressed as
PLS may be easily understood geometrically if we imagine the matrices X and Y as N points in two spaces. The X-space with K axes, and the Y-space with M axes, where K is the number of columns in X matrix and M the number of columns in Y matrix. The objectives of PLS is to find a latent variable so that the best approximate X-space, the best approximate Y–space, and the greatest possible correlation between X-space and Y space.
A PLS model can be written as
where contains the X average; contains the Y average; T is a matrix of scores that summarizes the X variables; U is a matrix of scores that summarizes the Y variables; E, F, H is a matrix of residuals (the deviations between the original and the predicted values) [12].
In a PLS algorithm, there are additional loading called weight. P is the matrix of weigh expressing the correlation between X and U and is used to calculate T. C is the matrix of weigh expressing the correlation between Y and T and is used to calculate U [12, 70].
OPLS has been developed in order to separate information in the X matrix that is correlated with Y matrix form Y-uncorrelated information. The idea of O-PLS algorithm was to remove systematic variation uncorrelated with the response with the goal and to reduce the number of components in order to increase interpretability of the model [55, 69, 71].
The main idea of O-PLS is to separate the systematic variation in X into two parts, one which is related to both X and Y (co-varying noise) and one which is orthogonal to Y (structured noise). Two O-PLS algorithms were developed, the first (O1-PLS) is unidirectional and the second (O2-PLS) is bi-directional and is able to separate these different types of variations in both X and Y matrices [63, 64]. The practical result of using O-PLS algorithms inside of PLS is cleaner models that are easier to display and interpret.
An O2-PLS model can be written as
where is a contain de X average; is a contain de Y average; T is a matrix of scores that summarizes the X variables; U is a matrix of scores that summarizes the Y variables; P is a matrix of weigh that express the correlation between X and U; C is a matrix of weigh that express the correlation between Y and T; E, F, HTU, HUT are the matrixes of residuals.
The matrixes TPT and UCT hold the joint X/Y information overlap [12, 63, 64].
In the last years, O2-PLS has become the preferred regression technique for the development of calibration models in NIR and Raman spectroscopy.
Another regression method recently proposed for multivariate calibration of spectroscopic methods is Bayesian ridge regression. The method presents similarities with least squares, and the estimated coefficients tend toward zero in order to avoid collinearity [44].
In a Bayes-RR regression model, higher-level prior Gaussian distributions can be introduced over α2 and α, and the prediction can be performed by integrating over α2, α, and the regression parameters w. Since this prior distribution is conjugate to the likelihood function, the predictive distribution is also Gaussian [72]
The Bayes-RR is a widely used regression technique in machine learning based on the ridge regression [73], and in the last years its performance for the development of excellent models for spectroscopic calibration has been proved [72, 74, 75].
The support vector machines (SVMs) are a set of learning methods mostly used for classification that can be used as a regression technique which is called the support vector regression. In the last years, SVM started to be used in chemometrics for NIR spectra classification and multivariate calibration. The SVR uses the same principles as the SVM and is based on finding the hyperplane maximizing the margin between classes. The hyperplane maximizing the margin is justified by statistical learning theory endowed with a probabilistic test error bound that is minimized when the margin is maximized. The regression is performed using kernel functions that transform the data into a higher dimensional feature space to make a linear separation possible. The models obtained by SVR are more complex and difficult to interpret in comparison with those obtained by other regression techniques [44, 76, 77].
Decision tree regression is a type of decision tree algorithm that can be applied to solve regression problems. Decision trees represent one of the main techniques used for discriminant analysis, classification, and prediction in knowledge discovery. It is widely used because it closely resembles human reasoning and is easy to understand. The principle is to compute a regression in a tree structure from breaking down a dataset into smaller and smaller subsets. Recently, some applications in multivariate calibration of spectroscopic methods have been proposed [44, 77, 78, 79].
This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number PN-III-P2-2.1-BG-2016-0201.
Mosquitoes in the family Culicidae are responsible for the transmission of viruses and other pathogens to man and animals. Arboviruses transmitted by mosquitoes of the genus Aedes (Meigen) (Diptera: Culicidae) promote the occurrence of diseases such as dengue fever, chikungunya fever, yellow fever and zika fever at alarming levels and have impacted public health in several countries, mainly Brazil [1, 2].
In Brazil, there is an outbreak of wild yellow fever [3] due to virus transmission, which should be kept in the wild, between non-human primates and arboreal wild mosquitoes, mainly of the genera Haemagogus (Linnaeus) (Diptera: Culicidae) and Sabethes (Robineau-Desvoidy) (Diptera: Culicidae). The possibility of a change in the ongoing yellow fever transmission cycle has been realized. To date, it has not been proven that the yellow fever mosquito Aedes aegypti (Linnaeus) (Diptera: Culicidae) mosquito is involved in transmission, but evidence has emerged from different studies [3].
With the exception of yellow fever and dengue, zika and chikungunya do not have specific vaccines or treatments. For this reason, the use of insecticides and chemical control constitutes the main insect vector control tools in urban areas. However, this practice has been disseminated and performed irresponsibly, resulting in actions that caused resistance to these insecticides. In this respect, new forms of control are desirable, especially with multifunctional products [3].
In Brazil, Ae. aegypti is found in more than 70% of the 5561 Brazilian municipalities. Most of these areas have a reduced number of health agents who, for different reasons, do not cover 80% of cities' urban areas. Thus, the country is left exposed to constant threats of outbreaks and epidemics. Thereby, it is necessary to develop new products, methodologies or even a combination of methods to control Aedes spp., populations. These methods should take into account effective surveillance and control, and be of a low cost and easy applicability. Additionally, efforts to improve infrastructure, population education, and training of health workers to combat and control the insect should be intensified. To this end, a better understanding of the different aspects of Culicidae biology and diversity, their relationships with humans and animals, and their reproduction in urban areas are important factors in assessing the adequacy of methodologies used for the selection and implementation of control methods, the development of new insecticides or the creation of new strategies that consider behavioral and adaptive aspects. However, information, techniques and/or scientific knowledge are still restricted to the majority of the population or have limited practical applications. It is up to the universities, research centers and zoonotic control agencies to obtain them and use the contents for discussion or establishment of new proposals and strategies of vector population control.
In this context, it is very useful to present a review of breeding sites, habits and behaviors of the Aedes species to understand the situations, conditions and/or details of the microenvironments, and reproduction at breeding sites, whether natural or artificial. Thus, this chapter is based on the collection and analysis of information available in the literature, or the authors' scientific experience, about the physicochemical and biological conditions of breeding sites and mosquito habits and behaviors. Considering these important findings will crucially enable the elaboration of strategies that control insect vector populations.
Mosquitoes use the aquatic environment to develop immature forms (egg, larva and pupa). In urban and peri-urban environments, breeding sites are any artificial reservoirs or containers and even natural breeding grounds where water is available for the reproductive process of the species. The availability of breeding sites and feeding facilities explains, in part, the spread of Aedes spp. Ae. aegypti is considered the main vector of (re)emerging diseases in Brazil (yellow fever, dengue, chikungunya and zika) and has been a constant concern for public health.
The genus Aedes [including Ae. aegypti and Asian tiger mosquito Aedes albopictus (Skuse) (Diptera: Culicidae)] has peculiar habits in relation to other mosquito species. The species oviposit in a split way, usually in aquatic environments with areas of restricted lighting or shaded spaces. At these locations, temperatures are milder compared to an area of direct solar sunshine. Shaded spaces and the availability of organic debris are physical and chemical signs that the breeding facility was suitable for previous generations and can successfully serve the breeding of a new generation [4].
Breeding sites are important spaces for the attraction, reproduction, survival guarantee and continuation of the species. These microenvironments are determinants for oviposition and reproductive success. Food availability in the aquatic environment appears to be the main attraction for oviposition, along with the physicochemical and biological conditions of the breeding sites. These related aspects can all influence the choice of the oviposition site by the females.
The abundance and population density of mosquitoes produce negative impacts on the quality of life of humans and animals. Diseases caused by arthropod-borne arboviruses are considered as neglected or emerging diseases. Viral diseases transmitted by Aedes spp., including yellow fever, dengue fever, chikungunya and zika, are serious public health problems that persist for decades in the form of periodic outbreaks with variable intensities due to non-continuous and deficient control methods. Since the early 1970s, the World Health Organization (WHO) has sought an effective way to control, manage and treat these diseases, but without much success.
Ae. aegypti undergoes four stages in its life cycle (Figure 1): egg (terrestrial form), larva, pupa (aquatic forms) and adult insect (winged; terrestrial form). Despite having a short shelf-life of at most 2 months in the laboratory (and less time in natural environments), the female can oviposit up to 270 eggs during her life span. As a strategy for species survival, egg laying and establishing an egg bank at breeding sites allows the maintenance, replacement and perpetuation of the species even under adverse conditions [5]. Split oviposition at the breeding sites performed by Ae. aegypti and Ae. albopictus allows them to increase survival conditions and establish the population in the ecosystem.
Rozeboom et al. [6] observed that females disperse their eggs through several breeding sites. In each site, 11–30 eggs are deposited by oviposition in small breeding sites and more than 60 eggs in large breeding sites [7]. Vector control strategies should include knowledge about the reproductive aspects of the insect due to the formation of an egg bank that allows for species reposition in the environment. Studies showed that both preferences and habits, as well as insect survival strategies, are important factors that must be considered by the vector control agent [7, 8].
The literature mentions that the breeding site must be viable for reproduction of the species, with food availability and adequate oxygenation, temperature and pH. The choice of oviposition sites is a critical factor for the survival and dynamics of populations, and knowledge of these factors is important for mosquito population control. The choice of such sites results in a complex interaction between chemical and physical factors (biotic and abiotic), which indicates the degree of preference in the selection of breeding and oviposition sites [4, 5, 6, 7, 8, 9]. Laboratory studies done by Madeira et al. [10] showed Ae. aegypti preferences for some types of breeding grounds. There are variations in the type of breeding sites chosen by this species for oviposition. Oviposition can occur in the water when the egg-laying surface does not allow egg adhesion. Non-submerged eggs usually do not hatch, in contrast to submerged eggs that hatch after hydration stimulation. Mosquitoes prefer porous or rough surfaces for oviposition.
Mitchell-Foster et al. [11] showed that new strategies can be used for integrated vector control. The imposition of unfavorable conditions for insects at breeding sites can help to increase insecticide effectiveness and vector control, since insecticides can be multifunctional for comprehensive secondary or tertiary control, due to changes in breeding sites physicochemical and/or microbiological conditions. These strategies could be integrated to consider applying disadvantageous situations and they include biological agents at different levels for in situ reproduction control. Exposure to competitors, physicochemical changes and others reduce the probability of egg hatching and species survival, besides affecting parameters such as larval maturation time, adult size, population density, fecundity, attraction and egg viability.
Rey and O'Connell [12] investigated Ae. aegypti and Ae. albopictus oviposition behavior by examining oviposition site characteristics and the influence of factors that alter egg laying. In cages, they prepared several combinations with females of both species for oviposition at breeding sites in vegetation (potted plants) or structural components (blocks of wood and concrete). Then they compared the number of eggs deposited per female among the species and the different types of breeding sites. Ae. aegypti deposited more eggs per female in breeding sites with vegetation compared to those with structural elements. The opposite is true for Ae. albopictus. Ae. aegypti females presented a higher oviposition frequency than Ae. albopictus. On average, 63% of breeding sites contained eggs from both species. The authors emphasized the possibilities of interactions between Ae. aegypti and Ae. albopictus in spaces and breeding grounds, especially when the two species are disseminated in the same space where specific interactions and mechanisms must contribute to reproductive cooperation.
After oviposition on the breeding walls, white eggs in the posture darken on contact with atmospheric oxygen. Eggs are deposited on the walls of the breeding site when the surfaces are porous or rough and close to the water surface (but not directly over the liquid when there is this availability of an oviposition surface). Humidity must be maintained for egg viability and hatching after submersion/hydration in water. Studies revealed that these eggs can survive 360–450 days after desiccation. Under favorable conditions, their hatching can occur between 10 minutes and 48 hours after humidification or immersion in breeding liquids, and after 7–10 days, they emerge as adult insects with vector potential, depending on the temperature and available feeding conditions [13, 14].
Mosquitoes seek shelter, dark spaces with microbiota and decomposing organic wastes available over a wide pH range (acidic, neutral and basic). If the insect does not find these conditions, it will look for other places, regardless of the size or condition, for their reproduction. The insect will never put all its eggs in a single breeding site. This survival strategy led to marked reproductive success and greater dispersion, features that make the mosquito resilient, competent and adapted to the different conditions offered by the medium for its survival, despite its apparent fragility. Its adaptive and vectoral capacities underscore its epidemic potential due to the ability to spread across different geographical areas [15].
After hydration and egg hatching, the larval stage begins, which has four evolutionary steps. Its duration depends on temperature, food availability (organic material or microorganisms) and number of larvae in the breeding site. Under these conditions, the period between hatching and the pupa (last stage before the adult insect) may not exceed 5 days. However, if there is low temperature and food shortage, the stage can be extended for up to a week [14].
The Ae. aegypti larva is composed of a head, thorax and abdomen. The abdomen is divided into eight segments. The posterior and anal segment of the abdomen has four lobulated bronchi for osmotic regulation and a siphon or air tube for breathing on the water surface. The siphon is short, thick and darker than the body. To breathe, the larva goes to the surface, where it is in almost vertical position. It moves in the shape of a serpent, making an "S" in its displacement. It is sensitive to sudden movements in the water and under a beam of light, it moves quickly, seeking refuge at the bottom of the vessel. This behavior is more related to antipredation because females select the oviposition site based on the success of previous generations as well as the presence of decomposing organic debris [4].
The pupae do not feed and at this stage metamorphosis of the larval stage occurs to generate the adult (winged) insect. This stage lasts 2–3 days and when the pupa is inactive it stays floating on the water surface to facilitate the emergence of the adult (winged) insect. The pupa is divided into the cephalothorax and abdomen. The head and thorax are united and constitute the cephalothorax, which gives the pupa, seen from the side, the appearance of a comma. The pupa has a pair of breathing tubes or "trumpets" that cross the water and allow it to breathe [14, 15].
The adult phase of the insect represents the reproductive and transmitter/vector phase. The adult is responsible for the spread of viruses and diseases and for the reproduction. Ae. aegypti is approximately 1 cm long and have black with white stripes distributed near the body and legs. Ae. aegypti incidence is usually greater in spaces with high temperature, low humidity, low vegetation and a concentration of heat sources. Unlike common mosquitoes, Ae. aegypti has diurnal habits, flies low and performs hematophagy, preferentially stinging the feet, ankles, arms and legs. Ae. aegypti does not like heat or radiated/lighted environments, so it is more active in the early morning and late afternoon. It is believed that the environment in these adverse conditions of irradiation and/or competitiveness with other species is more aggressive for the adult mosquito, which imposes an additional metabolic cost to its survival. The female feeds on blood (hematophagy), which is a ready and metabolized food source that is still rich in proteins, and minerals along with other micronutrients necessary for its survival, oviposition and the maintenance of its vector competence. However, the female only transmits a virus if she contracted it more than 2 weeks ago, since the dengue virus needs 10–14 days in mosquito metabolism to become viable for transmission during hematophagy [16].
The availability of different types of discarded containers with organic remains allows rainwater containment. Maintaining these conditions for a certain period of time allows the formation of new breeding sites that attract females for oviposition and ultimately promotes Aedes larval infestation.
Reproductive habitat characteristics may affect larval population density. Thus, the mosquito will be more abundant and the potential for disease transmission will increase. In this situation, quality of life is deeply affected by vector diseases, especially for individuals who live in high-population-density areas where disease will be transmitted more quickly.
Getachew et al. [17] showed the importance of basic sanitation and the simple treatment of water with chlorine, which represents an important control for the restriction of disease outbreaks. In containers that contain tap water, mosquito larvae are not abundant, contrary to what happens in containers filled with a mixture of tap water and rainwater or only rainwater. De Brito-Arduíno and Ávila [18] researched and discussed the reproduction and characteristics of urban breeding sites, and the implications for Ae. aegypti and Ae. albopictus control. The authors' results corroborate studies performed in Australia, where 28% of immature individuals came from tanks and wells, considered to be the main breeding sites [19]. Other studies performed in Venezuelan cemeteries showed that the breeding sites with the highest proportion of pupae have been those that containing 1–5 liters of liquid [20]. Additionally, the results presented great variability in the parameters, since immature individuals of Aedes spp., occur in water with pollutant load [19].
The species can develop in breeding grounds with a high degree of pollution and organic matter. The polluted breeding sites found in De Brito-Arduino and Ávila's studies [18] are boats, drains and sites that contained the residues of various substances, such as oil, salt, rust, among others [21]. In studies performed in Puerto Rico, a large number of Ae. aegypti larvae grew in tanks and septic tanks, cooperatively with Culex quinquefasciatus (Say) (Diptera: Culicidae) populations [22, 23]. In Brazil, laboratory analysis showed that Ae. aegypti can complete its reproductive cycle up to the adult (winged insect) phase in waters with different degrees of pollution [21, 22, 23, 24]. In Merida, Mexico, the main Ae. aegypti breeding sites are composed of rainwater runoff from the streets and localities that have a high pollutant load [25].
Ae. aegypti prefers breeding sites with high food availability when there are no conspecifics (Ae. albopictus), while Ae. albopictus prefers high food availability when larval density is relatively low [25, 26]. The literature suggests that Ae. aegypti possesses the ability, genetics and competence to adapt and survive in different biotic and abiotic conditions that result from anthropogenic changes. Studies revealed that larvae and pupae are found in breeding grounds with water of different quality and portability. Some breeding sites contain residues of different substances, as well as different temperatures, pH, dissolved oxygen (DO) and conductivity. These results demonstrate that the species can survive in non-conventional breeding sites [27, 28, 29].
Taken together, these facts highlight the challenge for mosquito population control and the reduction of viral disease incidence in urban and peri-urban environments. Effective Ae. aegypti population control to reduce disease incidence should focus on multifactorial strategies and take into account the different physicochemical and biological aspects of breeding sites.
The physicochemical characteristics of aquatic habitats influence the survival rate of mosquitoes [15, 16, 17]. Some factors may be important for mosquito oviposition, including water temperature, pH, ammonia, nitrate, sulfate and phosphate content, conductivity and dissolved solids [30, 31].
Following oviposition on the breeding wall, eggs hatch after hydration, and the insects develop through the four larval stages and pupa (which does not feed) until adulthood. The immature stages, namely eggs and larvae, are important parts to consider in population control. From egg hatching (embryo), the larvae consume decomposing organic material, debris and microorganisms from the aquatic microenvironment to obtain nutrients for their development, which are actions that contribute to reproductive cycle closure. These aspects can and should be considered for the establishment of new insecticides, formulations and strategies for insect population control. Control via the food chain, studies on microbiota composition (including prevalent and non-prevalent species), mosquito characteristics and specificities, the attractiveness of the breeding grounds to females and other details are important considerations to develop products and strategies for target species population reduction. Current research reveals that the type of container, water quality and the available physical, chemical, biological and nutritional conditions found at the breeding microenvironment are important parameters for the attraction, reproduction and continuation of the species. Thus, these factors are likely imperative for population control.
According to the historical incidence of vector-borne diseases, infection peaks occur during hot and rainy seasons. At these times, water storage lasts for prolonged periods, with the potent combination of humidity, temperature and food availability. The hot and humid climate increases mosquito population density from these breeding grounds, food and conditions favorable for reproduction of Aedes spp., all of which increase the risk of the incidence of the different diseases transmitted by them [17].
Studies in Africa, Asia and various parts of the world indicate that the predominance of Aedes spp. (Ae. aegypti and Ae. albopictus) in a given region depends on the climatic conditions, type of breeding grounds and form of species dispersal. The use of abiotic parameters like water conductivity (salt dispersion/electrolytes) as an index for mosquito proliferation in garbage dumps can be explored in mosquito control strategies [30].
Getachew et al. [17] observed that water conductivity in the range of 162.9–616.9 μS cm−1 negatively correlates with larval density. Fillinger et al. [32] also showed a negative correlation of larval density to malaria vector. A conductivity of 2000 μS cm−1 significantly reduces larval density at breeding sites. The authors also showed that the breeding site pH ranged from 6.72 to 7.63, with a positive correlation with larval density.
Breeding site ionic composition and pH are limiting factors for the distribution and number of organisms found in these habitats. However, Clark et al. [33] showed that the mosquito has a high adaptation capacity to the breeding site conditions. Aedes spp. can develop in extreme pH (4.3–9.9), wide temperature ranges (18–35.9°C) and different salinity levels. The insects prefer environments with leaves and decomposing organic material, although it is not an absolute rule for Aedes spp., with reproduction capacity in waters polluted by oil, salt and rust, among other factors. Indeed, De Brito-Arduino and Ávila [18] observed breeding sites in boats with motor oil residues, data that may also justify the minimum dissolved oxygen (DO) value of 0.40 mg L−1. The drains also had low DO (0.20–0.52 mg L−1) in the various collected samples, besides the presence of soap residue and detergents that would justify these DO values.
Another factor that influences the choice of breeding sites is salinity. Oviposition decreases with increasing salinity at concentrations above 12% [17, 30, 32, 34]. De Brito-Arduíno and Ávila [18] showed that these limits are flexible, with salinity values up to 13.5%. Increases in conductivity (salinity) impose unfavorable conditions for oviposition [18, 35]. In the littoral strip of Brazil and Asia, there are records of Ae. aegypti reproducing in water with high salinity (brackish water), data that demonstrate the existence of the species adaptation to the salinity of the breeding sites [21, 24].
Another study showed that breeding sites can be shared by different species for survival at different pH ranges. Ae. aegypti and Cx. quinquefasciatus (common stilt) have been found in breeding sites with pH in the range of 6.5–8 [36]. Brito-Arduino and Ávila [18] also observed that the breeding pH range varies from acidic to basic, with values from 4.3 to 9.9. These results are related to other studies performed in Australia, which showed a breeding site pH range of 5.5–11.3 [37], and in Venezuela, with pH between 6.5 and 10.5 [20]. Since the hydrogen ion concentration [38] is dependent on the water containment medium (breeding sites) and temperature, due to the water ionization constant, temperature is another factor that deserves careful analysis.
Temperature influences the immature development of Ae. aegypti, and thus, the availability of energy from the medium may interfere with metabolic activity, but mainly with the enzymatic activities of the organism. High temperatures can compromise the conformation, stability and function of insect metabolic enzymes, as well as activate and deactivate them or even alter metabolic processes that are involved in the development, adaptability and survival of these organisms [33, 37, 39]. As previously stated, the species can successfully develop and breed between 18 and 35.9°C [18]. Studies performed in Colombia revealed that Ae. aegypti larvae could grow from 24 to 30°C at breeding sites [39, 40]. Another related study conducted in Nukulaelae (Tuvulu), with breeding grounds in metal/plastic drums, showed that Ae. aegypti occur in environments with temperatures between 20 and 35°C [41]. The pH and salinity may limit the distribution of organisms. However, they cannot prevent reproduction under unfavorable abiotic conditions. The conductivity (salinity) of the water in the breeding sites of De Brito-Arduíno and Ávila [18] study, ranged from 0 to 22,400 μS cm−1 (0–13.5% salinity) and pH from 4.5 to 9.9 (depending on the collection and type of breeding sites). These results suggest that Ae. aegypti can reproduce under unfavorable conditions and thus overcome limiting factors for species development in extreme temperature, pH, DO and conductivity conditions.
In a study conducted by Rao et al. [30] in Ae. albopictus breeding sites, there is a positive correlation between larval density and total dissolved solids, turbidity, conductivity and salinity, and a negative correlation for pH. Dom et al. [42] and Nazri et al. [43] showed the potential risk of mosquito peri-domiciliary establishment and the characteristics of the breeding sites for Aedes spp. Ae. albopictus is the predominant species in breeding sites. Turbidity, pH, total organic carbon (TOC), magnesium, calcium and sodium are among the characteristics that are positively significant for larval density, species composition and breeding water characteristics, respectively. Mosquito breeding sites characteristics affect larval density and increase mosquito infestation, which ultimately impact the quality of life of the population.
Breeding characteristics play an important role in providing nutrients and a safe environment for increasing the larval survival rate. Different studies showed that turbidity (due to the presence of organic material and microorganisms), pH, TOC and magnesium concentration influence the larval population density. Ae. aegypti prefers less turbid water, while Ae. albopictus prefers more turbid water. These differences may indicate why one species prevails over another. The results suggest that Ae. aegypti is a less-demanding species in terms of food for oviposition and reproduction.
TOC, related to the presence of organic material in breeding sites, also influences larval density. This fact has been confirmed by a study of Thangamathi et al. [44], which indicated that sulfate ion () content in the breeding habitat of Aedes larvae (Ae. aegypti and Ae. albopictus) is high compared to other ionic species. Aedes spp. have a rapid adaptation capacity to environmental conditions, even if they appear to be averse to the species. According to Thangamathi et al. [44], Ae. aegypti survives most at pH between 6.5 and 8.0; however, values close to neutrality are better for the species [45]. This pH range may be altered to affect species adaptation and potentially to control the population.
Micro and macro nutrients availability in breeding sites is also important for Ae. aegypti and Ae. albopictus. Magnesium plays a role in larval density, as shown by Thangamathi et al. [44], who carried out studies that analyzed breeding site mineral composition. Calcium, potassium, sodium, sulfur (elemental or ) and magnesium [2] can provide an ideal broth for Ae. aegypti. The larval habitat characteristics have been analyzed and related to the species by larval number. Calcium and sodium provide significantly different results for the proposed composition that favors Aedes spp. in breeding sites.
These results show that any container that can store water for a prolonged period for domestic or industrial use becomes a potential mosquito breeding ground, since it can allow the deposition of minerals and organic material, colonization by microalgae and microorganisms, and thus gather conditions for the development of a microenvironment favorable to the life and reproduction of Aedes spp. In this regard, strategies such as insecticides or other products could potentially change breeding site physicochemical parameters in terms of pH, conductivity and nutrient availability for secondary vector control (due to reproductive microenvironment changes).
The Ae. aegypti oviposition strategy differs from other mosquito species. The literature shows that the oviposition stimulus is mainly food availability, signaled by microbiota semiochemicals that result from the degradation of organic materials and/or microorganisms. Semiochemicals are signaling molecules, used to carry information between living organisms that cause changes in Ae. aegypti behavior. These semiochemicals are perceived by the female, stimulate oviposition and consequently, allow species continuation, possibly because they indicate food availability for the larvae.
There are few studies that relate the attractiveness, selectivity and the relationship between microorganisms (i.e., bacteria) and products of organic degradation of breeding sites for attraction and oviposition. The available reports pay little attention to the density and colonization of breeding grounds by bacteria and other organisms and the female's attraction or repulsion and oviposition. Hasselschwert and Rockett [46] examined Culex larvae growth at breeding sites with different bacterial cultures and observed that the presence of Bacillus cereus and Pseudomonas aeruginosa induces attraction for Ae. aegypti oviposition. Trexler et al. [47] showed that isolates of Sphingobacterium multivorum (Sphingobacteriales: Sphingobacteriaceae), Psychrobacter immobilis (Juni and Heym) (Pseudomonadales: Moraxellaceae) and an unidentified Bacillus (Cohn) (Bacillales: Bacillaceae) species, when inoculated in water, induce high Ae. albopictus attraction and oviposition compared to water without these bacteria [40]. Poonam et al. [48] produced filtrates from bacterial cultures and used them for analysis of attraction and oviposition with female Cx. quinquefasciatus. These females oviposited more in certain concentrations of B. cereus (Frankland and Frankland), Bacillus thuringiensis (Berliner) and Pseudomonas fluorescens (Flügge) (Pseudomonadales: Pseudomonadaceae) compared to tap water. Other filtrates from Bacillus megaterium (Bary) and Azospirillum brasilense (Tarrand, Krieg and Döbereiner) (Rhodospirillales: Rhodospirillaceae) did not stimulate oviposition in water at any concentration of the filtrate tested. These results demonstrate that there is selectivity in the attraction and response of the mosquito to different bacteria. Bacteria grown in disparate nutritional media and this medium is not used as a control because it may contain substances that are attractive or deterrents for oviposition.
Huang et al. [49], in a study with 14 isolates of bamboo leaf infusion bacteria, showed that Ae. aegypti are repelled at densities of 109 cells mL−1. The mixture of 14 bacterial isolates attracts Ae. aegypti and Ae. albopictus at densities of 107–108 cells mL−1. These studies showed a concentration range dependence of 106–109 cells mL−1 for Aedes spp. attraction. Ae. aegypti and Ae. albopictus females are attracted by an infusion of bamboo leaves and a mixture of 14 bacterial species [50, 51, 52]. The results indicate that volatile substances from plants or bacteria can be attractive to mosquitoes. Despite the differences in the responses of Ae. aegypti and Ae. albopictus to different bacterial concentrations and densities, Ae. aegypti females tend to be attracted to lower bacterial concentrations and densities compared to Ae. albopictus females.
Other data available in the literature indicate that not all bacterial species produce the same semiochemicals (attractive or repellent) or sufficient amounts of attractants. These results suggest that there is a possible biological balance in the microbiota that controls, from the population density of the present species, the emission of volatile substances that can be perceived by the females in the process of attraction or repulsion to the breeding sites. Semiochemical composition variations from different bacterial species that stimulate the attraction or repulsion and oviposition of Aedes spp. explain the different species response. Further, other parameters, including variations in bacterial production time, can promote quantitative or qualitative differences for the chemical stimulus in the process of attraction or repulsion to the breeding sites and humans skins emanations [49, 53]. Thorn et al. [54] described significant differences in the type and concentration of volatiles produced by different bacteria grown in the laboratory and in nature for oviposition [6]. Ponnusamy et al. [50] showed the complexity of a microbiota balance and its importance in breeding sites for mosquito breeding. Their studies reinforce the hypothesis that the main stimulus for female ovipositors is the availability of food indicators or breeding site microbiota.
Diseases transmitted by Ae. aegypti are one of the major public health problems in many regions of the world; hence, their control is import. Thousands of years of natural selection increased vectoral competence and even species survival to the cultural habits of populations/communities. Additionally, lack of health education, high availability of breeding sites in domiciles and peri-domestic areas, and the absence in many places of sanitary infrastructure, among other factors, contribute to increase the survival and reproduction success of mosquito species and prevent their population control.
Despite of reports and dissemination of the polyvalent vaccine for dengue, chikungunya and zika that may be an option to reduce and control these diseases, this use can take up to 10 years for successful control, according to the Oswaldo Cruz Foundation (Fiocruz, RJ), due to the conditions required to guarantee efficiency and biosafety. There are four dengue serotypes and the number of serotypes for the other two viruses is still unknown, and must also be combated efficiently. For this reason, there is enormous dependence on chemical control and the need for eliminating Ae. aegypti breeding sites to reduce the mosquito population and consequently, the incidence of the diseases. The vaccine may be an important part for dengue control; however, other fronts should also be considered, such as health education, continuous epidemiological surveillance, infrastructure improvements and social responsibility by governments.
Chemical methods are intensively used for adult insect vector population control. Non-implementation of preventive control strategies is in part, responsible for the occurrence and constant aggravation of epidemic outbreaks during hot periods in Brazil.
The difficulty for Ae. aegypti population control centers are on the adaptive process, species resilience and acquired resistance to conventional insecticides. The first report of resistance occurred with organophosphates followed by pyrethroids. Over time, other resistance records have been reported to organophosphates, pyrethroids and carbamates in regions with intense mosquito occurrence. Before the year 2000, resistance to the organophosphate insecticides Temephos used as a larvicide and Malathion used to combat adult mosquitoes, was detected in some Brazilian municipalities.
Reproductive capacity favors the emergence of insecticide-resistant individuals used for vector control, because resistance varies with the time of use and concentration of the products that act at specific sites of toxicity. Medeiros [55] presents four categories of enzymatic activity profiles: (1) greater activities in the adult stage; (2) greater activities in the larval stage (esterases "α-EST" and "β-EST"); (3) activities that increase during each stage evaluated (mixed function oxidase [MFO]); and (4) activities that tend to increase in the larval stage and decrease in the first days of adult life (DVA) [esterase "ρNPA" and glutathione S-transferase (GST)]. Biochemical assays with larvae and adults from field populations revealed alterations in acetylcholine esterase (AChE) and other esterases at the larval stage, changes in GST more restricted to the adult stage, and MFO alterations have been limited to the two vector stages.
The results from these experiments allow detailed evaluation of the resistance mechanisms in different vector populations and can be used for the development and choice of insecticides more suitable for Aedes spp. control. Guirado and Bicudo [56] showed some aspects of population control and resistance to insecticides in Ae. aegypti and concluded that mosquito population control, as long as no more modern vaccines or genetic techniques of epidemiological control are available, is exclusively dependent on chemical control and human population awareness for breeding site elimination.
The available insecticide resistance studies demonstrate that resistance is due to three main mechanisms: (a) reduction of insecticide penetration due to changes in the insect cuticle; (b) increased metabolism of the insecticide by the action of esterases, mono-oxygenases or glutathione-transferases that inactivate the molecule; and (c) modification of the insecticide's biological target. The literature also shows a behavioral resistance mechanism, where insects avoid contact with sites that contain the toxic substance used for control. The results of insecticide resistance and loss of control efficacy indicate the need for continuous monitoring of Ae. aegypti susceptibility to insecticides and the use of chemical control in more rational ways. These smarter considerations include analysis of insect populations and their resistance, use of integrated management techniques and different methodologies and/or control products, in addition to continuous monitoring during all periods of the year by endemic control programs.
The availability of water by precipitation or accumulation at home and breeding grounds is an important factor for the reproductive process and establishment of Aedes spp. [57, 58]. Additionally, food availability is the most important factor for larval survival and development. Researchers have analyzed breeding site physicochemical conditions and water parameters. The studies reveal species adaptations and tolerance to the physicochemical conditions of breeding sites, besides the presence of other species that share breeding site spaces and cooperate with each other. Gil et al. [29], through collection and analysis of breeding water, showed a close relationship between the conditions of drinking and non-potable water composition, temperature, availability of organic material, waste, and reproduction and development of Ae. aegypti. Beserra et al. [26] showed that Ae. aegypti develops in different environments with organic material availability. The temperature, pH and water composition (total solids, total nitrogen, ammoniacal nitrogen, total dissolved phosphate and DO) are important variables for Ae. aegypti reproduction at egg, larval and pupal stages.
Silva and Silva [59] studied the influence of egg quiescence (interruption in development induced by low humidity) period in the life cycle of Ae. aegypti under laboratory conditions, in search of information that could improve control actions. Notably, the egg is the most resistant form of the biological cycle, and this feature allows mosquito development and resistance to climatic adversities. The experiments have been performed in a biological chamber, maintained at 28 ± 1°C, with relative humidity of 80 ± 5% and a 12-hour photoperiod. Quiescence is highly significant for larval hatching. Eggs from the same quiescence period had statistically different incubation periods and the larvae hatched in groups (defined by incubation). Indeed, in 99.8% of the cycles, the variation was determined by incubation.
Quiescence is a very important adaptation in the passive dispersion of Ae. aegypti because it enables the transport of resistant (or quiescent) eggs into all kinds of artifacts, such as used tires. Its non-destruction can signal a feasible mechanism to increase Ae. aegypti dispersion, a factor that makes entomological and/or epidemiological surveillance essential. This context gains another dimension when one adds the highly significant effect of quiescence on larval hatching. Quiescence periods of up to 720 days can occur, but the literature demonstrated that eggs are still viable up to 492 days. In some studies, the highest hatching rate (97.2%) occurs after 121 days quiescence [59, 60]. In another study, high hatching occurs after 180 days [23]. Among these quiescence periods, the 121 days period is the most favorable to Ae. aegypti, with significantly higher hatching rate and number of cycles (groups) than for other periods.
Other authors [32, 34, 36] also observed the same phenomenon attributing to different factors these prolonged periods of viability that include resistant, durable, inactive and residual eggs. Whatever the factor is, quiescence provoked by climatic variables, such as the decrease of humidity and temperature, increases Ae. aegypti life expectancy by enabling re-growth of the species. This behavior is relevant to the control of this mosquito, since the eggs adhered to container walls resist desiccation and can hatch when the water level rises.
In dechlorinated water at 26°C, there is a 92% Ae. aegypti larvae survival rate [60]. The Ae. aegypti larvae grown in raw sewage water show shorter developmental periods, probably due to the high concentrations of organic materials that serve as nutrients, although the authors hypothesized that the surface tension and high viscosity of the water are responsible. Further, the formation of material on the surface may makes it difficult for the larvae to obtain atmospheric oxygen. Beserra et al. [60] showed that Ae. aegypti can develop in polluted environments such as domestic sewage, where there is a high concentration of organic material and practically zero DO (0.12 mg L−1), as well as in treated effluents, with carbonaceous material removed, as the post-treated effluent after anaerobic filter and in a polishing pond.
Notably, food availability is a decisive condition in the selection of the breeding site by the female, even in conditions considered unfavorable with regards to the available water quality, including high pollution levels. Beserra et al. [60] analyzed water quality and food availability for Ae. aegypti larvae. Water quality elongates the breeding period, with or without food and the medium turbidity decreases. Further, turbidity is an important parameter and low light penetrability in the aquatic environment is beneficial for the species, since Ae. aegypti larvae are photophobic. The Ae. aegypti is a competent vector for the transmission of flavivirus, which causes yellow fever and dengue fever. In this organism, there are at least 10 different rhodopsins. Mosquitoes have similar sets of rhodopsin and retinal organization, including the malaria vector Anopheles gambiae (Giles) (Diptera: Culicidae) that diverged from Ae. aegypti millions of years ago. Conservation of molecules within these visual systems implies that these species have visual processing capabilities similar to those of the common ancestor. Thus, these capabilities remain important for the suitability of existing species and their adaptability to environmental conditions. In Ae. aegypti, several mechanisms contribute to the ability of a photoreceptor to adapt to ambient light conditions. The Ae. aegypti expresses long wavelength rhodopsin Aaop1 on all R1–6 photoreceptors and most R8 photoreceptors. These photoreceptors alter the cellular location of Aaop1 and rearrange their photosensitive rabdomeric membranes daily [61].
The effects of temperature on Ae. aegypti larval behavior have been examined for different food availability conditions at breeding sites. With reduced food availability and low temperature, the larvae spend more time and energy to feed. At the temperature extremes, there are negative impacts on feeding behavior and larval activity [62].
Increasing viscosity by elevating the concentration of dissolved solids imposes unfavorable conditions, including limiting movement of the organisms. However, females adapt, choosing to perform oviposition even under unfavorable conditions. This phenomenon shows that for Ae. aegypti, the water physicochemical quality is not the most important parameter, but rather the conditions that the breeding site presents for the development and survival of the immature individuals are crucial.
Albeny-Simões et al. [63] showed that the choice of breeding grounds for oviposition and reproduction with aquatic larvae is influenced by the risk of offspring mortality and survival, and food availability. Studies revealed that Ae. aegypti is attracted to do the oviposition in breeding sites with predatory larvae (Toxorhynchites (Theobald) (Diptera: Culicidae)), as the density of dead larvae and metabolic products increase the bacterial abundance in the breeding location. Bacterial biomass and species composition are environmental determinants for the occurrence and abundance of Aedes spp., breeding sites with competing species [64, 65].
Soares Pinheiro et al. [66] evaluated Ae. aegypti viability in the Amazon region that has high humidity. The Ae. aegypti eggs have been stored in plastic cups, paper envelopes or plastic bags and maintained in internal and external areas for different times. Overall, the storage form is important for egg viability. The authors concluded that Ae. aegypti viability in the Amazon region is maintained at high levels up to 4 months, at which time there are drastic reductions and hatchings up to 8 months occur at very low percentages. These results show that excess humidity and temperature can induce constant stimuli to hatching eggs and possibly due to water stress, reduce their viability. Comparatively, in dry environments this viability can be preserved for up to 492 days [59]. In this regard, the selection of suitable place for oviposition is fundamental for the distribution and establishment of Ae. aegypti. Studies indicate that Ae. aegypti females are not attracted to breeding grounds with clean water. This flexibility to choose breeding sites for posture, despite the apparent risks to reproduction, is an important fact. This posture behavior demonstrates the adaptability of the insect to the different environmental situations, even though they appear to represent unfavorable conditions [61, 62].
Additionally, breeding sites of principal dengue virus spreader Ae. aegypti [67] and secondary vector Ae. albopictus [68] or both [69] can be stopped through biological control [70]; with environmental modifications [71]; utilizing copepods (H. Milne-Edwards) (Copepoda: Cyclopidae) [72]; and using ecological services by frogs, toads and tadpoles (Anura) [73].
Studies aimed the understanding of the life cycle and development of Culicidae mosquitoes and their association with breeding sites are extremely important, since the main control actions are performed in these microhabitats. Although many researchers have devoted themselves to this topic, elucidating the main factors associated with the development of immature forms and seeking new control alternatives, what we have observed is that these insects can colonize new microhabitats, expanding to new areas and reaching high levels of infestation, resulting in outbreaks and epidemics of some arboviruses, such as dengue, chikungunya and zika.
The main factor contributing to this fact is the high adaptive power that these insects have in colonizing new breeding sites, the natural ones, but especially the artificial ones, located in urban environments, which are increasingly available due to the lack of investment in the improvement of sanitary conditions in cities, disorderly expansion of urban centers, increase of consumable products in disposable packaging, among others.
The available studies on the type, frequency and colonization of Aedes spp. breeding sites show that artificial breeding sites are preferred, and can be colonized by Aedes and other associated species. Natural breeding sites in plants, rocks and organic remains can be colonized for long periods, preferably by Ae. albopictus and occasionally by Ae. aegypti. These preferences can be reversed based on climatic influences, food availability, and the breeding water physicochemical and biological conditions. The breeding sites most suitable for Aedes spp. reproduction have a considerable amount of decomposing organic matter and conditions that guarantee a reproductive advantage to these species.
Mosquito population control is dependent on adherence and awareness of populations and knowledge of breeding conditions. Population control interventions with new products with potentially wide use or efficient strategies that are based on habits and ecological knowledge of breeding sites can be advantageous for Ae. aegypti population and reproduction control.
Acknowledgments are due to CNPq (477012/2013-2\\307390/2016-0) and FUNDECT (35308.522.6490.25062016) for the financial supports.
IntechOpen's Academic Editors and Authors have received funding for their work through many well-known funders, including: the European Commission, Bill and Melinda Gates Foundation, Wellcome Trust, Chinese Academy of Sciences, Natural Science Foundation of China (NSFC), CGIAR Consortium of International Agricultural Research Centers, National Institute of Health (NIH), National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), National Institute of Standards and Technology (NIST), German Research Foundation (DFG), Research Councils United Kingdom (RCUK), Oswaldo Cruz Foundation, Austrian Science Fund (FWF), Foundation for Science and Technology (FCT), Australian Research Council (ARC).",metaTitle:"Open Access Funding",metaDescription:"Open Access Funding",metaKeywords:null,canonicalURL:"/page/open-access-funding",contentRaw:'[{"type":"htmlEditorComponent","content":"
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.\\n\\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:\\n\\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).\\n\\n
Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at email@example.com\\n\\n
Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.\\n\\n
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.\n\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:\n\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).\n\n
Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at firstname.lastname@example.com\n\n
Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.\n\n