Biomimic models for 
Introduction Diabetes is a chronic metabolic disease marked by persistently high blood glucose levels, resulting in progressive damage to key organs, including blood vessels, heart, kidneys, eyes, and nerves. Predominantly, Type 2 Diabetes (T2D) manifests in adults and is either marked by inadequate insulin production or insulin resistance. According to the International Diabetes Federation's latest report in 2021, diabetes affects 537 million people worldwide, primarily those aged 20 to 79. By 2030 and 2045, this number is anticipated to go up to 643 million and 783 million, respectively. Furthermore, 75 % of individuals with diabetes reside in developing and underdeveloped countries ( ). Dietary composition is one of the pivotal risk factors in hyperglycemia, particularly the type and quantity of carbohydrates consumed. Research by the Madras Diabetes Research Foundation team, in India, unveiled a five-fold higher risk of T2D associated with the highest carbohydrate intake (587 g/day) compared to the lowest (294 g/day) among selected individuals ( ). This risk is primarily attributed to our carbohydrate-rich diet, commonly explained through various indices like glycemic response (GR), glycemic index (GI), glycemic load (GL), and inherent glycemic potential (IGP) ( ; ). GR refers to the spike in blood glucose concentration that occurs after a meal or food containing carbohydrates has been consumed. GI is defined as the GR elicited by a serving of food containing either 50 g or 25 g of available carbohydrates, relative to the GR evoked by an equivalent amount of a reference carbohydrate food (such as glucose or white wheat bread). The product of the GI and the total amount of carbohydrates that are available in a specific meal is the GL ( ). In 2021, Krishnan et al. reported IGP as another index that endorses the glycemic amplitude and could be used in assessing food crops as a quick way to identify donors for breeding low glycemic traits. The presence of available carbohydrate quantity and quality are the key determinants of glycemic amplitude. Understanding the glycemic amplitude of food is crucial, as it can vary even among different varieties of the same crop due to factors such as the amylose/amylopectin ratio, starch crystallinity, matrix composition, and starch-matrix component (protein/lipid/fiber/polyphenol) interactions ( ; ; ). The modern shift towards processed, fiber-depleted carbohydrate foods correlate with chronic hyperglycemia and its associated complications. Diets characterized by a high GI or GL, which consistently elevate postprandial blood glucose levels, have been implicated in increased cancer risk, and purportedly mediated by insulin-like growth factor dynamics ( ). There has been a prevalent negative perception in light of associated health concerns, particularly regarding high GI carbohydrates. Whereas, a higher intake of dietary fiber has been linked to a reduced risk of T2D in the general population. Incorporating fiber-rich foods into the diet could play a significant role in diabetes prevention ( ). These observations have been corroborated across genders, with females exhibiting more pronounced risk reduction ( ). Furthermore, evidence suggests that adopting low GL and low GI diets may confer protective benefits against coronary heart disease and specific cancer types such as breast and colorectal cancers ( ). The proposed mechanism underlying these favorable outcomes may entail the delayed carbohydrate digestion characteristic of low GI foods, the presence of viscous fibers, complex molecular structures, and inhibitors targeting carbohydrate-digesting enzymes ( ). Thus, emphasizing the importance of scientifically informed assessments of food GI and advocating for further research aimed at developing economically viable low GI staple options are imperative for promoting public health awareness in this domain. One of the major barriers to rapid trial and error testing of foods during their developmental stages is the laborious and time-consuming nature of in vitro assays. With the advancement of nanotechnology in the field of science, a glucose nanosensor offers an excellent potential to facilitate the procedure. Nanosensors present benefits compared to traditional methods in terms of speed, cost-effectiveness, accuracy, sensitivity, selectivity, portability, and miniaturization ( ). Among these nanosensors, non-enzymatic electrochemical nanosensors are preferred due to their additional benefits over enzymatic glucose sensors. Even though the glucose enzyme sensor has high selectivity and sensitivity, it also has significant owing due to the presence of enzymes in its structure ( ). For example, the enzyme fixation process is complicated, it is difficult to ensure the exact amount of enzyme, and it can be significantly influenced by temperature and pH. As a result, glucose enzyme-free sensors are gaining popularity. Non-enzymatic sensors are easier to prepare than enzyme sensors, are less impacted by their surroundings, and may be preserved for a long period ( ). Detecting this presents a distinct challenge compared to the direct measurement of carbohydrates, as digested food samples often contain simulated fluids or buffers with electrolytes that aid digestion. Final digestive aliquots include various components of the digested food matrix, such as peptides, amino acids, and fatty acids, which can interfere with the detection process. These biomolecules can interfere with the accurate detection of glucose in the matrix. Consequently, there is a need to optimize a suitable nanosensor for this specific purpose. Our genuine interest in using Artificial Intelligence (AI) to simulate in vitro digestion opens up promising possibilities in this exciting research area. The application of AI/Machine Learning (ML) techniques offers robust solutions for mitigating interfering compound signals and normalizing glucose measurements in non-enzymatic sensors designed for complex food matrices. This approach begins with sophisticated data preprocessing, employing techniques such as wavelet transforms for signal de-noising and Principal Component Analysis (PCA) for feature extraction. Supervised learning methods, including Decision Tree, Random Forest Regression, Support Vector Regression (SVR), and Deep Neural Networks (DNN), can be trained on datasets containing known concentrations of glucose and interferents to accurately predict true glucose levels. This review explores the current state of integrating these techniques into in vitro protocols and presents a potential model for the proposed approach. This development will support the creation of low-GI food products, GI labeling, and the ongoing efforts to breed food crops with low-GI traits. Along with that, the model can also be used as an alternative to preliminary studies in pharmacology, toxicology, microbiology, and nutrition. 

Glycemic index (GI) estimation – Current scenario and bottlenecks Evaluating carbohydrate quality indices through in vitro methods requires a thorough understanding of each step of carbohydrate digestion - oral, gastric, and intestinal phases, and understanding the enzymes and other interacting elements that play crucial roles in these processes ( ) . This knowledge is vital for comprehending how various foods can influence glucose metabolism and ultimately contribute to the prevention and management of diabetes. The primary challenge is the absence of an efficient method to quickly and painlessly determine the GI. The glycemic amplitude of dietary ingredients may be assessed both in vitro and in vivo . Ideally, a precise and reliable analysis of food digestion should be conducted in vivo . Although further steps and validations have been implemented to enhance accuracy and precision, the basic in vivo GI estimation methodology that was developed in 1981 remains intact ( ). Human clinical investigations, however, present difficulties since they are often linked with poor repeatability because of individual variances and are not always economically, technically, or ethically acceptable ( ; ). The International Organization for Standardization (ISO) issued an official in vivo GI measurement method in 2010 ( ), which is highly accurate and precise but involves significant effort in managing individuals and obtaining ethical approval, making it relatively expensive. Studying human digestive processes must adhere to stringent international guidelines and regulatory frameworks, as outlined by organizations like the European Union in their directives addressing clinical research protocols and ethical standards ( ; ). In clinical trials for postprandial glycemic response (PPGR) assessment, researchers prioritize non-invasive and cost-effective methodologies. Thus, experimental animal models have emerged as valuable screening tool, allowing for more invasive procedures like cannulation and catheterization to access gastrointestinal tract (GIT) and central organs. While invasive techniques such as intubation, intraluminal manometry, and gastric barostat can provide detailed data on gastric motility and emptying, their disruptive nature and impact on normal physiological processes make them impractical for routine PPGR studies in animal models too. These animal models face their own limitations, including interspecies differences in metabolism, digestive physiology, and stress responses, which can affect the translation of findings to human applications ( ; ). Thus, use of animal models as alternatives is also discouraged by the National Institutes of Health (NIH), which advocates for complementary approaches that are both rigorous and reproducible. Since 2022, the NIH has promoted the adoption of innovative in vitro methods that adhere to the principles of the 3Rs: reduction, refinement, and replacement of animal models. These challenges have prompted researchers to develop in vitro models that simulate the human digestive system, often incorporating computational and mathematical modeling. Consequently, there is growing interest in creating reliable in vitro digestion systems that can closely replicate the human GIT. Thus, researchers have explored in vitro approaches, to determine the GI levels of foods. A major challenge in developing these methods was simulating in vitro carbohydrate digestion across oral, gastric, and intestinal phases. reviewed various static and dynamic digestive models for this purpose. Furthermore, to understand the physiological effects of carbohydrate digestion on GR in humans, measurements should consider factors such as the precise dose and type of carbohydrate ( e.g. , starch) as consumed. Additional considerations include characterization of the food matrix (including microstructure and nutrient composition), and assessment of starch gelatinization and retrogradation, among other variables ( ). According to , multiple factors influence different phases of carbohydrate digestion, including mastication and salivation in the oral phase, gastric emptying, enzymatic hydrolysis by pepsin as well as lipase in the gastric chamber, and intestinal motility as well as hormonal feedback in the small intestine phase. Another existing limitation is the gap in knowledge in understanding the interaction of mass transfer and reaction engineering for replicating in vitro. In 2014, with a revision in 2019, the COST INFOGEST network reached an international consensus to develop a gold standard protocol. Over 35 countries participated, aligning digestive conditions into a widely accepted protocol known for its consistency across different laboratories, robustness, simplicity, affordability, and straightforward evaluation of each digestive phase ( ). However, conducting a complete in vitro analysis for determining the GI of a meal is not only expensive but also challenging. To determine the GI of composite meals, it is essential to calculate based on the GI of each carbohydrate-containing food or ingredient in the meal, akin to computing the average GI of a diet. Rather than employing the entire INFOGEST method or other complete food digestion protocols, focusing solely on starch/carbohydrate digestion may suffice. Nonetheless, discrepancies in GI for the identical meal as reported by the International GI table pose an issue, as it's difficult to determine the GI value of specific foods consumed by different individuals due to factors like variety, processing, and cooking methods ( ). However, broadly categorizing foods into high-GI (≥ 70), medium-GI (55–70), and low-GI (≤ 55), with glucose as the reference material at a GI value of 100, can be useful for identifying and distinguishing their glycemic potential ( ). 

Static in vitro protocols for glycemic index (GI) estimation Static in vitro digestion models, also referred to as biochemical methods, present a straightforward and cost-effective means for investigating the digestibility of various food items ( ). They serve as a simple and valuable tool applicable across diverse fields including nutrition, pharmaceuticals, and toxicology. Previous efforts to determine GI have predominantly relied on static models. These approaches specifically tailored for assessing glycemic potential have been documented in diverse research studies ( ; ; ; ). These in vitro methods offer a range of benefits in terms of accuracy and simplicity ( ) . These models are mostly mono-compartmental, focusing solely on simulating a specific phase such as intestinal digestion, or sequentially replicating the complete oro-gastro-intestinal simulation. In static models, key parameters like the ratio of meal to secretions, pH level, and concentrations of enzymes and biosurfactants are kept constant. These parameters are predetermined at the onset of each phase and remain constant throughout their duration ( ). In a broad classification of static in vitro digestion models, a dichotomy exists between mono-compartmental and multi-compartmental systems. Mono-compartmental models, known for their simplicity and efficiency, utilize a single reaction vessel for all digestive phases. In contrast, multi-compartmental models simulate the physiological progression of digesta through distinct compartments representing specific digestive phases. Contemporary research predominantly favors mono-compartmental static models for their pragmatic balance of simplicity and efficacy ( ). GI estimation methods can be placed into two main groups: single enzyme-based methods and multi-enzyme-based in vitro digestion models. Single enzyme-based approaches primarily focus on a specific enzyme, such as α-amylase or pancreatin. In contrast, multi-enzyme digestion methods mimic the human digestive process, incorporating oral, gastric, and intestinal phases with a range of enzymes involved in starch digestion. Both categories now commonly include a final step with amyloglucosidase, reflecting a trend in method refinement. This inclusion addresses the product inhibition effects of maltose, which can reduce reaction rates. By converting maltose and maltotriose into glucose (an inhibitor free product), researchers mitigate these inhibitory effects, although maltose's impact is noted to be minimal ( ). 3.1 In vivo and in vitro characterization of digestive phases 3.1.1 Oral phase The process of food digestion initiates in the oral cavity, where mastication and mixing occur. In the context of human digestion, saliva, a complex and viscous fluid generated by the salivary glands, serves as the primary medium. Saliva consists of approximately 99.5 % water, 0.3 % proteins (including about 30 diverse enzymes), and a variety of electrolytes such as potassium, sodium, magnesium, calcium, bicarbonate, and phosphate ( ). Notable enzymes present in saliva include salivary amylase and lingual lipase ( ). Other than these, invertase, maltase, carbonic anhydrase, urease, oxidase, catalase, protease, phosphatase, lysozyme, hyaluronidase, peroxidase, superoxide dismutase, and alcohol dehydrogenase have been reported. Early methodologies for GI estimation typically bypassed the oral phase of digestion in their protocols. However, contemporary protocols now universally incorporate this phase ( ). Oral processing simulators developed since the 19th century were mainly to study dental aspects rather than studying the physiological process of digestion. Early models laid the groundwork for advancements in simulating oral conditions. For instance, bacterial culture-based systems capable of continuous nutrient replenishment have been refined over the decades. Similarly, more sophisticated artificial mouth devices now incorporate features such as jaw movements, salivation, breathing, temperature regulation, and fluid dynamics, reflecting improvements in mimicking oral environments ( ). More advanced systems now feature multi-chamber designs and multichannel peristaltic pumps for precise delivery of nutrient-rich broths and simulated saliva, enabling improved modeling of oral processes. Additionally, artificial mouth models have been developed to facilitate the analysis of plaque biofilms, bacterial metabolic byproducts, and real-time pH monitoring, offering greater understanding into oral health ( ; ). Unfortunately, oral processing stimulators have not been tried much in combination with in vitro digestion models. The specific oral characteristics crucial for oral digestion were then thoroughly examined and subsequently replicated in many dynamic oral models, as reviewed by . The transit time of food through the oral cavity varies widely, ranging from 5 s to 2 min for different food items. Similarly, the chewing cycle varies significantly depending on the food's rheological properties. The maximum force exerted by the human jawline is approximately 400 N, with biting forces typically ranging from 100 N to 400 N. Saliva pH ranges from 5 to 7, which plays an important role in the initial stages of digestion ( ). In efforts to replicate oral conditions in vitro , static models employ buffers such as tris maleate buffer, salivary simulated fluid (SSF), phosphate buffer, carbonate buffer, and sodium acetate buffer. The pH of these buffers typically ranges from 6 to 7, with a majority falling within the range of 6.9 to 7 ( ) . In current static in vitro protocols for GI estimation, the challenge lies in replicating oral mastication. Some approaches involve direct human mastication followed by in vitro gastric and intestinal simulation ( ), while others employ quick strokes/mixing with glass rods/spatula for seconds to mimic oral mixing ( ). Alternatively, certain protocols opted for a 2 min incubation in shakers. Salivary amylase/α-amylase is the enzyme predominantly used in this phase, with enzyme activity ranging from 55.5 U mL −1 to 250 U mL −1 across different protocols ( ) . Following the INFOGEST protocol, many protocols now adhere to a standardized enzyme activity of 75 U mL −1 ( ). 3.1.2 Gastric phase Food bolus after the initial oral processing, traverses into the stomach via esophageal peristalsis, where it undergoes processing into a homogeneous semi-solid chyme. The J-shaped human stomach is a cylindrical muscular organ that holds approximately 1.5 L. The stomach is divided into several sections: the cardia, where the esophagus connects to the stomach, the fundus (the upper part), the body (the central and main part), the antrum (the lower part), and the pylorus, which connects the stomach to the duodenum ( ; ). Major physiological processes occurring within the stomach encompass food breakdown and mixing, wall contractions and peristaltic movements, gastric sieving, and transport ( ). These processes involve both enzymatic and acid hydrolysis, where enzymatic hydrolysis breaks down proteins through pepsin and lipids through gastric lipase, while acid hydrolysis, driven by gastric acid, primarily softens and dissolves the food. The pH of the gastric environment typically ranges from 1 to 3, with transit times varying from 15 min to 4 h ( ). The gastric emptying rate is controlled by the transfer of 2 Kcal min −1 from a meal volume of 500 mL, according to in vivo observations ( ). The contraction frequency within the stomach is approximately 3 cycles per minute. Concerning gastric models, mono-compartmental systems commonly employ diluted HCl to adjust the pH to 1.5–2. Subsequently, pepsin is often utilized for GI estimation, despite its primary role in protein digestion. The inclusion of pepsin not only facilitates protein digestion but also liberates starch molecules involved in protein interactions, rendering starch accessible for subsequent digestion in the intestinal phase. Consequently, the duration of digestion for carbohydrate-rich foods exhibits considerable variability, ranging from 30 min to 4 h, accompanied by corresponding variations in pepsin activity levels ( ) . reported that the lowest Starch hydrolysis % (SH %) was observed when only α-amylase and amyloglucosidase were utilized. Introducing an acidic pepsin step in the digestion protocol resulted in a slight increase in starch gel hydrolysis. Yet, the optimal hydrolysis rate occurred when trypsin and chymotrypsin were incorporated during intestinal digestion after initial treatment with pepsin. In recent times, numerous approaches have endeavored to integrate the viscous characteristics of gastric digestion, primarily by employing hydrocolloids, notably guar gum. Meals with high viscosity have been observed to augment the size of the gastric antrum and the rate of gastric retention. This alteration in viscosity has led to a postponement in gastric emptying, consequently constraining the surge in postprandial glucose levels ( ). For lipid-rich foods, the incorporation of gastric lipase in in vitro systems has also been proposed. Incubation in in vitro systems is typically conducted using methods such as a shaker, shaking water bath, or magnetic stirring. 3.1.3 Intestinal phase The intestinal digestion is characterized by its complexity, particularly in vivo . This phase spans from the stomach's distal part, beginning with the pyloric sphincter, through the duodenum, jejunum, and ileum, and ends with the large intestine's proximal entry at the ileocecal valve ( ). Nutrient absorption and digestion from food are the two major activities of the small intestine. Notably, its pH ranges from 6 to 7.5, while transit times vary between 1 and 5 h, influenced by various factors ( ). Predominantly, enzymatic digestion occurs within the small intestine, encompassing the breakdown of starch, proteins, lipids, and nucleic acids. Starch breakdown involves enzymes such as pancreatic amylase, sucrase, lactase, dextrinase, maltase, and amyloglucosidase. Protein digestion is facilitated by chymotrypsin, trypsin, elastase, and carboxypeptidase. Lipid breakdown is driven by pancreatic lipases and phospholipase, while nucleic acids are broken down by nucleases, nucleosidases, and phosphatases ( ; ; ; ). Replicating intestinal digestion in vitro involves multi-compartmental and mono-compartmental, with the former mostly seen in dynamic digestion systems. Conversely, static in vitro GI estimation protocols predominantly emulate mono-compartmental conditions continuous with the gastric phase. Therefore, an additional step of pH adjustment is often required, mostly using aqueous NaOH ( ). Buffers such as sodium acetate, sodium potassium phosphate, or simulated intestinal fluid within the pH range of 6–6.9 are commonly utilized in this phase ( ) . In in vitro GI estimation protocols, SH is a primary focus, and enzymes such as pancreatin and α-amylase are commonly used for analyzing starchy foods. For lipid-rich foods, pancreatic lipase is added alongside pancreatin. These digestion processes are typically conducted in incubator shakers, magnetic stirrers, or shaking water baths. In static protocols, the concentrations of enzymes employed can vary significantly. While this phase commonly utilizes pancreatin or α-amylase alone, the enzymatic activity employed can range considerably. Reported enzyme activity spans from as low as 2.6 U mL −1 to as high as 7000 U mL −1 , or from 28.4 μg mL −1 to 9 g in case of pancreatin ( ) . 

In vivo and in vitro characterization of digestive phases 3.1.1 Oral phase The process of food digestion initiates in the oral cavity, where mastication and mixing occur. In the context of human digestion, saliva, a complex and viscous fluid generated by the salivary glands, serves as the primary medium. Saliva consists of approximately 99.5 % water, 0.3 % proteins (including about 30 diverse enzymes), and a variety of electrolytes such as potassium, sodium, magnesium, calcium, bicarbonate, and phosphate ( ). Notable enzymes present in saliva include salivary amylase and lingual lipase ( ). Other than these, invertase, maltase, carbonic anhydrase, urease, oxidase, catalase, protease, phosphatase, lysozyme, hyaluronidase, peroxidase, superoxide dismutase, and alcohol dehydrogenase have been reported. Early methodologies for GI estimation typically bypassed the oral phase of digestion in their protocols. However, contemporary protocols now universally incorporate this phase ( ). Oral processing simulators developed since the 19th century were mainly to study dental aspects rather than studying the physiological process of digestion. Early models laid the groundwork for advancements in simulating oral conditions. For instance, bacterial culture-based systems capable of continuous nutrient replenishment have been refined over the decades. Similarly, more sophisticated artificial mouth devices now incorporate features such as jaw movements, salivation, breathing, temperature regulation, and fluid dynamics, reflecting improvements in mimicking oral environments ( ). More advanced systems now feature multi-chamber designs and multichannel peristaltic pumps for precise delivery of nutrient-rich broths and simulated saliva, enabling improved modeling of oral processes. Additionally, artificial mouth models have been developed to facilitate the analysis of plaque biofilms, bacterial metabolic byproducts, and real-time pH monitoring, offering greater understanding into oral health ( ; ). Unfortunately, oral processing stimulators have not been tried much in combination with in vitro digestion models. The specific oral characteristics crucial for oral digestion were then thoroughly examined and subsequently replicated in many dynamic oral models, as reviewed by . The transit time of food through the oral cavity varies widely, ranging from 5 s to 2 min for different food items. Similarly, the chewing cycle varies significantly depending on the food's rheological properties. The maximum force exerted by the human jawline is approximately 400 N, with biting forces typically ranging from 100 N to 400 N. Saliva pH ranges from 5 to 7, which plays an important role in the initial stages of digestion ( ). In efforts to replicate oral conditions in vitro , static models employ buffers such as tris maleate buffer, salivary simulated fluid (SSF), phosphate buffer, carbonate buffer, and sodium acetate buffer. The pH of these buffers typically ranges from 6 to 7, with a majority falling within the range of 6.9 to 7 ( ) . In current static in vitro protocols for GI estimation, the challenge lies in replicating oral mastication. Some approaches involve direct human mastication followed by in vitro gastric and intestinal simulation ( ), while others employ quick strokes/mixing with glass rods/spatula for seconds to mimic oral mixing ( ). Alternatively, certain protocols opted for a 2 min incubation in shakers. Salivary amylase/α-amylase is the enzyme predominantly used in this phase, with enzyme activity ranging from 55.5 U mL −1 to 250 U mL −1 across different protocols ( ) . Following the INFOGEST protocol, many protocols now adhere to a standardized enzyme activity of 75 U mL −1 ( ). 3.1.2 Gastric phase Food bolus after the initial oral processing, traverses into the stomach via esophageal peristalsis, where it undergoes processing into a homogeneous semi-solid chyme. The J-shaped human stomach is a cylindrical muscular organ that holds approximately 1.5 L. The stomach is divided into several sections: the cardia, where the esophagus connects to the stomach, the fundus (the upper part), the body (the central and main part), the antrum (the lower part), and the pylorus, which connects the stomach to the duodenum ( ; ). Major physiological processes occurring within the stomach encompass food breakdown and mixing, wall contractions and peristaltic movements, gastric sieving, and transport ( ). These processes involve both enzymatic and acid hydrolysis, where enzymatic hydrolysis breaks down proteins through pepsin and lipids through gastric lipase, while acid hydrolysis, driven by gastric acid, primarily softens and dissolves the food. The pH of the gastric environment typically ranges from 1 to 3, with transit times varying from 15 min to 4 h ( ). The gastric emptying rate is controlled by the transfer of 2 Kcal min −1 from a meal volume of 500 mL, according to in vivo observations ( ). The contraction frequency within the stomach is approximately 3 cycles per minute. Concerning gastric models, mono-compartmental systems commonly employ diluted HCl to adjust the pH to 1.5–2. Subsequently, pepsin is often utilized for GI estimation, despite its primary role in protein digestion. The inclusion of pepsin not only facilitates protein digestion but also liberates starch molecules involved in protein interactions, rendering starch accessible for subsequent digestion in the intestinal phase. Consequently, the duration of digestion for carbohydrate-rich foods exhibits considerable variability, ranging from 30 min to 4 h, accompanied by corresponding variations in pepsin activity levels ( ) . reported that the lowest Starch hydrolysis % (SH %) was observed when only α-amylase and amyloglucosidase were utilized. Introducing an acidic pepsin step in the digestion protocol resulted in a slight increase in starch gel hydrolysis. Yet, the optimal hydrolysis rate occurred when trypsin and chymotrypsin were incorporated during intestinal digestion after initial treatment with pepsin. In recent times, numerous approaches have endeavored to integrate the viscous characteristics of gastric digestion, primarily by employing hydrocolloids, notably guar gum. Meals with high viscosity have been observed to augment the size of the gastric antrum and the rate of gastric retention. This alteration in viscosity has led to a postponement in gastric emptying, consequently constraining the surge in postprandial glucose levels ( ). For lipid-rich foods, the incorporation of gastric lipase in in vitro systems has also been proposed. Incubation in in vitro systems is typically conducted using methods such as a shaker, shaking water bath, or magnetic stirring. 3.1.3 Intestinal phase The intestinal digestion is characterized by its complexity, particularly in vivo . This phase spans from the stomach's distal part, beginning with the pyloric sphincter, through the duodenum, jejunum, and ileum, and ends with the large intestine's proximal entry at the ileocecal valve ( ). Nutrient absorption and digestion from food are the two major activities of the small intestine. Notably, its pH ranges from 6 to 7.5, while transit times vary between 1 and 5 h, influenced by various factors ( ). Predominantly, enzymatic digestion occurs within the small intestine, encompassing the breakdown of starch, proteins, lipids, and nucleic acids. Starch breakdown involves enzymes such as pancreatic amylase, sucrase, lactase, dextrinase, maltase, and amyloglucosidase. Protein digestion is facilitated by chymotrypsin, trypsin, elastase, and carboxypeptidase. Lipid breakdown is driven by pancreatic lipases and phospholipase, while nucleic acids are broken down by nucleases, nucleosidases, and phosphatases ( ; ; ; ). Replicating intestinal digestion in vitro involves multi-compartmental and mono-compartmental, with the former mostly seen in dynamic digestion systems. Conversely, static in vitro GI estimation protocols predominantly emulate mono-compartmental conditions continuous with the gastric phase. Therefore, an additional step of pH adjustment is often required, mostly using aqueous NaOH ( ). Buffers such as sodium acetate, sodium potassium phosphate, or simulated intestinal fluid within the pH range of 6–6.9 are commonly utilized in this phase ( ) . In in vitro GI estimation protocols, SH is a primary focus, and enzymes such as pancreatin and α-amylase are commonly used for analyzing starchy foods. For lipid-rich foods, pancreatic lipase is added alongside pancreatin. These digestion processes are typically conducted in incubator shakers, magnetic stirrers, or shaking water baths. In static protocols, the concentrations of enzymes employed can vary significantly. While this phase commonly utilizes pancreatin or α-amylase alone, the enzymatic activity employed can range considerably. Reported enzyme activity spans from as low as 2.6 U mL −1 to as high as 7000 U mL −1 , or from 28.4 μg mL −1 to 9 g in case of pancreatin ( ) . 

Oral phase The process of food digestion initiates in the oral cavity, where mastication and mixing occur. In the context of human digestion, saliva, a complex and viscous fluid generated by the salivary glands, serves as the primary medium. Saliva consists of approximately 99.5 % water, 0.3 % proteins (including about 30 diverse enzymes), and a variety of electrolytes such as potassium, sodium, magnesium, calcium, bicarbonate, and phosphate ( ). Notable enzymes present in saliva include salivary amylase and lingual lipase ( ). Other than these, invertase, maltase, carbonic anhydrase, urease, oxidase, catalase, protease, phosphatase, lysozyme, hyaluronidase, peroxidase, superoxide dismutase, and alcohol dehydrogenase have been reported. Early methodologies for GI estimation typically bypassed the oral phase of digestion in their protocols. However, contemporary protocols now universally incorporate this phase ( ). Oral processing simulators developed since the 19th century were mainly to study dental aspects rather than studying the physiological process of digestion. Early models laid the groundwork for advancements in simulating oral conditions. For instance, bacterial culture-based systems capable of continuous nutrient replenishment have been refined over the decades. Similarly, more sophisticated artificial mouth devices now incorporate features such as jaw movements, salivation, breathing, temperature regulation, and fluid dynamics, reflecting improvements in mimicking oral environments ( ). More advanced systems now feature multi-chamber designs and multichannel peristaltic pumps for precise delivery of nutrient-rich broths and simulated saliva, enabling improved modeling of oral processes. Additionally, artificial mouth models have been developed to facilitate the analysis of plaque biofilms, bacterial metabolic byproducts, and real-time pH monitoring, offering greater understanding into oral health ( ; ). Unfortunately, oral processing stimulators have not been tried much in combination with in vitro digestion models. The specific oral characteristics crucial for oral digestion were then thoroughly examined and subsequently replicated in many dynamic oral models, as reviewed by . The transit time of food through the oral cavity varies widely, ranging from 5 s to 2 min for different food items. Similarly, the chewing cycle varies significantly depending on the food's rheological properties. The maximum force exerted by the human jawline is approximately 400 N, with biting forces typically ranging from 100 N to 400 N. Saliva pH ranges from 5 to 7, which plays an important role in the initial stages of digestion ( ). In efforts to replicate oral conditions in vitro , static models employ buffers such as tris maleate buffer, salivary simulated fluid (SSF), phosphate buffer, carbonate buffer, and sodium acetate buffer. The pH of these buffers typically ranges from 6 to 7, with a majority falling within the range of 6.9 to 7 ( ) . In current static in vitro protocols for GI estimation, the challenge lies in replicating oral mastication. Some approaches involve direct human mastication followed by in vitro gastric and intestinal simulation ( ), while others employ quick strokes/mixing with glass rods/spatula for seconds to mimic oral mixing ( ). Alternatively, certain protocols opted for a 2 min incubation in shakers. Salivary amylase/α-amylase is the enzyme predominantly used in this phase, with enzyme activity ranging from 55.5 U mL −1 to 250 U mL −1 across different protocols ( ) . Following the INFOGEST protocol, many protocols now adhere to a standardized enzyme activity of 75 U mL −1 ( ). 

Gastric phase Food bolus after the initial oral processing, traverses into the stomach via esophageal peristalsis, where it undergoes processing into a homogeneous semi-solid chyme. The J-shaped human stomach is a cylindrical muscular organ that holds approximately 1.5 L. The stomach is divided into several sections: the cardia, where the esophagus connects to the stomach, the fundus (the upper part), the body (the central and main part), the antrum (the lower part), and the pylorus, which connects the stomach to the duodenum ( ; ). Major physiological processes occurring within the stomach encompass food breakdown and mixing, wall contractions and peristaltic movements, gastric sieving, and transport ( ). These processes involve both enzymatic and acid hydrolysis, where enzymatic hydrolysis breaks down proteins through pepsin and lipids through gastric lipase, while acid hydrolysis, driven by gastric acid, primarily softens and dissolves the food. The pH of the gastric environment typically ranges from 1 to 3, with transit times varying from 15 min to 4 h ( ). The gastric emptying rate is controlled by the transfer of 2 Kcal min −1 from a meal volume of 500 mL, according to in vivo observations ( ). The contraction frequency within the stomach is approximately 3 cycles per minute. Concerning gastric models, mono-compartmental systems commonly employ diluted HCl to adjust the pH to 1.5–2. Subsequently, pepsin is often utilized for GI estimation, despite its primary role in protein digestion. The inclusion of pepsin not only facilitates protein digestion but also liberates starch molecules involved in protein interactions, rendering starch accessible for subsequent digestion in the intestinal phase. Consequently, the duration of digestion for carbohydrate-rich foods exhibits considerable variability, ranging from 30 min to 4 h, accompanied by corresponding variations in pepsin activity levels ( ) . reported that the lowest Starch hydrolysis % (SH %) was observed when only α-amylase and amyloglucosidase were utilized. Introducing an acidic pepsin step in the digestion protocol resulted in a slight increase in starch gel hydrolysis. Yet, the optimal hydrolysis rate occurred when trypsin and chymotrypsin were incorporated during intestinal digestion after initial treatment with pepsin. In recent times, numerous approaches have endeavored to integrate the viscous characteristics of gastric digestion, primarily by employing hydrocolloids, notably guar gum. Meals with high viscosity have been observed to augment the size of the gastric antrum and the rate of gastric retention. This alteration in viscosity has led to a postponement in gastric emptying, consequently constraining the surge in postprandial glucose levels ( ). For lipid-rich foods, the incorporation of gastric lipase in in vitro systems has also been proposed. Incubation in in vitro systems is typically conducted using methods such as a shaker, shaking water bath, or magnetic stirring. 

Intestinal phase The intestinal digestion is characterized by its complexity, particularly in vivo . This phase spans from the stomach's distal part, beginning with the pyloric sphincter, through the duodenum, jejunum, and ileum, and ends with the large intestine's proximal entry at the ileocecal valve ( ). Nutrient absorption and digestion from food are the two major activities of the small intestine. Notably, its pH ranges from 6 to 7.5, while transit times vary between 1 and 5 h, influenced by various factors ( ). Predominantly, enzymatic digestion occurs within the small intestine, encompassing the breakdown of starch, proteins, lipids, and nucleic acids. Starch breakdown involves enzymes such as pancreatic amylase, sucrase, lactase, dextrinase, maltase, and amyloglucosidase. Protein digestion is facilitated by chymotrypsin, trypsin, elastase, and carboxypeptidase. Lipid breakdown is driven by pancreatic lipases and phospholipase, while nucleic acids are broken down by nucleases, nucleosidases, and phosphatases ( ; ; ; ). Replicating intestinal digestion in vitro involves multi-compartmental and mono-compartmental, with the former mostly seen in dynamic digestion systems. Conversely, static in vitro GI estimation protocols predominantly emulate mono-compartmental conditions continuous with the gastric phase. Therefore, an additional step of pH adjustment is often required, mostly using aqueous NaOH ( ). Buffers such as sodium acetate, sodium potassium phosphate, or simulated intestinal fluid within the pH range of 6–6.9 are commonly utilized in this phase ( ) . In in vitro GI estimation protocols, SH is a primary focus, and enzymes such as pancreatin and α-amylase are commonly used for analyzing starchy foods. For lipid-rich foods, pancreatic lipase is added alongside pancreatin. These digestion processes are typically conducted in incubator shakers, magnetic stirrers, or shaking water baths. In static protocols, the concentrations of enzymes employed can vary significantly. While this phase commonly utilizes pancreatin or α-amylase alone, the enzymatic activity employed can range considerably. Reported enzyme activity spans from as low as 2.6 U mL −1 to as high as 7000 U mL −1 , or from 28.4 μg mL −1 to 9 g in case of pancreatin ( ) . 

Dynamic models for glycemic index (GI) estimation In food digestion, several parameters are crucial, including the enzyme-substrate ratio, pH transitions, structural mimicry of the GIT, gastric emptying, transit time, volume, transport of digested products, and absorption. Static in vitro models, which do not replicate dynamic and mechanical actions, have constraints in accurately evaluating nutrient accessibility or food dynamics throughout digestion ( ; ). While these static models are extensively employed for evaluating the glucose release of different food items based on digestibility, food structure, and composition, they overly simplify the complexities of the human digestive system. It also lacks the dynamic elements including fluid dynamics and mechanical forces since static models are conducted mostly with a series of falcon tubes/beakers/conical flasks under constant stirring. As such, mechanistic research and hypothesis-generating are the main uses of static models, with screening being one of their specialized applications ( ). Among dynamic models, there are mono-compartmental models which simulate any single compartment of the digestive system while bi-compartmental and multi-compartmental, simulate two or multiple segments of the digestive system, respectively. The mono-compartmental model is primarily designed for the stomach, as exemplified by the Dynamic Gastric Model (DGM), widely used in both the pharmaceutical and food industries, and the Human Gastric Simulator (HGS) for mainly investigating gastric digestion of food. These models integrate variations in gastric pH, sequential enzyme release, and gastric emptying dynamics ( ). Multi-compartmental models are more relevant for GI estimations as they encompass the oral, gastric, and intestinal phases. The TNO Gastrointestinal Model (TIM-1), is a noteworthy example of a multi-compartmental model created by TNO Nutrition and Food Research, Netherlands ( ). TIM-1 uses computer programs to emulate dynamic simulations within the lumen of the digestive tract. For better visualization during digestion, chambers have a glass tube or clear plastic. The programmed gastric emptying curve adheres to intestinal delivery and Elashoff's equation, depending on the kind and quantity of food consumed. A pump action on the surrounding water occasionally squeezes the tubes to simulate peristalsis while keeping the body temperature at 37 °C. The SH rate of fermented oats and barley was evaluated using the aforementioned model, demonstrating that this in vitro method can accurately assess the starch breakdown rate of cereal products ( ). Results also exhibited a close correlation with corresponding in vivo findings from related investigations using equivalent products. This model has also been employed for various other applications, including the bioaccessibility rate of glucose, fatty acids, proteins, and vitamins ( ). The French National Research Institute for Agriculture, Food, and Environment (INRAE) designed the DIDGI® system, another bi-compartmental model. The stomach and small intestine are replicated in the model by employing two glass-jacketed vessels. These jackets are filled with water that is circulated via a temperature-controlled water bath. A teflon membrane with 2 mm pores is placed at the top of the transfer pump, separating the stomach and intestinal compartments, to mimic the pyloric sieving action seen in vivo . The model includes regulated pH conditions in the small intestine, enzyme secretion, gastric emptying, and dynamic shifts in pH in the stomach. Additionally, it regulates the controlled secretion of bile, pancreatin, and intestinal electrolytes ( ). investigated the SH in pasta and bread using this model. They found that before being inactivated due to the low pH of the stomach, salivary α-amylase digested over 80 % of the starch in bread and 30 % in pasta, hydrolyzing more than half of it into oligosaccharides. The results demonstrated a strong correlation with in vivo data, highlighting the significant role of salivary α-amylase in the oro-gastric digestion of carbohydrate-rich foods. Additionally, this model has been employed in various other studies, such as examining protein hydrolysis kinetics and lipid digestion kinetics ( ). The TIM-1 and DIDGI® systems are among the most extensively utilized dynamic digestion models, having been applied in numerous studies ( ). Apart from these, several other models for different applications have been developed, particularly focusing on specific phases of digestion. For instance, the Simulator of the Human Intestinal Microbial Ecosystem (SHIME®) was designed to replicate the microbial environment in the GIT ( ). Colon model has been used to study probiotic bacteria ( ), and realistic stomach force and peristaltic frequency are integrated into the in vitro mechanical gastric system (IMGS) ( ). Other notable models include the Artificial Gastric Digestive System (AGDS) ( ), the SIMulator of the Gastro-Intestinal tract (SIMGI) ( ), and the Dynamic in vitro Human Stomach (DIVHS) ( ). However, concerning SH, the TIM-1 and DIDGI® systems have been predominantly employed. Even compared to static models, the utilization of dynamic models for predicting GI values in foods has been relatively limited. presented a unique approach to forecast PPGR trends in people by combining TIM and in silico mathematical modeling. Similarly, a recent study successfully predicted human oral glucose tolerance test (OGTT) curves by integrating a modified intestinal system with chicken jejunum to replicate the breakdown and assimilation of carbohydrates ( ). Nevertheless, this method has been used to predict GI values or analyze PPGR curves of actual food products. However, for the sole purpose of predicting the GR of food items, these models are costly. As a result, there has been little emphasis on automating or simplifying the process. introduced a novel miniaturized digestion chip that simulates human gastrointestinal digestion in a semi-dynamic fashion, featuring gradual acidification, controlled enzyme delivery, and real-time control of pH and temperature. While this innovation addresses some limitations of conventional in vitro models and is ideal for assessing bioaccessibility of nano-engineered materials and bioactive compounds, it still falls short in replicating the full complexity of human digestion and requires specialized expertise. 

Variability in in vitro digestion models and predictive glycemic index (pGI) estimation As seen above, while estimating GI, variations can be expected due to differences in digestion models (static/semi-dynamic/dynamic), presence or absence of different phases of digestion, enzyme combinations and concentrations, buffer combinations and concentrations, pH at different phases, incubation conditions (restricted/non-restricted system) and colorimetric estimation of glucose ( e.g. , glucose oxidase-peroxidase (GOPOD)/ 3,5-Dinitrosalicylic acid (DNS)/ PAHBAH reagent). The disparities among various static models expose several constraints, particularly in the oral phase. In vivo mastication is a multifaceted process where food particles undergo comminution after the initial bite. Concurrently, saliva is secreted, which is absorbed by the food particles, leading to their agglomeration and further enzymatic treatment. This salivary action dilutes the particles, forming a cohesive bolus ready for gastric processing. However, in vitro methodologies often oversimplify the intricate process of oral processing through mechanical shaking or stroke movements. These methods fail to accurately replicate the variable retention times of different-sized food particles, a factor influenced by the food's rheological and biochemical properties ( ). Additionally, there is a paucity of detailed information regarding the enzymes employed in these studies, including their catalytic activity and specific activity. This gap complicates the reproducibility of inter-laboratory studies. To address this issue, included a requirement in the INFOGEST protocol for conducting enzyme assays prior to in vitro experimentation. This step is essential as the enzymatic activity provided by manufacturers can fluctuate significantly over time. Enzymes require specific storage conditions, and their catalytic efficiency can degrade due to various environmental factors or intrinsic instability, often exacerbated by the presence of proteases. Such degradation highlights the importance of rigorous enzyme assays to ensure the reliability and reproducibility of in vitro digestion studies ( ). 5.1 Predictive glycemic index (pGI) calculation Prediction of GI through in vitro methods often differs from in vivo measurements, primarily due to the sensitivity of enzymatic SH. SH is influenced by numerous factors that, in turn, affect the calculation of in vitro GI. Consequently, in vitro GI is frequently reported as predictive GI (pGI) or estimated GI (eGI) ( ; ). pGI can be determined using the hydrolysis index (HI), SH %, or the area under the curve (AUC) from SH % vs. time plots. The HI-based pGI calculation requires reference foods, such as white bread or glucose, to be digested alongside test samples. However, in vitro methods using established pGI equations based on AUC or SH % do not require reference foods. Some methodologies incorporate both types of pGI equations, those dependent on reference foods (HI-based) and those independent of reference foods (AUC/SH %-based) as seen in the recent studies ( ). Other studies have adopted these equations even when not originally intended for use in their specific methods ( ). illustrates some of the pGI equations. These equations can be classified into two categories: primary and derived. Primary pGI equations refer to those developed as an integral part of an original in vitro method, such as the methods by and . In contrast, derived pGI equations are those established later through modifications or extensions of existing methods. For instance, introduced modified pGI equations based on AUC data. Certain equations are specifically tailored for particular food items; for example, developed an equation for estimating the eGI of commercial noodles ( ). Due to the lack of a standardized GI estimation protocol, researchers frequently employ diverse methodologies. Despite the variety of approaches, GI estimation generally depends on established predictive equations, such as pGI or eGI. These equations are derived based on particular SH profiles, which are influenced by specific enzyme combinations and concentrations. Ensuring the reliability of GI predictions requires strict adherence to the protocol used for deriving these equations. However, replicating the same conditions with a different protocol presents challenges, as deriving pGI for each method demands substantial experimental data. 

Predictive glycemic index (pGI) calculation Prediction of GI through in vitro methods often differs from in vivo measurements, primarily due to the sensitivity of enzymatic SH. SH is influenced by numerous factors that, in turn, affect the calculation of in vitro GI. Consequently, in vitro GI is frequently reported as predictive GI (pGI) or estimated GI (eGI) ( ; ). pGI can be determined using the hydrolysis index (HI), SH %, or the area under the curve (AUC) from SH % vs. time plots. The HI-based pGI calculation requires reference foods, such as white bread or glucose, to be digested alongside test samples. However, in vitro methods using established pGI equations based on AUC or SH % do not require reference foods. Some methodologies incorporate both types of pGI equations, those dependent on reference foods (HI-based) and those independent of reference foods (AUC/SH %-based) as seen in the recent studies ( ). Other studies have adopted these equations even when not originally intended for use in their specific methods ( ). illustrates some of the pGI equations. These equations can be classified into two categories: primary and derived. Primary pGI equations refer to those developed as an integral part of an original in vitro method, such as the methods by and . In contrast, derived pGI equations are those established later through modifications or extensions of existing methods. For instance, introduced modified pGI equations based on AUC data. Certain equations are specifically tailored for particular food items; for example, developed an equation for estimating the eGI of commercial noodles ( ). Due to the lack of a standardized GI estimation protocol, researchers frequently employ diverse methodologies. Despite the variety of approaches, GI estimation generally depends on established predictive equations, such as pGI or eGI. These equations are derived based on particular SH profiles, which are influenced by specific enzyme combinations and concentrations. Ensuring the reliability of GI predictions requires strict adherence to the protocol used for deriving these equations. However, replicating the same conditions with a different protocol presents challenges, as deriving pGI for each method demands substantial experimental data. 

Sensors for glycemic index (GI) estimation 6.1 Enzymatic and non-enzymatic sensors The advancement of nanotechnology in the scientific realm has sparked considerable interest in the utilization of glucose nanosensors, offering promising avenues for process optimization. Nanosensors offer a plethora of advantages over traditional methodologies, including enhanced speed, cost-effectiveness, accuracy, sensitivity, selectivity, portability, and miniaturization. Within the realm of nanosensors, non-enzymatic electrochemical sensors stand out, as they offer distinct advantages over their enzymatic counterparts. While enzymatic sensors excel in selectivity and sensitivity, they are encumbered by complexities associated with enzyme integration, challenges in maintaining precise enzyme quantities, and vulnerability to environmental factors such as temperature and pH fluctuations. Consequently, enzyme-free glucose sensors are gaining momentum as viable alternatives. These sensors are simpler to fabricate, less susceptible to external influences, and boast extended shelf lives ( ). Electrochemical sensors, which translate target substance-induced alterations into electrical signals, have become common for glucose detection. These sensors typically operate within a three-electrode system, consisting of a working electrode, counter electrode, and reference electrode. The judicious selection of nanomaterials, including nanotubes and various metal combinations, facilitates sensitive glucose detection ( ). 6.2 Glucose sensors Intending to improve food quality and management methods, the field of food-based sensors has experienced significant growth in recent years, as evidenced by the development of many sensors specialized in quality monitoring and contaminant detection. With certain chemical or biological interactions, these sensors find the presence or quantity of analytes that indicate the safety or quality of food ( ). The utilization of electronic and optical food sensors spans various applications, encompassing pH indicators, temperature indicators, and gas sensors, as well as the detection of residual pesticides, pathogens, antioxidants, sugars, toxins, adulterants, and heavy metals ( ). This diverse range of sensors plays a crucial role in ensuring and optimizing food quality and processing standards. At present numerous non-enzymatic electrochemical sensors have been developed for the detection of blood glucose concentration and urine glucose concentration ( ; ). There are Ni, Pt, Au, Cu, or Co-based and various other metal-based non-enzymatic glucose sensors focusing on detecting blood glucose concentrations. These sensors are calibrated for the detection of a lower range of concentrations, mostly 1–20 mM ( ). Additionally, these sensors have been developed to overcome interferences such as fructose, sucrose, ascorbic acid, NaCl, dopamine hydrochloride, uric acid, and L-cysteine that limit the detection of glucose in blood and serum ( ). 6.3 Scope of sensor integration for in vitro glycemic index (GI) estimation Most in vitro GI estimation techniques involve at least two digestion phases, gastric and intestinal, with the oral phase increasingly recognized as significant. A three-phase digestion process better mimics in vivo conditions, despite inherent limitations. Enzyme preparations are recommended to be conducted on the same day, requiring a minimum of 30 min. The oral phase typically lasts 20 s to 2 min, while gastric phase durations range from 30 min to 1 h, with extensive protocols extending to 2 h. The intestinal phase, once laborious at 4–5 h, is now commonly performed for 3 h, during which aliquots are collected at various time points to monitor glucose levels as food items reach a saturating SH level ( ). After aliquot collection, a centrifugation step lasting 10–15 min is essential to remove undigestible particles and macromolecules. Mostly, glucose estimation post-centrifugation was conducted using colorimetric methods, primarily the GOPOD coupled method or DNS methods. Contemporary protocols now often include an additional step with amyloglucosidase to convert oligosaccharides into glucose, requiring an extra 20 min. This is followed by glucose estimation procedures that take about 40 min, including sample preparation like dilution and vortexing. The more accurate and sensitive GOPOD reagent, although costlier than DNS, is commonly used for the final glucose estimation step. This labor-intensive process for aliquots from in vitro digestion models, which requires at least 30 min per sample and involves multiple time points, could benefit from a glucose sensor, yet there are few sensors specifically designed for estimating glucose in in vitro digesta. Spectrophotometry, a commonly employed method, functions as a batch process that disrupts the enzyme-to-substrate balance by removing samples from the digestion mixture. This removal concentrates the remaining substrate, prolongs the experiment duration, and reduces throughput. In contrast, sensors require only a few microliters of sample volume, minimizing their effect on enzyme concentration and the overall volume of the digestion mixture ( ). Developing such a sensor would significantly expedite GI estimation by enabling rapid assessment based on SH % or AUC derived from single enzymatic hydrolysis, such as with pancreatin or amylase, within 60 min, followed by quick sensor-based estimation. Surprisingly, the development of nanosensors for GI detection has received limited attention in current research, with relatively few sensors focused on assessing carbohydrate quality in food despite the abundance of sensors designed for food quality assessment. 6.4 Current developments in sugar detection sensors for agricultural and food applications demonstrated for the first time that the Accu-Chek® Performa® glucometer exhibited high specificity to glucose in digested food matrices, with a strong linear correlation (r 2 = 0.999), unaffected by pH (3.9–6), benzoic acid, starch enzymes, or temperature (25–37 °C), and a coefficient of variation (CV) below 5 %. This made it a reliable tool for in vitro digestion studies, as confirmed by other studies ( ; ; ). However, enzymatic glucose sensors like the Accu-Chek® Performa® face challenges in time-dependent hydrolysis studies requiring multiple measurements, often exceeding ten time points. These include limited reusability, high material costs, performance variability, and enzyme instability over time. Non-enzymatic electrochemical glucose sensors address these limitations, offering superior reusability, stability, and robustness for prolonged analyses. This enhances throughput in large-scale screening while maintaining accuracy, making them more suitable for in vitro digestion studies and advancing applications in GI analysis. Due to issues with sensor reusability, many non-enzymatic glucose and carbohydrate sensors, including blood glucose sensors, have had limited applicability in the agricultural sector. However, non enzymatic glucose sensing in beverages has advanced significantly. designed a Platinum-Nickel (Pt—Ni) sensor with reduced graphene oxide using ultrasonic hydroxide-assisted reduction, achieving high sensitivity (171.92 μA mM −1 cm 2 ), a low detection limit (6.3 μM), and stability for beverage analysis. Nickel Oxide (NiO) nanoparticle sensors offered versatile sugar detection, featuring a rapid 3-s response, high sensitivity (3.06 A M −1 cm −2 ), and a detection limit of 0.39 μM ( ). A Schiff base Ni complex sensor enabled precise glucose and fructose detection in honey using partial least squares regression (PLSR), with relative standard deviations under 9 % ( ). Nickel cobaltite (NiCo₂O₄) nanocomposite sensors excelled in glucose oxidation, with a broad range (0.4 μM–5.2 mM) and a low detection limit (1.1 μM) ( ). Addressing NiO's conductivity limitations, Mn-doped NiO nanosheets enhanced oxidation, achieving high sensitivity (3.53 mA mM −1 cm −2 ) and a low detection limit (0.6 μM) for sports beverages ( ). Cuprous oxide (Cu₂O) micro-nano cubes demonstrated nanocubes with superior glucose sensitivity (2864 μA mM −1 cm 2 ) and microcubes with an extended linear range (10.65 mM), useful for food and biological monitoring ( ). Recent innovations, such as NiCo-layered double hydroxide nanosheets, utilized microplasma-assisted etching for exceptional sensitivity (98,000 μA mM −1 cm 2 ) and a low detection limit (48.76 nM) ( ). Similarly, the WO₃-TiO₂ hybrid sensor integrated WO₃ and TiO₂ nanotubes, enhancing electron transfer with a sensitivity of 1228.12 μA/mM/cm 2 , a detection limit of 0.19 mM, and stable biological application performance ( ). developed a non-enzymatic sugar sensor using carboxylated graphene, carbon nanotubes, and gold nanoparticles (COOH-GR–COOH-MWNT–AuNPs) for broad sugar detection with high stability. further improved this with nanocomposites (GO-COOH-Cu-MOFs) and artificial neural networks, achieving a wider detection range (1 nM to 1 M) and lower limits. A recurring theme in glucose sensing of food is the reliance on advanced nanocomposites to overcome challenges in food glucose detection. However, these sensors have never been adapted for GI prediction or in vitro digestion analysis. introduced a microdroplet sensor for point-of-care GI testing using a gold-amylase nanocomposite (Au-Am-NC). This system cleaves starch iodine complexes, releasing iodine and enabling GI estimation via color intensity reduction. While selective and efficient, challenges in scalability, stability, and repeatability remain, limiting its application to food digestion studies. Further optimization is required for robustness and broader food matrix integration. 6.5 Optimization of nanosensor for glucose detection from in vitro digesta The in vitro digesta intended for sensor analysis presents a complex milieu, primarily comprising intestinal buffer medium, digestive enzymes, and an array of post-digestion biomolecules including monosaccharides, amino acids, oligopeptides, and fatty acids. Additionally, certain food matrices contain phytochemicals such as phenolics and various secondary metabolites. These diverse components, in conjunction with varied food matrices, can significantly influence amperometric and cyclic voltammetric measurements, necessitating sophisticated signal processing. From the perspective of in vitro digestion models for GR analysis, the focus typically centers on carbohydrate-rich foods, with most models incorporating carbohydrate-specific digestive enzymes. The resultant monosaccharides, including glucose, can be analyzed using fundamental electrochemical techniques such as cyclic voltammetry and amperometry. Particular attention should be given to monosaccharides structurally similar to glucose, such as galactose (C4 epimer) and mannose (C2 epimer), as well as the ketohexose fructose, which is abundant in many food matrices. Additionally, the sensor should be calibrated for common disaccharides, including reducing sugars like maltose and lactose, and the non-reducing disaccharide sucrose. To mitigate the complexity introduced by higher oligosaccharides, the incorporation of an amyloglucosidase incubation step post-digestion can effectively hydrolyze these polymers into simpler units. The sensor's response to each compound should be individually assessed and compared to the signal generated by an equimolar concentration of glucose under standardized conditions. However, given that real food matrices contain complex mixtures of these interferents alongside glucose, it is imperative to evaluate glucose quantification in the presence of various combinations and concentrations of interfering compounds, thereby simulating artificial food matrices ( ). This comprehensive approach to interference characterization and mitigation is particularly crucial for the development of non-enzymatic electrochemical glucose nanosensors. By systematically analyzing the sensor's response to individual compounds and their combinations, it becomes possible to develop robust algorithms for signal processing and glucose quantification in complex food-derived samples. This methodology not only enhances the accuracy of glucose detection but also provides valuable insights into the sensor's performance under diverse physiological conditions, potentially expanding its applicability beyond GR analysis to other biomedical and food science applications. 6.6 Existing challenges in sensor development and optimization Most research on nanosensors has focused predominantly on sensor fabrication, often neglecting in-depth biochemical applications. Concurrently, studies involving biochemical or in vitro digestion models tend to neglect the sensor component, often resulting in reliance on traditional blood glucose sensors. This gap in application arises largely from insufficient interdisciplinary collaboration, which hampers the integration of sensor technology into broader biochemical research contexts. This oversight is primarily due to a lack of interdisciplinary collaboration. Consequently, nanosensor research in the food science and agricultural sectors has traditionally targeted more straightforward applications, such as detecting toxins, antioxidants, and sugars directly from juices ( ; ). However, there is currently a positive shift towards broader applications in this field. Accurately assessing the GR of in vitro- digested food via sensors presents significant challenges. A potential electrochemical glucose nanosensor operating on amperometry would face numerous issues beyond those encountered with conventional glucose sensors. Non-enzymatic electrochemical glucose sensors determine glucose concentration by facilitating direct electron transfer during oxidation-reduction reactions. Detecting glucose amidst various interfering compounds is notably more complex than direct carbohydrate or sugar measurement. Digested foods may contain simulated fluids or buffers with electrolytes to aid digestion, along with digested dietary components such as fatty acids, amino acids, and peptides which could interfere with glucose detection within the matrix. These compounds can undergo oxidation or reduction at specific potentials. Additionally, high glucose concentrations in aliquots may hinder direct estimation if it exceeds the sensor's linear range of detection, a problem that can be mitigated by dilution of test aliquot. Therefore, the sensor's linear range can be accommodated by both higher concentrations (without dilution) and lower concentrations (with dilution). Selectivity can be compromised by various components, as the activity of interfering compounds tends to increase with the conversion of macromolecules to micromolecules. For an electrochemical glucose nanosensor, numerous reducing sugar groups can create more signals compared to the lower number of reducing groups in starch or polysaccharides. Sensitivity is crucial, particularly when estimating glucose levels at closely spaced time points (differences of less than 5–10 min) or between final time points (when the reaction becomes saturating), as glucose concentration changes may be minimal yet must still be detectable. Stability is also important for measuring multiple time points from multiple samples. Thus, there is a need to optimize an electrochemical nanosensor specifically designed for this purpose. 

Enzymatic and non-enzymatic sensors The advancement of nanotechnology in the scientific realm has sparked considerable interest in the utilization of glucose nanosensors, offering promising avenues for process optimization. Nanosensors offer a plethora of advantages over traditional methodologies, including enhanced speed, cost-effectiveness, accuracy, sensitivity, selectivity, portability, and miniaturization. Within the realm of nanosensors, non-enzymatic electrochemical sensors stand out, as they offer distinct advantages over their enzymatic counterparts. While enzymatic sensors excel in selectivity and sensitivity, they are encumbered by complexities associated with enzyme integration, challenges in maintaining precise enzyme quantities, and vulnerability to environmental factors such as temperature and pH fluctuations. Consequently, enzyme-free glucose sensors are gaining momentum as viable alternatives. These sensors are simpler to fabricate, less susceptible to external influences, and boast extended shelf lives ( ). Electrochemical sensors, which translate target substance-induced alterations into electrical signals, have become common for glucose detection. These sensors typically operate within a three-electrode system, consisting of a working electrode, counter electrode, and reference electrode. The judicious selection of nanomaterials, including nanotubes and various metal combinations, facilitates sensitive glucose detection ( ). 

Glucose sensors Intending to improve food quality and management methods, the field of food-based sensors has experienced significant growth in recent years, as evidenced by the development of many sensors specialized in quality monitoring and contaminant detection. With certain chemical or biological interactions, these sensors find the presence or quantity of analytes that indicate the safety or quality of food ( ). The utilization of electronic and optical food sensors spans various applications, encompassing pH indicators, temperature indicators, and gas sensors, as well as the detection of residual pesticides, pathogens, antioxidants, sugars, toxins, adulterants, and heavy metals ( ). This diverse range of sensors plays a crucial role in ensuring and optimizing food quality and processing standards. At present numerous non-enzymatic electrochemical sensors have been developed for the detection of blood glucose concentration and urine glucose concentration ( ; ). There are Ni, Pt, Au, Cu, or Co-based and various other metal-based non-enzymatic glucose sensors focusing on detecting blood glucose concentrations. These sensors are calibrated for the detection of a lower range of concentrations, mostly 1–20 mM ( ). Additionally, these sensors have been developed to overcome interferences such as fructose, sucrose, ascorbic acid, NaCl, dopamine hydrochloride, uric acid, and L-cysteine that limit the detection of glucose in blood and serum ( ). 

Scope of sensor integration for in vitro glycemic index (GI) estimation Most in vitro GI estimation techniques involve at least two digestion phases, gastric and intestinal, with the oral phase increasingly recognized as significant. A three-phase digestion process better mimics in vivo conditions, despite inherent limitations. Enzyme preparations are recommended to be conducted on the same day, requiring a minimum of 30 min. The oral phase typically lasts 20 s to 2 min, while gastric phase durations range from 30 min to 1 h, with extensive protocols extending to 2 h. The intestinal phase, once laborious at 4–5 h, is now commonly performed for 3 h, during which aliquots are collected at various time points to monitor glucose levels as food items reach a saturating SH level ( ). After aliquot collection, a centrifugation step lasting 10–15 min is essential to remove undigestible particles and macromolecules. Mostly, glucose estimation post-centrifugation was conducted using colorimetric methods, primarily the GOPOD coupled method or DNS methods. Contemporary protocols now often include an additional step with amyloglucosidase to convert oligosaccharides into glucose, requiring an extra 20 min. This is followed by glucose estimation procedures that take about 40 min, including sample preparation like dilution and vortexing. The more accurate and sensitive GOPOD reagent, although costlier than DNS, is commonly used for the final glucose estimation step. This labor-intensive process for aliquots from in vitro digestion models, which requires at least 30 min per sample and involves multiple time points, could benefit from a glucose sensor, yet there are few sensors specifically designed for estimating glucose in in vitro digesta. Spectrophotometry, a commonly employed method, functions as a batch process that disrupts the enzyme-to-substrate balance by removing samples from the digestion mixture. This removal concentrates the remaining substrate, prolongs the experiment duration, and reduces throughput. In contrast, sensors require only a few microliters of sample volume, minimizing their effect on enzyme concentration and the overall volume of the digestion mixture ( ). Developing such a sensor would significantly expedite GI estimation by enabling rapid assessment based on SH % or AUC derived from single enzymatic hydrolysis, such as with pancreatin or amylase, within 60 min, followed by quick sensor-based estimation. Surprisingly, the development of nanosensors for GI detection has received limited attention in current research, with relatively few sensors focused on assessing carbohydrate quality in food despite the abundance of sensors designed for food quality assessment. 

Current developments in sugar detection sensors for agricultural and food applications demonstrated for the first time that the Accu-Chek® Performa® glucometer exhibited high specificity to glucose in digested food matrices, with a strong linear correlation (r 2 = 0.999), unaffected by pH (3.9–6), benzoic acid, starch enzymes, or temperature (25–37 °C), and a coefficient of variation (CV) below 5 %. This made it a reliable tool for in vitro digestion studies, as confirmed by other studies ( ; ; ). However, enzymatic glucose sensors like the Accu-Chek® Performa® face challenges in time-dependent hydrolysis studies requiring multiple measurements, often exceeding ten time points. These include limited reusability, high material costs, performance variability, and enzyme instability over time. Non-enzymatic electrochemical glucose sensors address these limitations, offering superior reusability, stability, and robustness for prolonged analyses. This enhances throughput in large-scale screening while maintaining accuracy, making them more suitable for in vitro digestion studies and advancing applications in GI analysis. Due to issues with sensor reusability, many non-enzymatic glucose and carbohydrate sensors, including blood glucose sensors, have had limited applicability in the agricultural sector. However, non enzymatic glucose sensing in beverages has advanced significantly. designed a Platinum-Nickel (Pt—Ni) sensor with reduced graphene oxide using ultrasonic hydroxide-assisted reduction, achieving high sensitivity (171.92 μA mM −1 cm 2 ), a low detection limit (6.3 μM), and stability for beverage analysis. Nickel Oxide (NiO) nanoparticle sensors offered versatile sugar detection, featuring a rapid 3-s response, high sensitivity (3.06 A M −1 cm −2 ), and a detection limit of 0.39 μM ( ). A Schiff base Ni complex sensor enabled precise glucose and fructose detection in honey using partial least squares regression (PLSR), with relative standard deviations under 9 % ( ). Nickel cobaltite (NiCo₂O₄) nanocomposite sensors excelled in glucose oxidation, with a broad range (0.4 μM–5.2 mM) and a low detection limit (1.1 μM) ( ). Addressing NiO's conductivity limitations, Mn-doped NiO nanosheets enhanced oxidation, achieving high sensitivity (3.53 mA mM −1 cm −2 ) and a low detection limit (0.6 μM) for sports beverages ( ). Cuprous oxide (Cu₂O) micro-nano cubes demonstrated nanocubes with superior glucose sensitivity (2864 μA mM −1 cm 2 ) and microcubes with an extended linear range (10.65 mM), useful for food and biological monitoring ( ). Recent innovations, such as NiCo-layered double hydroxide nanosheets, utilized microplasma-assisted etching for exceptional sensitivity (98,000 μA mM −1 cm 2 ) and a low detection limit (48.76 nM) ( ). Similarly, the WO₃-TiO₂ hybrid sensor integrated WO₃ and TiO₂ nanotubes, enhancing electron transfer with a sensitivity of 1228.12 μA/mM/cm 2 , a detection limit of 0.19 mM, and stable biological application performance ( ). developed a non-enzymatic sugar sensor using carboxylated graphene, carbon nanotubes, and gold nanoparticles (COOH-GR–COOH-MWNT–AuNPs) for broad sugar detection with high stability. further improved this with nanocomposites (GO-COOH-Cu-MOFs) and artificial neural networks, achieving a wider detection range (1 nM to 1 M) and lower limits. A recurring theme in glucose sensing of food is the reliance on advanced nanocomposites to overcome challenges in food glucose detection. However, these sensors have never been adapted for GI prediction or in vitro digestion analysis. introduced a microdroplet sensor for point-of-care GI testing using a gold-amylase nanocomposite (Au-Am-NC). This system cleaves starch iodine complexes, releasing iodine and enabling GI estimation via color intensity reduction. While selective and efficient, challenges in scalability, stability, and repeatability remain, limiting its application to food digestion studies. Further optimization is required for robustness and broader food matrix integration. 

Optimization of nanosensor for glucose detection from in vitro digesta The in vitro digesta intended for sensor analysis presents a complex milieu, primarily comprising intestinal buffer medium, digestive enzymes, and an array of post-digestion biomolecules including monosaccharides, amino acids, oligopeptides, and fatty acids. Additionally, certain food matrices contain phytochemicals such as phenolics and various secondary metabolites. These diverse components, in conjunction with varied food matrices, can significantly influence amperometric and cyclic voltammetric measurements, necessitating sophisticated signal processing. From the perspective of in vitro digestion models for GR analysis, the focus typically centers on carbohydrate-rich foods, with most models incorporating carbohydrate-specific digestive enzymes. The resultant monosaccharides, including glucose, can be analyzed using fundamental electrochemical techniques such as cyclic voltammetry and amperometry. Particular attention should be given to monosaccharides structurally similar to glucose, such as galactose (C4 epimer) and mannose (C2 epimer), as well as the ketohexose fructose, which is abundant in many food matrices. Additionally, the sensor should be calibrated for common disaccharides, including reducing sugars like maltose and lactose, and the non-reducing disaccharide sucrose. To mitigate the complexity introduced by higher oligosaccharides, the incorporation of an amyloglucosidase incubation step post-digestion can effectively hydrolyze these polymers into simpler units. The sensor's response to each compound should be individually assessed and compared to the signal generated by an equimolar concentration of glucose under standardized conditions. However, given that real food matrices contain complex mixtures of these interferents alongside glucose, it is imperative to evaluate glucose quantification in the presence of various combinations and concentrations of interfering compounds, thereby simulating artificial food matrices ( ). This comprehensive approach to interference characterization and mitigation is particularly crucial for the development of non-enzymatic electrochemical glucose nanosensors. By systematically analyzing the sensor's response to individual compounds and their combinations, it becomes possible to develop robust algorithms for signal processing and glucose quantification in complex food-derived samples. This methodology not only enhances the accuracy of glucose detection but also provides valuable insights into the sensor's performance under diverse physiological conditions, potentially expanding its applicability beyond GR analysis to other biomedical and food science applications. 

Existing challenges in sensor development and optimization Most research on nanosensors has focused predominantly on sensor fabrication, often neglecting in-depth biochemical applications. Concurrently, studies involving biochemical or in vitro digestion models tend to neglect the sensor component, often resulting in reliance on traditional blood glucose sensors. This gap in application arises largely from insufficient interdisciplinary collaboration, which hampers the integration of sensor technology into broader biochemical research contexts. This oversight is primarily due to a lack of interdisciplinary collaboration. Consequently, nanosensor research in the food science and agricultural sectors has traditionally targeted more straightforward applications, such as detecting toxins, antioxidants, and sugars directly from juices ( ; ). However, there is currently a positive shift towards broader applications in this field. Accurately assessing the GR of in vitro- digested food via sensors presents significant challenges. A potential electrochemical glucose nanosensor operating on amperometry would face numerous issues beyond those encountered with conventional glucose sensors. Non-enzymatic electrochemical glucose sensors determine glucose concentration by facilitating direct electron transfer during oxidation-reduction reactions. Detecting glucose amidst various interfering compounds is notably more complex than direct carbohydrate or sugar measurement. Digested foods may contain simulated fluids or buffers with electrolytes to aid digestion, along with digested dietary components such as fatty acids, amino acids, and peptides which could interfere with glucose detection within the matrix. These compounds can undergo oxidation or reduction at specific potentials. Additionally, high glucose concentrations in aliquots may hinder direct estimation if it exceeds the sensor's linear range of detection, a problem that can be mitigated by dilution of test aliquot. Therefore, the sensor's linear range can be accommodated by both higher concentrations (without dilution) and lower concentrations (with dilution). Selectivity can be compromised by various components, as the activity of interfering compounds tends to increase with the conversion of macromolecules to micromolecules. For an electrochemical glucose nanosensor, numerous reducing sugar groups can create more signals compared to the lower number of reducing groups in starch or polysaccharides. Sensitivity is crucial, particularly when estimating glucose levels at closely spaced time points (differences of less than 5–10 min) or between final time points (when the reaction becomes saturating), as glucose concentration changes may be minimal yet must still be detectable. Stability is also important for measuring multiple time points from multiple samples. Thus, there is a need to optimize an electrochemical nanosensor specifically designed for this purpose. 

Role of AI in food digestion model optimization AI and ML are transformative technologies poised to revolutionize fields like food science and nutrition. ML, a subset of AI, uses algorithms and statistical models to execute tasks based on patterns and inference, rather than explicit instructions. Integrating AI/ML into biomimetic models for GI estimation enhances accuracy, efficiency, and predictive capability, enabling precise simulation of human digestion and GI prediction of foods. Significant strides have been made in applying AI/ML to glucose monitoring and diabetes management, particularly in wearable devices and in vivo contexts, but their application to in vitro digestion models and non-enzymatic electrochemical sensors for GR analysis remains underexplored. A multitude of studies has investigated the application of AI/ML in clinical diabetes management and glucose prediction, as comprehensively reviewed by and . detailed how wearable devices with AI enhance disease management and quality of life for diabetes patients. Some of the developments in this regard include mobile-based systems for gestational diabetes that utilize continuous glucose monitoring data and linear regression models for precise blood glucose predictions and personalized recommendations. Hybrid systems combining decision trees, support vector machines (SVM), and neural networks enhance diagnostic accuracy and individualized treatment. Wearable devices extend functionality to physical activity classification and fatigue detection, leveraging ensemble classifiers and k-nearest neighbors (KNN). These devices demonstrate robust gradient-boosted decision tree performance, ensuring timely alerts ( ). implemented a robust data-driven method to classify 99.0 % of daily CGM profiles, correlating them with representative daily patterns. addressed challenges in artificial pancreas development, emphasizing its potential to improve glucose control and patient autonomy. Despite incorporating CGM, intelligent controllers, and insulin pumps, challenges persist in dose calculation and using novel substances for glucose regulation. Reinforcement learning algorithms excel in closed-loop insulin delivery, providing personalized dosing tailored to patients' immediate needs. They efficiently analyze glucose patterns and manage complex data, with cross-validation and regularization ensuring optimal performance. proposed a deep reinforcement learning model for single-hormone and dual-hormone insulin delivery in individuals with Type 1 diabetes, employing double Q-learning with dilated Recurrent Neural Networks (RNNs). tackled the challenges of small and imbalanced datasets in diabetic blood glucose prediction by evaluating three neural network architectures: Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Self-Attention Networks (SANs). They developed four transfer learning strategies for glucose prediction and explored novel pre-processing approaches to address data imbalance issues. Their findings demonstrated that the CNN model outperformed other models in terms of mean absolute error (MAE) for both 30- and 60-min prediction horizons, exhibiting superior sensitivity and clinical relevance. conducted a comprehensive analysis of 61 articles, focusing on algorithm evaluation using real-world human data, in silico trials, and clinical studies. All these studies underscore the growing prevalence of AI/ML/Deep learning methodologies in clinical applications and diabetes management. However, the application of advanced AI/ML techniques to in vitro digestion models and GI prediction has been comparatively limited. Conventional approaches have primarily relied on basic regression techniques and linear models for predicting GI from in vitro data. While non-linear models have been employed for SH fitting, their direct application to GI prediction has been constrained. Early work by utilized ANN to predict GI based on HPLC analysis of sugars and compositional information. However, this approach required comprehensive food product profiling, limiting its practical applicability. More recent investigations have begun to explore the potential of advanced AI techniques in this domain. demonstrated the efficacy of Genetic Algorithm-Artificial Neural Network (GA-ANN) and Group Method of Data Handling (GMDH) modeling techniques in predicting glucose release from starch gels during simulated gastrointestinal digestion. Another study by evaluated the effects of baking temperature and time on resistant starch, pGI, and GL of cookies, utilizing ANN as a predictive model for dependent variables. employed a Swarm Intelligence Supervised Neural Network (SISNN) to simulate digestion kinetics and predict the GI of fortified bread, demonstrating superior performance compared to traditional mathematical models. Their study also incorporated PCA combined with Proportional Odds Modeling (POM) to identify key sensory attributes, supporting the potential of Euryale ferox seed shell extract as an additive for producing lower GI functional bread. These studies either narrow their approach to specific food items or focus on particular applications, limiting their general applicability for predicting GI. Subsequent GI prediction studies thus largely focused on in vitro - in vivo GI interpolation using basic correlation analyses ( ; ). Despite recent advancements, a significant research gap remains in the integration of AI/ML techniques with non-enzymatic electrochemical sensors in general food settings, particularly in in vitro models. Current research predominantly focuses on either in vitro digestibility studies using AI/ML approaches or on in vitro digestibility analyses using enzymatic blood glucose sensors. However, a comprehensive study combining non-enzymatic electrochemical glucose nanosensor with an optimized in vitro digestion model, integrated with advanced AI/ML techniques, remains limited. This gap presents a compelling opportunity for future research to develop more sophisticated, integrated approaches to GR analysis. Such studies could potentially enhance the accuracy and predictive power of in vitro models, bridging the gap between laboratory-based assessments and clinical outcomes in diabetes management and nutrition science. The synergistic integration of electrochemical glucose nanosensors with optimized in vitro digestion models and advanced AI/ML methodologies represents a promising frontier in GR analysis and diabetes research. AI/ML integration can significantly enhance biomimetic models for GI determination through several key mechanisms: 7.1 Real-time data collection and interference mitigation in in vitro digestion AI-powered sensors and data acquisition systems offer exceptional capabilities for real-time, high-resolution monitoring of digestive processes, capturing dynamic changes with greater accuracy than conventional methods. In in vitro digestion studies, particularly those involving time-dependent hydrolysis, the number of intestinal phase aliquots typically exceeds seven (corresponding to time points 0, 30, 60, 90, 120, 150, and 180 min), which can increase with additional sampling or extended digestion times. Collecting cyclic voltammetric or amperometric data from each time point can further enhance the dataset. AI-enhanced sensors enable direct measurement of glucose concentrations alongside potential interfering compounds, streamlining data collection for large-scale sample screening. These advanced systems can simultaneously monitor critical parameters such as pH, temperature, enzyme activity, and glucose concentration in real time, providing a comprehensive profile of the digestive environment. A key advantage of AI-based sensor systems is their ability to accurately quantify glucose concentrations by normalizing signals from interfering compounds, requiring extensive data collection from pure forms of each interferent and artificial food matrices with known compositions. Systematic testing of various combinations is essential for developing robust and reliable sensor systems. explored multicomponent testing of glucose and insulin using electrochemical methods, which would be beneficial for comprehensive metabolic assessments in an in vitro system. Cyclic voltammetry identified seven features, including oxidation/reductive currents, voltages, and curve area. Feature ranking using the random forest algorithm revealed that all inputs exhibited comparable significance, warranting their inclusion in subsequent analyses. They employed linear regression models to decouple insulin and glucose concentrations. The models achieved R 2 values of 0.9863 for glucose and 0.9979 for insulin, demonstrating their efficacy in clinical analysis ( ). Moreover, the incorporation of Internet of Things (IoT) devices facilitates seamless data collection and transmission, enabling real-time monitoring of various optimization parameters and outcomes during in vitro processes. These IoT-enabled devices connect sensors to a centralized database, allowing for comprehensive real-time monitoring and data acquisition. 7.2 Automated data analysis ML algorithms are employed to streamline the processing and analysis of large datasets obtained from non-enzymatic electrochemical glucose sensors used in in vitro digestion studies. These algorithms help in identifying patterns and correlations that enhance the accuracy and reliability of GI predictions. Automated data loggers continuously record sensor data, reducing human error and improving data collection efficiency. Furthermore, cloud-based systems facilitate easy access to large datasets for collaborative analysis. Data preprocessing techniques such as filtering and smoothing, driven by covariance analysis and confidence intervals, are used to remove noise from the data, ensuring cleaner inputs for ML models. Fully automated schemes for detecting and handling outliers, using statistical methods or ML algorithms, ensure that only consistent and reliable data are used. Additionally, Data normalization strategies like Z -score normalization and Min-Max scaling maintain consistency across different experiments, enabling more accurate cross-comparisons ( ). Advanced feature engineering is utilized to create new features, such as glucose release rates and enzyme activity profiles, further enriching the dataset and improving the precision of GI predictions through more informative inputs to ML models. 7.3 Predictive modeling for glycemic index (GI) prediction Cutting-edge ML models have the potential to transform the prediction of GI responses by analyzing in vitro digestion data collected using non-enzymatic glucose nanosensors. These advanced models, such as neural networks, decision trees, and clustering algorithms, incorporate various features from digestion profiles such as AUC, slope, and intercept, to provide highly accurate and specific estimates of pGI. Supervised learning methods, particularly regression models (linear, polynomial, and ridge), can effectively predict GR based on critical variables from digestion kinetics, including SH %. Deep learning architectures, notably CNNs and RNNs have exhibited remarkable capabilities in capturing complex, non-linear relationships within glucose release patterns ( ). CNNs commonly employed for spatial data analysis, can be ingeniously adapted to process structured SH % datasets. Meanwhile, RNNs, especially those integrating Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) components, excel in modeling sequential digestion data, effectively capturing temporal dependencies in glucose release kinetics. SVMs can be used for classifying foods into discrete GI categories (low, medium, high) based on SH data, utilizing optimized kernels such as linear, polynomial, or radial basis functions. tried to predict the GI of food items from images with these classifiers. The efficacy of these ML models is fundamentally dependent on extensive training datasets comprising known glucose concentrations and interfering compounds. This approach enables AI systems to discern the unique signal signature of SH from other molecular species. The integration of multiple sensing modalities (electrochemical, optical, and spectroscopic) can provide complementary data streams, substantially enhancing the robustness and reliability of glucose measurements. Decision trees and random forests are both supervised ML techniques that can be used in GI prediction. They leverage a combination of features including AUC, SH %, total starch content, degree of polymerization, and starch fractions - Rapidly Digestible Starch (RDS), Slowly Digestible Starch (SDS), and resistant starch (RS). These methods are effective in handling non-linear interactions between features. Decision trees simplify the dataset through key feature-based splitting, while random forests provide enhanced stability and robustness in predictions by aggregating multiple decision trees. To mitigate the risk of over-fitting in decision trees, pruning or depth limitation can be applied. Additionally, hyperparameter tuning via cross-validation optimizes random forests for an ideal balance between accuracy and model simplicity ( ). Unsupervised learning techniques, such as K-means clustering and hierarchical clustering, provide a method for categorizing food into distinct GI groups based solely on digestion profiles at specific time points of hydrolysis, without relying on predefined labels. These methods are effective in identifying natural patterns within the data, with K-means clustering benefiting from advanced centroid initialization techniques like the K-means++ algorithm. Gradient Boosting Machines (GBMs), including XGBoost and LightGBM, are particularly effective in handling noisy data and large feature sets, improving predictive performance through sophisticated ensemble learning techniques ( ). The integration of supervised and unsupervised models offers a comprehensive analytical approach to GI prediction. This combined approach, along with in vitro digestion models and non-enzymatic glucose sensing, enables efficient and accurate GI prediction. 7.4 Model implementation Efficient data preprocessing is crucial for accurate predictions of glucose concentration in complex food matrices. Signal denoising techniques such as wavelet or Fourier transforms are used to eliminate high-frequency noise from raw sensor data. Feature extraction methods like PCA or t-SNE help in identifying key features that differentiate glucose signals from interferents. Supervised learning techniques, including Random Forest Regression and SVR, are employed to predict true glucose levels by mapping complex relationships between sensor readings and actual concentrations. DNNs like CNNs are utilized for learning hierarchical features to enhance glucose quantification. For anomaly detection, unsupervised methods like Autoencoders and Isolation Forests help in identifying interferent-induced irregularities in sensor data. Ensemble methods such as bagging, boosting, and stacking enhance model robustness and accuracy by combining multiple models like Random Forests and GBMs. Transfer learning techniques, including domain adaptation and Few-Shot Learning, ensure model adaptability across different food matrices and new interferents with minimal retraining. For real-time processing, RNNs such as LSTM or GRU networks handle time-series sensor data, while Kalman Filters combine with ML models for optimal state estimation. Explainable AI techniques, including SHAP values and LIME, provide transparency by interpreting the impact of different interferents on glucose measurements. Model validation involves conducting controlled experiments and pilot studies to ensure reproducibility and accuracy. Statistical analysis, including descriptive and inferential methods like t -tests and ANOVA, establishes relationships between input variables and GI outcomes, while metrics such as MAE, Root Mean Squared Error (RMSE), and R-squared assess model performance, accuracy, robustness, and interpretability, with feature importance scores identifying key GI determining variables. provides a comprehensive overview of the proposed work plan, summarizing all key components. 

Real-time data collection and interference mitigation in in vitro digestion AI-powered sensors and data acquisition systems offer exceptional capabilities for real-time, high-resolution monitoring of digestive processes, capturing dynamic changes with greater accuracy than conventional methods. In in vitro digestion studies, particularly those involving time-dependent hydrolysis, the number of intestinal phase aliquots typically exceeds seven (corresponding to time points 0, 30, 60, 90, 120, 150, and 180 min), which can increase with additional sampling or extended digestion times. Collecting cyclic voltammetric or amperometric data from each time point can further enhance the dataset. AI-enhanced sensors enable direct measurement of glucose concentrations alongside potential interfering compounds, streamlining data collection for large-scale sample screening. These advanced systems can simultaneously monitor critical parameters such as pH, temperature, enzyme activity, and glucose concentration in real time, providing a comprehensive profile of the digestive environment. A key advantage of AI-based sensor systems is their ability to accurately quantify glucose concentrations by normalizing signals from interfering compounds, requiring extensive data collection from pure forms of each interferent and artificial food matrices with known compositions. Systematic testing of various combinations is essential for developing robust and reliable sensor systems. explored multicomponent testing of glucose and insulin using electrochemical methods, which would be beneficial for comprehensive metabolic assessments in an in vitro system. Cyclic voltammetry identified seven features, including oxidation/reductive currents, voltages, and curve area. Feature ranking using the random forest algorithm revealed that all inputs exhibited comparable significance, warranting their inclusion in subsequent analyses. They employed linear regression models to decouple insulin and glucose concentrations. The models achieved R 2 values of 0.9863 for glucose and 0.9979 for insulin, demonstrating their efficacy in clinical analysis ( ). Moreover, the incorporation of Internet of Things (IoT) devices facilitates seamless data collection and transmission, enabling real-time monitoring of various optimization parameters and outcomes during in vitro processes. These IoT-enabled devices connect sensors to a centralized database, allowing for comprehensive real-time monitoring and data acquisition. 

Automated data analysis ML algorithms are employed to streamline the processing and analysis of large datasets obtained from non-enzymatic electrochemical glucose sensors used in in vitro digestion studies. These algorithms help in identifying patterns and correlations that enhance the accuracy and reliability of GI predictions. Automated data loggers continuously record sensor data, reducing human error and improving data collection efficiency. Furthermore, cloud-based systems facilitate easy access to large datasets for collaborative analysis. Data preprocessing techniques such as filtering and smoothing, driven by covariance analysis and confidence intervals, are used to remove noise from the data, ensuring cleaner inputs for ML models. Fully automated schemes for detecting and handling outliers, using statistical methods or ML algorithms, ensure that only consistent and reliable data are used. Additionally, Data normalization strategies like Z -score normalization and Min-Max scaling maintain consistency across different experiments, enabling more accurate cross-comparisons ( ). Advanced feature engineering is utilized to create new features, such as glucose release rates and enzyme activity profiles, further enriching the dataset and improving the precision of GI predictions through more informative inputs to ML models. 

Predictive modeling for glycemic index (GI) prediction Cutting-edge ML models have the potential to transform the prediction of GI responses by analyzing in vitro digestion data collected using non-enzymatic glucose nanosensors. These advanced models, such as neural networks, decision trees, and clustering algorithms, incorporate various features from digestion profiles such as AUC, slope, and intercept, to provide highly accurate and specific estimates of pGI. Supervised learning methods, particularly regression models (linear, polynomial, and ridge), can effectively predict GR based on critical variables from digestion kinetics, including SH %. Deep learning architectures, notably CNNs and RNNs have exhibited remarkable capabilities in capturing complex, non-linear relationships within glucose release patterns ( ). CNNs commonly employed for spatial data analysis, can be ingeniously adapted to process structured SH % datasets. Meanwhile, RNNs, especially those integrating Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) components, excel in modeling sequential digestion data, effectively capturing temporal dependencies in glucose release kinetics. SVMs can be used for classifying foods into discrete GI categories (low, medium, high) based on SH data, utilizing optimized kernels such as linear, polynomial, or radial basis functions. tried to predict the GI of food items from images with these classifiers. The efficacy of these ML models is fundamentally dependent on extensive training datasets comprising known glucose concentrations and interfering compounds. This approach enables AI systems to discern the unique signal signature of SH from other molecular species. The integration of multiple sensing modalities (electrochemical, optical, and spectroscopic) can provide complementary data streams, substantially enhancing the robustness and reliability of glucose measurements. Decision trees and random forests are both supervised ML techniques that can be used in GI prediction. They leverage a combination of features including AUC, SH %, total starch content, degree of polymerization, and starch fractions - Rapidly Digestible Starch (RDS), Slowly Digestible Starch (SDS), and resistant starch (RS). These methods are effective in handling non-linear interactions between features. Decision trees simplify the dataset through key feature-based splitting, while random forests provide enhanced stability and robustness in predictions by aggregating multiple decision trees. To mitigate the risk of over-fitting in decision trees, pruning or depth limitation can be applied. Additionally, hyperparameter tuning via cross-validation optimizes random forests for an ideal balance between accuracy and model simplicity ( ). Unsupervised learning techniques, such as K-means clustering and hierarchical clustering, provide a method for categorizing food into distinct GI groups based solely on digestion profiles at specific time points of hydrolysis, without relying on predefined labels. These methods are effective in identifying natural patterns within the data, with K-means clustering benefiting from advanced centroid initialization techniques like the K-means++ algorithm. Gradient Boosting Machines (GBMs), including XGBoost and LightGBM, are particularly effective in handling noisy data and large feature sets, improving predictive performance through sophisticated ensemble learning techniques ( ). The integration of supervised and unsupervised models offers a comprehensive analytical approach to GI prediction. This combined approach, along with in vitro digestion models and non-enzymatic glucose sensing, enables efficient and accurate GI prediction. 

Model implementation Efficient data preprocessing is crucial for accurate predictions of glucose concentration in complex food matrices. Signal denoising techniques such as wavelet or Fourier transforms are used to eliminate high-frequency noise from raw sensor data. Feature extraction methods like PCA or t-SNE help in identifying key features that differentiate glucose signals from interferents. Supervised learning techniques, including Random Forest Regression and SVR, are employed to predict true glucose levels by mapping complex relationships between sensor readings and actual concentrations. DNNs like CNNs are utilized for learning hierarchical features to enhance glucose quantification. For anomaly detection, unsupervised methods like Autoencoders and Isolation Forests help in identifying interferent-induced irregularities in sensor data. Ensemble methods such as bagging, boosting, and stacking enhance model robustness and accuracy by combining multiple models like Random Forests and GBMs. Transfer learning techniques, including domain adaptation and Few-Shot Learning, ensure model adaptability across different food matrices and new interferents with minimal retraining. For real-time processing, RNNs such as LSTM or GRU networks handle time-series sensor data, while Kalman Filters combine with ML models for optimal state estimation. Explainable AI techniques, including SHAP values and LIME, provide transparency by interpreting the impact of different interferents on glucose measurements. Model validation involves conducting controlled experiments and pilot studies to ensure reproducibility and accuracy. Statistical analysis, including descriptive and inferential methods like t -tests and ANOVA, establishes relationships between input variables and GI outcomes, while metrics such as MAE, Root Mean Squared Error (RMSE), and R-squared assess model performance, accuracy, robustness, and interpretability, with feature importance scores identifying key GI determining variables. provides a comprehensive overview of the proposed work plan, summarizing all key components. 

Conclusion In vitro static models have significantly advanced our understanding of food digestion mechanisms and GR predictions. However, their limitations in replicating human digestion accurately lead to variability in outcomes, necessitating optimization for consistent GI estimation. While traditional models are simple and cost-effective, differences in digestion phase parameters often produce inconsistent predictions, highlighting the need for methodological improvements. Non-enzymatic electrochemical sensors offer a promising solution, enhancing both the speed and accuracy of glucose measurements in in vitro models. These sensors, optimized through cyclic voltammetric and amperometric techniques, provide faster, more reliable results than traditional enzymatic assays, which are often time-consuming and environmentally sensitive. AI and machine learning further improve GI prediction by processing complex data from in vitro digestion screenings, enabling real-time monitoring and adaptive control to better replicate in vivo conditions. Collectively, these innovations enhance the scalability and reliability of in vitro models, expanding their application across various food matrices. This integration of sensors and AI/ML technologies strengthens the precision of GI predictions, supporting food product development, GI labeling, crop breeding, and personalized dietary interventions. As these technologies evolve, AI-enhanced in vitro models will bridge the gap between lab-based assessments and practical dietary applications, offering more effective tools for nutrition science and glycemic health management. 

Mohammed Salman C K: Writing – original draft, Visualization, Methodology, Investigation, Data curation, Conceptualization. Muskan Beura: Visualization, Validation, Methodology, Conceptualization. Archana Singh: Resources, Project administration, Formal analysis. Anil Dahuja: Validation, Supervision, Project administration. Vinayak B. Kamble: Resources, Project administration, Formal analysis. Rajendra P. Shukla: Resources, Project administration, Formal analysis. Sijo Joseph Thandapilly: Writing – review & editing, Supervision, Funding acquisition, Formal analysis, Data curation, Conceptualization. Veda Krishnan: Writing – review & editing, Supervision, Funding acquisition, Formal analysis, Data curation, Conceptualization. 

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 

