increases the magnitude of the signal from the reflected excitation source decreases owing to an increase in absorption. Clearly, the closer in wavelength the excitation source is to the absorption maximum, the more pronounced this effect. Whilst not a 'clean' absorption measurement like those shown in Figure , the reflected LED light represents a 'pseudo-absorption' measurement, containing information on the absorption of the analyte at the LED wavelengths used for excitation. Indeed, so called diffuse reflectance spectroscopy is used very commonly for the analysis of opaque samples. . We have considered if the reflectance data provides useful information on analyte concentration. Figure shows the comparison between structurally different BZDs, showing their absorption spectra and HSFs. The HSFs are reported at three different concentrations: a 'low', 'medium' and 'high' concentration relative to the pharmaceutical dose, respectively. In all three examples the basic structure of the fingerprint is consistent with similar excitation emission maxima, but showing variance that is obvious even to the eye as the concentration changes. These changes are apparent in both the emitted light (intensity and wavelength variation) as well as in the reflected excitation light (intensity variation). These data suggest that combining both the information from LED reflectance and fluorescence emission can not only identify different BZDs, but also report on variation in BZD concentration. Deep learning enables high accuracy discrimination of both BZD identity and concentration. Ultimately, whilst the general principle of assessing concentration from a hybridised emission and pseudo-absorption measurement is feasible, we do not find an obvious generalisable model that allows extrapolation to different concentrations. For example, the inner filter effect will manifest differently for different analytes and different analytes will have varying extinction coefficients. That is, the exact relationship is highly multi-variate, making defining global rules for concentration measurement challenging, though we do not say impossible. Instead, we have explored whether deep learning can be deployed not only to identify BZDs from the fingerprints, but also through exploiting the variants of drugs as well as their concentrations. In this way, we can enable rule generation by scanning a range of concentrations for each BZD into a data library. Our device makes this technically tractable since data are collected via high intensity LEDs, with an optimized spectrometer positioning, resulting in full fingerprint scans that are collected in ~1 minute, enabling very high sampling volumes. For data collection, we captured HSFs for 86 distinct drugvariant-concentration classes, as shown in Table . Of these classes, 11 are BZDs at a range of concentrations that relate to relevant harm reduction advice; low, medium and high [relative to pharmaceutical dose or as harm reduction information from community forums (29)], and a range of other relevant drugs and in combination. We acquired 20 HSFs for each condition, where possible. However, for robustness, it was important to have a model that could provide accurate predictions for drugs even if very few samples were available for it, because this is expected to occur for many real-world drug samples. Before the device made predictions, the dataset was preprocessed as follows: (i) non-spectral data, such as excitation wavelengths and integration times, were removed; (ii) spectral data were trimmed to only the range of wavelengths containing useful fluorescence information; (iii) spectral data of similar wavelengths were averaged together, to reduce its dimensionality; (iv) spectral data from each excitation wavelength were normalised to the range [0,1] to improve computational efficiency; and (v) data were padded and reshaped into a 66x66x1 matrix. To ensure that a sophisticated deep learning model, such CNN, was the most appropriate choice of model for this task despite its larger computational overhead, three standard machine learning models were also trained with the same dataset, namely: Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM). Adjustments were made so that models from these three algorithms could function in a comparable manner to CNN, as follows: (i) each model was configured to be a One-vs-Rest (OvR) classifier so that they could provide multiclass classification; (ii) a large hyperparameter search was conducted for each model to determine optimal model configuration, for example, by Taken together, by leveraging a hybridised fluorescence and pseudo-absorption measurement, implemented on an optimised device geometry, we can apply deep learning to discriminate not just BZD type, but also variant and concentration information that is relevant for harm reduction information.