Clearly, there are other potential drugs of abuse that could be detected using this approach. Figure shows a range of HSF data for different molecules, including drugs that are topical because of their association with significant rates of overdose and death including, fentanyl, xylazine, heroin and examples of nitazenes. From Figure each of the fingerprints are trivial to identify, even by eye. Whilst these drugs individually are responsible for drug overdose, the combinations present a significantly enhanced risk of harm and death and so their discrimination is critical to useful harm reduction strategies that could employ the approach. Figure , F and I show the fingerprint of the combined drug mixture from the panels at left. Similar to our findings in Figure , the HSFs are essentially additive, with the key spectral features of each drug being retained. We note that the main peak for bromazolam (λEx = 283 nm, λEm = ~310 nm) is retained in the mixture, but is difficult to observe by the eye in the contour plot shown in Figure . Using our deep learning approach described above (Table ), we find that these drugs and mixtures are easily discriminated both from one and other and the rest of the library of benzodiazepines.