Cocaine is often cut by multiple diluents and adulterants that increase the volume or alter the pharmacological activity. Handheld near-infrared (NIR) and Raman spectroscopy are rapid and can be carried on the site for detection of drugs of abuse. Both techniques are complementary and give a chemical and physical fingerprint of the drugs measured. This research combined NIR and Raman spectroscopy for quantifying cocaine in mixtures non-destructively. Mixtures of cocaine with adulterants and/or diluents were prepared and stored in glass vials. Then each mixture was measured three times through the glass vials. Likewise, pure substances were measured through glass vials. NIR and Raman spectra were exported in Matlab 2024b where partial least square regression (PLSR) was applied. PLSR models were evaluated for accuracy and precision considering the correlation coefficient (r2) and root mean square errors of calibration and prediction (RMSEC and RMSEP) values. In this respect, models based on NIR spectral data showed higher accuracy and precision than those based on Raman spectral data. Thus, the r2values of calibration and prediction sets for NIR spectral models were in the range of 0.9815-0.9925 and 0.9604-0.9907 respectively. Yet, the r2values of calibration and prediction sets for Raman spectral models were in the range of 0.8891-0.9905 and 0.6882-0.9394 respectively. Moreover, the RMSEC and RMSEP values for models based on NIR spectral data were in the range of 2.77-6.04 and 3.13-8.34% m/m respectively. In addition, the RMSEC and RMSEP values for models based on Raman spectral data were in the range of 3.01-8.61 and 6.13-12.7% m/m respectively. The difference in accuracy could be related to NIR spectral data showing more information regarding the measured powders being collected in diffuse reflectance mode. On the other hand, the Raman measurement mode comprised surface reflection. However, the difference in accuracy and precision was not major and both techniques proved accurate and precise in predicting cocaine in mixtures of drug and food products.

This study investigated using near-infrared (NIR) and Raman spectroscopy with chemometrics for detecting counterfeit antibiotics. Antibiotics were measured nondestructively using portable spectrometers in diffuse reflectance (NIR), conventional reflectance and spatially offset modes (Raman). Spectra were exported to Matlab 2025a where two chemometric algorithms were applied being correlation (CM) and principal component analysis (PCA) methods. The results showed that powders had stronger spectroscopic activity than tablets. CM and PCA were accurate in differentiating genuine from counterfeit antibiotics with exception observed in Fabamox Duo and Lamivir products. Spatially offset Raman spectroscopy, validated most sample identities and detected discrepancies not visible to NIR spectroscopy. The findings highlighted the synergistic strengths of both techniques being: sensitivity of near-infrared spectroscopy to physical properties and Raman's specificity to chemical properties. Moreover, chemometrics showed powerful in classifying antibiotics of different manufacturers.

Oral fast dissolving films (FDFs), have gained attention as an alternative dosage form to solid oral dosage forms such as tablets as their ease of use increases patient acceptability and adherence. This study aimed to investigate spatially offset Raman spectroscopy (SORS) and attenuated total reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy as quality control methods, to assess the suitability of these techniques in hospital pharmacy settings for individualised dosage forms. The findings showed that both Raman spectroscopy and FTIR were successful in identifying the active pharmaceutical ingredient (API) present in the samples however, further investigation with more sensitive techniques such as Surface Enhanced Raman Spectroscopy (SERS) should be conducted.

Near infrared (NIR) spectroscopy offers portable and rapid analysis of endogenous constituents and drugs within fingernails. Fingernails are a useful alternative biological matrix to blood and urine specimen as they provide the advantage of being non-invasive and require minimal sample size (1–3 mm). This work utilised NIR spectroscopy for the detection of (1) drugs in fingernails including benzocaine, calcium carbonate, cocaine hydrochloride (HCl), levamisole HCl, lidocaine HCl and procaine HCl; and (2) endogenous constituents such as carbohydrates, lipids, proteins and water. Fingernails were analysed initially ‘as received’ to identify the aforementioned endogenous constituents. Seven sets of fingernails were then spiked with one the identified drugs and measured over a six-week period. Spectra were exported into Matlab 2019a for spectral interpretation and machine learning analytics (MLAs). MLAs included correlation wavenumber space (CWS), principal component analysis (PCA) and Artificial Neural Networks Self-Organising Maps (SOM). The results showed that NIR spectra of spiked nails showed key characteristic features at specific wavelengths that corresponded to their spiked drug (1). When combined with CWS and PCA, NIR spectroscopy was able to differentiate between spiked and un-spiked nails and distinguish between the drugs that did not share similar chemical structures. CWS values (r values) and PCA loading scores highlighted spectra/spectral features that were significant. In addition, SOM showed further classes beyond PCA that corresponded to changes in physical properties of the fingernails. Thus, finding confirmed that NIR spectroscopy combined with MLAs possessed the ability to characterise fingernails based on their endogenous constituents and to detect the presence of drugs within fingernails.

The use of handheld Raman spectroscopy has increased in popularity based on its ability to provide portable and rapid analysis of endogenous compounds and diagnostic biomarkers in alternative biological matrices, such as fingernails. The application of fingernails as a diagnostic matrix allows for non-invasive, non-intrusive sampling, which can be carried out in the comfort of the patient’s home. This study aimed to identify diagnostic biomarkers in fingernails related to cardiovascular diseases (CVDs) and diabetes mellitus (DM) using Raman spectroscopy and machine learning algorithms (MLAs). The findings showed that Raman spectroscopy successfully identified the presence of disease specific biomarkers in CVD and diabetic fingernails. Furthermore, when used in combination with MLAs, Raman spectroscopy was able to differentiate between healthy, CVD and diabetic fingernails. Further investigation will look at applying additional MLAs for determining the prognosis of disease.