Drug detection in oral fluid has become popular over the last few years with oral fluid being a non-invasive matrix. Handheld surface enhanced Raman spectroscopy (SERS) is a rapid technique that detects drugs at low concentration and in any field. This work proposes using handheld SERS with machine learning algorithms for detecting drugs in oral fluid. Three drugs were evaluated being cocaine, its metabolite (benzoylecgonine) and paracetamol. Raman spectra of drug solutions mixed with gold or silver nanoparticles were collected through glass vials. These spectra showed high signal to noise ratios and each spectrum of a drug solution showed characteristic drugs bands for each drug. Then three machine learning algorithms were applied and were correlation in wavenumber space, principal component analysis and partial least square regression. The correlation in wavenumber space and principal component analysis classified the different drugs in oral fluid with accuracy up to 99% depending on the model. The partial least square regression informed about the levels of drugs in the oral fluid solution with high accuracy and precision. In conclusion, handheld SERS could instantly detect and predict concentrations of drugs in oral fluid.

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.

In recent years, saliva has emerged as an alternative biological matrix, offering several advantages such as its accumulative nature and non-invasive, non-intrusive sampling. Moreover, paired with novel vibrational spectroscopic techniques such as attenuated total reflectanceFourier transform infrared (ATR-FTIR) and Raman spectroscopy, saliva can be utilized for the detection and monitoring of drug use. Previous detection methods required extensive sample preparation and do not allow for rapid and portable analysis. Therefore, this work employed two vibrational spectroscopic instruments, those being: the Agilent 4500a ATR-FTIR spectrometer and the Agilent Resolve Raman spectrometer for the detection of drugs and their metabolites in artificial saliva. Both instruments were successful in the detection of illicit and over-the-counter drugs including benzoylecgonine, cocaine hydrochloride (HCl), diazepam, delta-9-tetrahydrocannabinol (THC) and paracetamol. A surface enhanced Raman spectroscopy (SERS) method was also developed for enhanced Raman signals. The chosen vibrational spectroscopic techniques utilized demonstrated the ability to detect key drug-related bands at concentrations as low as 0.05 mg/mL and over a four-week period. However, particularly in the case of SERS, the activity of drug-related bands decreased over this time.

As an alternative biological matrix, fingernails act as a non-invasive window into systemic diseases such as cardiovascular diseases (CVDs) or diabetes mellitus (DM). The presence of such diseases manifests physically, altering the fingernail plate's topography, as well as its elemental composition. Scanning electron microscopy (SEM) equipped with energy dispersive X-ray spectrometry (EDX) and scanning transmission electron microscopy detector (STEM), allows for detailed composition analysis, elemental mapping and spectroscopy. As complementary detectors, the presence of key elements such as calcium, oxygen, potassium and silicon can be quantified and investigated in relation to the presence of disease. Furthermore, topographical STEM images provide insight into the prevalence of tissue damage to the fingernail, which is often attributed to hyperglycemia or poor circulation. The EDX weight% values of identified elements within the fingernails were exported into Matlab R2024a, where a machine learning algorithm, principal component analysis (PCA), was applied to identify patterns between the elemental composition of healthy and diseased fingernails. Analysis of variance (ANOVA) results demonstrated relationships between confounding variables such as biological sex and diet and elemental composition. Female participants demonstrated a significantly higher weight% value of titanium than males due to the exposure of titanium in personal-care products and sun creams. The weight% value of calcium was also significantly different in fingernails of individuals who practiced extremely healthy, healthy and unhealthy diets. In the presence of disease, calcium and sodium showed significantly different weight% values in healthy versus diseased fingernails. STEM images further confirmed the presence of disease, with CVD and/or diabetic fingernail clippings showing rough, topographical textures. In contrast, fingernails taken from healthy participants displayed smooth areas, with small amounts of rough texture. Therefore, demonstrating the ability of SEM to detect the presence and severity of disease.

The prevalence of cardiovascular diseases (CVDs) and diabetes mellitus (DM) has become a global concern with figures as high as $\mathbf{1 7. 9}$ and $\mathbf{1. 5}$ million lives lost annually [1, 2]. Global figures also suggested that the majority of CVDs and DM are present within low- and middle-income countries (LMICs), where medical equipment, staff and training is limited. As a result, many patients go underdiagnosed or undertreated and instead are left to manifest into further complications such as heart failure or diabetic ketoacidosis, respectively. Therefore, this study aimed to investigate the use of nearinfrared (NIR) spectroscopy paired with machine learning algorithms (MLAs) for detection of CVDs and DM in fingernails. The findings showed key NIR bands related to the glycation of proteins within the fingernails and indicated the presence of disease. Furthermore, binary and multi-class classification models were explored for the classification of healthy, unhealthy, CVD and diabetic fingernails.

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.