Drugs of abuse are often cut by pharmacological and non-pharmacological impurities that impact their adverse events. Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) offers a rapid technique that requires minimal sample preparation and can be carried to the field in portable form. This study utilized ATR-FTIR spectroscopy for detecting street drugs of abuse (n = 291). Few milligrams of powder or powdered tablets were measured directly on the instrument. Reference analysis was made using immunoassays and gas chromatography-mass spectrometry. The results showed that ATR-FTIR spectroscopy could identify more than 50% of the drugs measured. The main drugs found contained cocaine, ketamine, benzodiazepines, piperazines, and amfetamine derivatives. Accurate drug identification depended on the number of impurities, types of impurities and concentration of the drug in the product. Hence, drugs could be identified via their IR signatures that were able to differentiate a base from a salt. When correlation method (CM) was applied to the spectra, drugs were correctly identified if they did not have multiple impurities. On the other hand, the presence of impurities affected the accuracy of identification and showed mismatches. Mismatches were addressed by applying principal component analysis (PCA) to the IR spectra of the products. PCA in this case four key clusters with few overlaps. corresponding to cocaine, piperazines, amfetamines and ketamine. There were yet some overlap in the PCA scores due to presence of common impurities between products example benzocaine, cocaine and lactose. In summary, ATR-FTIR combined with chemometrics was accurate in identifying street drugs on the spot.

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.

Identifying cosmetics on substrates is essential in any crime scene processing. It is important in such cases to warrant the sample integrity and continuity. Spectroscopic techniques offer the advantage of analyzing samples non-destructively thus addressing the aforementioned requirements. When used in handheld modality, spectroscopic techniques offer rapid and on-site analysis with the ability to collect numerous datasets in minimal time. |Each spectrum is a unique fingerprint of the sample measured and that urges the need to collect large datasets and apply machine learning algorithms to make meaningful conclusions from analysis. Therefore, this work involved applying machine learning algorithms to infrared, near-infrared, and Raman spectroscopic data of cosmetics applied to different substrates. The cosmetics included powder, creams, lipsticks and nail polish; and the substrates included paper, fabric and glass. Machine learning algorithms were correlation method and principal component analysis. Both algorithms showed to be complementary in identifying cosmetics on different substrates, Thus, the approach proved accurate and precise for identifying substrates encountered at crime scenes.

This research proposes a computer vision-based solutions to identify whether a patient is covid19/normal/Pneumonia infected with comparable or better state-of-the-art accuracy. Proposed solution is based on deep learning technique CNN (Convolutional Neural networks) with multiple approaches to cover all open issues. First approach is based on CNN models based on pre-trained models; second approach is to create CNN model from scratch. Experimentation and evaluation of multiple approaches helps in covering all open points and gaps left unattended in related work performed to solve this problem. Based on the experimentation results of both the approaches and study of related work done by other researchers, Both the approaches are equally effective can be recommended for multi-class classification of lung disease.