Handheld Raman spectroscopy is an emerging technique for rapid on‐site detection of drugs of abuse. Most devices are developed for on‐scene operation with a user interface that only shows whether cocaine has been detected. Extensive validation studies are unavailable, and so are typically the insight in raw spectral data and the identification criteria. This work evaluates the performance of a commercial handheld Raman spectrometer for cocaine detection based on (i) its performance on 0–100 wt% binary cocaine mixtures, (ii) retrospective comparison of 3,168 case samples from 2015 to 2020 analyzed by both gas chromatography–mass spectrometry (GC–MS) and Raman, (iii) assessment of spectral selectivity, and (iv) comparison of the instrument's on‐screen results with combined partial least square regression (PLS‐R) and discriminant analysis (PLS‐DA) models. The limit of detection was dependent on sample composition and varied between 10 wt% and 40 wt% cocaine. Because the average cocaine content in street samples is well above this limit, a 97.5% true positive rate was observed in case samples. No cocaine false positives were reported, although 12.5% of the negative samples were initially reported as inconclusive by the built‐in software. The spectral assessment showed high selectivity for Raman peaks at 1,712 (cocaine base) and 1,716 cm⁻¹ (cocaine HCl). Combined PLS‐R and PLS‐DA models using these features confirmed and further improved instrument performance. This study scientifically assessed the performance of a commercial Raman spectrometer, providing useful insight on its applicability for both presumptive detection and legally valid evidence of cocaine presence for law enforcement.

On‐scene drug detection is an increasingly significant challenge due to the fast‐changing drug market as well as the risk of exposure to potent drug substances. Conventional colorimetric cocaine tests involve handling of the unknown material and are prone to false‐positive reactions on common pharmaceuticals used as cutting agents. This study demonstrates the novel application of 740–1070 nm small‐wavelength‐range near‐infrared (NIR) spectroscopy to confidently detect cocaine in case samples. Multistage machine learning algorithms are used to exploit the limited spectral features and predict not only the presence of cocaine but also the concentration and sample composition. A model based on more than 10,000 spectra from case samples yielded 97% true‐positive and 98% true‐negative results. The practical applicability is shown in more than 100 case samples not included in the model design. One of the most exciting aspects of this on‐scene approach is that the model can almost instantly adapt to changes in the illicit‐drug market by updating metadata with results from subsequent confirmatory laboratory analyses. These results demonstrate that advanced machine learning strategies applied on limited‐range NIR spectra from economic handheld sensors can be a valuable procedure for rapid on‐site detection of illicit substances by investigating officers. In addition to forensics, this interesting approach could be beneficial for screening and classification applications in the pharmaceutical, food‐safety, and environmental domains.

Both the increasing number and diversity of illicit‐drug seizures complicate forensic drug identification. Traditionally, colorimetric tests are performed on‐site, followed by transport to a laboratory for confirmatory analysis. Higher caseloads increase laboratory workload and associated transport and chain‐of‐evidence assurance performed by police officers. Colorimetric tests are specific only for a small set of drugs. The rise of new psychoactive substances therefore introduces risks for erroneous results. Near‐infrared (NIR)‐based analyzers may overcome these encumbrances by their compound‐specific spectral selectivity and broad applicability. This work introduces a portable NIR analyzer that combines a broad wavelength range (1300–2600 nm) with a chemometric model developed specifically for forensic samples. The application requires only a limited set of reference spectra for time‐efficient model training. This calibration‐light approach thus eliminates the need of extensive training sets including mixtures. Performance was demonstrated with 520 casework samples resulting in a 99.6% true negative and 97.6% true positive rate for cocaine. Similar results were obtained for MDMA, methamphetamine, ketamine, and heroin. Additionally, 236 samples were analyzed by scanning directly through their plastic packaging. Also here, a >97% true positive rate was obtained. This allows for non‐invasive, operator‐safe chemical identification of potentially potent drugs of abuse. Our results demonstrate the applicability for multiple drug‐related substances. Ideally, the combination of this NIR approach with other portable techniques, such as Raman and IR spectroscopy and electrochemical tests, may eventually eliminate the need for subsequent laboratory analysis; therefore, saving tremendous resources in the overall forensic process of confirmatory illicit drug identification.