Sulaf Assi

Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FT-IR) provides portable and rapid analysis of biomarkers and drugs within fingernails. Fingernails offer a suitable alternative to traditional biological matrices and provide advantages such as non-invasive collection and requiring small sample sizes. This work utilized ATR-FT-IR for detecting biomarkers and cocaine within fingernails. Fingernails were analyzed initially “as received” to identify biomarkers such as carbohydrates, lipids, and proteins over the range 650–4000 cm

. Spectra were collected for fingernails before and after spiking with cocaine hydrochloride. Measurements were taken at one week and up to six weeks. Principal component analysis (PCA) revealed distinct clusters within the PC scores of cocaine-spiked versus unspiked fingernails. Findings showed that ATR-FT-IR spectroscopy could characterize fingernails based on intrinsic components and identify the presence or absence of cocaine within them.

The study of keratinized matrices, such as hair and fingernails, has become important in clinical and forensic toxicology because of their potential for drug accumulation. Keratinized matrices offer a greater window of detection for drugs over traditional body fluids such as blood and urine (1–3). Hair analysis has been used for detecting historic drug use in a range of forensic applications including suspicious deaths and drug-facilitated crimes (4,5). However, hair analysis is often subject to contamination by microorganisms and environmental dust or smoke (6–8).

Fingernail sampling is non-invasive, and it does not require medical personnel for its collection, making it an ideal alternative matrix to hair. Fingernail sampling offers a further advantage in detecting a wide range of analytes because of the slow growth rate of fingernails and their higher drug accumulation rate. Chemical analysis of nails has been used for a variety of purposes such as forensic investigations, occupational, and environmental exposure (9–11). Fingernail analysis has also been used to diagnose various diseases and monitor specific medicines (antidepressants and benzodiazepines) and drugs of abuse (amphetamines, cocaine, and opiates) (12–16).

Many studies have applied analytical techniques for measurement of drugs of abuse in fingernails. These studies have focused on classical analytical techniques such as gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS), and were semi-quantitative in nature (17,18). However, both GC–MS and LC–MS required expensive and extensive extraction of the analytes prior to analysis. Considering these concerns, we propose a unique method for cocaine analysis based on ATR-FT-IR spectroscopy, which is cost-effective, rapid, and requires minimum sample preparation. ATR-FT-IR spectroscopy has become a crucial technique for forensic researchers and investigators to assess trace evidence alongside drugs, inks, explosives, biological fluids, and cosmetics (19–23).

Cocaine is Europe’s most used illicit stimulant and remains in high supply and demand (24). Cocaine addicts must be monitored in a therapeutic or forensic/legal context, necessitating the use of multi-analyte methods that may identify several substances in a single run. This study presents a simple and sensitive ATR-FT-IR method in conjunction with chemometric tools for the detection of constituents and drugs in fingernails.

This work comprised ATR-FT-IR spectroscopic analysis of 19 sets (11 males and eight females) of fingernails from adults of different ethnicities, each with various diets, medication or drug use, and lifestyles (Table I). Fingernail clippings were collected alongside a checklist regarding their medication use and lifestyle. No intervention was made by the researchers in terms of collecting the fingernail clippings, considering the procedure as an everyday activity. Ethical approval was granted from the two institutions that held the study: Liverpool John Moores University in the UK (PBS/2021-22/04) and the Lebanese University in Lebanon (2022-0104).

One fingernail set was placed in a glass vial and spiked with 50 mg of cocaine hydrochloride. The vial was vortexed for 2 min, coating the fingernails in the cocaine. Measurements were taken at weekly intervals over a six-week period. Spectral measurements were made using the Agilent 4500 ATR-FT-IR spectrometer over the wavenumber range of 650–4000 cm

. Three spectra were collected per fingernail such that each spectrum was the sum of 64 scans.

Spectra were exported into Matlab 2019a for spectral interpretation, spectral quality evaluation, and analysis. For spectral interpretation, the absorption bands of cocaine hydrochloride and fingernails were evaluated. The number of peaks (

), wavenumber range, maximum peak intensity and position, and signal-to-noise ratio (S/N) were all considered while evaluating spectral quality (Table II). Principal component analysis (PCA) was applied to the nail spectra to visualize the patterns among the different participants’ fingernails, and the patterns among cocaine-spiked and unspiked fingernails. For PCA analysis, PC scores and loading plots were evaluated. PC scores demonstrated the patterns of clusters among the different fingernails, and PC loadings justified the importance of the clusters. PC loading plots showed the key functional groups that expressed the highest variance among the data. In this respect, PC1 expressed the highest variance, PC2 the second highest variance, and PC3 the third highest variance. For accuracy and precision of the PCA model, clusters were inspected for type I and type II errors. Type I errors were encountered when scores of the same fingernail set were not grouped together, whereas type II errors were seen when a score of one fingernail set was grouped with a different set.

The use of ATR-FT-IR for fingernail inspection showed many advantages in forensic analysis. First, the collection procedure was non-invasive and non-destructive, and it needed a small sample size (1–3 mm), requiring minimal intervention from the researcher.

Second, FT-IR spectra provided unique identifiable features relating to endogenous constituents within nails. These identifiable features can be used to understand individuals’ diets, disease states, drug/medicine usage, and their lifestyle characteristics. Third, previous studies have shown that spectral data of fingernails could highlight the presence of some physical and psychological conditions. For example, conditions such as anxiety, depression and diabetes affect the fingernail’s keratin, and this is observed through the nail’s appearance (12). Individuals who have chronic depression or systematic diseases are likely to possess discolored nails, fingernail plate alterations, or brittle fingernails (12). Disfiguration to the nail plate is often the consequence of altered trace element levels during chronic depression. Research has highlighted a strong association between decreased levels of nail constituents and depression (12). By optimizing the method for a portable device, it could be used on-site at airports, police stations, hospitals, and crime scenes.

This study aimed to complement previous studies by exploring the effects of different diets and lifestyles on the IR spectra of fingernails. Moreover, it assessed deposition of cocaine in nails upon chronic exposure over a period of six weeks. In turn, this enabled discrimination of chronic dealing with cocaine that has been of interest in forensic investigations. In this respect, it has been important to distinguish individuals suspected of possessing cocaine for a “one-use” from those who possess drugs chronically with intent to supply.

 = 19) showed bands corresponding to the absorbances of key endogenous constituents usually present in fingernails (Figure 1). These constituents included proteins, lipids, carbohydrates, and amino acids (25). The identified bands were assigned vibrational bands (Table II).

FIGURE 1: Raw IR spectra of fingernail subject (nail set) #1 (black) and subject (nail set) #9 (red) measured using the portable ATR-FT-IR spectrometer.

Table III shows the IR spectra quality of the fingernail sets (

 = 19). In this respect, fingernail sets showed strong (

 = 12) IR activity. Considering the four evaluation parameters, the number of bands (

) ranged from 19–25 bands for fingernail sets with strong IR activity, and from 16–25 bands for fingernail sets with medium IR activity. Having more IR bands allowed better discrimination between fingernails’ sets. This is especially advantageous in early disease diagnosis, where pattern recognition between healthy and diseased fingernails can be established (12). Despite differences in the number of bands, all fingernail sets shared the same maximum band position (between 1630–1650 cm

) that corresponded to the fingernails’ proteins. The absorbance intensity of the protein band was in the range of 0.02–0.25 absorbance units. It is worth noting that the absorbance of 0.02 was seen for an individual with anxiety (LJMUWN1), confirming the findings of Cashman and Sloan’s 2010 study, which suggested that psychological conditions can affect a nail’s keratin (12). In addition, S/N varied between nails and was seen in the range of 50.5–130 for fingernails with strong IR activity, and in the range of 5.4–43.3 for fingernails with medium IR activity. Low S/N was seen for fingernails with high moisture content; this could be attributed to the individuals’ diets.

PCA analysis was often applied to large data sets to reduce dimensionality of data and to increase interpretability without information loss (Figure 2). During this analysis, spectral data was classified into the subspaces of PC scores and PC loading plots. The PC scores showed patterns of clusters among the different fingernails; whereas the PC loadings indicated which absorbances were important for each PC loading. When applied to the 19 sets of fingernails, the first three PC scores captured 93.1% of the variance but showed overlap among the fingernail scores apart from two fingernail sets. The latter two sets (5 and 6) were from individuals of Arab ethnicity; this finding demonstrated the ability of IR to classify participants’ demographic characteristics. This would be beneficial in disease management where personalized therapy is required.

FIGURE 2: PC scores plot of the FT-IR spectra of the fingernail sets (

 = 19) measured using the portable ATR-FT-IR spectrometer. Nail set one (black circles), nail set two (blue circles), nail set three (red circles), nail set four (green circles), nail set five (magenta circles), nail set six (cyan circles), nail set seven (black squares), nail set eight (blue squares), nail set nine (red squares), nail set ten (green squares), nail set eleven (magenta squares), nail set twelve (cyan squares), nail set thirteen (black pentagons), nail set fourteen (blue pentagons), nail set sixteen (red pentagons), nail set seventeen (magenta pentagons), nail set eight (cyan pentagons), nail set nineteen (black triangles).

The ability of ATR-FT-IR to detect cocaine has been evaluated through the analysis of fingernail clippings spiked with cocaine hydrochloride for a period of six weeks (Figure 3). Table IV shows the main bands attributed to cocaine hydrochloride that were featured in the spectra of fingernails. The key band attributed to the benzene ring stretching at 1072 cm

 was featured in the nail spectra in weeks 1–6. This further confirmed that fingernails act as a drug depot that is accumulating drugs over time. This latter finding supports previous literature that identified historic drug use through the analysis of nails as an alternative matrix. A greater window of detection could be attributed to how the drug is deposited into the fingernail through the blood supply. In this sense, drugs are deposited by diffusion into the germinal matrix and nail bed, which grows at a rate of 3.47 mm per month (1). As the fingernail grows, the drug is deposited horizontally and vertically into the fingernail, which can explain drug detection up to six months after consumption.

FIGURE 3: (a) Raw FT-IR spectra of fingernails, and (b) spiked with cocaine for a period of six weeks measured using the portable ATR-FT-IR spectrometer.

Cocaine has a high affinity to proteins, resulting in cocaine–protein interactions (28). These interactions are reliant on the concentration of the cocaine one consumed or was exposed to (28). Therefore, individuals who are chronically exposed to cocaine are more likely to have higher concentrations of cocaine within their fingernails and more cocaine–protein interactions. As previously mentioned, fingernails are composed of a laminated keratinized structure of alpha keratin (29). Keratins are a group of structural fibrous proteins that are often found in ectodermal tissues, hair, and nails (29). Unlike other structural proteins, such as collagen and elastin, which are found in bones and teeth, keratin has sulphur-rich amino acids, cysteine, and disulfide links (30). Therefore, cocaine has a high affinity for proteins within the fingernail and is easily deposited.

To visualize patterns among the spectra of the cocaine-spiked fingernails’ spectra over the six-week period, PCA models were applied. The first three PCs contributed to 96.7% of the variance among the data (Figure 4). PC scores indicated three distinct clusters, yet an overlap was found within and between the cluster. Hence, the first cluster showed the highest variance among the data and contained scores corresponding to weeks five and six. This indicated that cocaine deposited over a longer duration of time; this was further confirmed by the PC1 loading that showed bands corresponding to cocaine. Cluster two showed the second highest variances among the data and contained scores of weeks one and three, in addition to one score for week four, four scores for week five, and three scores for week six. This showed type II errors where the scores of weeks four, five and six were clustered with weeks one and three. Cluster three showed scores with the least variance and corresponded to week four with one score of week five. This could be attributed to the change resulting from the cocaine interacting with the fingernail’s proteins; however, further research is required for confirming such interaction. Nonetheless, the pattern of clustering has shown that PCA was successful in monitoring the depot.

FIGURE 4: PC scores plot of fingernail clippings spiked with cocaine for one to six weeks using the portable ATR-FT-IR spectrometer. Week one of spiking (black circles), week two of spiking (blue circles), week three of spiking (red circles) week four of spiking (green circles), week five of spiking (magenta circles), and week six of spiking (black pentagons).

ATR-FT-IR spectroscopy offered a rapid and portable technique for the detection of endogenous constituents and cocaine within fingernails. The results showed that FT-IR spectra of fingernails had bands corresponding to carbohydrates at 926 and 1045 cm

. Analysis of cocaine-spiked fingernails demonstrated cocaine bands at 978 cm

 (monosubstituted out-of-plane bending vibration), 1010 cm

 (monosubstituted benzene ring stretching), 1055 cm

 (monosubstituted benzene ring stretching), and 1105 cm

 (CO stretching). The results also demonstrated that the IR spectra of the fingernail sets indicated individuals’ characteristics and disease statuses that influenced the protein constituents featured in the IR spectra. Furthermore, the length of exposure to cocaine influenced the depositing of the drug within the fingernail and increased the likelihood of cocaine-protein interactions. Subsequently, chronic exposure increased the probability of drug detection, providing useful information for courts when investigating charges of possession with intent to supply. In the future, a multivariate quantitative approach (principal component regression) would be ideal to track changes in cocaine concentration within fingernails overtime.

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 are with the School of Pharmacy and Biomolecular Sciences at Liverpool John Moores University, in Liverpool, United Kingdom. 

 is with the Faculty of Science at Lebanese University, in Beirut, Lebanon. 

 is with the Chemical Analysis Group at Agilent Technologies, in Cheadle, United Kingdom. 

 is with the School of Computer Sciences and Mathematics at Liverpool John Moores University, in Liverpool, United Kingdom, Direct correspondence to: 

Articles

ATR-FT-IR spectroscopy can provide rapid and portable measurements in forensic applications, demonstrating its ability to rapidly detect biomarkers and the presence of cocaine in fingernails.

The Detection of Biomarkers and Cocaine in Fingernails Using Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy

Handheld Raman spectroscopy offers a rapid and mobile technique for determining drugs nondestructively. The technique offers further advantage for analyzing drug products (DPs) thanks to its ability to measure samples through packaging, thus preserving evidence integrity and continuity. This research evaluated the use of handheld Fourier transform (FT)-Raman spectroscopy for characterization of street DPs (

 = 254) of diverse formulations. Raman spectra of DPs inside their packaging were collected and matched against the instrumental in-built library, and then exported to Matlab 2020a for offline spectral interpretation. Reference analysis was confirmed using gas chromatography–mass spectrometry (GC–MS) and infrared (IR) spectroscopy. The main drugs present were 1,3-trifluoro- methylphenylpiperazine, cocaine, ketamine, 3,4-methylenedioxymethamfetamine, mephedrone, and 1-benzylpiprerazine. The main impurities present in DPs were benzocaine, caffeine, and lactose. The impurities affected the Raman signature (enhanced or inhibited) of DPs, enabling profiling of DPs and visualizing patterns among different products. These latter patterns were featured in the similarities highlighted by the correlation coefficient values between different DPs, and by adjacent clusters of different DPs envisaged in the principal component analysis scores plot.

Crime and violent behaviors have increased because of the widespread use of illicit drugs in contemporary societies. Amsterdam (1027.7), London (701.1), and Copenhagen (780 mg/1000 p/day) had the highest weekend daily mean cocaine usage in 2020 (1). Drug addiction endangers public health, safety, and welfare, as well as the security of the nation. Researchers and authorities require quick and accurate drug detection methods that can be adapted to the ever-increasing spectrum of illicit drugs. Currently, the primary goal of drug analysis is to detect and identify drugs of abuse.

Analytical techniques such as thin layer chromatography, gas chromatography–mass spectrometry (GC–MS), gas chromatography-flame ionization detection (GC-FID), high performance liquid chromatography (HPLC), and liquid chromatography–mass spectrometry (LC– MS) have all been proposed, developed, and used to detect illicit drugs (2). These techniques provide reliable and accurate results in drug analysis, but they are costly, time consuming, and destructive, which limits their practical ability. Vibrational spectroscopic methods, such as Fourier transform infrared (FT-IR) and Raman, address the aforementioned limitations, and they have also been applied for qualitative and quantitative investigations of illicit drugs (3,4).

Raman spectroscopy measures the vibrational modes of a sample. The spectrum is a wavelength distribution of bands that correspond to molecular vibrations in the sample under investigation. The Raman band frequencies and intensities could be used to identify and quantify the measured samples (5). Raman spectroscopy generates molecular-specific spectra with minimum or no sample preparation, permitting non-destructive in situ analysis for different forms of drugs (liquids, tablets, and powders) (6), which is very useful when a substantial number of samples need to be analyzed, and it is essential for avoiding sample contamination, reducing analysis time, and protecting the evidence under investigation. Raman spectroscopy has been used in a variety of applications, including forensics (6), industrial process control (7), biomedical purposes (8), and medical diagnostics (9). When identifying drugs, Raman can distinguish between salts, stereoisomers, and homologs with a sample size of more than 5 mg (10), and it also can reveal a high level of specificity for drugs concealed in alcohol (11). Minor variations in chemical structures can be identified, thus providing a characteristic spectrum for each chemical compound and making the identification of drugs more reliable (12). Raman spectroscopy has recently been used to check drug contents within their packaging (13), evaluate the composition and homogeneity of drug tablets (14), classify street drugs (15), and determine drug authenticity (16). Raman spectra of drug blends are often highly complex because they include spectral features corresponding to all components in the sample. Consequently, chemometric methods must be used to obtain quantitative information from complex spectra (17). This study complements previous studies in evaluating the use of handheld Raman spectroscopy for on-spot identification and characterization of street drugs in their packaging.

 = 254) were measured as received, and included powders, tablets, capsules, liquids, and rocks of different colors. Samples were measured through glass vials using a handheld FT-Raman spectrometer equipped with 490 mW of laser power, a 1064 nm laser wavelength, and cooled InGaAs detector. Spectra were collected over the wavenumber range of 250–2000 cm

. The in-built spectral algorithm was used to search matches of the drug samples against the instrumental libraries. For each sample, the library algorithm gave matches for the drugs or additives that showed spectral features. For offline analysis, the spectra of the products were interpreted considering spectral quality, functional groups, and similarities among products using correlation in wavenumber space (CWS) and principal component analysis (PCA) algorithms. Spectral quality was evaluated for the measured drug products, considering the low spectral resolution in FT-Raman (18). For the CWS method, the momentum product was calculated between the spectra of different products reporting a common drug. A threshold of 0.95 or more was considered a match and vice versa (19). For PCA, drug products’ spectra were explored for patterns among their scores. A false negative was seen when a drug product score was not grouped with product scores of the same drug, whereas false positives were seen when a drug product score was clustered with scores of different drugs. Reference analysis was conducted using GC–MS and attenuated total reflectance-Fourier transform infrared (ATR-FT-IR) spectroscopy. GC–MS and ATR-FT-IR confirmed the identity of the drugs or common impurities present in DPs, respectively (Table I).

Out of the measured DPs, 153 contained drugs or drug mixtures, most of which were not pure. The main drugs found were 1,3-trifluoromethylphenylpiperazine (TFMPP) (

 = 29), 3,4-methylenedioxymethamfetamine (MDMA) (

 = 2), 3,4-methylenedioxypyrovalerone (MDPV) (

 = 2), 1-(3-chlorophenyl)piperazine (mcPP) (

 = 1), and 1-methyl-4-benzylpiperazine (mBZP) (

The main impurities detected were caffeine (

 = 35), which were both Raman active. Lidocaine and procaine were detected less than benzocaine, and they were found in seven and two products, respectively. Benzocaine and lidocaine were mainly found as impurities in cocaine drug products, alongside other impurities such as phenacetin and caffeine. Thus, the pattern of adulterants varied between DPs. For instance, caffeine was also an adulterant in MDMA and piperazine derivatives. On the other hand, lactose was a bulking agent reported in diazepam tablets, and it contributed to the low Raman activity of these products. Likewise, dextrose and sucrose were also found in DPs of tablet- and powder-formulations and contributed to the low Raman activity of these products. Hence, drugs adulterated with the aforementioned sugars were not detected in tablets or powders using Raman spectroscopy where the Raman signal of these tablets was masked by lactose fluorescence.

Spectral quality was evaluated considering these parameters: the number of peaks (

); maximum peak position and intensity; wavenumber range; and signal-to-noise ratio (S/N) (19). In this respect, synthetic piperazine, cocaine, and mephedrone products showed strong Raman activity, ketamine products showed medium Raman activity, and MDMA products showed low Raman activity.

Synthetic piperazines’ spectral quality varied depending on the purity of the DPs and the types of impurities present in them (Figure 1). TFMPP samples showed stronger Raman activity than other piperazines, and that was related to their being either pure or cut with caffeine that in turns was Raman active. The ranges of N, maximum intensity, and S/N values for piperazine derivatives were 15–25, 1500–15000 a.u., and 10–20, respectively. Cocaine products demonstrated strong Raman activity, but that depended on the adulterants present in the cocaine sample, which affected the Raman activity (Figure 2). For cocaine, Raman active samples, 

, maximum intensity, and S/N were in the range of 11–27, 1,744–51,242 a.u. and 15–39, respectively. Similarly, mephedrone showed strong Raman activity with ranges of 

, maximum intensity and S/N in the ranges of 25–29, 1500–8000 a.u., and 18–37, respectively (Figure 3). Ketamine products showed medium Raman activity with 

, maximum intensity, and S/N in the ranges of 16–30, 700–4000 a.u., and 5.75–39, respectively (Figure 4). On the other hand, MDMA products exhibited low Raman activity with low S/N < 20), despite the high 

 in most spectra (up to 25) (Figure 5). The maximum intensity seen for MDMA products was in the range of 1500–5000 a.u.

FIGURE 1: Raman spectra of (a) TFMPP and (b) BZP DPs measured using an FT-Raman spectrometer equipped with 1064-nm laser excitation wavelength.

FIGURE 2: Raman spectra of DPs containing (a) cocaine, (b) cocaine and benzocaine, and (c) cocaine and caffeine measured using an FT-Raman spectrometer equipped with 1064-nm laser excitation wavelength.

FIGURE 3: Raman spectra of mephedrone samples in crystal (black) and powder (red) forms measured using an FT-Raman spectrometer equipped with 1064-nm laser excitation wavelength.

FIGURE 4: Raman spectra of ketamine DPs measured using an FT-Raman spectrometer equipped with 1064-nm laser excitation wavelength.

FIGURE 5: Raman spectra of MDMA DPs in the following colors or forms: (a) gray crystal, (b) white crystal, (c) orange powder, and (d) pink tablet measured using an FT-Raman spectrometer equipped with 1064-nm laser excitation wavelength.

Spectral interpretation showed characteristic scattering bands for the major drugs and impurities present in the measured DPs (Table II). Most drugs showed key bands between 500–1700 cm

 (21–25). In this respect, Raman spectra indicated key differences between different drug analogues as well as base or salt forms of the same drug. For drug analogues, piperazine derivatives (TFMPP and BZP) could be differentiated over the ranges 500–1000 and 1300–1700 cm

, respectively (Figure 1). Similarly, cocaine could be discriminated from cocaine HCl mainly in the range between 850–890 cm

, where cocaine base showed three bands around 850, 870, and 890 cm

 corresponding to the C-C stretch of the tropane ring in the cocaine base (20). On the other hand, cocaine HCl only showed two C-C stretching bands corresponding to the tropane ring around 850 and 890 cm

 (20,21). Where benzocaine was present in cocaine DPs, it could be traced by the strong absorption intensity and by the characteristic bands in the range of 850–1700 cm

When CWS was applied to the spectra of the top encountered DPs, variable correlation (

 value) ranges were observed depending on the amount of drug in DPs and on the purity of the specific DP (Figure 6). For instance, TFMPP-related products (DPN213–DPN254) did not show high 

 values against each other, and that they could not be considered a false negative because of the inconsistency in constituents in TFMPP products, indicating the presence of other additives, such as caffeine, dextrose, ephedrine, lactose, phenmetrazine, and sucrose. In this respect, the Raman activity of the constituents impacted the Raman scattering in terms of number of bands and Raman activity of the DP. For instance, caffeine had strong Raman scattering, and that influenced the signature of the TFMPP products containing them (DPN234–249). Nevertheless, the latter products contained impurities such as lactose that was only detected by Raman (and not GC–MS), which in turn influenced their Raman activity. Lactose and other sugars introduced fluorescence in the Raman spectra of TFMPP products (Figure 1). It is important to mention that the physical properties of the DPs did not influence the Raman signature. Hence, TFMPP products of similar chemical makeup and different physical characteristics (formulation, color) showed high 

 values against each other. For example, DPN217 (white tablet formulation) showed an r value of 0.95 against DPN220 (white crystal formulation). Also, DPN228 (pink tablets), DPN235 (white tablets), and DPN246 (blue tablets) showed 

 values of 0.95 against each other. It is worth noting that no false positives were seen for TFMPP-DPs except against a DP containing BZP, and this mismatch could be related to the structural similarity between TFMPP and BZP.

FIGURE 6: (a) Correlation map of the Raman spectra of TFMPP (1:42), cocaine (43:78), ketamine (79:107), MDMA (108:131), caffeine (132:150), mephedrone (151:159), and BZP (160:166) DPs, where 

 values above 0.95 are marked in yellow and those below 0.95 are marked in red. (b) PCA scores plot of the Raman spectra of TFMPP (black circle), cocaine (blue square), ketamine (red star), MDMA (green triangle), mephedrone (cyan triangle) and BZP (black star) DPs.

Cocaine DPs featured less false negatives, and that could be attributed to the consistency in the types of impurities detected in the measured cocaine DPs. For instance, samples with a cocaine/benzocaine mixture (DPN48–DPN62) showed 

 values > 0.95 against each other where both constituents in the matrix were Raman active and were strong scatterers. Where more than two constituents were present in a cocaine DP, the 

 value changed, and that also depended on the Raman activity of the constituents. Overall, the constituents were unique to cocaine mixtures where no false positives were observed with cocaine mixtures against other drugs. Similar patterns in 

 values results were seen for ketamine samples that were mainly pure, and showed 

 values < 0.95 against different drugs. Only four ketamine DPs showed low 

 values against other ketamine products (one Raman active and three Raman inactive samples). The remaining DPs (containing MDMA and mephedrone) showed false positives and false negatives related to the high number of impurities present in them, and that had been reported in the literature, especially in mephedrone DPs.

Hence, CWS results were only conclusive when the exact number of impurities were present in the reference and test tablets. Considering the aforementioned challenge in identification, PCA recognized patterns in the DPs scores that corresponded to spectral features contributing to high variances (Figure 6). The first three PCs contributed 88.8% of the variance among the DPs’ spectra, with cocaine DPs’ scores recording the highest variance. This was followed by ketamine DPs’ scores and TFMPP DPs’ scores. Cocaine, ketamine, and TFMPP DPs’ scores showed three distinct clusters with slight overlap in few scores, and that could be related to the caffeine or lactose that were impurities present in DPs of the three drugs. Some MDMA DPs that contained lactose overlapped with products of the same impurity. On the other hand, mephedrone DPs were clearly distinguished from other DPs, and that showed their unique composition.

In summary, handheld FT-Raman spectroscopy complemented GC–MS and IR in characterizing street DPs. It indicated information regarding certain impurities that could not be detected by GC–MS (such as lactose and sucrose). Another advantage encountered over IR was the ability of Raman to characterize Raman active drugs nondestructively regardless of the physical differences between the matrices (tablets, powders, or liquids). However, some limitations were encountered in processing the spectra of the drug products relating to the sensitivity of Raman in detecting drugs that contained impurities, especially fluorescent ones such as lactose. Moreover, the presence of multiple impurities introduced a challenge in the identification and characterization of the drug product. Further understanding of Raman spectra, particularly in samples of multiple impurities, is needed. Therefore, future work will consider investigating drugs in mixtures using a more quantitative approach.

The authors would like to thank Bournemouth University for access to drug products and instrumentation. Special thanks for Amy Williams, Chelsea Bailey, and Angela Buffardi for their help with spectral data collection.

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(10) Lanzarotta, A.; Witowski, M.; Batson, J. Identification of Opioids and Related Substances using Handheld Raman Spectrometers. 

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 are with the School of Pharmacy and Biomolecular Sciences at John Moores University, in Liverpool in the United Kingdom. 

 is also with the School of Allied Health at Anglia Ruskin University, in Chelmsford in the United Kingdom. Direct correspondence to: 

Handheld FT-Raman spectroscopy can complement GC–MS and IR in characterizing street drugs.

Characterization of Street Drugs Using Handheld Fourier Transform Raman Spectroscopy

Fingernails can accumulate drugs as a result of chronic exposure. This work employed Raman spectroscopy for detecting cocaine hydrochloride (HCl) and its impurities within fingernails, utilizing orbital raster scanning (ORS) technology, where the laser beam hits multiple positions within the sample. Doing so maintained sensitivity and ensured that more of each sample’s components were represented. Fingernails were spiked with powder and solution forms of cocaine HCl and its impurities, including benzocaine HCl, levamisole HCl, lidocaine HCl, and procaine HCl. The strong Raman scattering observed for these substances indicated a high drug accumulation in the fingernails. Key cocaine HCl bands were seen at 848, 874, and 898 cm

(symmetric stretching-aromatic ring), 1278 cm

 (C=C and C=O stretching). Principal components analysis (PCA) confirmed that 90% (nails spiked with drug powders) and 77.2% (nails spiked with drug solutions) were accounted for in the variance among the data. The findings showed that Raman spectroscopy identified the presence of cocaine HCl and its impurities within fingernails.

 (symmetric stretching-aromatic ring), 1278 cm

The pervasive use of illicit drugs in modern society is a phenomenon that has never occurred in the history of humanity (1). Approximately 275 million individuals have used illicit drugs in 2021, which is an increase of 22% from 2010. It is estimated that projected population growth for 2030 translates into a possible increase of 11% in the percentage of illicit drug users worldwide, with a far higher impact in low-income nations rather than high-income nations. Drug usage has resulted in numerous severe problems, especially addiction (2). Drug addiction has caused young individuals to drop out of school and has made it difficult for parents to care for their children. It undermines ambition, drags individuals into poverty and crime, and wrecks lives. Furthermore, the illicit drug trade hinders social and economic advancement, and it can cause serious danger to both global security and stability in some parts of the world. Within the illegal drug market, cocaine has been one of the most common substances with an estimated 2,000 metric tons produced per year (3). Cocaine is a stimulant that exerts its mechanism of action by producing a build-up of dopamine between synapses. Three main forms of cocaine are available: free base, “crack” rocks, and hydrochloride (HCl) powder (4,5).

Many investigating authorities across the world routinely seize cocaine and take judicial actions. Forensic scientists and law enforcement organizations require rapid, accurate, and adaptable techniques of drug detection to combat drugs addiction. Despite the existence of numerous detection techniques, including immunoassays (6), capillary electrophoresis (7), deoxyribonucleic acid (DNA) aptameric sensors (8), and nuclear magnetic resonance (NMR) spectroscopy (9), hyphenated methods are currently the industry standard for analyzing illicit drugs in fingernails and other matrices. Of the hyphenated techniques, gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–MS (LC–MS) are often used in these analyses (10). Both GC–MS and LC–MS are destructive and time-consuming, involving the use of chemicals that can be harmful to both the operator and the environment despite their great sensitivity and molecular specificity. Hence, the use of vibrational techniques, such as Fourier transform infrared (FT-IR) (11) and Raman spectroscopy (12), is considered as a reliable alternative because these methods offer a portable, quick, non-destructive, and cost-effective solution for drug analysis. In particular, Raman spectroscopy is a versatile technique that is applied to analyze different types of forensic samples that cannot be determined by other spectroscopic methods, such as IR spectroscopy (13). Raman spectroscopy is used for both qualitative and quantitative purposes (14,15). Numerous drug classes, including amphetamines, date rape drugs like ketamine, and designer drugs, have been studied using Raman spectroscopy (16). Raman is used because it has better chemical specificity than IR (17). The chemical specificity offers advantages of Raman in detecting physical and chemical differences between isomers and analogues of the same drug (18). Recent developments in data analysis, portable Raman spectrometers, and new excitation wavelengths offer exciting new opportunities for Raman spectroscopy applications in the detection and quantification of drugs of abuse, including investigations carried out immediately at the crime scene. Raman spectroscopy is regarded as a “Category A” technology, offering the maximum level of selectivity through structural information, according to worldwide recommendations given by the Scientific Working Group on Drugs (SWGDRUG) Core Committee (19). An emerging technique for the quick onsite detection of illicit drugs is handheld Raman spectroscopy because it offers additional features over conventional Raman in safety and security where the operator does not need to touch the sample that can be measured inside original packaging (20–23). The aim of this study is to demonstrate the application of rapid, portable methods for the quantitative detection of cocaine HCl and its impurities in human fingernails using handheld Raman spectroscopy in combination with chemometrics.

This work comprised the Raman spectroscopic analysis of 20 sets (11 males and nine females) of fingernails of adults of two ethnicities. Fingernail clippings of 1–2 mm were collected and measured through glass vials. No intervention was made by the researchers during the collection of the fingernail clippings, considering this procedure as an everyday activity. Ethical approval was provided from two institutions: Liverpool John Moores University (PBS/2021/22/04) and the Lebanese University in Lebanon (2022-0104). Five sets of fingernail clippings were then spiked with one of the following drugs or drug impurities: benzocaine (BEN); cocaine HCl (COC); levamisole HCl (LEV); lidocaine HCl (LID); and procaine HCl (PRO). After six weeks of treatment, the spiked nails were measured using a handheld Metrohm Mira XTR Raman spectrometer equipped with a 785-nm laser wavelength, and less than 100 mW of laser output power was used. Raman spectra were collected over a wavenumber range of 400–2300 cm

. The laser beam of the spectrometer used orbital raster scanning (ORS) technology where the beam sampled multiple positions on the sample.

Reference samples of the chosen drugs and impurities were measured through glass vials and included in the instrumental library. The in-built spectral algorithm searched for matches between the reference samples and spiked fingernail clipping samples. Matches were shown for the chosen drug and its impurities and highlighted the key spectral features between the drug and impurities. Offline analysis was also carried out in Matlab 2022b software, whereby the spectra of the drug and impurities were interpreted based on the spectral quality, functional groups, and similarities among the substances identified through the principal component analysis (PCA) algorithm. The spectral quality was evaluated for the measured drug and drug impurities. For PCA, the Raman spectra were investigated for patterns among their PC scores. False negatives were observed when a drug or impurity score was not grouped with the scores of the same drug or impurity and where false positives were seen when a drug or impurity score was clustered with scores of a different drug or impurity.

The measured nails consisted of 20 sets of fingernails obtained from different individuals within the age group of 18–64 years old. The male:female ratio of these individuals was 11:9, and they were of (two) ethnicities: white British and Arab. Each nail set consisted of 7–10 fingernail clippings, and the dimension of each fingernail clipping was 1–2 mm. Of the 20 sets, five were spiked with BEN, COC, LEV, LID, and PRO, respectively. Two types of spiking were made: powder form of the drug (3–5 nails per set) and liquid form (3–5 nails per set) of the drug. A minimum of 20 spectra were collected for each nail set (whether plain or spiked).

Raman spectroscopy was able to detect COC and its impurities in fingernails six weeks post-spiking (Figure 1). In this respect, nail samples showed strong spectral quality with a maximum intensity up to 30,000 a.u.

FIGURE 1: Raman spectra of (a) BEN, (b) COC HCl, (c) LEV HCl, (d) LID HCl, and (e) PRO HCl measured using the handheld Raman spectrometer equipped with a 785-nm laser wavelength; powder (black) and liquid (red) spiked spectra.

Raman activity of the chosen drug and impurities was determined through investigating their Raman spectral quality (Table I). In turn, Raman spectra were evaluated using the following parameters: the number of peaks (

), the maximum peak position and intensity, the wavenumber range, and the signal-to-noise (S/N) ratio. In this respect, the BEN powder, LID powder, and LID solution showed strong Raman activity with N (29–45), maximum intensity (11–74 a.u.), and S/N (33–42). The BEN liquid, COC HCl powder, LEV HCl powder, LEV HCL liquid, PRO HCl powder, and PRO HCl liquid displayed medium Raman activity with N (16–28), maximum intensity (12–66 a.u.), and S/N (20–29). COC HCl liquid showed low Raman activity with N (19), maximum intensity (28 a.u.), and S/N (25).

Effect of Physical Form on Raman Activity

Key variations were seen in the Raman activity between drugs in solid versus liquid forms (Figure 2). The degree of variation depended on the type of drug being analyzed because some drugs showed no difference in Raman activity in their solid forms compared to their liquid forms, which was the case for LEV and PRO; both showed medium Raman activity in their solid and liquid forms. LID also showed strong Raman activity in both its solid and liquid form. Some drugs, such as BEN and COC, showed differences in Raman activity between their solid and liquid forms. In this respect, BEN and COC showed stronger Raman activity in their solid form compared to their liquid forms (that was medium). Variations between drugs and impurities in their solid and liquid forms suggested that physical properties of the substance influenced their Raman activity.

FIGURE 2: Raman spectra of fingernails spiked with BEN powder (black) and BEN liquid (red) forms measured using the handheld Raman spectrometer equipped with 785-nm laser wavelength.

Previous work has reported the Raman activity of key endogenous compounds such as carbohydrates, lipids, and proteins (24). Figure 3 shows the Raman spectra of endogenous compounds. Carbohydrates have been associated with the bands 1471 (CH

 (C-C stretching) (25). The amino acids cystine and cysteine have been linked to C-S stretching vibrations that showed Raman scattering at 623 and 643 cm

 (26). These amino acids make up the disulfide bridges related to the microstructure of fingernails. Disulfide cross-linking is determinant for physical and mechanical properties of a fingernail’s keratin, and it can often be used to identify disease or nail degradation such as diabetes (27). For example, bands at 830 and 850 cm

The results of this study showed that orbital raster scanning (ORS) technology, which ensured a greater interrogation area, also resolved issues of conventional Raman spectroscopy, such as low spectrum intensity and resolution.

Sulaf Assi

Articles

The Detection of Biomarkers and Cocaine in Fingernails Using Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy

Characterization of Street Drugs Using Handheld Fourier Transform Raman Spectroscopy

Using Handheld Raman Spectroscopy Equipped with Orbital Raster Technology for Field Detection of Cocaine and its Impurities in Fingernails

Identification of Novel Psychoactive Substances Using Hyphenated Mass Spectrometric Techniques