Rapid qualitative and quantitative analysis of methamphetamine, ketamine, heroin, and cocaine by near-infrared spectroscopy

Liu, Cui-mei; Han, Yu; Min, Shungeng; Wang, Jia; Meng, Xin; Li, Peipei

doi:10.1016/j.forsciint.2018.07.008

Cited by 42 publications

(31 citation statements)

References 14 publications

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“…In our study, SNV processing proved to be a suitable technique for baseline correction (Figure 2A(ii)), in line with other studies. 22,24,30 As NIR spectra, in general, and these small-range spectra, in particular, are relatively information poor, a subsequent derivative preprocessing step is often suggested to put emphasis on the spectral differences. Consistent with an earlier NIR study on narcotics from Liu et al, 22 Figure S3 (supporting information).…”

Section: Spectral Reproducibility and Selectivitymentioning

confidence: 99%

“…Several studies have already shown that chemometric analysis of the data is of great use to apply NIR devices operating at longer-wavelength ranges in forensic casework. [22][23][24] Liu et al 22 successfully demonstrated the benefits of a multimodel approach on forensic drug samples. They used soft independent modeling of class analogy (SIMCA) for the classification of spectra with a methamphetamine, ketamine, heroin, or cocaine class followed by individual partial least square (PLS) regression models for quantification.…”

Section: Introductionmentioning

confidence: 99%

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Rapid and robust on‐scene detection of cocaine in street samples using a handheld near‐infrared spectrometer and machine learning algorithms

Kranenburg

Verduin

Weesepoel

et al. 2020

Drug Testing and Analysis

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On-scene drug detection is an increasingly significant challenge due to the fastchanging 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.

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Section: Spectral Reproducibility and Selectivitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid and robust on‐scene detection of cocaine in street samples using a handheld near‐infrared spectrometer and machine learning algorithms

Kranenburg

Verduin

Weesepoel

et al. 2020

Drug Testing and Analysis

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“…Liu et al 184 proposed a two-step qualitative and quantitative classification approach to determine the content of methamphetamine, ketamine, heroin, and cocaine in confiscated samples, using NIR spectroscopy and classification methods. A total of 282 samples were used for calibration and 836 samples were used for prediction.…”

Section: Infrared Spectroscopymentioning

confidence: 99%

Vibrational Spectroscopy and Chemometrics in Forensic Chemistry: Critical Review, Current Trends and Challenges

Silva¹,

Braz²,

Pimentel³

2019

J. Braz. Chem. Soc.

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The present manuscript makes an extensive review of the scientific approaches developed in the last decade involving infrared and Raman spectroscopy combined with chemometrics for solving several issues in the investigation of the most relevant forensic traces, such as questioned documents and currency, explosives, gunshot residues, illicit drugs and body fluids. In addition, current trends, main challenges and the adequate use of several chemometric techniques are discussed. Principal component analysis (PCA) was found to be the most used technique. This unsupervised approach, however, has sometimes been misunderstood as a classification technique. Discriminant analysis techniques are widely employed, leaving a range of possibilities for application of class-modeling techniques, particularly in cases of problems regarding only one target class. In addition, increasingly complex dataset structures frequently require nonlinear approaches or flexible techniques such as multivariate curve resolution-alternating least squares (MCR-ALS). Results reporting, however, still lack reliable quality parameters and sample representativeness, posing a significant challenge to the solution of forensic problems. Regarding the analytical techniques, Raman has been playing an important role, especially in the area of questioned documents and of body fluids. Portable and hyperspectral imaging infrared spectrometers have also been showing significant potential in forensic applications.

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“…To avoid the flaws from previous research, relatively larger (51 HD subjects and 40 CN subjects) and more homogeneous (unmedicated, cigarette, and alcohol abuse matched with professional questionnaire) samples were collected in the current study to investigate the ALFF differences in the whole brain between the HD and CN groups. In addition, to investigate potential biomarkers for heroin addiction, the support vector machine (SVM) classifier, which had already been successfully used in fMRI studies about heroin addiction ( 25 , 26 ), was adopted on clusters that showed significant ALFF differences to discriminate HD subjects from healthy controls.…”

Section: Introductionmentioning

confidence: 99%

Increased Amplitude of Low-Frequency Fluctuation in Right Angular Gyrus and Left Superior Occipital Gyrus Negatively Correlated With Heroin Use

Luo

Yang

et al. 2020

Front. Psychiatry

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Abnormal amplitude of low-frequency fluctuation has been implicated in heroin addiction. However, previous studies lacked consistency and didn’t consider the impact of confounding factors such as methadone and alcohol. Fifty-one heroin-dependent (HD) individuals and 40 healthy controls underwent resting-state functional magnetic resonance imaging. The ‘amplitude of low-frequency fluctuation’ (ALFF) value was calculated and support vector machine (SVM) classification analysis was applied to analyze the data. Compared with healthy controls, heroin addicts exhibited increased ALFF in the right angular gyrus (AG) and left superior occipital gyrus (SOG). A negative correlation was observed between increased ALFF in the right angular gyrus and left superior occipital gyrus and the duration of heroin use ( p 1 =0.004, r 1 =-0.426; p 2 =0.009, r 2 =-0.361). Moreover, the ALFF in the right AG and left SOG could discriminate the HD subjects from the controls with acceptable accuracy (Acc 1 =64.85%, p 1 =0.004; Acc 2 =63.80%, p 2 =0.005). HD patients showed abnormal ALFF in the brain areas involved in semantic memory and visual networks. The longer HD individuals abused heroin, the less the ALFF of associated brain regions increased. These observed patterns suggested that the accumulative effect of heroin’s neurotoxicity overpowered self-recovery of the brain and may be applied as a potential biomarker to identify HD individuals from the controls.

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Rapid qualitative and quantitative analysis of methamphetamine, ketamine, heroin, and cocaine by near-infrared spectroscopy

Cited by 42 publications

References 14 publications

Rapid and robust on‐scene detection of cocaine in street samples using a handheld near‐infrared spectrometer and machine learning algorithms

Rapid and robust on‐scene detection of cocaine in street samples using a handheld near‐infrared spectrometer and machine learning algorithms

Vibrational Spectroscopy and Chemometrics in Forensic Chemistry: Critical Review, Current Trends and Challenges

Increased Amplitude of Low-Frequency Fluctuation in Right Angular Gyrus and Left Superior Occipital Gyrus Negatively Correlated With Heroin Use

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