Wavenumber selection method to determine the concentration of cocaine and adulterants in cocaine samples

Discrimination and quantification of volatile compounds in beer by FTIR combined with machine learning approaches

The composition of volatile compounds in beer is crucial to the quality of beer. Herein, we identified 23 volatile compounds, namely, 12 esters, 4 alcohols, 5 acids, and 2 phenols, in nine different beer types using GC-MS. By performing PCA of the data of the flavor compounds, the different beer types were well discriminated. Ethyl caproate, ethyl caprylate, and phenylethyl alcohol were identified as the crucial volatile compounds to discriminate different beers. PLS regression analysis was performed to model and predict the contents of six crucial volatile compounds in the beer samples based on the characteristic wavelength of the FTIR spectrum. The R2 value of each sample in the prediction model was 0.9398-0.9994, and RMSEP was 0.0122-0.7011. The method proposed in this paper has been applied to determine flavor compounds in beer samples with good consistency compared with GC-MS.

Gaussian process regression coupled with mRMR to predict adulterant concentration in cocaine

Street cocaine is often mixed with various substances that intensify its harmful effects. This paper proposes a framework to identify attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) intervals that best predict the concentration of adulterants in cocaine samples. Wavelengths are ranked according to their relevance through ReliefF and mRMR feature selection approaches, and an iterative process removes less relevant wavelengths based on the ranking suggested by each approach. Gaussian Process (GP) regression models are constructed after each wavelength removal and the prediction performance is evaluated using RMSE. The subset balancing a low RMSE value and a small percentage of retained wavelengths is chosen. The proposed framework was validated using a dataset consisting of 345 samples of cocaine with different amounts of levamisole, caffeine, phenacetin, and lidocaine. Averaged over the four adulterants, the GP regression coupled with the mRMR retained 1.07 % of the 662 original wavelengths, outperforming PLS and SVR regarding prediction performance.

Overview of cocaine identification by vibrational spectroscopy and chemometrics

The use of non-destructive forensic methods for cocaine identification is of outstanding importance, given the amount of samples seized. Techniques such as ATR-FTIR, Raman, and NIR spectroscopy have become alternatives to circumvent this problem, as they allow fast, cheap analysis, and enable the reanalysis of samples. When combined with chemometrics, these spectroscopic methods can be used to determine and quantify cocaine samples, meaning that the limitations of existing techniques can be overcome. This review article covers spectroscopic techniques for identifying cocaine in different forms and matrices, such as food and textiles, which are materials used for smuggling. The chemometric identification of cocaine in oral fluid and water is also discussed. In addition, vibrational spectroscopy techniques using portable equipment are described. This work seeks to evaluate the main chemometric applications of spectroscopic data and to find new perspectives on the identification of cocaine using chemometrics.

A network pharmacology approach to predict potential targets and mechanisms of "Ramulus Cinnamomi (cassiae) - Paeonia lactiflora" herb pair in the treatment of chronic pain with comorbid anxiety and depression

BACKGROUND

Traditional Chinese medicine (TCM) prescriptions have multiple bioactive properties. "Gui Zhi-Shao Yao" herb pair is widely used to treat chronic pain (CP), as well as anxiety and depression. However, its related targets and underlying mechanisms have not been deciphered.

METHODS

In this study, the network pharmacology method was used to explore the bioactive components and targets of "Gui Zhi-Shao Yao" herb pair and further elucidate its potential biological mechanisms of action in the treatment of CP with comorbid anxiety disorder (AD) and mental depression (MD).

RESULTS

Following a series of analyses, we identified 15 active compounds, hitting 130 potential targets. After the intersections the targets of this herb pair and CP, AD and MD - sorted by the value of degree - nine targets were identified as the vital ones: Akt1, IL6, TNF, PTGS2, JUN, CASP3, MAPK8, PPARγ and NOS3. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results demonstrated 11 pathways, such as AGE-RAGE signalling pathway, IL-17 signalling pathway, TNF signalling pathway, which primarily participate in the pathological processes.

CONCLUSIONS

This study preliminarily predicted and verified the pharmacological and molecular mechanisms of "Gui Zhi-Shao Yao" herb pair for treating CP with comorbid AD and MD from a holistic perspective. In vivo and in vitro experiments will be required to further investigate the mechanisms.KEY MESSAGEA network pharmacology approach was applied to identify key targets and molecular mechanisms.Nine targets were regarded as the vital targets for chronic pain with comorbid anxiety and depression.Predicted 11 pathways were the potential therapy targets and pharmacological mechanism of "Gui Zhi-Shao Yao" herb pair.

Combining wavelength importance ranking to the random forest classifier to analyze multiclass spectral data

Near Infrared (NIR) is a type of vibrational spectroscopy widely used in different areas to characterize substances. NIR datasets are comprised of absorbance measures on a range of wavelengths (λ). Typically noisy and correlated, the use of such datasets tend to compromise the performance of several statistical techniques; one way to overcome that is to select portions of the spectra in which wavelengths are more informative. In this paper we investigate the performance of the Random Forest (RF) classifier associated with several wavelength importance ranking approaches on the task of classifying product samples into categories, such as quality levels or authenticity. Our propositions are tested using six NIR datasets comprised of two or more classes of food and pharmaceutical products, as well as illegal drugs. Our proposed classification model, an integration of the χ² ranking score and the RF classifier, substantially reduced the number of wavelengths in the dataset, while increasing the classification accuracy when compared to the use of complete datasets. Our propositions also presented good performance when compared to competing methods available in the literature.

An infrared spectroscopic approach to characterise white powders, easily applicable in the context of drug checking, drug prevention and on-site analysis

More and more events, such as the summer music festivals, are considering the possibilities for implementing on-site testing of psychoactive drugs in the context of prevention and harm reduction. Although the on-site identification is already implemented by plenty of drug checking services, the required rapid quantitative dosing of the composition of illicit substances is still a missing aspect for a successful harm reduction strategy at events. In this paper, an approach is presented to identify white powders as amphetamine, cocaine, ketamine or others and to estimate the purity of the amphetamine, cocaine and ketamine samples using spectroscopic techniques hyphenated with partial least squares (PLS) modelling. For identification purposes, it was observed that mid-infrared spectroscopy hyphenated with PLS-discriminant analysis allowed the distinction between amphetamine, cocaine, ketamine and other samples and this with a correct classification rate of 93.1% for an external test set. For quantitative estimation, near-infrared spectroscopy was more performant and allowed the estimation of the dosage/purity of the amphetamine, cocaine and ketamine samples with an error of more or less 10% w/w. An easily applicable, practical and cost-effective approach for on-site characterisation of the majority of the psychoactive samples encountered in Belgian nightlife settings based on IR spectroscopy was proposed.

A data fusion model merging information from near infrared spectroscopy and X-ray fluorescence. Searching for atomic-molecular correlations to predict and characterize the composition of coffee blends

This article aims to develop and validate a multivariate model for quantifying Robusta-Arabica coffee blends by combining near infrared spectroscopy (NIRS) and total reflection X-ray fluorescence (TXRF). For this aim, 80 coffee blends (0.0-33.0%) were formulated. NIR spectra were obtained in the wavenumber range 11100-4950 cm-1 and 14 elements were determined by TXRF. Partial least squares models were built using data fusion at low and medium levels. In addition, selection of predictive variables based on their importance indices (SVPII) improved results. The best model reduced the number of variables from 1114 to 75 and root mean square error of prediction from 4.1% to 1.7%. SVPII selected NIR regions correlated with coffee components, and the following elements were chosen: Ti, Mn, Fe, Cu, Zn, Br, Rb, Sr. The model interpretation took advantage of the data fusion between atomic and molecular spectra in order to characterize the differences between these coffee varieties.

Wavenumber selection based on Singular Value Decomposition for sample classification

The dissemination of falsified medicines is a public health risk. Techniques such as attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy are commonly adopted for fraudulent drug detection. However, the spectrum generated by the ATR-FTIR typically results in hundreds of wavenumbers, reducing the performance of classification methods aimed at discriminating between authentic and falsified medicines. This article proposes a novel method for selecting a reduced size subset of wavenumbers that improves the classifier performance. The singular value decomposition SVD is used to generate a wavenumber importance index. An iterative process creates k-nearest neighbor (KNN) models by adding the wavenumbers in a decreasing order according to the importance index. Wavenumbers that increase classification accuracy are selected. When applied to Cialis® ATR-FTIR data, the proposed approach retained average 0.51% of the original wavenumbers with 100% accurate classifications; as for the Viagra® data set, the method yielded perfect classifications retaining average 0.17% of the original wavenumbers.