Headspace-gas chromatographic fingerprints to discriminate and classify counterfeit medicines.

&lt;p&gt;Counterfeit medicines are a global threat to public health. These pharmaceuticals are not subjected to quality control and therefore their safety, quality and efficacy cannot be guaranteed. Today, the safety evaluation of counterfeit medicines is mainly based on the identification and quantification of the active substances present. However, the analysis of potential toxic secondary components, like residual solvents, becomes more important. Assessment of residual solvent content and chemometric analysis of fingerprints might be useful in the discrimination between genuine and counterfeit pharmaceuticals. Moreover, the fingerprint approach might also contribute in the evaluation of the health risks different types of counterfeit medicines pose. In this study a number of genuine and counterfeit Viagra(®) and Cialis(®) samples were analyzed for residual solvent content using headspace-GC-MS. The obtained chromatograms were used as fingerprints and analyzed using different chemometric techniques: Principal Component Analysis, Projection Pursuit, Classification and Regression Trees and Soft Independent Modelling of Class Analogy. It was tested whether these techniques can distinguish genuine pharmaceuticals from counterfeit ones and if distinct types of counterfeits could be differentiated based on health risks. This chemometric analysis showed that for both data sets PCA clearly discriminated between genuine and counterfeit drugs, and SIMCA generated the best predictive models. This technique not only resulted in a 100% correct classification rate for the discrimination between genuine and counterfeit medicines, the classification of the counterfeit samples was also superior compared to CART. This study shows that chemometric analysis of headspace-GC impurity fingerprints allows to distinguish between genuine and counterfeit medicines and to differentiate between groups of counterfeit products based on the public health risks they pose.&lt;/p&gt;</p

Discrimination of legal and illegal Cannabis spp. according to

Aerial parts containing cannabidiol can be purchased in a legal way but cannabis&nbsp;used as recreational drug is illegal in most European countries. Δ9-tetrahydrocannabinol&nbsp;is one of the main cannabinoids responsible for the psychotropic effect.&nbsp;European Union countries and Switzerland authorize a concentration of THC of 0.2 % and&nbsp;1.0 % w/w, respectively, for smoking products and industrial hemp. Public health&nbsp;inspectors and law enforcement officers need to check the legality of samples.&nbsp;Therefore there is a need for innovative approaches, allowing quality control of these&nbsp;products in an easy way and preferably on site. In many countries, cultivation of&nbsp;industrial hemp is permitted if the THC content does not exceed 0.2 % w/w. A portable&nbsp;equipment could be a useful measuring tool for farmers to check for the THC content&nbsp;at regular time. In this work, 189 samples were analysed with a benchtop and a&nbsp;handheld NIR device in order to create two classification methods according to&nbsp;European and Swiss laws. All samples were also analysed by GC-FID to determine their THC concentration. Supervised analysis was applied in order to establish the best&nbsp;model. For the first classification, the accuracy was 91% for the test set with the&nbsp;benchtop data and 93 % for the test set with the handheld data. For the second&nbsp;classification, the accuracies were respectively 91 % and 95 %. The obtained models,&nbsp;hyphenating spectroscopic techniques and chemometrics, enable to discriminate legal and&nbsp;illegal cannabis samples according to European and Swiss&nbsp;laws.</p

Development and validation of a HS/GC-MS method for the simultaneous analysis of diacetyl and acetylpropionyl in electronic cigarette refills

&lt;p&gt;The use of e-cigarettes as alternative for tobacco cigarettes has become increasingly popular, even though their safety has not yet been scientifically established. One of the frequently raised concerns is the potential toxicity of certain flavours present in the e-liquids, such as diacetyl and acetylpropionyl. It is therefore important to be able to identify and quantify both compounds. Numerous analytical methods have been published for determining e-liquid compositions, but concerns exist with respect to the lack of analytical evaluation. Hence in this study, a new HS/GC-MS-based method was developed for the screening and quantification of diacetyl and acetylpropionyl in e-liquids. This method was fully validated using the &amp;#39;total error&amp;#39; approach. The LOQ of the analytical method was 5ppm for diacetyl and acetylpropionyl. The obtained accuracy profiles show that the beta-expectation tolerance intervals did not exceed the acceptance limits of+/-10%, meaning that 95% of future measurements will be included in the [-10%, 10%] bias limits. As proof of applicability, the validated method was successfully applied on a small set of e-liquid samples, indicating that this methodology could be used for routine quality control analyses of e-liquids&lt;/p&gt;</p

Evaluation of the residual solvent content of counterfeit tablets and capsules

A group of counterfeit samples of Viagra® and Cialis® were screened for their residual solvent content and compared to the content of the genuine products. It was observed that all counterfeit samples had higher residual solvent contents compared to the genuine products. A more diverse range of residual solvents was found as well as higher concentrations. In general these concentrations did not exceed the international imposed maximum limits. Only in a few samples the limits were exceeded. A Projection Pursuit analysis revealed clusters of samples with similar residual solvent content, possibly enabling some future perspectives in forensic research

Factors Influencing Benzene Formation from the Decarboxylation of Benzoate in Liquid Model Systems

Benzene may occur in foods due to the oxidative decarboxylation of benzoate in the presence of hydroxyl radicals. This study investigated factors influencing benzene formation in liquid model systems. The type of buffer, other sources of hydroxyl radical formation in food (photo oxidation of riboflavin and lipid oxidation), transition metal ion concentrations, and the inhibitory effect of antioxidants were tested in benzoate containing model systems. Regarding the hydroxyl radical sources tested, the highest benzene formation was observed in light exposed model systems containing ascorbic acid, Cu2+, and riboflavin in Na-citrate buffer (1250 -¦ 131 ?g kg?1). In practice, it seems that the combination ascorbic acid/transition metal ion remains the biggest contributor to benzene formation in food. However, the concentration of Cu2+ influences significantly benzene formation in such a system with highest benzene yields observed for Cu2+ 50 ?M (1400 ?g kg?1). The presence of antioxidants with metal chelation or reduction properties could prevent completely benzene formationBenzene may occur in foods due to the oxidative decarboxylation of benzoate in the presence of hydroxyl radicals. This study investigated factors influencing benzene formation in liquid model systems. The type of buffer, other sources of hydroxyl radical formation in food (photo oxidation of riboflavin and lipid oxidation), transition metal ion concentrations, and the inhibitory effect of antioxidants were tested in benzoate containing model systems. Regarding the hydroxyl radical sources tested, the highest benzene formation was observed in light exposed model systems containing ascorbic acid, Cu2+, and riboflavin in Na-citrate buffer (1250 -¦ 131 ?g kg?1). In practice, it seems that the combination ascorbic acid/transition metal ion remains the biggest contributor to benzene formation in food. However, the concentration of Cu2+ influences significantly benzene formation in such a system with highest benzene yields observed for Cu2+ 50 ?M (1400 ?g kg?1). The presence of antioxidants with metal chelation or reduction properties could prevent completely benzene formation</p

Full Characterisation of Heroin Samples Using Infrared Spectroscopy and Multivariate Calibration

The analysis of heroin samples, before use in the protected environment of user centra, could be a supplementary service in the context of harm reduction. Infrared spectroscopy hyphenated with multivariate calibration could be a valuable asset in this context, and therefore 125 heroin samples were collected directly from users and analysed with classical chromatographic techniques. Further, Mid-Infrared spectra were collected for all samples, to be used in Partial Least Squares (PLS) modelling, in order to obtain qualitative and quantitative models based on real live samples. The approach showed that it was possible to identify and quantify heroin in the samples based on the collected spectral data and PLS modelling. These models were able to identify heroin correctly for 96% of the samples of the external test set with precision, specificity and sensitivity values of 100.0, 75.0 and 95.5%, respectively. For regression, a root mean squared error of prediction (RMSEP) of 0.04 was obtained, pointing at good predictive properties. Furthermore, during mass spectrometric screening, 10 different adulterants and impurities were encountered. Using the spectral data to model the presence of each of these resulted in performant models for seven of them. All models showed promising correct-classification rates (between 92 and 96%) and good values for sensitivity, specificity and precision. For codeine and morphine, the models were not satisfactory, probably due to the low concentration of these impurities as a consequence of acetylation. For methacetin, the approach failed