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In this study, we investigated the combined effects of MoS2 QDs' catalytic properties and the colorimetric responses of organic reagents to create a sniffing device based on the sensor array concept of the mammalian olfactory system. The aim was to differentiate the volatile organic compounds (VOCs) present in cigarette smoke. The designed optical nose device was utilized for the classification of various cigarette VOCs. Unsupervised Principal Component Analysis (PCA) and supervised Linear Discriminant Analysis (LDA) methods were employed for data analysis. The LDA analysis showed promising results, with 100% accuracy in both training and cross-validation. To validate the sensor's performance, we assessed its ability to discriminate between five cigarette brands, achieving 100% accuracy in the training set and 82% in the cross-validation set. Additionally, we focused on studying four popular Iranian cigarette brands (Bahman Kootah, Omega, Montana Gold, and Williams), including fraudulent samples. Impressively, the developed sensor array achieved a perfect 100% accuracy in distinguishing these brands and detecting fraud. We further analyzed a total of 126 cigarette samples, including both original and fraudulent ones, using LDA with a matrix size of (126 × 27). The resulting LDA model demonstrated an accuracy of 98%. Our proposed analytical procedure is characterized by its efficiency, affordability, user-friendliness, and reliability. The selectivity exhibited by the developed sensor array positions it as a valuable tool for differentiating between original and counterfeit cigarettes, thus aiding in border control efforts worldwide. [ABSTRACT FROM AUTHOR]

Comprehensive two-dimensional gas chromatography and flame ionization detection combined with unfolded-partial least squares is proposed as a simple, fast and reliable method to assess the quality of gasoline and to detect its potential adulterants. The data for the calibration set are first baseline corrected using a two-dimensional asymmetric least squares algorithm. The number of significant partial least squares components to build the model is determined using the minimum value of root-mean square error of leave-one out cross validation, which was 4. In this regard, blends of gasoline with kerosene, white spirit and paint thinner as frequently used adulterants are used to make calibration samples. Appropriate statistical parameters of regression coefficient of 0.996-0.998, root-mean square error of prediction of 0.005-0.010 and relative error of prediction of 1.54-3.82% for the calibration set show the reliability of the developed method. In addition, the developed method is externally validated with three samples in validation set (with a relative error of prediction below 10.0%). Finally, to test the applicability of the proposed strategy for the analysis of real samples, five real gasoline samples collected from gas stations are used for this purpose and the gasoline proportions were in range of 70-85%. Also, the relative standard deviations were below 8.5% for different samples in the prediction set. [ABSTRACT FROM AUTHOR]

A new colorimetric sensor array based on mixing of Molybdenum disulfide quantum dots (MoS 2 QDs) and organic reagents is introduced in this study. MoS 2 QDs shows a specific and higher affinity to oxygen functionalized volatile compounds like aldehydes and ketones. Therefore, this designed sensor array is used for classification of eight different aldehydes and ketones based on Linear Discriminate Analysis (LDA) at first. The classification accuracy of 96% and 83% was obtained for training and prediction phases, respectively. Then the introduced colorimetric sensor array is used for the semi-quantitative and quantitative analysis of formaldehyde in milk samples. Formaldehyde is an adulteration that is added to the milk for increasing the storage time. Cow milk samples were provided directly from dairy farmer and from supermarkets and were spiked by formaldehyde in the concentration range of 1-25 ppm. The response of sensor array to these samples were analyzed by partial least squares regression (PLS-R) method and were calibrated for concentration of formaldehyde. The PLSR results (R 2  = 0.94 and RMSEC = 2.36) shows that proposed sensor is useable in direct analysis of formaldehyde in milk as a complex matrix., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)