Solvent-based mid-infrared spectroscopy paired with modern machine learning approaches for saffron authentication.

Rapid authentication of intact saffron stigma thorough the package using Vis-SWNIR hyperspectral imaging coupled with chemometrics

Multiple adulterants detection in turmeric powder using Vis-SWNIR hyperspectral imaging followed by multivariate curve resolution and classification techniques

Using UV-induced fluorescence images integrated with a CACHAS-based two-step hierarchical classification approach for rapid detection of extra virgin olive oil adulteration.

Chemometrics-assisted isotope ratio fingerprinting based on gas chromatography/combustion/isotope ratio mass spectrometry for saffron authentication

Evaluation of adulteration in soy-based beverages by water addition using chemometrics applied to ATR-FTIR spectroscopy

The high market value of saffron (Crocus sativus L.) has made it an attractive candidate for adulteration. Safflower (Carthamus tinctorius L.) and tartrazine are among the most common herbal and synthetic foreign materials that may be added to pure saffron for the purpose of adulteration. In spite of encouraging advances achieved in the identification of adulteration in saffron samples, the lack of a simple method with sufficient power for discrimination of pure high grade saffron from meticulously adulterated saffron samples persuaded us to perform this study. In this work, we show that 1H NMR spectroscopy together with chemometric multivariate data analysis methods can be used for the detection of adulteration in saffron. Authentic Iranian saffron samples (n = 20) and adulterated samples that were prepared by adding either different quantities of natural plant materials such as safflower, or synthetic dyes such as tartrazine or naphthol yellow to pure saffron (n = 22) composed the training set. This training set was used to build multivariate Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) models. The predictive power of the PLS-DA model was validated by testing the model against an external dataset (n = 13). PCA and PLS-DA models could both discriminate between the authentic and adulterated samples, and the external validation showed 100% sensitivity and specificity for predicting the authenticity of suspicious samples. Peaks specific to authentic and adulterated samples were also characterized. Proximity of samples with unknown adulteration status to the samples adulterated with known compounds in the PCA provided insight regarding the identity of the adulterant in the suspicious samples. Furthermore, the authentic samples could be distinguished based on their cultivation site. The present study demonstrates that the application of 1H NMR spectroscopy coupled with multivariate data analysis is a suitable approach for detection of adulteration in saffron specimens. Outstanding sensitivity and specificity of the PLS-DA model in discriminating the authentic from adulterated samples in external validation confirmed the high predictive power of the model. The advantage of the present method is its power for detecting a wide spectrum of adulterants, ranging from synthetic dyes to herbal materials, in a single assay.

Determination of main raw material source in bar soaps using mid-infrared spectroscopy combined with classification tools

This study explores the application of Mid-Infrared (MIR) and Near-Infrared (NIR) spectroscopy combined with various multivariate calibration techniques to detect the presence of cannabis in tobacco samples and tobacco in herbal smoking products. Both MIR and NIR spectra were recorded for self-prepared samples, followed by data exploration using Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), and the calculation of binary classification models with Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares-Discriminant Analysis (PLS-DA). PCA demonstrated a clear differentiation between tobacco samples containing and not containing cannabis. On the other hand, based on PCA, only NIR was able to distinguish herbal smoking products adulterated and not adulterated with tobacco. HCA further clarified these results by revealing distinct clusters within the data. Modelling results indicated that MIR and NIR spectroscopy, particularly when paired with preprocessing techniques like Standard Normal Variate (SNV) and autoscaling, demonstrated high classification accuracy in SIMCA and PLS-DA, achieving correct classification rates of 90% to 100% for external test sets. Comparison of MIR and NIR revealed that NIR spectroscopy resulted in slightly more accurate models for the screening of tobacco samples for cannabis and herbal smoking products for tobacco. The developed approach could be useful for the initial screening of tobacco samples for cannabis, e.g., in a night life setting by law enforcement, but also for inspectors visiting shops selling tobacco and/or herbal smoking products.

Detection of illegal additives in Brazilian S-10/common diesel B7/5 and quantification of Jatropha biodiesel blended with diesel according to EU 2015/1513 by MIR spectroscopy with DD-SIMCA and MCR-ALS under correlation constraint

Rice is a staple food in Vietnam, and the concern about rice is much greater than that for other foods. Preventing fraud against this product has become increasingly important in order to protect producers and consumers from possible economic losses. The possible adulteration of this product is done by mixing, or even replacing, high-quality rice with cheaper rice. This highlights the need for analytical methodologies suitable for its authentication. Given this scenario, the present work aims at testing a rapid and non-destructive approach to detect adulterated rice samples. To fulfill this purpose, 200 rice samples (72 authentic and 128 adulterated samples) were analyzed by near infrared (NIR) spectroscopy coupled, with partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA). The two approaches provided different results; while PLS-DA analysis was a suitable approach for the purpose of the work, SIMCA was unable to solve the investigated problem. The PLS-DA approach provided satisfactory results in discriminating authentic and adulterated samples (both 5% and 10% counterfeits). Focusing on authentic and 10%-adulterated samples, the accuracy of the approach was even better (with a total classification rate of 82.6% and 82.4%, for authentic and adulterated samples, respectively).

Untargeted metabolomics based on nuclear magnetic resonance spectroscopy and multivariate classification techniques for identifying metabolites associated with breast cancer patients

A methodology was developed to monitor the adulteration of the B10 blend of diesel and crambe biodiesel using proton nuclear magnetic resonance (1H NMR) spectroscopy combined with data driven soft independent modeling of class analogy (DD-SIMCA) model. The training was performed only with samples of the target class (B10) while the validation was performed with a test set consisting of new samples of the target class (B10) and samples of B10 adulterated with crambe oil, used frying oil, and residual automotive lubricating oil. The efficiency of this methodology was characterized based on the sensitivity parameters for the training set and specificity for the test set, in which a value of 1 was obtained for both parameters. This sensitivity value for the training set indicates that no target class samples were classified as extreme or outliers. The specificity for the test set shows that all samples in the test set were correctly classified into their respective classes, demonstrating the high efficiency of the DD-SIMCA model in monitoring adulterants in B10 mixture of diesel and crambe biodiesel. The DD-SIMCA model is simpler to construct than the multivariate control chart and the partial least squares discriminant analysis (PLS-DA) because its development does not require prior information about the adulterants. The excellent obtained results in the application of this model suggest that this analytical methodology is efficient, feasible and suitable for use by inspection agencies to characterize the quality of this fuel.

Hierarchical authentication of the geographical origin of instant coffee using digital image-based fingerprints and chemometrics.

Grape is the most consumed nectar in Brazil and a relatively expensive beverage. Therefore, it is susceptible to fraud by substitution with other less expensive fruit juices. Adulterations of grape nectars by the addition of apple juice, cashew juice, and mixtures of both were evaluated by using low-field nuclear magnetic resonance (LF-NMR) and supervised multivariate classification methods. Two different approaches were investigated using one-class (only unadulterated samples (UN) were modeled) and multiclass (three classes were modeled: UN, adulterated with cashew (CAS), and adulterated with apple (APP)) strategies. For the one-class approach, soft independent modeling of class analogy (SIMCA), one-class partial least squares (OCPLS), and data-driven SIMCA (DD-SIMCA) models were built. For the multiclass approach, partial least squares discriminant analysis (PLS-DA) and multiclass SIMCA models were built. The results obtained demonstrated good performances by all the one-class methods with efficiency rates higher than 93%. For the multiclass approach, the classification of samples containing only one type of adulterant presented efficiencies higher than 90% and 97% using SIMCA and PLS-DA, respectively. The classification of samples containing blends of two adulterants was satisfactory for the CAS class, but not for the APP class when applying PLS-DA. Nevertheless, multiclass SIMCA did not provide satisfactory predictions for either of these two classes.

The detection and quantification of additives in tobacco products are critical for ensuring consumer safety and compliance with regulatory standards. Traditional analytical techniques, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and others, although effective, suffer from drawbacks, including complex sample preparation, high costs, lengthy analysis times, and the requirement for skilled operators. This study addresses these challenges by evaluating the efficacy of mid-infrared (MIR) spectroscopy and near-IR (NIR) spectroscopy, coupled with multivariate analysis, as potential solutions for the detection and quantification of additives in tobacco products. So, a representative set of tobacco products was selected and spiked with the targeted additives, namely caffeine, menthol, glycerol, and cocoa. Multivariate analysis of MIR and NIR spectra consisted of principal component analysis (PCA), hierarchical clustering analysis (HCA), partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) to classify samples based on targeted additives. Based on the unsupervised techniques (PCA and HCA), a distinction could be made between spiked and non-spiked samples for all four targeted additives based on both MIR and NIR spectral data. During supervised analysis, SIMCA achieved 87-100% classification accuracy for the different additives and for both spectroscopic techniques. PLS-DA models showed classification rates of 80% to 100%, also demonstrating robust performance. Regression studies, using PLS, showed that it is possible to effectively estimate the concentration levels of the targeted molecules. The results also highlight the necessity of optimizing data pretreatment for accurate quantification of the target additives. Overall, NIR spectroscopy combined with SIMCA provided the most accurate and robust classification models for all target molecules, indicating that it is the most effective single technique for this type of analysis. MIR, on the other hand, showed the overall best performance for quantitative estimation.