Rapid Detection Of Adulteration Research Articles

Rapid Detection of Adulteration in Boswellia Extracts with Citric Acid by UPLC–HRMS and 1H NMR

Boswellia serrata ole-gum-resin extracts (BSEs) are commonly used as food supplements, especially in osteoarthritis management. The quality standard is established by determining 11-keto-β-boswellic acid (KBA) and acetyl-11-keto-boswellic acid (AKBA) content using high-performance liquid chromatography (HPLC) or assessing the total boswellic acid (TBA) content by titrimetry. The limited geographical distribution of Boswellia species and increasing industrial demand could increase the risk of adulteration in Boswellia-containing products. In this study, 14 BSEs from commercial sources, used in food supplements, were analyzed in comparison with a USP Reference Standard extract. The KBA and AKBA content was determined by HPLC, whereas the TBA content was determined by titration. Targeted UHPLC-high-resolution mass spectrometry (HRMS) was applied to identify the carboxylic acid content in the samples. The 1H NMR spectra of extracts were also analyzed. Only two products met the criteria for KBA and AKBA content. Although, the TBA content complied with the expected amount, 10 extracts contained citric acid levels of 6–11% even though citric acid is not a cha­racteristic component of BSEs. Our results suggest undeclared addition of citric acid to comply with declared contents of TBA when using titration methods. Incorporation of citric acid to industrial samples – in order to alter the outcomes of the titration analysis – was demonstrated for the first time.

Facile and rapid detection of adulteration in mustard oils: NMR and unsupervised machine learning

Facile and rapid detection of adulteration in mustard oils: NMR and unsupervised machine learning

Rapid detection of adulteration in pistachio based on deep learning methodologies and affordable system

Rapid detection of adulteration in pistachio based on deep learning methodologies and affordable system

Rapid detection of adulteration in powder of ginger (Zingiber officinale Roscoe) by FT-NIR spectroscopy combined with chemometrics.

Ginger powder (GP) is a popular spice in the world. Duo to its nutritional value, GP is regarded as an attractive target for adulteration, which is not easily detected. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics were developed to identify and quantify of GP and its adulterants. The result showed that GPs and adulterated GPs cannot be completely distinguished by chromaticity analysis. While, the optimized NIR spectra could accurately distinguish the authentic GPs from those adulterated samples. Random forest and gradient boosting algorithms exhibited the highest accuracies (100%) in classification. Moreover, a quantitative model was successfully established to predict the adulteration level in GP. The optimal parameters of prediction to deviation were 8.92, 13.68, 14.61, and 4.30, for pure and adulterated GPs. Overall, FT-NIR spectroscopy is a promising tool, which can quickly identify potential adulteration in GP and track the types of adulterants.

Rapid detection of adulteration in desiccated coconut powder: vis-NIR spectroscopy and chemometric approach

Adulteration of desiccated coconut powder (DCP) with coconut milk residue (CMR) is an emerging problem in the coconut processing industry. Consumers and industries are looking for a simple non-destructive device to measure the purity of DCP. vis-NIR (350–2500 nm) spectroscopy along with the chemometric techniques have been used to assess the purity of DCP. In this study, DCP was adulterated with CMR at different levels such as 0 (pure DCP), 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100% (pure CMR). Partial least squares regression (PLSR) models were developed using whole spectral data and selected wavelengths. The spectral data were pre-processed using different techniques such as raw, MSC + SNV, SG-smoothing, and detrending. The R 2 of the models constructed with the pre-processed spectral data was higher than 0.950, irrespective of pre-processing technique. Pre-processing of spectral data does not have a significant effect on model performance when compared with the model developed using raw spectral data (R 2 P = 0.973; SE P = 9.681; RPD P = 9.381; RER P = 10.389), but the prediction accuracy was decreased. The wavelengths 653, 933, 1189, 1383, 1444, 1670, and 1911 nm were selected as the featured wavelengths for quantification of adulteration level in DCP. No significant difference in statistical results was observed between the PLSR model developed with selected wavelengths (R 2 P = 0.869; SE P = 11.701; RPD P = 9.381; RER P = 8.595) and the PLSR model for whole spectral data. The developed model can be used to predict the level of adulteration in DCP if the adulterant concentration was more than 10%. The overall results obtained in present study suggest that the vis-NIR spectroscopy along with suitable chemometric techniques have a great potential for rapid measurement of adulteration level in DCP. • Vis-NIR (350–2500 nm) spectroscopy has been used to predict the purity of DCP • The wavelengths responsible for quantification of level of adulteration in DCP were selected. • The developed PLSR model can be used to predict the level of adulteration more than 10%. • The PLS-DA technique showed more than 70% classification accuracy.

Rapid Detection of Adulteration in Mixing Sesame, Sunflower, and Canola Vegetable Oils by Mathematical Model

The aim of this study was to investigate the application of a mathematical model to rapid detect the adulteration in sesame, canola, and sunflower oils. To hit this target, we combined the refined sesame oil with canola and sunflower oils in different concentrations of 30–60%. Furthermore, fatty acid content of 12 samples of sesame, canola, and sunflower oils was analyzed using the gas chromatography (GC). Chromatograms were analyzed to diagnose and classify the fatty acid types. The results achieved from the experiments were analyzed using Excel 2016. The results showed that decreasing the amounts of sesame oil in different mixture oils reduces the stearic acid content and increases the amount of linolenic acid. For the model development, the mathematical model using the polynomial function was used. Finally, a mathematical formula was successfully designed to determine the amount of sesame oil in the mixture of sesame, canola, and sunflower vegetable oils (R2= 0.995). Finally, application of the mathematical models can be a quick, low-cost, and effective method to detect adulteration in sesame oils.

Rapid Detection of Adulteration in Extra Virgin Olive Oil by Low-Field Nuclear Magnetic Resonance Combined with Pattern Recognition

Intentional addition of cheaper oils into olive oil (OL) for economic motivation has been becoming particularly attractive due to the favorable flavor and healthy characteristics of OL, but it is very challenging to identify such adulteration because of the compositional similarity between the oils. In this study, low-field nuclear magnetic resonance (LF-NMR) in combination with multivariate statistical analysis was used to identify the adulterated olive oil with different rations of soybean oil (SO) or corn oil (CO). Significant differences in multi-component relaxation time (T21 and T22) and peak area proportions (S21 and S22) were detected between pure and adulterated OL. As the adulteration ratio increased, S21 and S22 changed linearly, while T21 and T22 only changed slightly. The detection by gas chromatography suggested that T21 and T22 values might be influenced by triacylglycerol components, and the changes of S21 and S22 were attributed to the varied mono-/polyunsaturated fatty acids. In the relaxation time-based pattern recognition models, the authentic OL could be correctly identified from the adulterated ones with at least 20% of SO or CO by principal component analysis (PCA) or partial least squares discriminant analysis (PLS-DA). The multi-blended oil could be 100% classified by orthogonal partial least squares discriminant analysis (OPLS-DA) and 98.8% classified by principal component analysis followed by linear discriminant analysis (PCA-LDA) when the adulteration ratio was above 30%, demonstrating a promising technique of LF-NMR combined with pattern recognition in rapid screening of the edible oils.

Rapid Detection of Adulteration in Dendrobium Huoshanense with Dendrobium Henanense by ATR-FTIR Combined with Multivariate Methods.

Dendrobium huoshanense (DHS) has long been used to make tea drink, soup, and porridge to protect eye and liver in many Southeast Asian countries for centuries. As a rare and endangered functional food, adulteration in DHS with visually similar but cheaper and more accessible plants such as Dendrobium henanense (DHN) because of their similarities in morphology has become prevalent in the market. In this study, the Attenuated Total Reflectance Fourier transform Infrared Spectroscopy (ATR-FTIR) combined with chemometric methods was established to detect fraudulent addition in DHS with DHN. The partial least squares (PLS) models based on the ATR-FTIR files of DHS mixed with different proportions of DHN were built under cross validation and tested with different independent data sets. To reduce the variables’ lack of information and increase the accuracy of the model, different wavelength selection methods including Moving Window Partial Least Squares (MW-PLS), Monte Carlo-uninformative variable elimination (MC-UVE), and interval random frog (iRF) were compared.The results showed that iRF performed the most perfectly with the number of latent variables (nLVs = 7), the lowest Root Mean Square Error of Cross-Validation (RMSECV = 7.37), and the maximum determination coefficients (R2 = 0.9721). The excellent performance of the model was proved by the low RMSEP value of 6.44% and the high R2 value of 0.9556. The developed method could rapidly quantify the adulteration DHN in DHS, and our study might provide an efficient and great potential technique tool for the rapid, green, low-cost, and nondestructive identification and quantification for DHS adulterated with DHN.

Rapid detection of adulteration in Anoectochilus roxburghii by near-infrared spectroscopy coupled with chemometric methods.

To determine the authenticity of Anoectochilus roxburghii, this study presents an application of near-infrared spectroscopy and chemometric methods for evaluating adulteration of A. roxburghii with two cheaper adulterants, i.e. C. Goodyera schlechtendaliana and Ludisia discolor. Partial least squares discriminant analysis models were built for the accurate classification of authentic A. roxburghii and A. roxburghii adulterated at 5-100% (w/w) levels. Partial least squares regression models were used to predict the level of adulteration in the A. roxburghii. After by compared different spectral pretreatment methods, and using interval PLS and synergy interval PLS for variable selection, optimum models were developed. These results show that the NIR spectroscopy combined with chemometric methods offers a simple, fast, and reliable method for classifying and quantifying the adulteration of A. roxburghii.

Development of an Indirect Enzyme-Linked Immunosorbent assay for Rapid Detection of Adulteration of Food Allergen Mackerel in Processed Marine Foods

Development of an Indirect Enzyme-Linked Immunosorbent assay for Rapid Detection of Adulteration of Food Allergen Mackerel in Processed Marine Foods

Rapid Detection of Adulteration in Extra-Virgin Olive Oil using Three-Dimensional Fluorescence Spectra Technology with Selected Multivariate Calibrations

To rapidly and efficiently detect the presence of adulterants in extra-virgin olive oil, 3D fluorescence spectra technology was employed with the help of multivariate calibration. Parallel factor analysis and characteristic parameters method were comparatively employed to compress and extract the data of 3D fluorescence spectra. Then, three different non-linear and linear classification tools (i.e., back-propagation artificial neural network, least-square support vector machine and k-nearest neighbor) were systemically studied and compared in developing the model. The number of principle components and parameters of models were optimized by cross-validation. Compared with parallel factor analysis, characteristic parameters method, in this article, has its own superiority. Experimental results also showed that the performance of least-square support vector machine model is the best among the three models. The optimal least-square support vector machine model was achieved when seven principle components were used, with the discrimination rate of 98.96% in calibration set and 96.88% in prediction set, respectively. The misclassified samples are adulterated extra-virgin olive oil, and their adulterated concentrations were lower than 2.5% (wt/wt). The overall results sufficiently demonstrated that 3D fluorescence spectroscopy technology coupled with characteristic parameters method and least-square support vector machine classification tool has the potential to detect adulterated extra-virgin olive oil products when their adulterant concentrations are more than 2.5% (wt/wt).