The fraudulent adulteration of goat milk with cheaper and more available milk of other species such as cow milk is occurrence. The aims of the present study were to investigate the effect of goat milk adulteration with cow milk on the mid-infrared (MIR) spectrum and further evaluate the potential of MIR spectroscopy to identify and quantify the goat milk adulterated. Goat milk was adulterated with cow milk at 5 different levels including 10%, 20%, 30%, 40%, and 50%. Statistical analysis showed that the adulteration had significant effect on the majority of the spectral wavenumbers. Then, the spectrum was preprocessed with standard normal variate (SNV), multiplicative scattering correction (MSC), Savitzky-Golay smoothing (SG), SG plus SNV, and SG plus MSC, and partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) were used to establish classification and regression models, respectively. PLS-DA models obtained good results with all the sensitivity and specificity over 0.96 in the cross-validation set. Regression models using raw spectrum obtained the best result, with coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) of cross-validation set were 0.98, 2.01, and 8.49, respectively. The results preliminarily indicate that the MIR spectroscopy is an effective technique to detect the goat milk adulteration with cow milk. In future, milk samples from different origins and different breeds of goats and cows should be collected, and more sophisticated adulteration at low levels should be further studied to explore the potential and effectiveness of milk mid-infrared spectroscopy and chemometrics.

Edible oils and fats are crucial components of everyday cooking and the production of food products, but their purity has been a major issue for a long time. High-quality edible oils are contaminated with low- and cheap-quality edible oils to increase profits. The adulteration of edible oils and fats also produces many health risks. Detection of main and minor components can identify adulterations using various techniques, such as GC, HPLC, TLC, FTIR, NIR, NMR, direct mass spectrometry, PCR, E-Nose, and DSC. Each detection technique has its advantages and disadvantages. For example, chromatography offers high precision but requires extensive sample preparation, while spectroscopy is rapid and non-destructive but may lack resolution. Direct mass spectrometry is faster and simpler than chromatography-based MS, eliminating complex preparation steps. DNA-based oil authentication is effective but hindered by laborious extraction processes. E-Nose only distinguishes odours, and DSC directly studies lipid thermal properties without derivatization or solvents. Mass spectrometry-based techniques, particularly GC-MS is found to be highly effective for detecting adulteration of oils and fats in food and non-food samples. This review summarizes the benefits and drawbacks of these analytical approaches and their use in conjunction with chemometric tools to detect the adulteration of animal fats and vegetable oils. This combination provides a powerful technique with enormous chemotaxonomic potential that includes the detection of adulterations, quality assurance, assessment of geographical origin, assessment of the process, and classification of the product in complex matrices from food and non-food samples.

The authenticity of fish products has become a widespread issue in markets due to substitution and false labeling. Lipidomics combined with chemometrics enables the fraudulence identification of food through the analysis of a large amount of data. This study utilized ultra-high-performance liquid chromatography (UHPLC)-QE Orbitrap MS technology to comprehensively analyze the lipidomics of commercially available basa catfish and sole fish. In positive and negative ion modes, a total of 779 lipid molecules from 21 lipid subclasses were detected, with phospholipid molecules being the most abundant, followed by glycerides molecules. Significant differences in the lipidome fingerprinting between the two fish species were observed. A total of 165 lipid molecules were screened out as discriminative features to distinguish between basa catfish and sole fish, such as TAG(16:0/16:0/18:1), PC(14:0/22:3), and TAG(16:1/18:1/18:1), etc. This study could provide valuable insights into authenticating aquatic products through comprehensive lipidomics analysis, contributing to quality control and consumer protection in the food industry.

This research introduces a novel approach for detecting sartan antihypertensive drug adulteration in herbal oral liquids using cotton fiber-supported liquid extraction (CF-SLE) combined with high-performance liquid chromatography-fluorescence detection (HPLC-FLD). Optimal extraction parameters were determined through systematic method development, establishing a sample solution with a pH of 3.0, using 200 mg of cotton fiber, ethyl acetate as the extraction solvent, and a solvent volume of 4 mL. These conditions demonstrated robust extraction efficiency and were further validated for precision and accuracy, with intra- and inter-day relative standard deviations consistently below 7.5 % and relative recoveries ranging from 88.5 % to 106.1 %. The method exhibited excellent linearity for sartans, with R² values greater than 0.993 across a concentration range of 10-2000 ng/mL. Detection limits were effectively established in the range of 2.6-3.1 ng/mL, indicating that the method\'s sensitivity is adequate for the intended screening purposes. This validated method was then applied to real sample analysis, confirming its potential for routine use in detecting illegal additives within complex herbal matrices, thereby ensuring consumer safety and supporting regulatory compliance.

Under the background of digitalization of traditional Chinese medicine (TCM), to realize the quick identification and adulteration analysis of Pulsatilla Radix (PR), adhering to digital conviction, this study conducted UHPLC-QTOF-MSE analysis on PR and its adulterant-Pulsatilla Cernua (PC) from different batches and based on digital conversion, the shared ions were extracted from different batches of PR and PC as their \"ions representation\", respectively. Further, the data set of unique ions of PR relative to PC and PC relative to PR were screened out as the \"digital identities\" of PR and PC respectively. Further, above the \"digital identities\" of PR and PC were used as the benchmarks for matching and identifying to feedback give a matching credibility (MC). The results showed that based on the \"digital identities\" of PR and PC, the digital identification of two herbal samples can be realized efficiently and accurately at the individual level with the MC≥70.00 %, even if 5 % of PC in the mixed samples can still be identified efficiently and accurately. The study is of great practical significance for improving the identification efficiency of PR and PC, cracking down on adulterated and counterfeit drugs, and strengthening the quality control of PR. In addition, it has important reference significance for developing non-targeted digital identification of herbal medicines at the individual level based on UHPLC-QTOF-MSE and the \"digital identity\", which was beneficial to the construction of digital Chinese medicine and digital quality control.

The geographical origin of foods greatly influences their quality and price, leading to adulteration between high-priced and low-priced regions in the market. The rapid detection of such adulteration is crucial for food safety and fair competition. To detect the adulteration of Polygonati Rhizoma from different regions, we proposed LIBS-VNIR fusion based on the deep learning network (LVDLNet), which combines laser-induced breakdown spectroscopy (LIBS) containing element information with visible and near-infrared spectroscopy (VNIR) containing molecular information. The LVDLNet model achieved accuracy of 98.75%, macro-F measure of 98.50%, macro-precision of 98.78%, and macro-recall of 98.75%. The model, which increased these metrics from about 87% for LIBS and about 93% for VNIR to more than 98%, significantly improved the identification ability. Furthermore, tests on different adulterated source samples confirmed the model\'s robustness, with all metrics improving from about 87% for LIBS and 86% for VNIR to above 96%. Compared to conventional machine learning algorithms, LVDLNet also demonstrated its superior performance. The results indicated that the LVDLNet model can effectively integrate element information and molecular information to identify the adulterated Polygonati Rhizoma. This work shows that the scheme is a potent tool for food identification applications.

Food adulteration involving the illegal addition of dyes to foodstuffs has become an alarming issue in recent years. This study developed and validated a high-performance liquid chromatography (HPLC)-DAD (diode array detector) method for the simultaneous determination of nine azo dyes (Butter Yellow, Sudan Orange G, Para Red, Sudan I, Sudan II, Sudan III, Sudan IV, Sudan Red 7B, and Scarlet 808). Moreover, a qualitative analysis method using liquid chromatography-tandem mass spectrometry was developed to more accurately identify peaks detected in HPLC-DAD. The calibration curve represented good linearity (r2 ≥ 0.9998) over the measured concentration range of 0.5-25 mg/kg. limit of detection and limit of quantification were 0.01-0.04 and 0.04-0.12 mg/kg, respectively. Accuracy and precision were 96.0-102.6 and 0.16-2.01 (relative standard deviation%), respectively. Additionally, the measurement uncertainty and HorRat value were estimated. Several Curcuma longa L. distributed in Korea were collected and monitored for azo dye contaminants. PRACTICAL APPLICATION: The proposed HPLC-DAD method represents a significant advancement in the field, offering a reliable means of quantifying azo dyes and identifying their presence even at trace levels in adulterated turmeric. This not only contributes to ensuring the safety and integrity of turmeric products but also establishes precedent for robust analytical techniques in addressing food safety challenges.

Packaged coconut water offers various options, from pure to those with added sugars and other additives. While the purity of coconut water is esteemed for its health benefits, its popularity also exposes it to potential adulteration and misrepresentation. To address this concern, our study combines Fourier transform infrared spectroscopy (FTIR) and machine learning techniques to detect potential adulterants in coconut water through classification models. The dataset comprises infrared spectra from coconut water samples spiked with 15 different types of potential sugar substitutes, including: sugars, artificial sweeteners, and sugar alcohols. The interaction of infrared light with molecular bonds generates unique molecular fingerprints, forming the basis of our analysis. Departing from previous research predominantly reliant on linear-based chemometrics for adulterant detection, our study explored linear, non-linear, and combined feature extraction models. By developing an interactive application utilizing principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), non-targeted sugar adulterant detection was streamlined through enhanced visualization and pattern recognition. Targeted analysis using ensemble learning random forest (RF) and deep learning 1-dimensional convolutional neural network (1D CNN) achieved higher classification accuracies (95% and 96%, respectively) compared to sparse partial least squares discriminant analysis (sPLS-DA) at 77% and support vector machine (SVM) at 88% on the same dataset. The CNN\'s demonstrated classification accuracy is complemented by exceptional efficiency through its ability to train and test on raw data.

Saffron, renowned for its aroma and flavor, is susceptible to adulteration due to its high value and demand. Current detection methods, including ISO standards, often fail to identify specific adulterants such as safflower or turmeric up to 20% (w/w). Therefore, the quest continues for robust screening methods using advanced techniques to tackle this persistent challenge of safeguarding saffron quality and authenticity. Advanced techniques such as time-of-flight secondary ion mass spectrometry (TOF-SIMS), with its molecular specificity and high sensitivity, offer promising solutions. Samples of pure saffron and saffron adulterated with safflower and turmeric at three inclusion levels (5%, 10%, and 20%) were analyzed without prior treatment. Spectral analysis revealed distinct signatures for pure saffron, safflower, and turmeric. Through principal component analysis (PCA), TOF-SIMS effectively discriminated between pure saffron and saffron adulterated with turmeric and safflower at different inclusion levels. The variation between the groups is attributed to the characteristic peaks of safflower and the amino group peaks and mineral peaks of saffron. Additionally, a study was conducted to demonstrate that semi-quantification of the level of safflower inclusion can be achieved from the normalized values of its characteristic peaks in the saffron matrix.

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.