Search

Heavy metal contaminants in vegetable oils can cause irreversible damage to human health. In this study, the quantitative detection of Cd in vegetable oils was investigated based on Raman spectroscopy combined with chemometric methods. The necessary preprocessing of the Raman signal was performed using baseline calibration and the Savitzky–Golay method. Three variable optimization methods were applied to the preprocessed Raman spectra. Namely, bootstrap soft shrinkage, multiple feature spaces ensemble strategy with least absolute shrinkage and selection operator, and competitive adaptive reweighted sampling (CARS), respectively. Partial least squares regression (PLSR) modeling for the determination of Cd in vegetable oils. The results show that three variable optimization algorithms improved the predictive performance of the model. Among them, the CARS–PLSR model has strong generalization performance and robustness. Its prediction coefficient of determination (RP2$R_{\mathrm{P}}^2$) was 0.9995, the root mean square error of prediction was 0.3533 mg/kg, and the relative prediction deviation was 44.3748, respectively. In summary, rapid quantitative analysis of Cd contamination in vegetable oils can be realized based on Raman spectroscopy combined with chemometrics. [ABSTRACT FROM AUTHOR]

• A method for detecting microplastics in flour by FT-NIR spectroscopy was proposed. • Various multiple regression models established based on feature optimization methods. • High-precision model for detecting polystyrene content in flour. • A new analytical perspective for non-destructive testing of microplastics in flour. Microplastic detection has been acknowledged as challenging so far. Despite advancements in rapid detection methods for analyzing environmental microplastics, limited research has been conducted on detecting microplastics in food substrates. The objective of this study was to investigate the feasibility of utilizing Fourier near-infrared (FT-NIR) spectroscopy optimized characteristic model for quantitative detection of polystyrene (PS) microplastics in flour. A Fourier transform infrared spectrometer was employed to gather spectral information on flour with varying concentrations of PS. Four variable selection methods, namely iterative variable subset optimization (IVSO), bootstrapping soft shrinkage (BOSS), Interval variable iterative space shrinkage approach (IVISSA), and variable-dimensional particle swarm optimization movement window (VDPSO-CMW), were introduced to select features from the preprocessed near-infrared spectrum. Detection models based on partial least squares (PLS) were constructed with the aim of achieving quantitative detection of PS in flour, and comparisons were conducted to evaluate the detection performance of the four models. The VDPSO-CMW-PLS model demonstrates the highest level of generalization performance, according to the research findings. The coefficient of determination (R p 2) is 0.9810, the root mean square error of prediction (RMSEP) is 0.0462%, and the relative percent deviation (RPD) is 7.3890. The research findings indicate that the constructed PLS detection model, utilizing FT-NIR spectral optimization characteristics, can rapidly and accurately detect PS in flour. This study presents a novel technical approach for the prompt quantitative identification of microplastics in food. [ABSTRACT FROM AUTHOR]

• Detection of heavy metals in vegetable oils using NIR spectroscopy and chemometrics. • Important variables were extracted using a variable optimization algorithm. • The CARS-PLS model was the most effective in detecting Cd. • NIR spectroscopy combined with chemometric methods is rapid and easy to implement. Vegetable oils contain traces of heavy metals that can cause irreversible damage to human health. The present study employed near-infrared spectroscopy and variable selection in conjunction with partial least squares (PLS) for the rapid determination of Cd content in peanut oil. Firstly, the spectral data of peanut oil test samples were preprocessed by different preprocessing methods, and the best preprocessing method was selected according to the results obtained by the PLS regression model. Then, PLS regression models were established to determine Cd content in peanut oil by variable iterative space shrinkage approach (VISSA), competitive adaptive reweighted sampling (CARS), multiple feature spaces ensemble strategy with least absolute shrinkage and selection operator (MFE-LASSO), and bootstrap soft shrinkage (BOSS), respectively. The results show that all four feature optimization algorithms could improve the prediction accuracy of the model. Among them, the CARS-PLS model had high prediction accuracy. Its prediction coefficient of determination (R P 2) was 0.9666, the root mean square error of prediction (RMSEP) was 2.8207 mg/kg, and the relative prediction deviation (RPD) was 5.4705, respectively. In summary, near-infrared spectroscopy combined with chemometrics could be used for rapid quantitative detection of Cd in peanut oil. [ABSTRACT FROM AUTHOR]