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As a common food raw material in daily life, the quality and safety of wheat flour are directly related to people's health. In this study, a model was developed for the rapid identification and detection of three illegal additives in flour, namely azodicarbonamide (ADA), talcum powder, and gypsum powder. This model utilized a combination of near-infrared spectroscopy with chemometric methods. A one-dimensional convolutional neural network was used to reduce data dimensionality, while a support vector machine was applied for non-linear classification to identify illegal additives in flour. The model achieved a calibration set F1 score of 99.38% and accuracy of 99.63%, with a validation set F1 score of 98.81% and accuracy of 98.89%. Two cascaded wavelength selection methods were introduced: The first method involved backward interval partial least squares (BiPLS) combined with an improved binary particle swarm optimization algorithm (IBPSO). The second method utilized the CARS-IBPSO algorithm, which integrated competitive adaptive reweighted sampling (CARS) with IBPSO. The two cascade wavelength selection methods were used to select feature wavelengths associated with additives and construct partial least squares quantitative detection models. The models constructed using CARS-IBPSO selected feature wavelengths for detecting ADA, talcum powder, and gypsum powder exhibited the highest overall performance. The model achieved validation set determination coefficients of 0.9786, 0.9102, and 0.9226, with corresponding to root mean square errors of 0.0024%, 1.3693%, and 1.6506% and residual predictive deviations of 6.8368, 3.5852, and 3.9253, respectively. Near-infrared spectroscopy in combination with convolutional neural network dimensionality reduction and support vector machine classification enabled rapid identification of various illegal additives. The combination of CARS-IBPSO feature wavelength selection and partial least squares regression models facilitated rapid quantitative detection of these additives. This study introduces a new approach for rapidly and accurately identifying and detecting illegal additives in flour.

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The skylight polarization pattern contains rich information for navigation, meteorological monitoring, and remote sensing. In this paper, we propose a high-similarity analytical model by considering the influence of the solar altitude angle on the neutral point position variations for the distribution pattern of the polarized skylight. A novel function is built to determine the relationship between the neutral point position and solar elevation angle based on a large number of measured data. The experimental results show that the proposed analytical model achieves a higher similarity to measured data compared with existing models. Furthermore, data from several consecutive months verifies the universality, effectiveness, and accuracy of this model.

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Excessive illegal addition of talc in flour has always been a serious food safety issue. To achieve rapid detection of the talc content in flour (TCF) by near-infrared spectroscopy (NIRS), this study used a Fourier transform near-infrared spectrometer technique. The identification of efficient spectral feature wavelength selection (FWS), such as backward interval partial-least-square (BiPLS), competitive adaptive reweighted sampling (CARS), hybrid genetic algorithm (HGA), and BiPLS combined with CARS; BiPLS combined with HGA; and CARS combined with HGA, was also discussed in this paper, and the corresponding partial-least-square regression models were established. Comparing with whole spectrum modeling, the accuracy and efficiency of regressive models were effectively improved using feature wavelengths of TCF selected by the above algorithms. The BiPLS, combined with HGA, had the best modeling performance; the determination coefficient, root-mean-squared error (RMSE), and residual predictive deviation of the validation set were 0.929, 1.097, and 3.795, respectively. BiPLS combined with CARS had the best dimensionality reduction effect. Through the FWS by BiPLS combined with CARS, the number of modeling wavelengths decreased to 72 from 1845, and the RMSE of the validation set was reduced by 11.6% compared with the whole spectra model. The results showed that the FWS method proposed in this paper could effectively improve detection accuracy and reduce modeling wavelength variables of quantitative analysis of TCF by NIRS. This provides theoretical support for TCF rapid detection research and development in real-time.

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Electrode sensitivity and selectivity in complex biological matrices are major challenges in the development of electrochemical sensors. Bimetallic nanoparticles provide a new perspective for enhancing electrocatalytic property because of some specific synergetic effects. In this work, platinum nanoparticles (PtNPs) and gold nanoparticles (AuNPs) modified carbon fiber microelectrode (PtNPs/AuNPs/CFME) was fabricated to determine aesculin and aesculetin simultaneously. Differential pulse voltammetry (DPV) method was conducted for the electrochemical sensing of aesculin and aesculetin, the modified electrode displayed high electrocatalytic activity for the redox of these two drugs. The linear ranges of aesculin and aesculetin were 0.4-10 µM and 0.04-1 µM, with the detection limits of 41 nM and 3.6 nM, respectively, which were the lowest values achieved. Furthermore, an electrochemical investigation of the interactions of these two drugs with Calf thymus double stranded DNA (dsDNA) was investigated by PtNPs/AuNPs/CFME, the decrease in peak currents is proportional to DNA concentration and can be used to detect DNA. The electrode was successfully used to measure aesculin and aesculetin in mouse serum and urine with 98.0-104.8% recovery. The novel electrochemical probe possessed excellent performances of high sensitivity, good reproducibility, and simplicity of fabrication, which will facilitate effective detection of aesculin and aesculetin for metabolic kinetics study.

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In this study, a rapid detection method based on near-infrared reflectance spectroscopy was proposed for measuring the contents of cellulose, hemicellulose and lignin in corn stover. In the basis of strategies of variable selection, feature extraction and nonlinear modeling, BiPLS-PCA-SVM was constructed using backward interval partial least squares combined with principal component analysis and support vector machine, which was used to improve the performance of spectral regression calibration model. For BiPLS-PCA-SVM model, the determination coefficients, root mean squared error and residual predictive deviation for the validation set were 0.906, 0.900% and 3.213 for cellulose; 0.987, 0.797% and 9.071 for hemicellulose; and 0.936, 0.264% and 4.024 for lignin, correspondingly. The results indicate that near-infrared reflectance spectroscopy combined with BiPLS-PCA-SVM can provide a reliable alternative strategy to detect contents of lignocellulosic components for pretreated corn stover in the anaerobic digestion process.

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