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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]

BACKGROUND: Tea‐garden pest control is crucial to ensure tea quality. In this context, the time‐series prediction of insect pests in tea gardens is very important. Deep‐learning‐based time‐series prediction techniques are advancing rapidly but research into their use in tea‐garden pest prediction is limited. The current study investigates the time‐series prediction of whitefly populations in the Tea Expo Garden, Jurong City, Jiangsu Province, China, employing three deep‐learning algorithms, namely Informer, the Long Short‐Term Memory (LSTM) network, and LSTM‐Attention. RESULTS: The comparative analysis of the three deep‐learning algorithms revealed optimal results for LSTM‐Attention, with an average root mean square error (RMSE) of 2.84 and average mean absolute error (MAE) of 2.52 for 7 days' prediction length, respectively. For a prediction length of 3 days, LSTM achieved the best performance, with an average RMSE of 2.60 and an average MAE of 2.24. CONCLUSION: These findings suggest that different prediction lengths influence model performance in tea garden pest time series prediction. Deep learning could be applied satisfactorily to predict time series of insect pests in tea gardens based on LSTM‐Attention. Thus, this study provides a theoretical basis for the research on the time series of pest and disease infestations in tea plants. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: The objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si‐PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables. RESULTS: The collected spectral data were divided into 19 sub‐interval selections totaling 246 variables, yielding the lowest root mean square error of cross‐validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (RC), prediction (RP), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si‐GA‐PLS model produced the following results: PCs = 9; RC = 0.915; RMSECV = 1.39; RP = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si‐CARS‐PLS model was noted to be best at PCs = 10, while RC = 0.9723, RMSECV = 0.81, RP = 0.9114, RMSEP = 1.45 and RPD = 2.59. CONCLUSION: The build model's prediction ability was amended in the order PLS < Si‐PLS < CARS‐PLS when full spectroscopic data were used and Si‐PLS < Si‐GA‐PLS < Si‐CARS‐PLS when interval selection was performed with the Si‐PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

As a source of vital nutrients for the normal functioning of the body, chicken meat plays an important role in promoting good health. This study examines the occurrence of total volatile basic nitrogen (TVB-N) as an index for evaluating freshness, using novel colorimetric sensor arrays (CSA) in combination with linear and nonlinear regression models. Herein, the TVB-N was determined by steam distillation, and the CSA was fabricated via the use of nine chemically responsive dyes. The corresponding dyes utilized, and the emitted volatile organic compounds (VOCs) were found to be correlated. Afterwards, the regression algorithms were applied, assessed, and compared, with the result that a nonlinear model based on competitive adaptive reweighted sampling coupled with support vector machines (CARS-SVM) achieved the best results. Accordingly, the CARS-SVM model provided improved coefficient values (Rc = 0.98 and Rp = 0.92) based on the figures of merit used, as well as root mean square errors (RMSEC = 3.12 and RMSEP = 6.75) and a ratio of performance deviation (RPD) of 2.25. Thus, this study demonstrated that the CSA paired with a nonlinear algorithm (CARS-SVM) could be employed for fast, noninvasive, and sensitive detection of TVB-N concentration in chicken meat as a major indicator of freshness in meat. [ABSTRACT FROM AUTHOR]

This study proposes a label-free rapid detection method for aflatoxin B1 (AFB1) in pressing peanut oil based on Raman spectroscopy technology combined with appropriate chemometric methods. A DXR laser Raman spectrometer was used to acquire the Raman spectra of the pressed peanut oil samples, and the obtained spectra were preprocessed by wavelet transform (WT) combined with adaptive iteratively reweighted penalized least squares (airPLS). The competitive adaptive reweighted sampling (CARS) method was used to optimize the characteristic bands of the Raman spectra pretreated by the WT + airPLS, and a partial least squares (PLS) detection model for the AFB1 content was established based on the features optimized. The results obtained showed that the root mean square error of prediction (RMSEP) and determination coefficient of prediction ( R P 2 ) of the optimal CARS-PLS model in the prediction set were 22.6 µg/kg and 0.99, respectively. The results demonstrate that the Raman spectroscopy combined with appropriate chemometrics can be used to quickly detect the safety of edible oil with high precision. The overall results can provide a technical basis and method reference for the design and development of the portable Raman spectroscopy system for the quality and safety detection of edible oil storage, and also provide a green tool for fast on-site analysis for regulatory authorities of edible oil and production enterprises of edible oil. [ABSTRACT FROM AUTHOR]

BACKGROUND: The acid value is an important indicator for evaluating the quality of edible oil during storage. This study employs a portable near‐infrared (NIR) spectroscopy system to determine the acid value during edible oil storage. Four MPA‐based variable selection methods, namely competitive adaptive reweighted sampling (CARS), the variable iterative space shrinkage approach (VISSA), iteratively variable subset optimization (IVSO), and bootstrapping soft shrinkage (BOSS) were introduced to optimize the preprocessed NIR spectra. Support vector machine (SVM) models based on characteristic spectra obtained by different selection methods were then established to achieve quantitative detection of the acid value during edible oil storage. RESULTS: The results revealed that, compared with the full‐spectrum SVM model, the SVM models established by the characteristic wavelengths optimized by the variable selection methods based on the MPA strategy exhibit a significant improvement in complexity and generalization performance. Furthermore, compared with the CARS, VISSA, and IVSO methods, the BOSS method obtained the least number of characteristic wavelength variables, and the SVM model established based on the optimized features of this method exhibited the optimal prediction performance. The root mean square error of prediction (RMSEP) was 0.11 mg g−1, the coefficient of determination (Rp2) was 0.92 and the ratio performance deviation (RPD) was 2.82, respectively. Conclusion: The overall results indicate that the variable selection methods based on the MPA strategy can select more targeted characteristic variables. This has good application prospects in NIR spectra feature optimization. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

The validity and reliability of visible–near infrared (Vis–NIR) hyperspectral imaging were investigated for the determination of lead concentration in lettuce leaves. Besides, a method involving wavelet transform and stacked auto-encoders (WT-SAE) is proposed to decompose the spectral data in the multi-scale transform and obtain the deep spectral features. The Vis–NIR hyperspectral images of 1120 lettuce leaf samples were obtained and the whole region of lettuce leaf sample spectral data was collected and preprocessed. In addition, WT-SAE the deep spectral features using db5 as wavelet basis function, and support vector machine regression (SVR) was used for regression modelling. Furthermore, the best prediction performances for detecting lead (Pb) concentration in lettuce leaves was obtained from raw data set, with coefficient of determination for calibration (Rc2) of 0.9911, root mean square error for calibration (RMSEC) of 0.05187 m g k g − 1 , coefficient of determination for prediction (Rp2) of 0.9590, root mean square error for prediction (RMSEP) of 0.05587 m g k g − 1 and residual predictive deviation (RPD) of 3.251 using db5 as wavelet basis function with wavelet fifth layer decomposition. The results of this study indicated that WT-SAE can effectively select the optimal deep spectral features and Vis–NIR hyperspectral imaging has great potential for detecting lead content in lettuce leaves. [ABSTRACT FROM AUTHOR]

BACKGROUND The study reports a portable near infrared (NIR) spectroscopy system coupled with chemometric algorithms for prediction of tea polyphenols and amino acids in order to index matcha tea quality. RESULTS: Spectral data were preprocessed by standard normal variate (SNV), mean center (MC) and first‐order derivative (1stD) tests. The data were then subjected to full spectral partial least squares (PLS) and four variable selection algorithms, such as random frog partial least square (RF‐PLS), synergy interval partial least square (Si‐PLS), genetic algorithm‐partial least square (GA‐PLS) and competitive adaptive reweighted sampling partial least square (CARS‐PLS). RF‐PLS was established and identified as the optimum model based on the values of the correlation coefficients of prediction (RP), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD), which were 0.8625, 0.82% and 2.13, and 0.9662, 0.14% and 3.83, respectively, for tea polyphenols and amino acids. The content range of tea polyphenols and amino acids in matcha tea samples was 8.51–14.58% and 2.10–3.75%, respectively. The quality of matcha tea was successfully classified with an accuracy rate of 83.33% as qualified, unqualified and excellent grade. CONCLUSION: The proposed method can be used as a rapid, accurate and non‐destructive platform to classify various matcha tea samples based on the ratio of tea polyphenols to amino acids. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

The acid value (AV) is an essential parameter for the quality and safety evaluation of peanut oil. In this study, for efficiently and real-time monitor of acid value (AV) in peanut oil, a portable spectroscopy system was first developed and combined with variables selection algorithms to measure acid value (AV) in peanut oils. Developed portable spectroscopy system was applied for transmittance spectrum data acquisition after which partial least squares (PLS) and several variables selection algorithms synergy interval partial least square (Si-PLS), genetic algorithm (GA), genetic algorithm combined with Si-PLS namely GA-Si-PLS, ant colony optimization (ACO) algorithms were systemically studied and comparatively used for modeling. The performances of these models were evaluated according to correlation coefficients squared in the prediction set ( R P ) and root mean square error of prediction (RMSEP). The results showed that the variables selection methods could select more significant variables and improve the model performance, especially for the GA-Si-PLS model with the best performance than other variables selection algorithms with R P = 0.9426 and RMSEP = 0.2980. Finally, the paper draws a conclusion that the developed portable spectroscopy system combined with a suitable variables selection methods could be used for the simultaneous and rapid measurement of acid value in peanut oil. [ABSTRACT FROM AUTHOR]

Epigallocatechin-3-gallate (EGCG) is credited with the majority of the health benefits associated with green tea consumption. It has a high economic and medicinal value. The feasibility of using different variable selection approaches in Fourier transform near-infrared (FT-NIR) spectroscopy for a rapid and conclusive quantitative determination of EGCG in green tea was investigated. Graphically oriented multivariate calibration modeling procedures such as interval partial least squares (iPLS), synergy interval partial least squares (siPLS), and genetic algorithm optimization combined with siPLS (siPLS-GA) were applied to select the most efficient spectral variables that provided the lowest prediction error. The performance of the final model was evaluated according to the root mean square error of prediction (RMSEP) and coefficient of determination (R 2) for the prediction set. Experimental results showed that the siPLS-GA model obtained the best results in comparison to other models. The optimal models were achieved with R 2p = 0.97 and RMSEP=0.32. The model can be obtained with only 36 variables retained and it provides a robust model with good estimation accuracy. This demonstrates the potential of NIR spectroscopy with multivariate calibration methods to quickly detect the bioactive component in green tea. [ABSTRACT FROM AUTHOR]

This study innovatively proposes a feature fusion technique to determine fatty acid content during rice storage. Firstly, a self-developed olfactory visualization sensor was used to capture the odor information of rice samples at different storage periods and a portable spectroscopy system was employed to collect the near-infrared (NIR) spectra during rice storage. Then, principal component analysis (PCA) was performed on the pre-processed olfactory visualization sensor data and the NIR spectra, and the number of the best principal components (PCs) based on the single technique model was optimized during the backpropagation neural network (BPNN) modeling. Finally, the optimal PCs were fused at the feature level, and a BPNN detection model based on the fusion feature was established to achieve rapid measurement of fatty acid content during rice storage. The experimental results showed that the best BPNN model based on the fusion feature had a good predictive performance where the correlation coefficient (RP) was 0.9265, and the root mean square error (RMSEP) was 1.1005 mg/100 g. The overall results demonstrate that the detection accuracy and generalization performance of the feature fusion model are an improvement on the single-technique data model; and the results of this study can provide a new technical method for high-precision monitoring of grain storage quality. [ABSTRACT FROM AUTHOR]

It is crucial for the efficacy of the apple storage to apply methods like electronic nose systems for detection and prediction of spoilage or infection by Penicillium expansum. Based on the acquisition of electronic nose signals, selected sensitive feature sensors of spoilage apple and all sensors were analyzed and compared by the recognition effect. Principal component analysis (PCA), principle component analysis-discriminant analysis (PCA-DA), linear discriminant analysis (LDA), partial least squares discriminate analysis (PLS-DA) and K-nearest neighbor (KNN) were used to establish the classification model of apple with different degrees of corruption. PCA-DA has the best prediction, the accuracy of training set and prediction set was 100% and 97.22%, respectively. synergy interval (SI), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) are three selection methods used to accurately and quickly extract appropriate feature variables, while constructing a PLS model to predict plaque area. Among them, the PLS model with unique variables was optimized by CARS method, and the best prediction result of the area of the rotten apple was obtained. The best results are as follows: Rc = 0.953, root mean square error of calibration (RMSEC) = 1.28, Rp = 0.972, root mean square error of prediction (RMSEP) = 1.01. The results demonstrated that the electronic nose has a potential application in the classification of rotten apples and the quantitative detection of spoilage area. [ABSTRACT FROM AUTHOR]

Fatty acids are a key index for measuring wheat storage quality. This study provides a quantitative method for determining fatty acids in stored wheat using an olfactory sensor technique. A 4 × 3 olfactory sensor array was prepared using 12 chemical dyes (porphyrins) to collect volatile odor information from stored wheat. A dung beetle optimizer (DBO) algorithm was used to determine the best characteristic color variable combination for the olfactory sensor, and a wheat fatty acid value detection model based on support vector regression (SVR) was established. The results showed that the DBO algorithm can effectively screen out characteristic color variables that are closely related to the wheat fatty acid value. The SVR detection model built on the 11 characteristic color variables selected by the DBO algorithm exhibited the best performance. The root mean square error of prediction was 3.0384 mg/100 g, and the coefficient of correlation (R P) was 0.9413. These results demonstrate that the olfactory sensor prepared by our team has a promising application potential in the quality inspection of stored grains. • Fatty acid is a key quality index to measure wheat storage quality. • A 4 × 3 olfactory sensor array was prepared using 12 chemical dyes. • The sensor was employed to collect the volatile odor information of stored wheat. • Support vector regression models were built to determine the fatty acid in wheat. [ABSTRACT FROM AUTHOR]

• Determination of procymidone residues in rapeseed oil by NIRS. • Feature optimization methods are compared for NIR spectral feature optimization. • Intelligent optimization algorithms are compared for SVM model parameter optimization. • This study verifies the usefulness of analysis of pesticide residues in edible oil by NIRS. The issue of pesticide residues has always been a hot topic at home and abroad. A method for the quantitative detection of procymidone residues in grain and oil products using near-infrared (NIR) spectroscopy has been proposed. First, a NIR spectrometer was used to collect spectral data from rapeseed oil samples with different concentrations of procymidone residues. Based on full-spectrum data, the wavelength points selected by bootstrapping soft shrinkage (BOSS) algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and variable combination population analysis (VCPA) algorithm then were compared and were quantified using support vector regression (SVR) model. Simultaneously, the prediction results of the SVR model optimized by dung beetle optimizer (DBO) algorithm and pigeon-inspired optimization (PIO) algorithm were compared using the full-spectrum data. Finally, the wavelength selection algorithms and parameter optimization algorithms with the best prediction results were selected for comparison and combination. In light of the outcomes, the three spectral characteristic wavelength selection algorithms and the two optimization algorithms can improve the coefficient of determination (R P 2) and reduce the root mean square error of prediction (RMSEP). The SVR model that utilizing CARS and PIO algorithm demonstrates the best generalization performance among all models evaluated, and the R P 2 is 0.9939 with a RMSEP of 2.3435 mg·kg−1. The results indicate that the high-precision and rapid detection of procymidone in edible oil can be achieved using the SVR model optimized by input feature and parameter based on NIR spectral data. This has great significance in ensuring the safety of grain and oil food. [ABSTRACT FROM AUTHOR]

Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables. In comparison with the other models, GA-BPANN models from spectra data information showed the best performance, with the correlation coefficient of 0.8935, and the root mean square error of 0.392 in the prediction set. In addition, models based on the spectra information provided better performance than that based on the color parameters. Therefore, the VIS-NIR spectroscopy technique is a promising tool for rapid and accurate evaluation of the sensory quality of black tea samples. [ABSTRACT FROM AUTHOR]

[Display omitted] • The absorbance spectra of wheat samples were obtained by using CSA-NIRS. • An optimization strategy combining several optimization algorithms is adopted. • The CARS-RIME-KELM model was used to improve the detection accuracy of zearalenone. In the current study, a colorimetric sensor array combined with near-infrared (NIR) spectroscopy was used to quantitatively analyze zearalenone in wheat. The portable NIR spectrometer was used to scan the porphyrin reaction points of the wheat colorimetric sensor and collect spectral data. Subsequently, based on all the NIR spectral data, the two models and three feature selection algorithms are compared, and the best performance model and the best feature variable input are selected. Concurrently, the Kernel-based Extreme Learning Machine (KELM) model optimized by the two parameter optimization algorithms was compared, and the best parameter optimization algorithm was selected. Among all evaluation models, the KELM model optimized by the Competitive Adaptive Reweighted Sampling algorithm combined with the rime optimization algorithm has the best prediction effect. The predicted R P 2 is 0.9900, and the root mean square error of prediction (RMSEP) is 18.4610 μ g ∙ k g - 1 . [ABSTRACT FROM AUTHOR]

The expansive potential of surface-enhanced Raman scattering (SERS) has been well-established; however, the primary bottleneck hindering its routine analytical and commercial implementation is the poor signal reproducibility and challenges in substrate fabrication. Thus, the current work attempts to synthesize a scalable and reproducible nanoporous gold (npAu) decorated with gold (Au) nanoparticles to generate a highly structured Au@npAu nanocomposite. The substrate fabrication completes via three distinct routes: i) selective dealloying to form npAu on the Au film, ii) the fast deposition (i-t = −0.8 V, t = 10.0 s) of Au atoms across the npAu surface, and finally iii) the precise growth control of the generated Au@npAu by a series of by oxidation-reduction cycles (−0.03 to −0.4 V for 80.0 segments at ν = 50.0 mVs−1). The simulations of the dealloyed npAu and the final Au@npAu nanocomposite showed that the reduced interparticle spacing and ligament size in the Au@npAu nanocomposite is crucial for forming abundant "hot spot" regions with highly concentrated electromagnetic fields. The Au@npAu substrate reproducibility was assessed on 400.0 sites for SERS spectral acquisition with a relative standard deviation of 9.22 %. Furthermore, the Au@npAu was checked under different preparation batches for intra- and inter-day analysis and storage for 20.0 days with good stability. Finally, the substrate was checked for direct SERS detection of ferbam residues with a 4.34 × 10−9 mol L−1 sensitivity and examined in real samples with satisfactory recoveries (97.63 ± 1.95%–99.16 ± 0.24 %). This work offers a promising avenue towards highly reproducible, scalable and universal Au@npAu SERS substrate fabrication in diverse SERS-related applications. [Display omitted] • A scalable and reproducible Au-grafted nanoporous gold for SERS detection of ferbam. • Fast and controlled electrochemical growth and programming of the Au@npAu composite. • Highly stable, reproducible, and replicable substrate with negligible variations over prolonged storage. • Ultra-sensitive and direct SERS detection of ferbam on Au@npAu with good recoveries in apples. [ABSTRACT FROM AUTHOR]

An anti-interference colorimetric sensor array (CSA) technique was developed for the qualitative and quantitative detection of target heavy metals in corn oil. This method involves a binding mechanism that triggers changes in atomic energy levels and visible color changes. A custom-built olfactory visualization device was employed to gather spectral data, revealing distinct CSA color difference patterns. Subsequently, three pattern recognition algorithms were used to create an identification model for the target heavy metals. The results showed that the ACO-KNN (Ant Colony Optimization-K-Nearest Neighbor) model outperformed the other models, achieving accuracy rates of 90.28% and 89.58% for the calibration and prediction sets, respectively. The ACO-PLS (Partial Least Square) model was more stable with the lowest root mean square error of prediction (RMSEP), which were 0.1730 and 0.1180, respectively. The limit of detection (LOD) and quantification (LOQ) of Pb and Hg were (0.3, 0.6, 1.1 and 2.2) x 10−3 mg/L, respectively. • Anti-interference colorimetric sensor array with spectral detected corn oil. • Binding mechanism between dyes and heavy metals was developed. • Different recognition algorithms were compared in developing identification models. • The ACO-KNN and ACO-PLS models had optimal performance of heavy metals detection. [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]

• This study proposes a rapid detection method of fatty acid value of peanuts during storage. • Building a portable spectroscopy system to collect the spectra of peanut samples during storage. • Optimizing spectral characteristics using variable selection methods based on the MPA strategy. • Developing SVM models using the features optimized to determine fatty acid content of peanuts. In the process of peanut storage, the acidity index can be used as one of the important evaluation criteria of peanut storage characteristics. This study proposed a rapid detection method of acidity index of peanuts during storage based on a portable near-infrared (NIR) spectroscopy system. The portable spectroscopy system was built to collect the NIR spectra of peanuts during storage. The Savitzky-Golay (SG) smoothing combined with multiple scattering correction (MSC) was used to preprocess the raw spectra and normalize it. Three model population analysis-based (MPA-based) wavelength variable selection methods (namely: variable combination population analysis (VCPA), iterative retained information variable (IRIV) and (VCPA-IRIV) were employed comparatively to optimize the characteristics of the pre-treated spectra. And support vector machine (SVM) models were established based on optimized features to achieve rapid detection of acidity index of peanuts during storage. The results obtained showed that compared with the full-spectrum SVM model, the prediction performance of the SVM model based on optimized features has been improved. In addition, the VCPA-SVM model obtained the best prediction performance. The root mean square error (RMSEP) of the model was 0.61 g·kg−1, the coefficient of determination of the prediction set (R P 2) was 0.95, and the residual predictive deviation (RPD) was 4.31. The overall results demonstrate that the portable NIR spectroscopy system can realize the rapid detection of the acidity index during peanut storage, and the VCPA algorithm can efficiently obtain the characteristic wavelengths of the NIR spectra of peanuts during storage. [ABSTRACT FROM AUTHOR]

• This work proposes a novel method for quantitative detection of yeast growth status by MSF. • The intrinsic characteristics of NIR and Raman spectra were optimized by VCPA. • The optimized spectral features were fused in feature layers. • The yeast growth status was monitored using the SVM models constructed on fusion features. This work innovatively proposes a novel method for quantitative detection of yeast growth status based on molecular spectroscopy fusion (MSF) technique. First, the Raman spectra and near-infrared (NIR) spectra of the samples during the yeast culture process were collected by spectrometers, and the raw spectra were respectively preprocessed using Savitzky-Golay (SG) smoothing + standard normal variate (SNV). Then, the two kinds of molecular spectra after preprocessing were respectively optimized for characteristic wavelength using variable combination population analysis (VCPA), and feature fusion was performed on the feature layer. Finally, support vector machine (SVM) models based on the fusion features were established to achieve quantitative detection of yeast growth status. The experimental results showed that, compared with the best VCPA-SVM model based on single-molecule spectral features, the best VCPA-SVM model based on the fusion features could obtain better prediction performance. The root mean square error of prediction (RMSEP), coefficient of determination (R P 2) and relative percent deviation (RPD) of the best model were 0.47, 0.98, and 6.69, respectively. The results obtained reveal that the MSF technique can effectively improve the detection accuracy of yeast growth status; in addition, the VCPA method has a certain potential in feature wavelength mining, which can effectively reduce the dimensionality of fusion features. [ABSTRACT FROM AUTHOR]

• This study compares the potential of using different NIR spectra to detect acidity index of peanut. • Selecting spectral intervals of FT-NIR and P-NIR spectra by the SiPLS algorithm. • Optimizing characteristic wavelength variables using the BOSS algorithm. • Developing SVM models using the features optimized to determine acidity index of peanuts. This study compared the potential of detection models based on different near-infrared (NIR) spectral characteristics in detecting acidity index of peanut during storage. Fourier transform near-infrared (FT-NIR) spectrometer and portable near-infrared (P-NIR) spectrometer were used to obtain the NIR spectra of peanut samples in different storage periods. The characteristic wavelength intervals of the preprocessed NIR spectra were roughly optimized by synergy interval partial least squares (SiPLS). The bootstrapping soft shrinkage (BOSS) was introduced to further fine select the characteristic wavelength variables, and the support vector machine (SVM) models based on the optimized characteristic wavelength variables were established. The results obtained showed that the SVM model based on the fusion of different NIR spectra wavelength variables obtained the best predictive performance. Root mean square error of prediction set (RMSEP) of the model was 0.73 g·kg−1, the determination coefficient of prediction set (R P 2) was 0.93, and the residual prediction deviation (RPD) was 3.83. The overall results indicate that although the commercial NIR spectrometer and portable near-infrared spectroscopy system overlap in band, the wavelength variables obtained can play a complementary effect to a certain extent. Therefore, the combination of two instrument data can effectively improve the generalization performance of the detection model. [ABSTRACT FROM AUTHOR]

In this study, a self-built portable Raman spectroscopy system was used for spectral acquisition. Three characteristic wavelength optimization methods were applied to optimize the characteristic wavelengths of the preprocessed Raman spectra; and Support vector machine detection models based on the combination of different optimized characteristic wavelength variables were established to realize the accurate detection of the AFB 1 in maize, and the detection performance of different optimized models were compared. [Display omitted] • This study proposes a new determination method of AFB 1 content in maize. • Building a portable Raman spectroscopy system to collect the spectra of maize samples. • Optimizing spectral features using different variable selection methods. • Developing SVM models based on the features optimized to determine the AFB 1 of maize. Aflatoxin B 1 (AFB 1) is the most widely distributed, most toxic, and most harmful, and it is widely present in moldy grains. This study proposes a new method for quantitative and rapid determination of the AFB 1 content in maize based on Raman spectroscopy. The Raman spectra of maize samples with different mildew degrees were collected by a portable laser Raman spectroscopy system. Three different spectral selection methods, which were bootstrapping soft shrinkage (BOSS), variable combination population analysis (VCPA) and competitive adaptive reweighted sampling (CARS), were applied to optimize the characteristic wavelength variables of the pretreated Raman spectra. The support vector machine (SVM) detection models based on different optimized characteristic wavelength variables were established, and the results of each detection model were compared. The results obtained showed that the performance of the SVM models established by optimized features was significantly better than the performance of the SVM model built by full-spectrum data. Among them, the SVM model based on the characteristic wavelength variables optimized by the CARS method had the best performance, and its root mean square error of prediction (RMSEP) was 3.5377 μg∙kg−1, the determination coefficient of prediction (R P 2) was 0.9715, and the relative prediction deviation (RPD) was 5.8258. The overall results reveal that the rapid quantitative detection of the AFB 1 in maize by Raman spectroscopy has a promising application prospect. In addition, the implementation of the characteristic wavelength optimization of Raman spectra in the model calibration process can effectively improve the detection accuracy of chemometric models. [ABSTRACT FROM AUTHOR]

A new method for quantitative detection of aflatoxin B 1 (AFB 1) in corn based on colorimetric sensor technology was proposed in this study. The characteristic color component combination of the colorimetric sensor was effectively mined by the SRCFS-SVR algorithm, and the PSO-SVR model based on the optimized characteristic combination was established to realize the high-precision detection of AFB 1 in corn. This study can provide a detection method and algorithm reference for the quantitative and accurate detection of mycotoxins contamination levels during grain storage. [Display omitted] • This study proposes a method to determine the aflatoxin B 1 in corn based on colorimetric sensor. • The SRCFS algorithm was used to select the sensor features. • The PSO algorithm was employed to optimize the parameters of the SVR models. • Seeking an optimal SVM model to achieve the determination of the aflatoxin B 1 in corn. A new method for quantitative detection of aflatoxin B 1 (AFB 1) in corn based on colorimetric sensor technology was proposed in this study. First, we chose 12 kinds of color sensitive materials to make the colorimetric sensor array. The prepared colorimetric sensor array was used to collect the olfactory sensory information of corn samples with different degrees of mildew, and the information was characterized in the form of images, and using a new unsupervised feature selection with multi-subspace randomization and collaboration (SRCFS) algorithm combined with support vector machine regression (SVR) to optimize the image features of the colorimetric sensor to determine the best color components combination. The SVR model was established by using the best color components combination to achieve rapid quantitative analysis of the AFB 1 in corn; in the process of SVR model calibration, optimization of SVR parameters C and g using particle swarm optimization (PSO). The research results showed that compared with the PSO-SVR model, the mean of correlation coefficient of prediction (R P) of the SRCFS-PSO-SVR model has increased from 0.97 to 0.98, and the mean of root mean square error of prediction (RMSEP) has decreased from 4.8 μg·kg−1 to 3.5 μg·kg−1; the R P of the best SRCFS-PSO-SVR model was 0.99 and the RMSEP was 2.8 μg·kg−1. The results reveal that the quantitative detection of the AFB 1 in corn can be achieved by using colorimetric sensor technology combined with chemometric methods. Moreover, the SRCFS algorithm can efficiently find the feature combination of colorimetric sensor. [ABSTRACT FROM AUTHOR]

Organophosphorus pesticides (OPPs) present in tea infusions pose a serious threat to human health. In this study, a sensitive method for the determination of OPPs was developed based on a direct-immersion solid-phase microextraction (DI-SPME) probe. By fine adjustment of the ratio and one-step polymerization of dihydroxy-functionalized zirconium-based metal-organic framework UiO-66-(OH) 2 and divinylbenzene- N -vinyl pyrrolidone (DVB-NVP) microspheres, the DVB-NVP@ UiO-66-(OH) 2 (D-N@U) composite with an optimal hydrophilic-lipophilic balance (HLB) was achieved. Furthermore, D-N@U was adhesively bonded to stainless-steel wires to fabricate a DI-SPME probe. OPPs, especially those with nonpolar properties characterized by a high octanol-water partition coefficient (log K OW), were selectively and efficiently enriched on the D-N@U-coated DI-SPME probe from tea infusions. Coupled with a gas chromatography-flame photometric detector, the as-fabricated D-N@U-coated DI-SPME probe achieved good performance for OPPs analysis with a wide linear dynamic range of 0.10–500.00 μg/L and low detection limits of 1.96–6.69 ng/L. Moreover, in spiked samples, the recoveries and relative standard deviations were in the ranges of 73.12%–101.20 % and 1.03%–6.56 %, respectively. Owing to its simple operation, high extraction efficiency, and high sensitivity, this approach has great potential for the rapid determination of multiple pesticide trace-level residues in food. Fig. 1. Schematic of the preparation and application of D-N@U-coated DI-SPME for OPPs. [Display omitted] • First composite of DVB-NVP and UiO-66-(OH) 2 for rapid analysis of trace OPPs. • The D-N@U-coated probe was optimized to achieve the best HLB in tea infusions. • Selective DI-SPME of a range of nonpolar OPPs with D-N@U-coated probe. • Higher sensitivity and extraction efficiency compared to commercial SPME probes. [ABSTRACT FROM AUTHOR]

In this study, covalent organic frameworks (COFs) were grown in situ on magnetic nitrogen-doped graphene foam (MNGF), and the resulting composite of COFs-modified MNGF (MNC) was wrapped by molecularly imprinted polymers (MNC@MIPs) for specifically capturing SAs. A magnetic solid phase extraction (MSPE) method for SAs was established using MNC@MIPs with good magnetic responsiveness. The adsorption performance of MNC@MIPs was superior to that of non-molecularly imprinted polymers (MNC@NIPs), with shorter adsorption/desorption time and higher imprinting factors. A high-efficiency SAs analytical method was developed by fusing HPLC and MNC@MIPs-based MSPE. This approach provides excellent precision, a low detection limit, and wide linearity. By analyzing fish samples, the feasibility of the approach was confirmed, with SAs recoveries and relative standard deviations in spiked samples in the ranges of 77.2–112.7 % and 2.0–7.2 %, respectively. This study demonstrated the potential use of MNC@MIPs-based MSPE for efficient extraction and quantitation of trace hazards in food. • MNC@MIPs were prepared by in situ growth method. • MNC@MIPs served as an advance absorbent for MSPE of SAs. • MNC@MIPs reveals shorter adsorption/desorption time and higher imprint factor than MNC@NIPs. • MNC@MIPs-based MSPE and HPLC were combined for sensitive analysis of SAs. [ABSTRACT FROM AUTHOR]

A neoteric measure for quantitative assay of Aflatoxin B 1 (AFB 1) in maize based on an optimized feature model of near-infrared (NIR) spectroscopy was proposed in the work. A portable near-infrared spectroscopy system constructed by the group was employed to collect maize samples with varying degrees of mildew. The variable selection methods of interval variable iterative space shrinkage approach (IVISSA), iterative retained information variable (IRIV), and particle swarm optimization combined moving window (PSO-CMW) were introduced to perform feature selection on the pretreatment NIR spectra. The characteristic wavelength variables after screening were used to constitute support vector machine (SVM) and partial least squares (PLS) test model respectively to implement the measurement of AFB 1 in maize, and the detection performance of the two types of models was compared. The results obtained showed that the overall performances of SVM models were higher than that of PLS models, and the SVM model based on the characteristic wavelength variables optimized by the PSO-CMW method had the most prominent generalization performance. The root mean square error of prediction (RMSEP) of the model was 3.5967 μg kg−1, the coefficient of determination (R P 2) was 0.9707, and the relative prediction deviation (RPD) was 5.7538. The overall results demonstrate that the optimized features of NIR spectra can realize the on-site quick testing of the AFB 1 in maize with high precision by constructing a nonlinear SVM detection model. This investigation provides an original approach for speedy quantitative detection of mycotoxins in cereals. [Display omitted] • This study proposes a neoteric measure for the quantitative assay of AFB 1 in maize. • A portable spectroscopy system was built for spectral data acquisition. • Optimizing characteristic wavelength variables using different chemometrics methods. • Developing SVM models using the features optimized to determine the AFB 1 in maize. [ABSTRACT FROM AUTHOR]

This study built a portable NIR spectroscopy system to acquire the NIR spectra of wheat samples at disparate storage stages by means of diffuse reflectance, and the obtained NIR spectra were pre-processed appropriately. In order to obtain highly targeted feature wavelength variables, three variable selection methods were used to optimize the feature wavelength of the pre-processed NIR spectra. Finally, PLS quantitative detection models were developed on the basis of optimized characteristic wavelength variables to realize rapid detection of the AFB1 in wheat during storage, and the outcomes of each PLS model were compared and analyzed. The figure shows the connection between the modules and the near-infrared spectroscopy collection process of wheat samples. [Display omitted] • A portable NIRS system was developed to determine the AFB 1 in wheat during storage. • The characteristic wavelengths were optimized by different variable selection algorithm. • PLS models were established using selected features to determine the AFB 1 in wheat. • This study provides theoretical reference for more targeted spectrometer development. Wheat is a widely grown grain crop around the world and is highly susceptible to environmental factors during storage and transportation, resulting in the production of fungal toxins that are harmful to humans. Of these, aflatoxin B 1 (AFB 1) is the most prevalent and most toxic. In view of this, this study used a self-built portable near-infrared spectroscopy system to predict the AFB 1 content of wheat during storage and investigated and compared the prediction effects of different wavelength selection algorithms on the constructed PLS model. Firstly, the NIR spectra of wheat samples at disparate storage stages were acquired using the NIR spectroscopy system. Secondly, the raw NIR spectra were pretreated by Savizkg-Golag (SG) smoothing, standard normal variate (SNV) and normalization in turn. Finally, three variable optimization methods, which were variable combination population analysis (VCPA), variable iterative space shrinkage approach (VISSA) and competitive adaptive reweighted sampling (CARS), were applied to select the characteristic wavelength variables of the pre-processed spectra. Partial least squares (PLS) models based on the optimized features of the three methods were established, respectively. The results obtained showed that the CARS-PLS model had the best overall effect. The root mean square error of prediction (RMSEP) for the best CARS-PLS was 2.0965 μ g ∙ k g -1, the prediction coefficient of determination (R p 2) was 0.9935, and the ratio of prediction to deviation (RPD) was 7.3279. The CARS variable screening method was used to effectively select the characteristic wavebands associated with AFB 1 in wheat, compressing the number of wavelength variables, simplifying the model structure and improving model performance. The results reveal that the self-built portable NIR spectroscopy system enables to determine the AFB 1 in wheat during storage. Furthermore, through the feature optimization of spectral wavelength variables can effectively exclude undesired wavelength variable information. [ABSTRACT FROM AUTHOR]

The fatty acid value of rice is one of the important indexes to reflect its freshness. A portable near-infrared spectroscopy (NIRS) system was designed and assembled to dynamically monitor fatty acid values in rice storage in this study. First, the near-infrared (NIR) spectra of rice samples in different storage periods were obtained using the portable NIRS system. Then, a weighted multiplicative scatter correction with variable selection (WMSCVS) algorithm was applied to the original spectra for scattering correction, and to compress variable space for achieving characteristic wavelengths. Finally, a partial least square (PLS) regression model was established using the characteristic wavelengths to realize the rapid monitoring of fatty acid values in rice storage. The results showed that the performance of the optimal PLS model based on the features by the WMSCVS algorithm was significantly better than those of the optimal PLS models based on SNV and MSC pre-processing spectra, with the determination coefficient (R P 2) of 0.9615 and the root mean square error of prediction (RMSEP) of 0.3626 in the predictive process. The overall results demonstrate that it is feasible to use the portable NIRS system developed by our team to quickly monitor the fatty acid values in rice storage. • Fatty acid value can be used as one of the criteria for determining the rice quality. • A portable NIRS system was developed to dynamically monitor fatty acid values in rice storage. • The WMSCVS algorithm was applied to correct and optimize original NIR spectra. • Developing a PLS model using optimized wavelengths to monitor the fatty acid value of rice. [ABSTRACT FROM AUTHOR]

• A novel color sensitive sensor was developed for the determination of polyphenols in green tea. • The sensor prepared including eight porphyrins and one pH indicator. • Optimizing color feature components from different sensitive spots using ant colony optimization. • Multivariate analysis was performed on the polyphenols and the color features. Tea polyphenols content in green tea has an indirect relationship with the aroma quality of tea. This study innovatively proposed a method for quantitative determination of tea polyphenols in green tea based on the self-developed color sensitive sensor. Firstly, the color sensitive sensor was prepared to acquire the aroma information of green tea. Secondly, color components were extracted and then optimized using ant colony optimization (ACO) algorithm. Finally, extreme learning machine (ELM) model was built using the optimized color feature components for quantitative determination of tea polyphenols content in green tea. Results showed that the correlation coefficient (R P) of the best ELM model is 0.8035, and the root mean square error prediction (RMSEP) is 1.6003% in the validation set. The overall results sufficiently demonstrate that it is feasible to quantitative detect tea polyphenols content in green tea by the homemade color sensitive sensor combined with appropriate chemometrics methods. [ABSTRACT FROM AUTHOR]

Quantitative analysis of surface-enhanced Raman scattering (SERS) spectra has been a critical step in trace level analysis. In this study, a novel variable selection method called interval combination iterative optimization approach coupled with SIMPLS (ICIOA-SIMPLS) was proposed for simultaneously predicting the volume ratios of various pesticides by quantitative analysis of the SERS spectra of the compounds. Four strategies, including interval selection, model population analysis (MPA), weighted bootstrap sampling (WBS) and soft shrinkage were combined in the current designed ICIOA-SIMPLS approach. Firstly, the SERS spectra were split into a series of equal-width spectral intervals. Secondly, WBS, as a random sampling method was applied based on the initial weights of spectral intervals to generate random combinations of spectral intervals, namely sub-datasets. On this basis, multivariate calibration sub-models were developed by applying SIMPLS followed by MPA to statistically analyze the outputs of sub-models and update the weights of spectral intervals. Finally, using an iterative optimization procedure the optimal spectral interval combination with the lowest root mean squares error of cross-validation (RMSECV) was searched in a soft shrinkage manner. For the sake of investigating the performance of ICIOA-SIMPLS, four methods including SIMPLS, VCPA-SIMPLS, VISSA-SIMPLS and ICIOA-SIMPLS were tested on two groups of SERS spectra for comparison. The findings revealed that the best prediction performance was obtained with ICIOA-SIMPLS. Hence, this proposed method offers a robust and effective variable selection strategy for quantitative analysis of spectroscopic datasets. Image 1 • A novel spectral interval selection method ICIOA-SIMPLS was proposed. • MPA, WBS, soft shrinkage and SIMPLS were embedded into ICIOA-SIMPLS. • The method was applied to two SERS spectra datasets for quantifying the volume ratios of three pesticides. • The performance of ICIOA-SIMPLS was assessed by its comparison with VCPA-SIMPLS and VISSA-SIMPLS. [ABSTRACT FROM AUTHOR]

[Display omitted] • Paper first predicted the TVC content of Fujian oysters using the CSA technique. • Three fusion strategies based on CSA image and Vis-NIR channels were proposed. • The high-level-fusion strategy showed the best performance. The total viable count (TVC) is a traditional method for evaluating seafood freshness. This study aimed to predict the TVC of Fujian oysters stored at 4℃ by using the colorimetric sensing array (CSA) technique combined with imaging and visible near-infrared (Vis-NIR) information channels. An optimal quantitative prediction model based on partial least squares (PLS) was established by combining feature variable selection with a data fusion strategy. All three data-fusion strategies showed better predictive performance than the individual datasets. The high-level fusion strategy performed the best, with a root mean square error of prediction (RMSEP) of 0.17, mean absolute error of prediction (MAEP) of 0.14, coefficient of determination of prediction (R p 2) of 0.9822, and residual prediction deviation (RPD) of 7.37. The experimental results showed that CSA combined with multiple detection channels could be effectively used to predict the TVC of Fujian oysters. In this study, a reliable and comprehensive evaluation model was established to achieve a rapid and accurate prediction of oyster freshness. [ABSTRACT FROM AUTHOR]

Aroma is an important index to evaluate the quality and grade of black tea. This work innovatively proposed the sensory evaluation of black tea aroma quality based on an olfactory visual sensor system. Firstly, the olfactory visualization system, which can visually represent the aroma quality of black tea, was assembled using a lab-made color sensitive sensor array including eleven porphyrins and one pH indicator for data acquisition and color components extraction. Then, the color components from different color sensitive spots were optimized using the particle swarm optimization (PSO) algorithm. Finally, the back propagation neural network (BPNN) model was developed using the optimized characteristic color components for the sensory evaluation of black tea aroma quality. Results demonstrated that the BPNN models, which were developed using three color components from FTPPFeCl (component G), MTPPTE (component B) and BTB (component B), can get better results based on comprehensive consideration of the generalization performance of the model and the fabrication cost of the sensor. In the validation set, the average of correlation coefficient (R P) value was 0.8843 and the variance was 0.0362. The average of root mean square error of prediction (RMSEP) was 0.3811 and the variance was 0.0525. The overall results sufficiently reveal that the optimized sensor array has promising applications for the sensory evaluation of black tea products in the process of practical production. In this study, an olfactory visualization sensor technology is adopted to "visualize" aroma substances of black tea, so as to realize the sensory evaluation of the aroma quality of black tea. The detailed contents of the research are as follows: (1) constructing the olfactory visualization system using a homemade color sensitive sensor array based on the optimizing color sensitive materials; (2) visual representation of black tea aroma and color characteristics extraction; and (3) optimizing feature sensors using PSO algorithm and sensory evaluation of aroma quality of black tea based on BPNN model. Unlabelled Image • Making a novel array of color sensor including eleven porphyrins and one pH indicator • Assembling an olfactory visualization system for sensory evaluation of black tea • Optimizing color components of the array using particle swarm optimization algorithm • Developing back propagation neural network model to evaluate aroma quality of black tea [ABSTRACT FROM AUTHOR]

[Display omitted] • A rapid and efficient technology for on-line monitoring kombucha is developed. • Near-infrared spectroscopy is used as a monitoring tool for total sugars in kombucha. • Linear and nonlinear calibration methods are combined and compared. • The combined model can be used to monitor the total sugar. Kombucha is widely recognized for its health benefits, and it facilitates high-quality transformation and utilization of tea during the fermentation process. Implementing on-line monitoring for the kombucha production process is crucial to promote the valuable utilization of low-quality tea residue. Near-infrared (NIR) spectroscopy, together with partial least squares (PLS), backpropagation neural network (BPANN), and their combination (PLS-BPANN), were utilized in this study to monitor the total sugar of kombucha. In all, 16 mathematical models were constructed and assessed. The results demonstrate that the PLS-BPANN model is superior to all others, with a determination coefficient (R2p) of 0.9437 and a root mean square error of prediction (RMSEP) of 0.8600 g/L and a good verification effect. The results suggest that NIR coupled with PLS-BPANN can be used as a non-destructive and on-line technique to monitor total sugar changes. [ABSTRACT FROM AUTHOR]

[Display omitted] • For detection of fatty acids, NIR spectroscopy in combination with CSA has been used. • Low and mid-level data fusion were used to build a stable model. • The best intervals from mobile NIR spectral data were selected using Si-PLS. • Mid-level fusion yielded better results for the prediction of fatty acids. The use of sensor fusion, a novel method of combining artificial senses, has become increasingly popular in the assessment of food quality. This study employed a combination of the colorimetric sensor array (CSA) and mobile near-infrared (NIR) spectroscopy to predict free fatty acids in wheat flour. In conjunction with a partial least squares model, Low- and mid-level fusion strategies were used for quantification. Accordingly, performance of the built model was evaluated based on higher correlation coefficients between calibration and prediction (R C and R P), lower root mean square error of prediction (RMSEP), and a higher residual predictive deviation (RPD). The mid-level fusion coupled PLS model produced superior data fusion findings, with R C = 0.8793, RMSECV = 7.91 mg/100 g, R P = 0.8747, RMSEP = 6.99 mg/100 g, and RPD = 2.27. The findings of the study suggest that the NIR-CSA fusion approach could be effectively applied to the prediction of free fatty acids in wheat flour. [ABSTRACT FROM AUTHOR]

Phytochemically, peanuts provide significant nutritional value to humans, animals, and the food industry as a whole. This study was conducted to explore the potential for near-infrared (NIR) spectroscopy and effective variable selection algorithms to quantify phenolic compounds in peanut seeds. The phenolics were extracted and then identified and quantified using high-performance liquid chromatography (HPLC). The spectroscopic data were acquired from the peanut samples using a tabletop NIR spectrometer with a wavelength range of 10,000–4000 cm–1. The acquired spectra were preprocessed using a synergistic effect of first- and second-order derivative (FOD and SOD) preprocessing techniques, and multivariate algorithms were used, examined, and evaluated using correlation coefficients of the validation set (R p), root mean square error of prediction (RMSEP), and residual predictive deviations (RPDs). The competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model produced optimum performance for chlorogenic acid (R p = 0.933, RPD = 2.77), kaempferol (R p = 0.928, RPD = 2.68), p -coumaric acid (R p = 0.900, RPD = 2.32), and quercetin (R p = 0.932, RPD = 2.88), respectively. Therefore, this study proved that NIR spectroscopy in combination with CARS-PLS was capable of nondestructively predicting phenolic content in peanut seeds. • Phenolic compounds in peanut seeds were individually quantified using HPLC. • NIR spectroscopy was effectively employed to evaluate phenolic compounds. • Spectral signal pretreatment improved the performance of the PLS. • Variable selection methods can be used to enhance model performance. • CARS-PLS produced optimal results for predicting phenolic contents. [ABSTRACT FROM AUTHOR]

Abstract Surface-enhanced Raman spectroscopy (SERS) combined with chemometric models were employed to develop a rapid, low-cost, and sensitive method for qualitative and quantitative analysis of chlorpyrifos residues in tea. Au@Ag nanoparticles (NPs) with high enhancement factor were synthesized and coupled with chemometric algorithms for SERS measurements. K-nearest neighbors (KNN) classification models gave the best performance model with high classification rates (90.84–100.00%) achieved. For the quantification models for predicting chlorpyrifos contents, the genetic algorithm-partial least squares (GA-PLS) models and synergy interval partial least squares-genetic algorithm (siPLS-GA) models applied to standard normal variate transformation (SNV) preprocessed training and validation data set showed better prediction performances with excellent regression quality (slope = 0.98–1.00), higher correlation coefficient of determination (r2 = 0.96–0.98), and lower root-mean-square error of prediction (RMSEP = 0.29, 0.31) than other quantification models. Paired sample t -test exhibited no statistically significant difference between the reference values determined by GC-MS and the predicted values in most quantification models. The proposed method would be a more effective and powerful tool for classification and determination of chlorpyrifos (CPS) residues in tea samples. Highlights • The Au@Ag NPs were synthesized for SERS measurements. • SERS spectra were preprocessed by SNV, 1st derivative, and 2nd derivative. • KNN, BPANN, and PAC were employed for qualitative analysis of CPS residues in tea. • PLS, GA-PLS, siPLS and siPLS-GA were employed for quantitative analysis of CPS residues in tea. [ABSTRACT FROM AUTHOR]

[Display omitted] • Quantification of ZEN in corn oil by SERS coupled with deep learning models. • The SERS spectra were augmented to boost the generalization ability of the models. • The performance of PLSR, GPR, RFR, 1D CNN, and 2D CNN was comparatively studied. • 1D CNN & 2D CNN showed the best prediction performance compared with other models. Surface-enhanced Raman spectroscopy (SERS) and deep learning models were adopted for detecting zearalenone (ZEN) in corn oil. First, gold nanorods were synthesized as a SERS substrate. Second, the collected SERS spectra were augmented to improve the generalization ability of regression models. Third, five regression models, including partial least squares regression (PLSR), random forest regression (RFR), Gaussian progress regression (GPR), one-dimensional convolutional neural networks (1D CNN), and two-dimensional convolutional neural networks (2D CNN), were developed. The results showed that 1D CNN and 2D CNN models possessed the best prediction performance, i.e., determination of prediction set (R P 2) = 0.9863 and 0.9872, root mean squared error of prediction set (RMSEP) = 0.2267 and 0.2341, ratio of performance to deviation (RPD) = 6.548 and 6.827, limit of detection (LOD) = 6.81 × 10−4 and 7.24 × 10−4 μg/mL. Therefore, the proposed method offers an ultrasensitive and effective strategy for detecting ZEN in corn oil. [ABSTRACT FROM AUTHOR]

[Display omitted] • Total flavonoids and phenolics content were evaluated by NIR. • Preprocessing techniques had no effect on raw data. • Total flavonoids and phenolic prediction were improved with variable selection algorithms. • Si-CARS-PLS exhibited optimal results for flavonoids and phenolic content prediction. Peanuts are nutritionally valuable for both humans and animals due to their high content of flavonoids and phenolic compounds. Herein, we explored the potential of near-infrared (NIR) spectroscopy coupled with efficient variable selection algorithms for quantitative prediction of total flavonoids (TFC) and total phenolics content (TPC) in raw peanut seeds. Spectrophotometrically, the reference results of the extracts for TFC and TPC were analysed and recorded. The integrated application of the synergy interval coupled competitive adaptive reweighted sampling-partial least squares (Si-CARS-PLS) were used for prediction. The model performance appraisal was based on the correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The Si-CARS-PLS performed optimally for TFC (Rp = 0.9137, RPD = 2.49) and TPC (Rp = 0.9042, RPD = 2.31), respectively. Moreover, the model (Si-CARS-PLS) was found to have an acceptable fit for the analytes under study since it achieved 0.88 for TFC and 0.86 for TPC based on the external validation. Therefore, these results showed that NIR coupled with Si-CARS-PLS could be used for the quantitative prediction of flavonoids and phenolic contents in raw peanut seeds. [ABSTRACT FROM AUTHOR]

[Display omitted] • This study proposes a method to determine the ZEN in wheat by NIR spectroscopy. • The spectral features were mined by different optimization algorithms. • An SVM model was constructed using reduced spectral features. • This study provides a new analytical perspective for determination of the ZEN in wheat. Zearalenone (ZEN) can easily contaminate wheat, seriously affecting the quality and safety of wheat grains. In this study, a near-infrared (NIR) spectroscopy detection method for rapid detection of ZEN in wheat grains was proposed. First, the collected original near-infrared spectra were denoised, smoothed and scatter corrected by Savitzky-Golay smoothing (SG-smoothing) and multiple scattering correction (MSC), and then normalized. Three wavelength variable selection algorithms were used to select variables from the preprocessed NIR spectra, which were random frog (RF), successive projections algorithm (SPA), least absolute shrinkage and selection operator (LASSO). Finally, based on the feature variables extracted by the above algorithms, support vector machine (SVM) models were established respectively to realize the quantitative detection of the ZEN in wheat grains. Eventually, the prediction effect of the LASSO-SVM model was the best, the prediction correlation coefficient (R P) was 0.99, the root mean square error of prediction (RMSEP) was 2.1 μg·kg−1, and the residual prediction deviation (RPD) was 6.0. This research shows that the NIR spectroscopy can be used for high-precision quantitative detection of the ZEN in grains, and the research gives a new technical solution for the in-situ detection of mycotoxins in stored grains. [ABSTRACT FROM AUTHOR]

[Display omitted] • This study proposes a monitoring method of fermentation by electronic nose technology. • A PEN3 electronic nose was employed to acquire the odor information of fermentation substrate. • Deep learning algorithms were used to realize feature self-learning and model calibration. • This study provides an effective analysis tool for in-situ monitoring of modern fermentation. This study innovatively proposes a high-precision monitoring method for key parameters in the process of ethanol production from simultaneous saccharification and fermentation (SSF) by electronic nose technology combined with recurrent neural network (RNN). A PEN3 electronic nose system was employed to acquire the odor information of the fermented samples, and four deep learning algorithms based on the RNN architecture were employed to design reasonable network structures to realize the deep learning of the electronic nose signal features and model calibration. The results obtained showed that each deep learning model based on the RNN architecture has good generalization performance for the determination of cassava SSF process parameters. Among them, the bidirectional long short-term memory network (BiLSTM) model has the best monitoring effect on ethanol content, with root mean square error of prediction (RMSEP) of 3.7 mg·mL−1 and coefficient of predictive determination (R P 2) of 0.98 and the relative percent deviation (RPD) of 8.1. The bidirectional gated recurrent unit (BiGRU) model had the best monitoring effect on glucose content, and its RMSEP, R P 2 and RPD were 2.9 mg·mL−1, 0.99 and 9.1, respectively. The overall results reveal that deep learning algorithms have promising application prospects in the feature mining and model calibration of electronic nose signals, which provides an effective analysis tool for in-situ monitoring of electronic nose technology in modern industrial fermentation processes. [ABSTRACT FROM AUTHOR]

The current study aims to enhance the colony number for different Aspergillus species infection in for early mildewing in wheat using a combination of Visible/near-infrared spectroscopy (Vis/NIRs) and colorimetric sensor array (CSA) technology. The CSA was fabricated from nanoparticles polymer styrene acrylic and porous silica nanospheres (PSN) doped chemo-responsive dyes for the detection of volatile organic compounds (VOCs). The composition and relative content of VOCs of the infected wheat samples by Aspergillus were determined using headspace solid-phase microextraction and gas chromatography-mass spectrometry (HS-SPME-GC-MS). The Vis/NIRs spectral data were preprocessed and subjected comparatively to chemometric models. The prediction model for the colony count established using synergy interval genetic algorithm partial least squares performs optimally for quantifying the three Aspergillus species. The correlation coefficients for all the established quantitative prediction models for A.glaucus, A.candidus and A.flavus in wheat were above 0.95 with a root mean square error < 0.5. The performance of the model based on the nanoparticles modified CSA was more sensitive, whereas, the sensor unit doped with PSN performed better at predicting the colonies number of Aspergillus. Based on accurate quantitative analysis of volatile markers, it would be possible to greatly improve the accuracy of early detection of wheat mildew within 95% confidence interval. The research offers enormous capability in mildewing analysis for food quality and safety control. • CSA based nanoparticles PSA and PSN doped chemo-responsive dyes system for wheat infection. • VOCs early detection by CSA-Vis/NIRs exposure to Aspergillus in wheat samples. • HS-SPME coupled GC-MS for VOCs composition and analysis. • The establishment of PLS, Si-PLS, GA-PLS, Si-GA-PLS prediction models for Aspergillus species. • An obtained correlation coefficient above 0.95 for all the prediction models with an RMSE less than 0.5. [ABSTRACT FROM AUTHOR]

Aflatoxin B 1 (AFB 1) is an important cause of human liver cancer. This study proposes a quantitative detection method for the AFB 1 in corn based on Fourier transform near-infrared (FT-NIR) spectroscopy technology. First, we acquired spectral data of corn samples with different degrees of mildew using FT-NIR spectrometer; then, we used the ant colony optimization (ACO) and the NSGA-Ⅱ algorithms to optimize the characteristic wavelengths of the spectra after SNV treatment respectively; Finally, the back propagation neural network (BPNN) models were established using the optimized characteristic wavelengths to realize the accurate detection of the AFB 1 in corn. The results obtained showed that the prediction performance of the BPNN model established using the four characteristic wavelength variables optimized by NSGA-Ⅱ algorithm is the best, and the best NSGA-Ⅱ-BPNN model's correlation coefficient of prediction (R P) is 0.9951, the root mean square error of prediction (RMSEP) is 1.5606 μg ⋅ kg−1. The overall results demonstrate that the quantitative detection of AFB 1 in corn by FT-NIR technique is feasible; in addition, the NSGA-Ⅱ algorithm has its unique advantages in the optimization of spectral characteristics, and it can obtain characteristic wavelength variables with strong pertinence and a small number. • This work proposes a FT-NIR chemometrics model to determine the AFB 1 in corn. • Intelligent swarm optimization algorithms were used to optimize characteristic wavelengths. • The NSGA-Ⅱ-BPNN model had a perfect performance for the AFB 1 determination. • This study can provide a method reference to detect the mycotoxins contamination levels in grain. [ABSTRACT FROM AUTHOR]

[Display omitted] • This work proposes a novel method to determine the AFB 1 in wheat during storage. • The colourimetric sensor array was developed to capture the VOCs of wheat samples. • The sensor features were optimized using a firefly algorithm. • The sparrow search algorithm was used to optimize the parameters of the BPNN model. Wheat is highly susceptible to contamination by fungal toxins during storage and transportation. Among them, aflatoxin B 1 (AFB 1) is the most common, has the highest degree of pollution and is the most toxic and carcinogenic. Therefore, this study proposed a novel means for the detection of AFB 1 content in wheat according to colourimetric sensor array technology. A highly specific colourimetric sensor array was prepared to collect volatile gas information of wheat samples. The firefly algorithm and the sparrow search algorithm (SSA) were applied to optimize the sensor features and the parameters of the back propagation neural network (BPNN), respectively. The results obtained showed that the average correlation coefficient of prediction (R P) of the optimized BPNN model improved from 0.94 to 0.97, the average root mean square error of prediction (RMSEP) decreased from 3.6 to 2.5 and the average relative percent deviation (RPD) increased from 4.3 to 6.2. The results of the study show that it is feasible to achieve AFB 1 detection in wheat using a specific colourimetric sensor array. In addition, the optimization of the enter characteristics and parameters of the model is necessary in the model calibration process to enhance the predictive performance and stability of the model while reducing the model complexity to a certain extent. [ABSTRACT FROM AUTHOR]

• Raman spectroscopic data was successfully acquired. • Deoxymyoglobin and oxymyoglobin were effectively measured and quantified. • CARS-PLS, GA-PLS and RF-PLS models were executed effectively. • The RF-PLS model was most optimum for myoglobin proportions prediction in pork. Because of the nutritional advantages and customer acceptance, it is vital to ensure pork meat quality. This study examined the quantification of myoglobin proportions (deoxymyoglobin and oxymyoglobin) by coupling Raman spectroscopy with efficient variables selection chemometrics. Prior to acquiring Raman spectroscopic data, the fractions of myoglobin were determined. Afterward, multivariate calibration methods like partial least square (PLS), competitive adaptive reweighted sampling (CARS-PLS), genetic algorithm-PLS (GA-PLS), and random frog-PLS (RF-PLS) were applied and evaluated. The models' performance was assessed using correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The RF-PLS model achieved optimal results for both deoxymyoglobin and oxymyoglobin, with Rp = 0.8936; RMSEP = 2.91 and RPD = 1.97 for the former and Rp = 0.9762; RMSEP = 1.23 and RPD = 4.47 for the latter, respectively. Therefore, this work demonstrated that Raman spectroscopy paired with RF-PLS could be employed for nondestructive, fast, and easy detection of deoxymyoglobin and oxymyoglobin. [ABSTRACT FROM AUTHOR]

[Display omitted] • Acid value and peroxide value were determined by NIRS. • Spectral data preprocessing improved the performance of the PLS model. • Acid value and peroxide value prediction were improved with variable selection models. • NIRS coupled CARS-PLS was most reliable for acid value and peroxide value prediction. Two key parameters (acidity and peroxide content) for evaluation of the oxidation level in crude peanut oil have been studied. The titrimetric analysis was carried out for reference data collection. Then, near-infrared spectroscopy in combination with chemometric algorithms such as partial least square (PLS); bootstrapping soft shrinkage-PLS (BOSS-PLS); uninformative variable elimination-PLS (UVE-PLS), and competitive-adaptive reweighted sampling-PLS (CARS-PLS) were attempted and assessed. The correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to individually evaluate the performance of the models. Optimum results were noticed with CARS-PLS, 0.9517 ≤ Rc ≤ 0.9670, 0.9503 ≤ Rp ≤ 0.9637, 0.0874 ≤ RMSEP ≤ 0.5650, and 3.14 ≤ RPD ≤ 3.64. Therefore, this affirmed that the near-infrared spectroscopy coupled with CARS-PLS could be used as a simple, fast, and non-invasive technique for quantifying acid value and peroxide value in crude peanut oil. [ABSTRACT FROM AUTHOR]

Arsenic (As) is one of the toxic, persistent, and lethal heavy metalloids and requires rapid, less costly, and sensitive detection methods. This study proposed a label-free cuprous oxide/silver (Cu 2 O/Ag) surface-enhanced Raman scattering (SERS) nanoprobe to detect total As in tea. Different total As spiked tea concentrations were mixed with the Cu 2 O/Ag SERS nanoprobe for the SERS detection. Quantitative models were established for predicting the total As in tea by comparatively applying chemometric algorithms. Amongst the algorithms, competitive adaptive reweighted sampling partial least squares (CARS-PLS) optimized the most effective spectral variables to predict the total As in tea efficiently. The CARS-PLS gave the highest correlation coefficient value (R p = 0.9935), very low root means square error (RMSEP = 0.0496 μg g−1) in the prediction set and recorded the highest RPD value of 8.819. The proposed nanoprobe achieved a lower detection limit (0.00561 μg g−1), excellent selectivity, satisfactory reproducibility, and stability. No significant difference was recorded when the performance of the Cu 2 O/Ag total As SERS sensor was compared with the inductively coupled plasma mass spectrometry (ICP-MS) method. Therefore, this developed Cu 2 O/Ag coupled chemometrics SERS sensing method could be used to efficiently determine, quantify, and predict total As in tea to promote monitoring of heavy metal contaminants. [Display omitted] • A chemometrics fused SERS method was developed to determine total As in tea. • CARS-PLS algorithm combined with SERS can efficiently predict total As in tea. • High selectivity, good reproducibility and stability were achieved. • The validity of the SERS sensor compared to ICP-MS was not significant (P > 0.05). • The proposed method could be employed for total As quantification and prediction. [ABSTRACT FROM AUTHOR]

In this study, SERS coupled with chemometric algorithms like PLS (partial least square), Si-PLS (synergy interval-PLS), GA-PLS (genetic algorithm-PLS) and UVE-PLS (uninformative variable elimination-PLS) were applied for the detection of 2,4-D and imidacloprid in milk. The fabricated gold and silver core-shell nanoflowers (Au@Ag NFs) were used as SERS nanosensor which exhibited strong signal over the concentration range of 1.0 × 10−3 to 1.0 × 102 ng/mL. Performances of the models were evaluated based on the attained correlation coefficients of the prediction (R p) and calibration (R c), root mean square error in calibration (RMSECV) and prediction (RMSEP), and the ratio of sample standard deviation to RMSEP (RPD). The models exhibited improved results in the order of PLS < Si-PLS < GA-PLS < UVE-PLS. The UVE-PLS model showed superior performance, yielded R c = 0.9801 and R p = 0.9878 with RPD = 6.32 for 2,4-D, and R c = 0.9732 and R p = 0.9726 with RPD = 4.30 for imidacloprid. The strategy could be employed for safety and quality monitoring of milk. • Hollow Au@Ag NFs were synthesized and served as active SERS substrates. • The SERS nanosensor exhibited strong signal at 1.0 × 10−3 to 1.0 × 102 ng/mL. • PLS, Si-PLS, GA-PLS, UVE-PLS were applied for the 2,4-D and imidacloprid detection. • The models exhibited improved results in the order of PLS < Si-PLS < GA-PLS < UVE-PLS. [ABSTRACT FROM AUTHOR]

• Colorimetric-bionic sensor array consists of nine chemically responsive dyes was fabricated. • Deoxymyoglobin, oxymyoglobin and metmyoglobin were simultaneously quantified by the sensor. • UVE, BOSS and RF were used to improve the performance of prediction modeling. • RF-PLS modeling was most robust for myoglobin proportions prediction in pork. The quality of pork meat is of considerable importance as one of the principal sources of proteins. The study proposed the quantification of myoglobin proportions (deoxymyoglobin, oxymyoglobin and metmyoglobin) in pork meat by exploring the feasibility of colorimetric-bionic sensor array combined with efficient variable selection algorithms. Specifically, a novel colorimetric-bionic sensor array consists of nine chemically responsive dyes was fabricated. Subsequently, multivariate calibration algorithms such as partial least square (PLS), uninformative variable elimination-PLS (UVE-PLS), bootstrapping soft shrinkage-PLS (BOSS-PLS) and random frog-PLS (RF-PLS) were applied and appraised. The correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were used for the performance appraisal of the models. Optimum results were noticed with RF-PLS for myoglobin proportions (0.8412 ≤ Rp≤0.9484, 1.69 ≤ RMSEP≤3.79, and 1.68 ≤ RPD≤3.14). Therefore, this study proved that the colorimetric-bionic sensors array combined with RF-PLS could be used for rapid, simple, and non-destructive detection of quality in pork meat. [ABSTRACT FROM AUTHOR]

• A novel quantitative method of volatile aldehydes was developed in this work. • Boron-dipyrromethene dyes were used to quantify a group of volatile aldehydes. • The interaction mechanism was explored by Density Functional Theory calculations. • This prediction model was successfully applied in rice aging determination. Current work proposed a novel quantitative method of volatile aldehydes (VAs) using chemoselective response dyes (CRDs) combined with multivariate data analysis. Multivariate spectral data of selected CRDs was obtained by visible near-infrared spectroscopy. The Synergy-interval Partial Least Squares (Si-PLS) algorithm processed multivariate spectral data to establish VAs quantitative prediction models at the level of 0.0002 v/v to 0.18 v/v. The prediction coefficient (Rp) values of models ranged from 0.8399 to 0.9886, and the Root Mean Square Error of Prediction (RMSEP) values were less than 0.01. These models were verified by classification of aging rice samples, and 93% samples were correctly identified in prediction set. In addition, Density Functional Theory (DFT) calculations explored the interaction mechanism between selected CRDs and VAs. The optimized Highest Occupied Molecular Orbital-Lowest Unoccupied Molecular Orbital (HOMO-LUMO) energy levels, dipole moment, distance between molecules were found to have strong correlations with the interaction. [ABSTRACT FROM AUTHOR]