Search

• Rapid detection of protein content in rice was performed by Raman-NIR fusion data. • IBPSO was proposed to select spectral characteristic wavelengths of rice protein. • IBPSO was superiority to other four algorithms in Raman-NIR wavelengths selection. • Detection accuracy of Raman-NIR fusion data regression model was improved by IBPSO. Protein content is an essential index for evaluating rice quality. This work discussed the feasibility of rapid detection of protein content in rice using spectral data fusion technology. An improved binary particle swarm optimization algorithm (IBPSO) was proposed to select the characteristic wavelength of Raman and near-infrared spectroscopy fusion data, which improved the detection accuracy of the partial least squares correction model. The determination coefficient of prediction, root mean square error of prediction, and mean relative error of prediction of the protein content detection model established by IBPSO were 0.903, 0.235%, and 2.768%, respectively, which were better than the modeling performance of the other four algorithms. The research shows that IBPSO can efficiently acquire high correlation modeling wavelength variables through the guiding optimization of binary bits with a value of '1′. The combination of IBPSO and spectral data fusion strategy can realize the rapid detection of protein content in rice, which provides theoretical support for developing related online detection equipment. [ABSTRACT FROM AUTHOR]

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering. [ABSTRACT FROM AUTHOR]

[Display omitted] • Uniting 1D-CNN dimensionality reduction with SVM classification to construct model. • BiPLS-IBPSO and CARS-IBPSO outperform the BiPLS and CARS in wavelengths selection. • Synchronous qualitative and quantitative test of three illegal additives in flour. 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. [ABSTRACT FROM AUTHOR]

• Rapid evaluation of ammonia nitrogen concentration in biogas slurry was performed. • CARS-GSA was superiority to CARS in wavelengths selection for NIR spectroscopy. • Detection accuracy of NIR spectroscopy regression model was improved by CARS-GSA. As an important intermediate product of the anaerobic digestion (AD) process, ammonia nitrogen (AN) is an essential indicator for analyzing the nitrogen nutrition level in the biogas system. To realize rapid evaluation of ammonia nitrogen concentration (ANC) in biogas slurry during AD, a detection model of ANC was established by combining near infrared (NIR) transmission spectroscopy with partial least squares. The CARS-GSA was constructed based on the competitive adaptive reweighted sampling (CARS) algorithm combined with the genetic simulated annealing algorithm (GSA), which was applied to characteristic wavelength selection of AN for improving the accuracy and efficiency of the spectral detection model. Through the selection of wavelengths by CARS-GSA, the modeling wavelengths decreased to 47 from 1557 of raw spectra, and the performance of the ANC regression model was effectively improved. For the CARS-GSA model, the determination coefficient, root mean squared error and residual predictive deviation of the validation set were 0.989, 12.549 and 9.662, respectively. For the independent test set, the above indexes were 0.990, 21.341 and 9.821, and consistent with those of the calibration set. The results showed that CARS-GSA could effectively reduce modeling wavelength variables and enhance prediction ability of the model. This study provides a theoretical support for the application of NIR transmission spectroscopy in online detection of ANC in biogas slurry. [ABSTRACT FROM AUTHOR]

• Biochemical methane potential was rapidly estimated in anaerobic co-digestion. • Mean relative errors of models based on physicochemical indexes were under 3.1%. • Mean relative error of model based on near infrared spectroscopy was 2.333%. • Model based on near-infrared spectroscopy had the highest predicted accuracy. To rapidly estimate the biochemical methane potential (BMP) of feedstocks, different multivariate regression models were established between BMP and the physicochemical indexes or near-infrared spectroscopy (NIRS). Mixed fermentation feedstocks of corn stover and livestock manure were rapidly detected BMP in anaerobic co-digestion (co-AD). The results showed that the predicted accuracy of NIRS model based on characteristic wavelengths selected by multiple competitive adaptive reweighted sampling outperformed all regression models based on the physicochemical indexes. For the NIRS regression model, coefficient of determination, root mean squares error, relative root mean squares error, mean relative error and residual predictive deviation of the validation set were 0.982, 6.599, 2.713%, 2.333% and 7.605. The results reveal that the predicted accuracy of NIRS model is very high, and meet the requirements of rapid prediction of BMP for co-AD feedstocks in practical biogas engineering. [ABSTRACT FROM AUTHOR]