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• Rapid determination of rice protein content was completed using NIR spectroscopy. • BiPLS-GSA and BiPLS-SABPSO was proposed to select feature wavelengths of protein. • BiPLS-SABPSO was superior to BiPLS-GSA in feature wavelengths selection. • Detection accuracy of regression model was efficiently improved using BiPLS-SABPSO. Protein content is a key indicator for rice nutritional value, and its accurate determination has become a necessary tool for rice quality evaluation and breeding. To solve the shortcomings of slow and costly testing by traditional methods, a rapid detection model of rice protein content was constructed based on near-infrared spectroscopy coupled with feature wavelength selection. The backward interval partial least squares (BiPLS) was combined with the genetic simulated annealing algorithm (GSA) and the simulated annealing binary particle swarm optimization algorithm (SABPSO), respectively, to construct BiPLS-GSA and BiPLS-SABPSO for selecting protein feature wavelengths, thereby establishing a corresponding partial least squares quantitative calibration model. Among them, the regression model established using feature wavelengths selected by BiPLS-SABPSO had the best performance. The determination coefficient of the model for the validation set and the independent test set were 0.949 and 0.956, the root mean square error were 0.174% and 0.214%, the relative root mean square error were 2.621% and 3.118%, and the residual predictive deviation being 4.235 and 4.500, respectively, which could meet the requirement for rapid and accurate determination of rice protein content. The combination of BiPLS-SABPSO and near-infrared spectroscopy has developed into a new method to achieve fast and reliable determination of rice protein content, providing an alternative strategy for rapid qualitative detection of related agricultural products. [ABSTRACT FROM AUTHOR]

• 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]

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

The contents of cellulose and hemicellulose (C and H) in corn stover (CS) have an important influence on its biochemical transformation and utilization. To rapidly detect the C and H contents in CS by near-infrared spectroscopy (NIRS), the characteristic wavelength selection algorithms of backward partial least squares (BIPLS), competitive adaptive reweighted sampling (CARS), BIPLS combined with CARS, BIPLS combined with a genetic simulated annealing algorithm (GSA), and CARS combined with a GSA were used to select the wavelength variables (WVs) for C and H, and the corresponding regression correction models were established. The results showed that five wavelength selection algorithms could effectively eliminate irrelevant redundant WVs, and their modeling performance was significantly superior to that of the full spectrum. Through comparison and analysis, it was found that CARS combined with GSA had the best comprehensive performance; the predictive root mean squared errors of the C and H regression model were 0.786% and 0.893%, and the residual predictive deviations were 3.815 and 12.435, respectively. The wavelength selection algorithm could effectively improve the accuracy of the quantitative analysis of C and H contents in CS by NIRS, providing theoretical support for the research and development of related online detection equipment. [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]

• 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]