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The moisture content (MC) and acidity content (AC) of the fermented grains used in liquor production directly affect the liquor quality and yield; as such, they are important indicators used to evaluate the quality of fermented grains. In this study, extreme gradient enhancement algorithm (XGBoost), partial least square regression (PLSR), and extreme learning machine (ELM) models were developed based on spectral data collected by near-infrared (NIR) hyperspectral imaging (HSI) technology. First, PLSR models were established after SNV and MSC algorithms preprocessed the HSI data, and the best preprocessing method was determined (MC: SNV; AC: MSC). Then, the competitive adaptive reweighting sampling (CARS) algorithm and principal component analysis (PCA), both combined with the successive projection algorithm (SPA), were used to extract the characteristic wavelengths from the full-band spectral data. Ultimately, the XGBoost model developed using the characteristic wavelengths extracted by CARS-SPA most accurately predicted the MC (RPD = 6.4167, RP2 = 0.9757, RMSEP = 0.0442 g·100 g−1) and AC (RPD = 13.0308, RP2 = 0.9941, RMSEP = 0.0216 mmol·10 g−1). The results showed that the XGBoost model could more accurately predict the MC and AC of the fermented grains from hyperspectral images of the grains, providing an effective method for the rapid analysis of raw materials used in the fermentation of liquor. [ABSTRACT FROM AUTHOR]

• A robust model was developed and validated for evaluation of sorghum quality. • Isolated forest and CARS-SPA were conducive to the optimization of the model. • The average CRR of the model in identifying sorghum varieties was greater than 91%. • The deviation of the predicted mixing ratio of adulterated samples was less than 4%. This study combined hyperspectral imaging (HSI) and deep forest (DF) to develop a reliable model for conducting a rapid and nondestructive determination of sorghum purity. Isolated forest (IF) algorithm and principal component analysis (PCA) were used to remove the abnormal data of sorghum grains. Competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA) were combined and used to extract the characteristic wavelengths. Gray-level co-occurrence matrix (GLCM) was used to extract the textural features. DF models were established based on the different types of data. Specifically, the DF models established using the characteristic spectra produced the best recognition results: the average correct recognition rate (CRR) of the models was greater than 91%. In addition, the average CRR of validation set Ⅰ was 88.89%. These results show that a combination of HSI and DF could be used for the rapid and nondestructive determination of sorghum purity. [ABSTRACT FROM AUTHOR]

• CF was used to predict the contents of amylose and amylopectin in sorghum. • PCA-SPA was used to extract characteristic wavelengths corresponding to amylose and amylopectin. • The average RPD values of the CF models were both greater than 4.7. • Accurate recognition for the varieties of sorghum grains was realized. • This study provides a new method for the starch content detection of cereals. The contents of amylose and amylopectin in sorghum directly affects the quality and yield of liquor. Hyperspectral imaging (HSI) is an emerging technology widely applied in the content analysis of food ingredients. In this study, the effects of different preprocessing methods on visible-light and near-infrared spectral data were analyzed, and the prediction accuracies of these spectral data were compared. Principal components analysis (PCA) and successive projections algorithm (SPA) were combined to extract the characteristic wavelengths. Using both the full and characteristic wavelengths, partial least square regression (PLSR) and cascade forest (CF) models were developed to predict the contents of amylose and amylopectin in different varieties of sorghum. The average RPD values of the CF models established by the characteristic wavelengths were 4.7622 and 5.5889, respectively. These results corroborated the utility of HSI in achieving the rapid and nondestructive prediction of amylose and amylopectin contents in different varieties of sorghum. [ABSTRACT FROM AUTHOR]

• The R P 2 values of the CF models were greater than 98.00 %. • PCA-SPA was used to extract characteristic wavelengths. • The distribution map of the substance content was obtained via visualization. • This study provides a new method for obtaining real-time index data on Daqu. In this research, the hyperspectral imaging technique was employed to realize the rapid and accurate detection of the reducing sugar and amino acid nitrogen contents of Daqu during fermentation. Different preprocessed data were employed to establish cascade forest (CF) models to determine the best preprocessing method. Principal component analysis (PCA) and the successive projection algorithm (SPA) were then combined to extract the characteristic wavelengths. Four types of models (CF, BPNN, SVR, and PLSR) were established based on the full and characteristic wavelengths, respectively. Among all the models, the CF model established by the characteristic wavelengths was determined to be superior for the detection of the reducing sugar and amino acid nitrogen contents: for reducing sugar, R P 2 = 0.9862, RMESP = 0.0812 g/100 g, and RPD = 6.0402; for amino acid nitrogen, R P 2 = 0.9876, RMSEP = 0.0500 g/100 g, and RPD = 6.3698. The best model achieved the visualization of the reducing sugar and amino acid nitrogen contents in the region of interest. The satisfactory detection results demonstrate that the hyperspectral imaging technique can be used to realize the rapid and accurate detection of the reducing sugar and amino acid nitrogen contents of Daqu during fermentation. [ABSTRACT FROM AUTHOR]

The contents of amylopectin and amylose are important indicators to evaluate the quality of sorghum. Hyperspectral imaging (HSI) is a technology based on machine vision and spectroscopy that has been widely used in the quality evaluation of various foods. Three combined algorithms were used to extract the characteristic wavelengths. The gray-level co-occurrence matrix (GLCM) was used to extract the textural features. Based on the spectra, textures, and fusion data, genetic algorithm-optimized back propagation neural network (BPNN-GA), cascade forest (CF), and partial least square regression (PLSR) models were established to predict the amylopectin and amylose contents. The BPNN-GA and CF models established using the characteristic wavelengths of visible-light were determined to be the best model, with RPD values of 8.2352 and 26.7308, respectively. These results demonstrated that HSI combined with machine learning algorithms could be used to enable the rapid and nondestructive prediction of amylopectin and amylose contents in sorghum. • The RPD values of the best models were both greater than 8. • The fusion data of the characteristic spectra and texture features was employed. • Three different combined algorithms were used to extract characteristic wavelengths. • This study provides a new method for obtaining the content of starch in sorghum. [ABSTRACT FROM AUTHOR]

Rice is one of the main raw materials used in the brewing of Chinese liquor. The fatty acid content of rice is one of the most important indicators of the degree of aging of rice, which greatly influences the flavor of the liquor. In this study, hyperspectral imaging (HSI) was used in combination with an integrated learning model to predict the fatty acid content in rice. Spectral, textural, and color information were extracted from the hyperspectral images of the rice grains derived from at different levels of aging. After analyzing, the best preprocessing method was determined to be SG-SNV. Principal component analysis with different kernel functions (KPCA) was used to extract the characteristic wavelengths, and PSO-SVR, SSA-KELM, and SVM-AdaBoost models were developed to predict the fatty acid content of rice based on full-wavelength spectral data, feature wavelengths, feature wavelengths combined with texture information, and feature wavelengths combined with color information. Comparison of model performance showed that the SVM-AdaBoost model, which was built by combining the texture information of the rice grains with the feature wavelengths, had the highest performance of all the models, with a RPD value of 22.3663. These results showed that combining HSI with an integrated learning model can be effective for the rapid and accurate prediction of fatty acid content in rice. [Display omitted] [ABSTRACT FROM AUTHOR]

• First-time fusion of spectral and color features of HSI to predict the total acid content (TAC) and reducing sugar content (RSC) of fermented grains. • CARS-SPA and RC-SPA were conductive to the optimization of the detection model. • For TAC and RSC, the RPD value of the model was greater than 19. • This study provides a new method for the rapid and accurate detection of total acid and reducing sugar contents in fermented grains. Total acid content (TAC) and reducing sugar content (RSC) are important evaluation indicators for the quality of fermented grains. In this study, the TAC and RSC of fermented grains were quantified using hyperspectral imaging (HSI). Two combined algorithms were used to extract the characteristic wavelengths of TAC and RSC. Nine color features of fermented grains were extracted based on H, S and V color channels. Multivariate analytical models were developed to predict TAC and RSC using full wavelengths, characteristic wavelengths, color features and fused data, respectively. The CF model established based on characteristic wavelengths extracted by CARS-SPA showed the best results in predicting TAC. Meanwhile, the PSO-SVR model built using fused data was the best model for predicting RSC. The visualization of the TAC and RSC was achieved using the optimal models. These results show that HSI can achieve non-destructive detection and visualization of TAC and RSC in fermented grains. [ABSTRACT FROM AUTHOR]