Using visible and NIR hyperspectral imaging and machine learning for nondestructive detection of nutrient contents in sorghum

Abstract

Nondestructive, rapid, and accurate detection of nutritional compositions in sorghum is crucial for agricultural and food industries. In our study, the crude protein, tannin, and crude fat contents of sorghum variety samples were taken as the research object. The visible near-infrared (VIS-NIR) hyperspectral of sorghum were measured by the indoor mobile scanning platform. The nutritional components were determined using chemical methods to analyze the differences in nutritional composition among different varieties. After preprocessing the original spectral, the competitive adaptive reweighted sampling (CARS) and bootstrapping soft shrinkage (BOSS) algorithms were used to coarsely extract the key variables. Subsequently, the iteratively retains informative variables (IRIV) was employed to assess the importance of these key variables, resulting in explanatory wavelength sets for crude protein, tannin, and crude fat. Finally, the partial least squares (PLS), back propagation (BP) and extreme learning machine (ELM) were utilized to establish detection models. The results indicated that the optimal wavelength variable sets for crude protein, tannin, and crude fat contained 41, 38, and 22 wavelength variables, respectively. The CARS-IRIV-PLS, BOSS-IRIV-PLS and BOSS-IRIV-ELM were suitable for detecting crude protein, tannin and crude fat, respectively. Meanwhile, the R_p², RMSE_p and RPD_p values of the model were 0.69, 0.80% and 1.80, 0.88, 0.22% and 2.84, 0.61, 0.32% and 1.61, respectively. These detection models can be used for the effective estimation of the nutritional compositions in sorghum with VIS-NIR spectral data, and can provide an important basis for the application of food nutrition assessment.

Keywords: Detection model; Hyperspectral; Machine learning; Nondestructive; Nutrient contents; Sorghum.

Fig. 1

The chemical analysis results of crude protein, tannin, and crude fat content in 93 sorghum varieties. (a) The box plots and normal distribution of crude protein, tannin, and crude fat in 279 sorghum samples. (b) The differences in crude protein, tannin, and crude fat content among 93 varieties of sorghum grains.

Fig. 2

The pretreatment of spectral data for sorghum samples; (a) Raw spectra. (b) Spectral pre-processing by SNV; (c) Spectral pre-processing by SNV and Detrending; (d) Spectral pre-processing by SNV, Detrending and MSC.

Fig. 3

The change of RMSE_CV value with the increase of iterations during the key wavelength extraction process of crude protein, tannin, and crude fat using the CARS algorithm. (a) Crude protein; (b) Tannin; (c) Crude fat.

Fig. 4

The change of RMSE_CV value with the increase of iterations during the key wavelength extraction process of crude protein, tannin, and crude fat using the BOSS algorithm. (a) Crude protein; (b) Tannin; (c) Crude fat.

Fig. 5

Distribution of the strong information, weak information, no information and interference information wavelengths of key wavelengths of the crude protein, tannin and crude fat in grain spectra of sorghum. (a) Crude protein, (b) Tannin, (c) Crude fat.

Fig. 6

Fitting results of the calibration set and prediction set for each index in the optimal simultaneous detection model. (a) Crude protein; (b) Tannin; (c) Crude fat.