Quantitative Determination of Rice Moisture Based on Hyperspectral Imaging Technology and BCC‐LS‐SVR Algorithm

Abstract

In this study, a method for quantitative determination of rice moisture based on hyperspectral imaging technology was proposed. First, the hyperspectral imaging system in the spectral range of 871-1766 nm was used to collect the hyperspectral images of 120 rice samples of 10 moisture grades. Support vector regression (SVR), least-squares support vector regression (LS-SVR), and bacterial colony chemotaxis least-squares support vector regression (BCC-LS-SVR) models were established to determine the moisture content by using full wavelengths spectra data. Among all the models, the BCC-LS-SVR model showed the best results. To simplify the calibration model, successive projections algorithm (SPA) was used for feature selection and the number of characteristic wavelengths was determined as 25. Principal component analysis (PCA) was used for feature extraction and the cumulative contribution rate of the first six principal components reached 99%, which could reflect most of the information of the full spectra data. Three new regression models based on the selected wavelengths were built and the results were improved obviously. The BCC-LS-SVR-SPA model got the best accuracy in prediction and calibration with R 2 p of 0.980, RMSEP of 0.967%, R 2 c of 0.985 and RMSEC of 0.591%. The overall results from this study demonstrated that hyperspectral image technology is feasible to detect rice moisture. PRACTICAL APPLICATIONSThe quality of rice has a direct relationship with the moisture content of rice. Because of the moisture content over standard, rice storage time becomes shorter and rice is easy to go bad. It's harmful to eat this rice for a long time. Traditional methods for identification of rice moisture mainly focus on the appearance of rice and depend on the feelings of professionals, which are tedious, time-consuming, expensive and greatly influenced by subjective factors. Hyperspectral imaging technology has the advantages of nondestructive, rapid, non-pollution, and so on. The results showed that hyperspectral imaging technology for the detection of the rice moisture is feasible and it can measure the moisture of rice.

Rapid quantitative analysis of adulterated rice with partial least squares regression using hyperspectral imaging system

“…Among them, the MSC transform assumes that the scatter for each spectrum is a linear deviation from a reference spectrum in a dataset . The SNV transform has equivalent results to the MSC transforms, and the main difference between them is that the application of the SNV transform does not need a reference spectrum . Furthermore, to correct non‐linear additive and multiplicative scatter effects, the PMSC transform has been introduced, whose principle is to fit a linear regression in a local wavelength region …”

“…Recently, HSI has been successfully employed to classify rice varieties, detect spice adulteration, determine rice moisture, quantify minced lamb meat adulteration, and determine pseudomonas loads in chicken fillets . To our knowledge, no studies have aimed to conduct a quantitative analysis of adulterated rice using HSI.…”

Quantitative detection of mixed pesticide residue of lettuce leaves based on hyperspectral technique

“…Least squares support vector regression (LSSVR) (Liu, Xu, Li, & Zeng, ; Liu, Zeng, Liu, Bai, & Song, ; Sun, Lu, Mao, Wu, & Gao, ; Zhang, Zhao, Li, & Niu, ) is an emerging nonlinear algorithm of quantitative correction. In LSSVR, the equality constraint is used to replace the inequality constraint, and the error squared sum loss function is used as the empirical loss of the training set.…”

Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy

“…Rice is a staple cereal crop in many countries, and quality assessment and process control is very important for rice grain due to the production seasonality and the huge demand [1,2]. The variation of moisture level not only affects the content of starch, fat and protein, but also affects the quality of rice grain to a certain extent [3][4][5]. Therefore, it is an indispensable prerequisite for safe crop storage to determine the rice grain moisture accurately and quickly.…”