Abstract
The detection of authenticity is essential to the development and management of Thai jasmine rice industry. In this study, the multispectral imaging system (405–970 nm) was used for the detection of adulteration in Thai jasmine rice combined with chemometric methods including principal component analysis (PCA), partial least squares (PLS), least squares-support vector machines (LS-SVM), and backpropagation neural network (BPNN). Three varieties of rice that were similar to Thai jasmine rice in appearance were selected to perform the classification and quantitative prediction experiments by multispectral images. For the classification experiment, four varieties of rice samples could be easily classified with accuracy achieved to 92% by the BPNN model. For the quantitative prediction of adulteration proportion experiments, the results showed that, among the different chemometric methods, LS-SVM achieved the best prediction performance comparing the results of coefficient of determination, root-mean-square error (RMSEP), bias, and residual predictive deviation (RPD). It can be concluded that multispectral imaging technology with chemometric methods can be applied in the rapid and nondestructive detection of authenticity of Thai jasmine rice.
Highlights
Rice (Oryza sativa), as one of the most important food crops, supplies staple foods for more than half of the world’s population [1,2,3]
To analyze the spectral data, some chemometric methods have been developed such as partial least square (PLS), least square-support vector machine (LSSVM), and backpropagation neural network (BPNN), which are important for the application of optical and spectroscopic techniques in nondestructive detection [29, 30]
600 rice samples (150 samples in each variety) were randomly allocated to the calibration set, and 200 rice samples (50 samples in each variety) were randomly allocated to the prediction set. en in adulteration experiment, ai jasmine rice was selected as unadulterated rice samples and parts of them were adulterated with different proportions of other three varieties of rice, respectively
Summary
Rice (Oryza sativa), as one of the most important food crops, supplies staple foods for more than half of the world’s population [1,2,3]. To study the authenticity of ai jasmine rice, the classification and adulteration experiments were developed using multispectral imaging technique combined with chemometric methods. Comparing the results from different chemometric methods, the best model for the detection of authenticity of ai jasmine rice was obtained.
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