数字农科院2.0

PREDICTION MODEL FOR EATING PROPERTY OF INDICA RICE

文献类型： 外文期刊

作者： Lu, Lin;Zhu, Zhiwei

作者机构：

期刊名称： JOURNAL OF FOOD QUALITY

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： One hundred and five indica rice samples were analyzed by determining head rice yield, chalkiness, protein content, gel consistency and amylose content. Taste scores of rice were obtained by sensory evaluation. Support vector machine (SVM) and K-nearest neighbors (KNN) models were established with physicochemical properties as attributes. In each linear correlation, amylose content had highest coefficient with taste score, but in the mutual influence, gel consistency showed highest correlation. In SVM model, the accuracy of the training set and testing set were 99.0% and 93.3%, respectively; in KNN model, they were 74.3% and 73.3%, respectively. These results showed that the nonlinear relationship between eating property and physicochemical properties of indica rice was found. It was concluded that SVM, which extracts nonlinear features, can be used to effectively predict the taste class of unknown.

分类号： TS2

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