数字农科院2.0

Feasibility of SSC Prediction for Navel Orange Based on Origin Recognition Using NIR Spectroscopy

文献类型： 外文期刊

作者： Lyu, Qiang;Liao, Qiuhong;Liu, Yanli;Lyu, Qiang;Lan, Yubin

作者机构：

关键词： Partial least squares regression;NIR spectroscopy;Linear discriminant analysis;Navel orange;Origin recognition;Soluble solids content

期刊名称： INTELLIGENT AUTOMATION AND SOFT COMPUTING

ISSN： 1079-8587

年卷期： 2015 年 21 卷 3 期

页码：

收录情况： SCI

摘要： Soluble solids content (SSC) is one of most important quality indicators of the navel orange. In order to explore the feasibility of SSC prediction for the navel orange from different origins using near infrared (NIR) spectroscopy, we collected seven groups Newhall navel orange (Citrus sinensis (L) Osb.) samples from seven origins (i.e. Beibei, Fengjie, Leibo, Linhai, Wusheng, Xinfeng, and Yizhang) in China, and all the samples were combined as the eighth group. The difference of the growing environments caused the variation of SSC of oranges from different origins. The partial least squares regression (PLS) models were applied to predict the SSC of its origin samples and other origin samples. The results predicted by origin-model were the best compared to cross-origin-prediction results. So it was necessary to recognize origin as the first steps of SSC prediction. Linear discriminant analysis (LDA) model with the top 18 principle components could recognize the origins of samples with 100% accuracy. The overall results demonstrated that it was feasible that SSC prediction for navel orange based on origin recognition using NIR spectroscopy.

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