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A novel strategy of "pick the best of the best" for the nondestructive identification of Poria cocos based on near-infrared spectroscopy

文献类型： 外文期刊

作者： Li, Jiayi 1 ; Yu, Mei 1 ; Li, Shangke 1 ; Jiang, Liwen 1 ; Zheng, Yu 3 ; Li, Pao 1 ;

作者机构： 1.Hunan Agr Univ, Coll Food Sci & Technol, Hunan Prov Key Lab Food Sci & Biotechnol, Changsha, Peoples R China

2.Hunan Acad Agr Sci, Hunan Agr Prod Proc Inst, Changsha, Peoples R China

3.Hunan Normal Univ, Sch Med, Changsha 410013, Peoples R China

关键词： near-infrared spectroscopy; nondestructive analysis; pick the best of the best; poria cocos; principal component analysis

期刊名称：FOOD SCIENCE & NUTRITION （ 影响因子：2.863； 五年影响因子：3.141 ）

ISSN： 2048-7177

年卷期： 2021 年 9 卷 8 期

页码：

收录情况： SCI

摘要： In this paper, a novel strategy of "pick the best of the best" was proposed for the nondestructive identification of different-origin and adulterated Poria cocos with near-infrared spectroscopy. First, various preprocessing methods were divided into three classes: baseline correction, scattering and trend correction, and scaling. The single preprocessing methods with the best predictions in each class were selected. Then, the selected preprocessing methods were combined in pairs according to three classes. The pair combination preprocessing methods with the best predictions and also better predictions than single methods were selected. Finally, the selected pair combination preprocessing method was combined with the methods in the unselected class. The three combination preprocessing methods with the best predictions and also better predictions than pair combination methods were selected as the final prediction. With this strategy, the optimized preprocessing combination can be obtained quickly, and the identification accuracy with principal component analysis method can be greatly improved. 0% identification accuracy of adulterated samples and 12.5% identification accuracy of different-origin samples were obtained with the raw data. However, 100% accuracy of adulterated samples, 93.8% accuracy of calibration dataset, and 75% accuracy of validation dataset can be obtained with the novel strategy. The developed technology can be regarded as a simple, rapid, and accurate nondestructive identification method for different-origin and adulterated samples, and has a broad application prospect in the future.

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