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一种基于近红外光谱的茯苓无损鉴别“优中选优”新策略。

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

作者信息

Li Jiayi, Yu Mei, Li Shangke, Jiang Liwen, Zheng Yu, Li Pao

机构信息

College of Food Science and Technology Hunan Provincial Key Laboratory of Food Science and Biotechnology Hunan Agricultural University Changsha China.

Hunan Agricultural Product Processing Institute Hunan Academy of Agricultural Sciences Changsha China.

出版信息

Food Sci Nutr. 2021 Jun 19;9(8):4176-4184. doi: 10.1002/fsn3.2383. eCollection 2021 Aug.

Abstract

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.

摘要

本文提出了一种“优中选优”的新策略,用于利用近红外光谱对不同产地及掺假茯苓进行无损鉴别。首先,将各种预处理方法分为三类:基线校正、散射和趋势校正以及归一化。在每一类中选择预测效果最佳的单一预处理方法。然后,根据三类将所选的预处理方法进行两两组合。选择预测效果最佳且比单一方法预测效果更好的两两组合预处理方法。最后,将所选的两两组合预处理方法与未选类中的方法进行组合。选择预测效果最佳且比两两组合方法预测效果更好的三种组合预处理方法作为最终预测方法。采用该策略能够快速获得优化的预处理组合,并能大幅提高主成分分析法的鉴别准确率。原始数据对掺假样品的鉴别准确率为0%,对不同产地样品的鉴别准确率为12.5%。然而,采用新策略对掺假样品的鉴别准确率可达100%,校正集准确率为93.8%,验证集准确率为75%。所开发的技术可被视为一种用于鉴别不同产地及掺假样品的简单、快速且准确的无损鉴别方法,在未来具有广阔的应用前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28b1/8358339/f0e9fd7cda4e/FSN3-9-4176-g007.jpg

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