Evidence map›Paper›PMID 34401069›Full record

ArticleFood science & nutrition2021

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

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

Abstract read
In one paragraph

Article in Food science & nutrition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
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4citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jiayi LiCollege of Food Science and Technology Hunan Provincial Key Laboratory of Food Science and Biotechnology Hunan Agricultural University Changsha China.
Mei YuCollege of Food Science and Technology Hunan Provincial Key Laboratory of Food Science and Biotechnology Hunan Agricultural University Changsha China.
Shangke LiCollege of Food Science and Technology Hunan Provincial Key Laboratory of Food Science and Biotechnology Hunan Agricultural University Changsha China.
Liwen JiangCollege of Food Science and Technology Hunan Provincial Key Laboratory of Food Science and Biotechnology Hunan Agricultural University Changsha China.
Yu ZhengSchool of Medicine Hunan Normal University Changsha China.
Pao LiCollege of Food Science and Technology Hunan Provincial Key Laboratory of Food Science and Biotechnology Hunan Agricultural University Changsha China.ORCID https://orcid.org/0000-0002-5386-9397

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

near‐infrared spectroscopynondestructive analysispick the best of the bestporia cocosprincipal component analysis

Identifiers

PMID34401069
PMCPMC8358339

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.