Accurate and fast identification of transgenic soybean plants by boosting methods with a handheld miniature spectrometer

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis Pub Date : 2024-10-26 DOI:10.1016/j.jfca.2024.106873

Yancong Zhang , Long Miao , Yuan Rao , Xiaobo Wang , Jiajia Li , Xiaodan Zhang , Youhui Deng , Lijing Tu , Xiu Jin

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引用次数: 0

Abstract

Rapid and economical classification of transgenic soybean and non-transgenic soybean is highly important for food processing and handling. This paper developed an efficient and low-cost identification method for different categories of soybeans on the basis of a handheld miniature near-infrared spectrometer. The dataset consists of transgenic modified and non-transgenic soybeans from soybean breeders, and different pretreatment methods and classifiers are used to establish models. The identification model with the best performance is selected for the boosting models. After the data are compared by different pretreatment methods and classifiers, SG+SNV is the best, and the performance of the model constructed by the gradient lifting tree is optimized. The accuracy is 98.03 % and the F1 score is 96.74 %. The results show that the near-infrared spectrum can be used to collect the all-band spectrum of soybean, and the model can be used to classify the soybean category accurately, and quickly via a handheld miniature spectrometer.

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利用手持式微型光谱仪的增强方法准确快速地识别转基因大豆植株

快速、经济地对转基因大豆和非转基因大豆进行分类对食品加工和处理非常重要。本文以手持式微型近红外光谱仪为基础，开发了一种高效、低成本的大豆分类鉴定方法。数据集包括来自大豆育种者的转基因改良大豆和非转基因大豆，并使用不同的预处理方法和分类器建立模型。选择性能最佳的识别模型用于增强模型。通过不同的预处理方法和分类器对数据进行比较后，SG+SNV 最佳，梯度提升树构建的模型性能得到优化。准确率为 98.03 %，F1 分数为 96.74 %。结果表明，近红外光谱可用于采集大豆的全波段光谱，该模型可用于通过手持式微型光谱仪准确、快速地对大豆类别进行分类。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Food Composition and Analysis 工程技术-食品科技

CiteScore

6.20

自引率

11.60%

发文量

601

审稿时长

53 days

期刊介绍： The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects. The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.