A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition

文献类型： 外文期刊

第一作者： He, Gang

作者： He, Gang;Yang, Shao-bing;Wang, Yuan-zhong;He, Gang;Yang, Shao-bing;Wang, Yuan-zhong

作者机构： Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming, Peoples R China;Yunnan Agr Univ, Coll Food Sci & Technol, Kunming, Peoples R China;Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Peoples R China

关键词： chemometrics; classification; Fourier transform-near infrared spectroscopy; image recognition; Lanxangia tsaoko

期刊名称：JOURNAL OF FOOD SCIENCE （ 2022影响因子：3.9； 五年影响因子：4.0 ）

ISSN： 0022-1147

年卷期： 2024 年 89 卷 4 期

页码：

收录情况： SCI

摘要： Lanxangia tsaoko's accurate classifications of different origins and fruit shapes are significant for research in L. tsaoko difference between origin and species as well as for variety breeding, cultivation, and market management. In this work, Fourier transform-near infrared (FT-NIR) spectroscopy was transformed into two-dimensional and three-dimensional correlation spectroscopies to further investigate the spectral characteristics of L. tsaoko. Before building the classification model, the raw FT-NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS-DA and SVM models can achieve 100% classification in origin classification, but what is difficult to avoid is the complex process of model optimization. The ResNet image recognition model classifies the origin and shape of L. tsaoko with 100% accuracy, and without the need for complex preprocessing and feature extraction, the model facilitates the realization of fast, accurate, and efficient identification.

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