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Open AccessArticle

Application of Near-Infrared Hyperspectral Imaging with Machine Learning Methods to Identify Geographical Origins of Dry Narrow-Leaved Oleaster (Elaeagnus angustifolia) Fruits

by Pan Gao 1,2,†, Wei Xu 3,4,†, Tianying Yan 1,2, Chu Zhang 5,6, Xin Lv 2,3 and Yong He 5,6,*
1
College of Information Science and Technology, Shihezi University, Shihezi 832000, China
2
Key Laboratory of Oasis Ecology Agriculture, Shihezi University, Shihezi 832003, China
3
College of Agriculture, Shihezi University, Shihezi 832003, China
4
Xinjiang Production and Construction Corps Key Laboratory of Special Fruits and Vegetables Cultivation Physiology and Germplasm Resources Utilization, Shihezi 832003, China
5
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
6
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
These two authors contributed equally to this manuscript.
Foods 2019, 8(12), 620; https://doi.org/10.3390/foods8120620
Received: 21 October 2019 / Revised: 22 November 2019 / Accepted: 23 November 2019 / Published: 27 November 2019
(This article belongs to the Special Issue Application of Analytical Chemistry to Foods and Food Technology)
Narrow-leaved oleaster (Elaeagnus angustifolia) fruit is a kind of natural product used as food and traditional medicine. Narrow-leaved oleaster fruits from different geographical origins vary in chemical and physical properties and differ in their nutritional and commercial values. In this study, near-infrared hyperspectral imaging covering the spectral range of 874–1734 nm was used to identify the geographical origins of dry narrow-leaved oleaster fruits with machine learning methods. Average spectra of each single narrow-leaved oleaster fruit were extracted. Second derivative spectra were used to identify effective wavelengths. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to build discriminant models for geographical origin identification using full spectra and effective wavelengths. In addition, deep convolutional neural network (CNN) models were built using full spectra and effective wavelengths. Good classification performances were obtained by these three models using full spectra and effective wavelengths, with classification accuracy of the calibration, validation, and prediction set all over 90%. Models using effective wavelengths obtained close results to models using full spectra. The performances of the PLS-DA, SVM, and CNN models were close. The overall results illustrated that near-infrared hyperspectral imaging coupled with machine learning could be used to trace geographical origins of dry narrow-leaved oleaster fruits. View Full-Text
Keywords: narrow-leaved oleaster fruits; near-infrared hyperspectral imaging; geographical origin; convolutional neural network; effective wavelengths narrow-leaved oleaster fruits; near-infrared hyperspectral imaging; geographical origin; convolutional neural network; effective wavelengths
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Gao, P.; Xu, W.; Yan, T.; Zhang, C.; Lv, X.; He, Y. Application of Near-Infrared Hyperspectral Imaging with Machine Learning Methods to Identify Geographical Origins of Dry Narrow-Leaved Oleaster (Elaeagnus angustifolia) Fruits. Foods 2019, 8, 620.

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