Rapid Assessment of Tomato Ripeness Using Visible/Near-Infrared Spectroscopy and Machine Vision-Reference-Cited by-同舟云学术

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Rapid Assessment of Tomato Ripeness Using Visible/Near-Infrared Spectroscopy and Machine Vision

Published:2016-11-26 Issue:6 Volume:10 Page:1721-1726
ISSN:1936-9751
Container-title:Food Analytical Methods
language:en
Short-container-title:Food Anal. Methods

Author:

Lu Huishan,Wang Fujie,Liu Xiulin,Wu Yuanyuan

Funder

Scientific and Technological Programs in Shanxi Province

Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi Province

Publisher

Springer Science and Business Media LLC

Subject

Safety Research,Safety, Risk, Reliability and Quality,Applied Microbiology and Biotechnology,Food Science,Analytical Chemistry

Link

http://link.springer.com/content/pdf/10.1007/s12161-016-0734-9.pdf

Reference26 articles.

1. Acevedo FJ, Jiménez J, Maldonado S, Domínguez E, Narváez A (2007) Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines. J Agric Food Chem 55:6842–6849

2. Büning-Pfaue H (2003) Analysis of water in food by near infrared spectroscopy. Food Chem 82:107–115

3. Camps C, Toussirot M, Quennoz M, Simonnet X (2011) Determination of artemisinin and moisture content of Artemisia annua L. dry powder using a hand-held near infrared spectroscopy device. J Near Infrared Spectrosc 19:191–198

4. El-Bendary N, El Hariri E, Hassanien AE, Badr A (2015) Using machine learning techniques for evaluating tomato ripeness. Expert Syst Appl 42(4):1892–1905

5. Elhariri, E., El-Bendary, N., Fouad, M. M. M., Platoš, J., Hassanien, A. E., & Hussein, A. M. (2014). Multi-class SVM based classification approach for tomato ripeness. In: Innovations in Bio-inspired Computing and Applications (pp. 175–186). Springer International Publishing.

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