NIR Spectroscopy Oranges Origin Identification Framework Based on Machine Learning

Songjian Dan

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)18(1)

ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.297039

MLA

Dan, Songjian. "NIR Spectroscopy Oranges Origin Identification Framework Based on Machine Learning." IJSWIS vol.18, no.1 2022: pp.1-16. http://doi.org/10.4018/IJSWIS.297039

APA

Dan, S. (2022). NIR Spectroscopy Oranges Origin Identification Framework Based on Machine Learning. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-16. http://doi.org/10.4018/IJSWIS.297039

Chicago

Dan, Songjian. "NIR Spectroscopy Oranges Origin Identification Framework Based on Machine Learning," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-16. http://doi.org/10.4018/IJSWIS.297039

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Abstract

Research on the identification model of orange origin based on machine learning in Near infrared (NIR) spectroscopy. According to the characteristics of NIR spectral data, a complete general framework for origin identification is proposed. It includes steps such as data preprocessing, feature selection, model building and cross validation. Compare multiple preprocessing algorithms and multiple machine learning algorithms under the framework. Based on NIR spectroscopy to identify the origin of orange, a good identification result was obtained. Improve the accuracy of orange origin identification and obtained the best origin identification accuracy of 92.8%.