Honey quality detection based on near-infrared spectroscopy

Abstract As a natural agricultural product, honey is favored by consumers, and its variety and adulteration have a huge impact on the quality. Acacia honey, red jujube honey and rape honey were used as experimental objects, and their spectral reflectance curves were obtained through a near-infrared spectral image acquisition system. Spectral features were extracted from the preprocessed spectral reflectance curves, and a honey variety classification model based on near-infrared spectral features was established by machine learning. After statistical analysis, Principal Component Analysis Support Vector Machine after processing data through Successive Projections Algorithm (SPA-SVM) is the optimal classification model for three varieties of acacia honey, red jujube honey and rape honey, and the correct rate of honey variety classification reaches 95.83%. The spectral reflectance curve was used to establish a honey adulteration identification model based on the partial least squares-discriiminate analysis (PLS-DA), and the classification accuracy was 97.92% in the test set.

Bibliographic Details

Main Authors:	ZHOU,Man, LONG,Tao, ZHAO,Zhengyang, CHEN,Jie, WU,Qingsong, WANG,Yue, ZOU,Zhiyong
Format:	Digital revista
Language:	English
Published:	Sociedade Brasileira de Ciência e Tecnologia de Alimentos 2023
Online Access:	http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612023000100412
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