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.
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Sociedade Brasileira de Ciência e Tecnologia de Alimentos
2023
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oai:scielo:S0101-206120230001004122022-10-31Honey quality detection based on near-infrared spectroscopyZHOU,ManLONG,TaoZHAO,ZhengyangCHEN,JieWU,QingsongWANG,YueZOU,Zhiyong honey quality machine learning adulteration 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.info:eu-repo/semantics/openAccessSociedade Brasileira de Ciência e Tecnologia de AlimentosFood Science and Technology v.43 20232023-01-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612023000100412en10.1590/fst.98822 |
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ZHOU,Man LONG,Tao ZHAO,Zhengyang CHEN,Jie WU,Qingsong WANG,Yue ZOU,Zhiyong |
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ZHOU,Man LONG,Tao ZHAO,Zhengyang CHEN,Jie WU,Qingsong WANG,Yue ZOU,Zhiyong Honey quality detection based on near-infrared spectroscopy |
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ZHOU,Man LONG,Tao ZHAO,Zhengyang CHEN,Jie WU,Qingsong WANG,Yue ZOU,Zhiyong |
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ZHOU,Man |
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Honey quality detection based on near-infrared spectroscopy |
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Honey quality detection based on near-infrared spectroscopy |
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Honey quality detection based on near-infrared spectroscopy |
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Honey quality detection based on near-infrared spectroscopy |
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Honey quality detection based on near-infrared spectroscopy |
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honey quality detection based on near-infrared spectroscopy |
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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. |
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Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
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2023 |
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http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612023000100412 |
work_keys_str_mv |
AT zhouman honeyqualitydetectionbasedonnearinfraredspectroscopy AT longtao honeyqualitydetectionbasedonnearinfraredspectroscopy AT zhaozhengyang honeyqualitydetectionbasedonnearinfraredspectroscopy AT chenjie honeyqualitydetectionbasedonnearinfraredspectroscopy AT wuqingsong honeyqualitydetectionbasedonnearinfraredspectroscopy AT wangyue honeyqualitydetectionbasedonnearinfraredspectroscopy AT zouzhiyong honeyqualitydetectionbasedonnearinfraredspectroscopy |
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