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...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | Brasil |
| Institución: | Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
| Repositorio: | Food Science and Technology (Campinas) |
| Idioma: | inglés |
| OAI Identifier: | oai:scielo:S0101-20612023000100412 |
| Acceso en línea: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612023000100412 |
| Access Level: | acceso abierto |
| Palabra clave: | honey quality machine learning adulteration |
| Sumario: | 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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