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首页> 外文期刊>Applied Engineering in Agriculture >IDENTIFICATION OF WINE GRAPE VARIETIES BASED ON NEAR-INFRARED HYPERSPECTRAL IMAGING
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IDENTIFICATION OF WINE GRAPE VARIETIES BASED ON NEAR-INFRARED HYPERSPECTRAL IMAGING

机译：基于近红外高光谱成像的葡萄酒葡萄品种鉴定

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摘要

Wine grape variety is one of the main determinants of wine quality. The objective of this study is to explore the feasibility of using hyperspectral imaging (HSI) to identify six red and six white wine grape cultivars during the ripening period. Abnormal spectral data were removed by the Mahalanobis distance, and six different methods were employed to preprocess the spectral data. Next, the effective wavelengths for the classification of grape varieties were selected using principal component analysis (PCA) loadings to improve the HSI processing speed. Finally, three methods were applied to classify grape samples: a support vector machine (SVM), a random forest (RF), and an AdaBoost model. The results indicated that the model established by Savitzky-Golay (S-G) Filter + PCA + SVM achieves the best classification result. The average calibration and validation accuracy for red grapes reached 93.06% and 90.01%, respectively, and for white grapes, they reached 83.77% and 81.09%, respectively, which are slightly lower than those achieved by the full-spectrum model. This study revealed that hyperspectral imaging has great potential for rapid variety discrimination of different wine grapes.
机译：葡萄酒葡萄品种是葡萄酒质量的主要决定因素之一。本研究的目的是探讨使用高光谱成像（HSI）在成熟时期识别六个红色和六个白葡萄葡萄品种的可行性。通过mahalanobis距离去除异常的光谱数据，采用六种不同的方法预处理光谱数据。接下来，使用主成分分析（PCA）负载来选择葡萄品种分类的有效波长，以提高HSI处理速度。最后，应用了三种方法来分类葡萄样品：支持向量机（SVM），随机林（RF）和ADABoost模型。结果表明，由Savitzky-Golay（S-G）滤波器+ PCA + SVM建立的模型实现了最佳分类结果。红葡萄的平均校准和验证精度分别达到93.06％和90.01％，分别达到83.77％和81.09％，分别略低于全频谱模型所实现的葡萄。本研究表明，高光谱成像对不同葡萄酒葡萄的快速歧视具有巨大潜力。

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