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Evaluation of Table Grape Flavor Based on Deep Neural Networks

文献类型： 外文期刊

作者： Liu, Zheng 1 ; Zhang, Yu 2 ; Zhang, Yicheng 1 ; Guo, Lei 1 ; Wu, Chase 3 ; Shen, Wei 1 ;

作者机构： 1.Zhejiang Sci Tech Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China

2.Zhejiang Acad Agr Sci, Inst Agr Safety & Nutr, Hangzhou 310022, Peoples R China

3.New Jersey Inst Technol, Dept Data Sci, Newark, NJ 07102 USA

关键词： grape flavor evaluation; deep neural networks; attention mechanism; evaluation model

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.7； 五年影响因子：2.9 ）

ISSN：

年卷期： 2023 年 13 卷 11 期

页码：

收录情况： SCI

摘要： For fresh table grapes, flavor is one of the most important components of their overall quality. The flavor of table grapes includes both their taste and aroma, involving multiple physical and chemical properties, such as soluble solids. In this paper, we investigate six factors, divide flavor ratings into a range of ?ve grades based on the results of trained food tasters, and propose a deep-neural-network-based flavor evaluation model that integrates an attention mechanism. After training, the proposed model achieved a prediction accuracy of 94.8% with an average difference of 2.657 points between the predicted score and the actual score. This work provides a promising solution to the evaluation of table grapes and has the potential to improve product quality for future breeding in agricultural engineering.

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