"Evaluation of Table Grape Flavor Based on Deep Neural Networks" by Zheng Liu, Yu Zhang et al.
 

Evaluation of Table Grape Flavor Based on Deep Neural Networks

Document Type

Article

Publication Date

6-1-2023

Abstract

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 five 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.

Identifier

85161715938 (Scopus)

Publication Title

Applied Sciences Switzerland

External Full Text Location

https://doi.org/10.3390/app13116532

e-ISSN

20763417

Issue

11

Volume

13

Grant

CARS-29

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