外文摘要：Artificial scent screening systems, inspired by the mammalian olfactory system, hold promise for fruit ripeness detection, but their commercialization is limited by low sensitivity or pattern recognition inaccuracy. This study presents a portable fruit ripeness prediction system based on colorimetric sensing combinatorics and deep convolutional neural networks (DCNN) to accurately identify fruit ripeness. Using the gas chromatography-mass spectrometry (GC-MS) method, the study discerned the distinctive gases emitted by mango, peach, and banana across various ripening stages. The colorimetric sensing combinatorics utilized 25 dyes sensitive to fruit volatile gases, generating a distinct scent fingerprint through cross-reactivity to diverse concentrations and varieties of gases. The unique scent fingerprints can be identified using DCNN. After capturing colorimetric sensor image data, the densely connected convolutional network (DenseNet) was employed, achieving an impressive accuracy rate of 97.39% on the validation set and 82.20% on the test set in assessing fruit ripeness. This fruit ripeness prediction system, coupled with a DCNN, successfully addresses the issues of complex pattern recognition and low identification accuracy. Overall, this innovative tool exhibits high accuracy, non-destructiveness, practical applicability, convenience, and low cost, making it worth considering and developing for fruit ripeness detection.
外文关键词：Volatile organic compounds;Deep convolutional neural networks;fruit ripeness detection;artificial olfactory sensor;colorimetric sensing combinatorics
作者：Ying, Yibin;Zhao, Mingming;You, Zhiheng;Chen, Huayun;Wang, Xiao;Wang, Yixian
作者单位：Zhejiang Univ;Key Lab Intelligent Equipment & Robot Agr Zhejiang;ZJU Hangzhou Global Sci & Technol Innovat Ctr
期刊名称：FOODS
期刊影响因子：0.0
出版年份：2024
出版刊次：13（5）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台