Integrated Fruit Ripeness Assessment System Based on an Artificial Olfactory Sensor and Deep Learning.

ABSTRACT: 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 s

SUBMITTER: Zhao M 

PROVIDER: S-EPMC10931156 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature