Integrating Sensor Data, Laboratory Analysis, and Computer Vision in Machine Learning-Driven E-Nose Systems for Predicting Tomato Shelf Life

Integrating Sensor Data, Laboratory Analysis, and Computer Vision in Machine Learning-Driven E-Nose Systems for Predicting Tomato Shelf Life

Assessing the quality of fresh produce is essential to ensure a safe and satisfactory product. Methods to monitor the quality of fresh produce exist; however, they are often expensive, time-consuming, and sometimes require the destruction of the sample. Electronic Nose (E-Nose) technology has been e...

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Bibliographic Details
Main Authors: Julia Marie Senge, Florian Kaltenecker, Christian Krupitzer
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Chemosensors
Subjects:
Electronic Nose
data fusion
freshness monitoring
prediction
machine learning
artificial intelligence
Online Access:https://www.mdpi.com/2227-9040/13/7/255
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