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Fruit Volatile Analysis Using an Electronic Nose

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

Olfactory visualization sensor system based on colorimetric sensor array and chemometric methods for high precision

Weidong Xu¹, Yingchao He¹, Jiaheng Li¹

¹College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang Engineering Laboratory of Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang University, Hangzhou 310058, China.

|September 10, 2022

Summary

This study developed a novel olfactory visualization system using a colorimetric sensor array to detect beef freshness. The system accurately assesses beef quality, helping to prevent foodborne illness and reduce societal costs.

Keywords:

Beef Chemometric method Colorimetric sensors array Freshness assessment Olfactory visualization

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Last Updated: Aug 29, 2025

Fruit Volatile Analysis Using an Electronic Nose

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Published on: June 29, 2017

Imaging Odor-Evoked Activities in the Mouse Olfactory Bulb using Optical Reflectance and Autofluorescence Signals

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Published on: October 31, 2011

Area of Science:

Food Science
Analytical Chemistry
Sensory Science

Background:

Beef spoilage leads to foodborne illnesses and significant economic losses.
Accurate and rapid detection of beef freshness is crucial for public health and food safety.

Purpose of the Study:

To develop and validate a novel olfactory visualization system for assessing beef freshness.
To utilize colorimetric sensor arrays and chemometric methods for objective freshness evaluation.

Main Methods:

Immobilization of 12 color-sensitive materials on a hydrophobic platform to capture beef volatile compounds.
Application of machine vision algorithms and principal component analysis (PCA) for scent fingerprint extraction and dimensionality reduction.
Construction of four machine learning models (k-NN, ELM, SVM, Random Forest) for freshness prediction based on TVB-N and TVC values.

Main Results:

The support vector machine (SVM) model demonstrated superior prediction accuracy.
SVM achieved 95.83% precision in the training set and 95.00% in the prediction set.
The olfactory visualization system proved effective in evaluating beef freshness.

Conclusions:

The developed olfactory visualization sensor system offers a rapid, robust, and accurate method for assessing beef freshness.
This technology has the potential to significantly improve food safety monitoring in the beef industry.
Objective assessment of beef quality can be achieved through integrated sensor and chemometric approaches.