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A smart olfactory visualization system based on colorimetric sensor array and chemometrics for the identification of
Muhammad Arslan1, Muhammad Zareef1, Mubrrah Afzal2
1Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013 Zhenjiang, Jiangsu, China.
Food Chemistry
|July 23, 2025
Summary
A new smart olfactory visualization system quickly identifies adulterants in premium rice. This colorimetric sensor array offers a rapid, simple, and low-cost method for detecting inferior quality grains.
Area of Science:
- Analytical Chemistry
- Sensor Technology
- Food Science
Background:
- Rice adulteration with lower quality grains is a significant concern for consumers and the industry.
- Accurate and rapid detection methods are crucial for ensuring rice authenticity and quality.
- Traditional methods for detecting rice adulteration can be time-consuming and require specialized equipment.
Purpose of the Study:
- To develop and validate a smart olfactory visualization system for the rapid identification of adulterants in premium Super India Kainat 1121 rice.
- To assess the system's ability to discriminate between freshly harvested and stored rice of varying quality.
- To evaluate the effectiveness of chemometric algorithms in analyzing sensor data for adulteration detection.
Main Methods:
- Fabrication of a smart olfactory visualization system utilizing a colorimetric sensor array.
- Adulteration of premium rice with inferior quality stored rice at various percentages (10-75%).
- Analysis of volatile compounds using Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC/MS).
- Application of Principal Component Analysis (PCA), Hierarchical Component Analysis (HCA), and k-Nearest Neighbors (kNN) algorithms for data analysis.
Main Results:
- The olfactory sensor array successfully captured characteristic volatile compound profiles of rice samples.
- Distinct colorimetric maps were generated based on the chemical environment of the rice samples.
- PCA and HCA models effectively discriminated rice samples based on different levels of adulteration.
- The kNN algorithm demonstrated high accuracy in identifying adulterated rice samples.
Conclusions:
- The developed smart olfactory visualization system provides a rapid, simple, and low-cost method for detecting rice adulteration.
- The system effectively discriminates between genuine and adulterated rice based on volatile compound analysis.
- This technology has the potential to ensure the quality and authenticity of premium rice varieties.
Keywords:
ChemometricsHS-SPME-GC/MS analysisOlfactory sensor arraysRice adulterationSuper India Kainat 1121More Related Videos
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