Detecting Mold Levels in Soybeans Using Electronic Nose Technology
Author Information
Author(s): Song Xuejian, Qian Lili, Zhang Dongjie, Wang Xinhui, Fu Lixue, Chen Mingming
Primary Institution: College of Food Science, Heilongjiang Bayi Agricultural University, Daqing, China
Hypothesis
Can electronic nose technology effectively differentiate mold levels in soybeans?
Conclusion
Electronic nose technology can effectively identify mold levels in soybeans, achieving a classification accuracy of 82.72%.
Supporting Evidence
- The electronic nose technology achieved a discriminative accuracy rate of 82.72% in LDA.
- The study identified that organic sulfur compounds were the most significant in differentiating mold levels.
- PCA and LDA analyses showed moderate effectiveness in distinguishing between different dominant fungal inoculations.
Takeaway
This study shows that a special device can smell and tell how moldy soybeans are, helping to keep food safe.
Methodology
The study used electronic nose technology to analyze artificially inoculated and naturally moldy soybean samples, employing PCA and LDA for data analysis.
Limitations
The study did not integrate other detection methods for qualitative and quantitative analyses of specific components in spoiled soybeans.
Digital Object Identifier (DOI)
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