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Accurate and non-destructive monitoring of mold contamination in foodstuffs based on whole-cell biosensor array
Junning Ma1, Yue Guan2, Fuguo Xing1
1Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture and Rural Affairs / Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Abstract:
Mold contamination in foodstuffs causes huge economic losses, quality deterioration and mycotoxin production. Thus, non-destructive and accurate monitoring of mold occurrence in foodstuffs is highly required. We proposed a novel whole-cell biosensor array to monitor pre-mold events in foodstuffs. Firstly, 3 volatile markers ethyl propionate, 1-methyl-1 H-pyrrole and 2,3-butanediol were identified from pre-mold peanuts using gas chromatography-mass spectrometry. Together with other 3 frequently-reported volatiles from Aspergillus flavus infection, the volatiles at subinhibitory concentrations induced significant but differential response patterns from 14 stress-responsive Escherichia coli promoters. Subsequently, a whole-cell biosensor array based on the 14 promoters was constructed after whole-cell immobilization in calcium alginate. To discriminate the response patterns of the whole-cell biosensor array to mold-contaminated foodstuffs, optimal classifiers were determined by comparing 6 machine-learning algorithms. 100 % accuracy was achieved to discriminate healthy from moldy peanuts and maize, and 95 % and 98 % accuracy in discriminating pre-mold stages for infected peanuts and maize, based on random forest classifiers. 83 % accuracy was obtained to separate moldy peanuts from moldy maize by sparse partial least square determination analysis. The results demonstrated high accuracy and practicality of our method based on a whole-cell biosensor array coupling with machine-learning classifiers for mold monitoring in foodstuffs.
Insights
A novel whole-cell biosensor array detects early mold contamination in food using volatile markers and machine learning. This method accurately identifies pre-mold stages, preventing economic losses and ensuring food safety.
Area of Science:
- Food Science
- Biosensor Technology
- Analytical Chemistry
Background:
- Mold contamination in foodstuffs leads to significant economic losses, quality degradation, and mycotoxin production.
- Accurate and non-destructive methods for monitoring mold occurrence in food are crucial for safety and quality assurance.
- Early detection of pre-mold stages is essential to prevent further spoilage and mycotoxin formation.
Purpose of the Study:
- To develop a novel whole-cell biosensor array for the non-destructive detection of pre-mold events in foodstuffs.
- To identify specific volatile organic compounds (VOCs) indicative of early mold growth.
- To integrate biosensor array data with machine learning algorithms for accurate classification of food contamination.
Main Methods:
- Identification of three key volatile markers (ethyl propionate, 1-methyl-1H-pyrrole, 2,3-butanediol) from pre-mold peanuts using gas chromatography-mass spectrometry (GC-MS).
- Construction of a whole-cell biosensor array using 14 stress-responsive Escherichia coli promoters, immobilized in calcium alginate, to detect differential responses to VOCs.
- Application of six machine learning algorithms (including random forest and sparse partial least square) to discriminate response patterns for classifying food samples.
Main Results:
- The biosensor array coupled with random forest classifiers achieved 100% accuracy in distinguishing healthy from moldy peanuts and maize.
- High accuracy (95% and 98%) was obtained in discriminating pre-mold stages for infected peanuts and maize.
- Sparse partial least square analysis achieved 83% accuracy in differentiating moldy peanuts from moldy maize.
Conclusions:
- The developed whole-cell biosensor array offers a highly accurate and practical method for monitoring mold contamination in foodstuffs.
- The integration of biosensor arrays with machine learning provides a powerful tool for early detection of food spoilage.
- This approach has the potential to significantly reduce economic losses and enhance food safety by enabling timely intervention.
