Accurate and non-destructive monitoring of mold contamination in foodstuffs based on whole-cell biosensor array coupling with machine-learning prediction models

文献类型： 外文期刊

第一作者： Ma, Junning

作者： Ma, Junning;Xing, Fuguo;Li, Xu;Tai, Bowen;Guan, Yue;Wang, Yan;Eltzov, Evgeni

作者机构：

关键词： Whole-cell biosensor array; Volatile markers; Mold monitoring; Machine-learning

期刊名称：JOURNAL OF HAZARDOUS MATERIALS （ 影响因子：13.6； 五年影响因子：12.7 ）

ISSN： 0304-3894

年卷期： 2023 年 449 卷

页码：

收录情况： SCI

摘要： 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 prac-ticality of our method based on a whole-cell biosensor array coupling with machine-learning classifiers for mold monitoring in foodstuffs.

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