Decoding wheat contamination through self-assembled whole-cell biosensor combined with linear and non-linear machine learning algorithms.

The contamination of mycotoxins is a serious problem around the world. It has detrimental effects on human beings and leads to tremendous economic loss. It is essential to develop a rapid and non-destructive method for contamination recognition particularly for early alarm. In this study, the whole-cell biosensor array was constructed and employed for rapid recognition of wheat contamination by combining with machine learning algorithms. Seven key VOCs were explored through univariate coupling to multivariate analysis of orthogonal partial least squares-discrimination analysis (OPLS-DA) models. The promoters of dnaK, katG, oxyR, soxS obtained from the stress-responsive of key VOCs were fused to the bacterial operon and fabricated on the whole-cell biosensor. The constructed whole-cell biosensor array was consisted with four kinds of sensors and 18 sensor unit. The bioluminescent intensity combined with linear machine learning algorithm of partial least squares discriminant analysis (PLS-DA) and non-linear algorithms of back propagating artificial neural network (BP-ANN) and least square support vector machine (LS-SVM) were employed to establish discrimination models for mold contamination especially for early warning. The Monte-Carlo strategy was performed to generate thirty subsets for modeling to give more reliable results. As a result, the whole-cell biosensor combined with non-linear algorithm of LS-SVM was practicable for detecting mold identification for wheat early-warning with the accuracy of 97.24%. Additionally, this study provides practical and effective methods not only for wheat quality guarantee and supervision but also for other foodstuffs.