Differential-based biosensor array for fluorescence-chemometric discrimination and the quantification of subtle chloropropanols by cross-reactive serum albumin scaffolding - Wong, Siew Fang; Low, Kah Hin; Khor, Sook Mei (Elsevier, 2020)
Food contamination is a serious concern because of a high level of chemicals in food causes severe health issues. Safeguarding the public from the risk of adulterated foods has become a challenging mission. Chloropropanols are of importance to food safety and food security because they are common chemical food contaminants and believed to be carcinogenic to humans. In chemical sensing, chloropropanols are challenging analytes owing to the lacking diversity of functional groups and difficulty in targeting the hydroxyl group in aqueous environments. Moreover, because of their small molecular size, the compositions of chloropropanols remain challenging for achieving chromatographic determination. Herein, to simulate human smell and taste sensations, serum albumins, which are protein-based receptors, were introduced as low-selective receptors for differential sensing. Utilizing serum albumins, a fluorophore (PRODAN), and an additive (ascorbic acid), a differential-based optical biosensor array was developed to detect and differentiate chloropropanols. By integrating the sensor array with linear discriminant analysis (LDA), four chloropropanols were effectively differentiated based on their isomerism properties and the number of the hydroxyl groups, even at ultra-low concentration (5 nM). This concentration is far below the maximum tolerable level of 0.18 μM for chloropropanols. The sensing array was then employed for chloropropanols differentiation and quantification in the complex mixtures (e.g., synthetic soy and dark soy sauces). Leave-one-out cross-validation (LOOCV) analysis demonstrated 100% accurate classification for all tests. These results signify our differential sensing array as a practical and powerful tool to speedily identify, differentiate, and even quantify chloropropanols in food matrices. © 2020 Elsevier B.V.