4.8 Article

Pattern-based colorimetric sensor array to monitor food spoilage using automated high-throughput analysis

期刊

BIOSENSORS & BIOELECTRONICS
卷 196, 期 -, 页码 -

出版社

ELSEVIER ADVANCED TECHNOLOGY
DOI: 10.1016/j.bios.2021.113687

关键词

Colorimetric array; Multivariate analysis; Biogenic amines; Food spoilage; Principal component analysis; Simultaneous estimation

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This study developed a portable sensor array and reader for determining food quality, effectively detecting different amines and linearly correlating colorimetric response with biogenic amine concentrations.
Despite the existing rapid and reliable analytical methods for determining biogenic amine in food matrices, recently special efforts have been devoted for development of portable and inexpensive devices for discrimination of biogenic amines (BAs) in food products to achieve onsite detection of food-spoilage. Thus, in this context, a field deployable cross-reactive sensor array and a field-portable array reader has been developed for determination of food quality. The sensor array consisting of metal complexes (C1 - C11) of single azophenol dye-based receptor generated a unique visible response on interaction with different amines (A1 - A7). Further, the colorimetric pattern and discrimination efficacy of the sensor array was evaluated using multivariate statistical techniques such as principal component analysis and linear discriminant analysis. Motivated by outstanding discriminatory power of sensor array, titration experiment was performed with BAs, and colorimetric response of array was linearly corelated to concentrations of BAs such as tryptamine and spermine with R-2 values of 0.9596 and 0.967 respectively. Finally, for practical utility and the field analysis, a portable reader was developed and utilized for quantification of biogenic amines in meat and cottage cheese samples spiked with spermine and tryptamine up to the concentrations of 40 mu M; therefore, apparently proving the potential applicability of the designed sensing method for food quality monitoring.

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