Issue 13, 2023

The use of a novel smartphone testing platform for the development of colorimetric sensor receptors for food spoilage

Abstract

This work presents a novel smartphone testing platform for the validation of colorimetric sensor receptors (CSRs) in the form of layers that enables reliable and straightforward determination of their color change in a closed system using a commercially available color sensor. The food-compatible model CSR used for the method development was made of black carrot extract and ethyl cellulose. The colorimetric responses were studied in detail for NH3, dimethylamine (DMA), and trimethylamine (TMA) by analyzing changes in the value of the total color difference (ΔE) with the increasing logarithm of the mass concentration (log γ) of the analytes. The method was partially validated for the detection limit (LOD), the limit of quantification, sensitivity, and linear γ range. The fastest reaction times were obtained for the NH3 analyte, while the calculated LOD values were quite similar (1.48 mg L−1 for NH3, 1.55 mg L−1 for DMA, and 1.58 mg L−1 for TMA). The applicability of CSRs was shown for different types of muscle food. Frozen (boneless and skinless) hake fillets were used for additional experimental work in which the color changes of the CSRs were correlated with the values of the total volatile basic nitrogen (TVB-N) and the total counts of aerobic and anaerobic microorganisms. The developed testing platform shows great promise for the development of CSRs that define the quality of a broad variety of muscle food.

Graphical abstract: The use of a novel smartphone testing platform for the development of colorimetric sensor receptors for food spoilage

Article information

Article type
Paper
Submitted
25 Dec 2022
Accepted
10 Mar 2023
First published
10 Mar 2023
This article is Open Access
Creative Commons BY-NC license

Anal. Methods, 2023,15, 1700-1712

The use of a novel smartphone testing platform for the development of colorimetric sensor receptors for food spoilage

T. Mastnak, G. J. Mohr and M. Finšgar, Anal. Methods, 2023, 15, 1700 DOI: 10.1039/D2AY02082C

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