Digital analysis of polydiacetylene quality tags for contactless monitoring of milk

Abstract

The incorporation of colorimetric sensors as quality indicators in food packaging is an exciting new area of research that could improve food management. The standard approach, however, demands a reliable interface between the sensor and the food and risks food contamination which is a significant consumer concern. To overcome this challenge, herein, we develop a polydiacetylene/phospholipid agarose-based sensor that encapsulates milk in the hydrogel matrix during synthesis. The chemical recognition of free fatty acids, a product of microbial spoilage of the encapsulated milk, induces a gradual blue to red color change in the sensor. We demonstrate that the new composite material exhibits the same spoilage kinetics as regular liquid milk (digital colorimetric response 28 ± 1% and 27 ± 3% respectively), indicating the agarose does not preserve the milk ingredients nor inhibit the detection mechanism of the polydiacetylene sensors. As a result, this sensor can be attached to the external surface of food packaging to provide an indirect indication of food quality without the need for contact with the milk product. The quality tags we present can be “switched” on and off using dehydration and rehydration, removing the need for in situ manufacturing and allowing storage before use. We show that the quality tags produce a similar digital colorimetric response of 21 ± 2% to indicate milk spoilage after rehydration. The color change of the quality tags could not be analyzed using absorption spectroscopy, the standard technique for polydiacetylenes, due to the opacity that milk imparts on the sample. To solve this problem, we develop digital colorimetric analysis software using the Python programming language to describe the extent of color change in polydiacetylene materials and develop a new metric termed the Digital Colorimetric Response that describes polydiacetylene response with excellent linearity (R2 = 0.96). The software is programmed to employ statistical cleaning techniques that automatically remove image noise and outliers based on a pixel’s grayscale Z-score. This new approach to sensor design increases practicality and could be extended to the contactless quality monitoring of other foods, medicines and other products whose safety or quality is jeopardized with direct sensor contact.