Smartphone application assisted optoelectronic nose for monitoring milk freshness

Abstract

This study introduces an innovative method for detecting the freshness of milk through the use of smart packaging and a smartphone application. The proposed technique employs a colorimetric sensor array and a decision tree classifier, with a curvature test for feature selection. The freshness of milk samples was assessed using six colorimetric sensors, of which three, namely (Methyl violet, Chlorophenol red, and Nile blue) were chosen based on their classification error rates of 4%, 35.67%, and 8.33%, respectively. Sensor 1 was ultimately selected due to its superior performance, and its color information, along with the decision tree classification rules, was incorporated into a smartphone application. The application underwent an evaluation process, during which it was tested on twenty milk samples until total bacterial counts (TBC) reach 7 log CFU.g−1. The results showed that the application achieved a zero-error rate in identifying each sample, with the exception of 4–4.5 log CFU.g−1, where an error rate of 35% was observed. Overall, the total error rate for the entire evaluation period was 3%. Given these results, it can be concluded that this application has the potential to transform milk quality control and improve food safety.