On-site colorimetric food spoilage monitoring with smartphone embedded machine learning

• Published in: Talanta (Oxford) 2024-01, Vol.266 (Pt 1), p.125021-125021, Article 125021
• Main Authors: Doğan, Vakkas, Evliya, Melodi, Nesrin Kahyaoglu, Leyla, Kılıç, Volkan
• Format: Article
• Language: English
• Subjects: Colorimetric detection; Fish gelatin; Machine learning; Smartphone application; Spoilage monitoring
• ISSN: 0039-9140; 1873-3573
• DOI: 10.1016/j.talanta.2023.125021

Real-time and on-site food spoilage monitoring is still a challenging issue to prevent food poisoning. At the onset of food spoilage, microbial and enzymatic activities lead to the formation of volatile amines. Monitoring of these amines with conventional methods requires sophisticated, costly, labor-intensive, and time consuming analysis. Here, anthocyanins rich red cabbage extract (ARCE) based colorimetric sensing system was developed with the incorporation of embedded machine learning in a smartphone application for real-time food spoilage monitoring. FG-UV-CD100 films were first fabricated by crosslinking ARCE-doped fish gelatin (FG) with carbon dots (CDs) under UV light. The color change of FG-UV-CD100 films with varying ammonia vapor concentrations was captured in different light sources with smartphones of various brands, and a comprehensive dataset was created to train machine learning (ML) classifiers to be robust and adaptable to ambient conditions, resulting in 98.8% classification accuracy. Meanwhile, the ML classifier was embedded into our Android application, SmartFood++, enabling analysis in about 0.1 s without internet access, unlike its counterpart using cloud operation via internet. The proposed system was also tested on a real fish sample with 99.6% accuracy, demonstrating that it has a great advantage as a potent tool for on-site real-time monitoring of food spoilage by non-specialized personnel. [Display omitted] •Real-time food spoilage monitoring by a machine learning embedded smartphone app.•A classification accuracy of 99.6% and a processing time in about 0.1 s.•Advantages of offline, accurate, and rapid analysis in resource-limited settings.•Exploitation of anthocyanins rich red cabbage extract in colorimetric sensing.