Prediction of fruit quality based on the RGB values of time-temperature indicator

Time-temperature indicators (TTIs) are cost-efficient tools that may be used to predict food quality. In this paper, a diffusion TTI was used to predict fruit quality during storage. Both the color changing characters of TTI and the quality parameters, including weight loss, soluble solids content, vitamin C content, titratable acidity, and antioxidant capacity of three kinds of fruits (kiwifruit, strawberry, and mango), were investigated for storage temperatures (5, 10, 15, and 20 °C). The relationships between the color changing properties and fruit quality parameters have been built based on the activation energy (E_a ). The results showed that the storage temperature and time had significant effects on the color changing of TTI and fruit quality. The RGB value of TTI decreased with time, and the higher the storage temperature, the faster the RGB value reduced. Also, the higher the storage temperature, the faster the fruit quality changed and the poorer they were. Furthermore, all of the differences of E_a between TTI color response and fruit quality change are less than 25 kJ/mol, which indicates that the TTI can be used to predict these fruit quality. Finally, prediction models were built and validated based on the RGB values of TTI. It provides the possibility for low-cost quality monitoring and has more application potential in food quality predicting. PRACTICAL APPLICATION: By monitoring the color change of diffuse time-temperature indicator (TTI) and the quality change of fruit, the feasibility of TTI for fruit quality monitoring was determined and the quality prediction model was established. The diffusion TTI and fruit quality prediction model can realize the monitoring and predicting of fruit quality based on the TTI, which provides a basis for the combination of TTI Quick Response Code and fruit quality monitoring, with a view to achieving fruit quality status by scanning the Quick Response Code of TTI with mobile phones in the future. This method may provide a new solution to monitor the fruit quality during storage and distribution based on visualization technology that can simplify the methods of detecting fruit quality and achieve fast quality detection. It provides the possibility for low-cost quality monitoring and has more application potential in food quality predicting. Further studies on diffusion TTI are needed to develop its application in more field of food and make the diffusion TTI an intelligent mean for food quality monitoring and predicting.