Flexible Vis/NIR sensing system for banana chilling injury

Abstract

The transportation stage of the banana production chain has influence on banana quality and other lifecycle parameters. Certain factors, such as chilling injury (CI), have detrimental effects on bananas, leading to increased losses. The commonly used monitoring and evaluation methods, such as machine vision, face challenges in achieving real-time detection and are susceptible to environmental interference, leading to deviations in the results. Therefore, a flexible visible (Vis)/near-infrared (NIR) real-time sensing system (FVN) was developed for real-time monitoring of the CI status of bananas. The color space of bananas was analyzed and predicted based on the multiple linear regression (MLR) model, and the results showed that the coefficient of determination (R2p) of a* of 0.97, and the residual prediction deviation (RPD) of 4.95, which indicated that the prediction of a* reached a high accuracy. The RPD values for the predictions of L* and b* exceeding 2.5 indicate that the FVN based on the MLR model could be applicable to the majority of market demands. The self-developed classification prediction model (SCP) exhibits evident advantages in predicting the occurrence and elapsed duration of CI, with prediction accuracies of 98.3 % and 95.5 %. In addition, a comprehensive comparative analysis of the FVN is carried out in terms of power consumption and cost, highlighting its great advantages. The application of FVN can effectively reduce the waste of bananas in the market supply chain, greatly alleviate the problem of unpredictable fruit chilling damage, and thus promote more sustainable and cleaner production in the banana industry.