Large-scale data-driven uniformity analysis and sensory prediction of commercial banana ripening process

Abstract

Artificial intelligence (AI) and machine learning (ML) have found prominent yet mostly academic applications in the food supply chain specifically to preserve and optimize the quality of fresh produce and achieve uniformity across the various stages of the cold chain. Nevertheless, the practical use of AI/ML for predictive analytics within real and large-scale commercial food processes, such as banana ripening, is sparse. This study proposes a novel data-driven approach tested and validated on two new large-scale datasets to automate and optimize the banana ripening process in refrigerated marine containers by successfully employing ML in uniformity analysis of the peel color and the pulp temperature of bananas based on atmospheric conditions. The results demonstrate high correlations between the gas concentrations and the uniformity of the process, suggesting that the uniformity of the peel color and the pulp temperature of fruit can be achieved by controlling the concentrations of the CO₂ and O₂ gas levels. Furthermore, this study, for the first time, achieves accurate algorithmic predictions of oxygen levels from other atmospheric variables to provide an alternative approach for continuous, improved and more cost-effective monitoring of the atmospheric conditions during ripening. A wide-range of predictive models are tested and validated where the Long Short Term Memory regression provides the lowest root-mean-square-errors (0.033 and 0.202) with robust R-squared values (0.999 and 0.959) for two datasets.