Building kinetic models to determine moisture content in apples and predicting shelf life based on spectroscopy

Abstract

AbstractMoisture content (MC) directly affects the storage of apples. This study explored the prediction of MC and the shelf life of apples stored at room temperature. Using wavelengths selected by the competitive adaptive reweighted sampling algorithm and chemical kinetic reactions, a quantitative prediction model based on optical fiber spectroscopy and a kinetic model were established to determine the MC of apples stored at room temperature. The shelf life of apples was predicted based on the developed quantitative prediction and kinetic models. The determination coefficients of the calibration and prediction of the quantitative prediction model were determined as .9810 and .9686, with root mean square errors of .0020 and .0022, respectively, and a residual predictive deviation of 6.55. In addition, the correlation coefficient of the kinetic model was derived as .9957, with a root mean square error of .0011. Results demonstrate that when the MC of the fresh apples exceeds 85% and the initial MC of apples stored at room temperature equals 87%, the freshness preservation time of the apples should not exceed 28 days. The proposed quantitative and kinetic models are able to rapidly and accurately monitor the MC of apples, and the real‐time prediction of apple shelf life can be realized according to the difference in the spectral information pre‐ and post‐storage at room temperature.Practical ApplicationThis study provides a method for the determination of moisture content (MC) in apples and the prediction of shelf life based on kinetic models. According to the proposed method, enterprises can not only quickly and accurately detect the MC of apples to determine whether they are fresh, but can also easily predict the shelf life of apples to determine whether they are safe.