Study on the quantitative measurement of firmness distribution maps at the pixel level inside peach pulp

Abstract

Firmness is a major quality index for peaches and many other fruits. Because firmness varies among different parts of the pulp, maps of the firmness distribution inside peach pulp can be used as an important indicator for deeply understanding and evaluating the ripening process of fruit and for further optimizing planting patterns and postharvest storage strategies. However, the commonly used Magness-Taylor firmness tester cannot provide the firmness distribution at the pixel level, because it measures only one or a few sampling points where the probe is located. In this study, a laboratory-based hyperspectral imaging system in the wavelength range of 380–1030nm was developed to detect and visualize the firmness distributions for different cross sections of peach pulp. Two calibration methods based on partial least squares regression (PLSR) and least squares support vector machines were applied to establish firmness calibration models. In addition, the successive projections algorithm, uninformative variable elimination, and competitive adaptive reweighted sampling (CARS) were applied separately to select the optimal wavelengths to improve the models’ accuracy and robustness. CARS-PLSR without preprocessing was determined to be the best model for determining the firmness of peach pulp, yielding prediction results with a correlation coefficient of 0.852 and a residual predictive deviation of 1.739. Using this model, the firmness distributions for different cross sections of peach pulp were quantitatively visualized at the pixel level. The firmness histogram revealed the existence of a wide range of firmnesses inside peach pulp. The overall results demonstrated that hyperspectral imaging shows great potential for visualizing the spatial variations in the firmness distribution inside peach pulp at the pixel level, which can provide a more detailed understanding of the ripening process of peach fruits during the preharvest and postharvest periods.