Predicting bruise susceptibility in apples using Vis/SWNIR technique combined with ensemble learning

作者：Jian Yao, Guan Jiyu, Qibing Zhu · 发表于：International journal of agricultural and biological engineering · 年份：2017 · DOI：10.25165/j.ijabe.20171005.2888 · 被引用次数：10 · 研究领域：Postharvest Quality and Shelf Life Management、Spectroscopy and Chemometric Analyses、Plant Surface Properties and Treatments

Bruise susceptibility in fruits is an important indicator in evaluating risk factors for bruising caused by external factors. Prediction of the bruising susceptibility of fruit can provide useful information for proper postharvest handling and storage operations. In this study, visible and shortwave near-infrared (Vis/SWNIR) technique was used to develop nondestructive method for predicting the bruise susceptibility of apples. Vis/SWNIR spectra covering 400-1100 nm were collected for 300 ‘Golden Delicious’ apples over a time period of three weeks after harvest. A pendulum-like device was used to simulate impact bruise at three impact energy levels of 1.11 J, 0.66 J and 0.33 J. Bruise volumes were estimated from the digital images of the bruised apples by using the bruise thickness model. Three prediction models, i.e. partial least squares model (PLS), partial least squares model combined with successful projection algorithm (SPA-PLS), and selective ensemble learning based on feature selection (SELFS), for bruise susceptibility were developed for each impact energy level as well as for the pooled data. Compared with PLS and SPA-PLS model, SELFS gave the better prediction results for bruise susceptibility, with the correlation coefficient of Rp=0.800-0.886 for the prediction set, the root-mean-square error of 38.7- 62.1 mm3/J for the prediction set (RMSEP), and the residual predictive deviation (RPD) of 1.78-2.14 for three impact energy level. For three impact energy levels, th...