Early detection of internal bruise is one of the major challenges in postharvest quality sorting processes in Lingwu long jujube. In this study, the visible/near infrared (VIS/NIR) hyperspectral imaging system (400–1000 nm) was used to rapidly detect the intact and damaged jujube at five time points after mechanical damage (2 h, 4 h, 8 h, 12 h and 24 h). The region of interest of samples was selected by ENVI software, and the average spectrum was calculated for modelling. Different preprocessing methods were used to transform and enhance the spectral signal. Partial least squares-discriminant analysis (PLS-DA) classification models of the original and preprocessed spectra were established. The de-trending-PLS-DA model had the best effect, the accuracy of the calibration set and prediction set were 85.56% and 92.22%, respectively. The spectra were pre-processed by the de-trending algorithm, and the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), cluster analysis (CA), two-dimensional correlation spectra (2D-COS), UVE-SPA, CARS-SPA, iVISSA-SPA, CA-SPA and 2D-COS-SPA method were used to select characteristic variables. The PLS-DA model based on feature variables was established. The de-trending-CARS-PLS-DA model with 63 variables was found to be the optimal model. In the de-trending-CARS-PLS-DA model, the accuracy of calibration set and prediction set were 86.67% and 91.11%, respectively. It was found that the model accurately detected bruising in jujube at 8 h after bruising, and the accuracy of calibration set and prediction set were 100%. The results showed that VIS/NIR hyperspectral imaging technology combined with PLS-DA could discriminate different stages of bruising in Lingwu long jujube. This study may help develop an online detection system of bruising in Lingwu long jujube.

早期发现内部瘀伤是灵武长枣采后质量分选过程的主要挑战之一。在这项研究中，使用可见/近红外（VIS / NIR）高光谱成像系统（400-1000 nm）在机械损坏（2 h，4 h，8 h， 12小时和24小时）。通过ENVI软件选择样品的感兴趣区域，并计算平均光谱以进行建模。使用了不同的预处理方法来变换和增强频谱信号。建立了原始光谱和预处理光谱的偏最小二乘判别分析（PLS-DA）分类模型。去趋势PLS-DA模型效果最好，校正集和预测集的准确度分别为85.56％和92.22％。光谱通过去趋势算法进行预处理，并进行了连续投影算法（SPA），无信息变量消除（UVE），竞争性自适应加权采样（CARS），区间变量迭代空间收缩法（iVISSA），聚类分析（ CA），二维相关光谱（2D-COS），UVE-SPA，CARS-SPA，iVISSA-SPA，CA-SPA和2D-COS-SPA方法用于选择特征变量。建立了基于特征变量的PLS-DA模型。发现具有63个变量的去趋势CARS-PLS-DA模型是最佳模型。在去趋势CARS-PLS-DA模型中，校准集和预测集的准确性分别为86.67％和91.11％。发现该模型在青紫后8 h准确地检测到枣青紫，校正集和预测集的准确性为100％。结果表明，VIS / NIR高光谱成像技术与PLS-DA结合可以判别灵武长枣的瘀伤不同阶段。该研究可能有助于开发灵武长枣瘀伤在线检测系统。