As a physiological disorder, chilling injury in kiwifruit may develop when the fruit are stored for long periods at a low storage temperature of 0–1°C. Presence of the disorder, inconsistent with marketing requirements for high-quality fruit, may lead to substantial financial and reputational losses. Thus, early detection or removal of chill-damaged fruit is desirable. This study demonstrates a novel dual-laser scanning system which has potential to be developed into a fast online system for the detection of chilling injury in Actinidia chinensis var. chinensis ‘Zesy002’ kiwifruit. The system consists of two laser modules at 730 and 880 nm wavelengths, a scanning mechanism and two detectors at partial (90°) and full (180°) light transmission. A sample of 231 kiwifruit was used to prove the concept, including 80 sound and 151 chill-damaged fruit of three different severity categories (slight, moderate and severe). A principal component analysis – back propagation neural network was used to classify fruit with 5-fold cross-validation. A comparison was made with standard visible-near infrared (Vis-NIR) interactance spectroscopy used to classify the same fruit using the same modelling algorithm. The dual-laser scanning system showed a slightly higher binary classification accuracy than the Vis-NIR spectroscopy, with an average accuracy of 95% for distinguishing sound and chill-damaged fruit. The classification error rate was 0% for severe damaged fruit. These experimental results demonstrate the potential of this dual-laser scanning system for the detection of chill-damaged fruit. The setup using only two wavelengths, its unique scanning operation and flexible system layout make it practical and attractive for future development for application on high-speed fruit graders.

作为一种生理疾病，猕猴桃在 0-1°C 的低温贮藏条件下长期贮藏可能会出现冷害。与优质水果的营销要求不一致的无序状态可能会导致重大的财务和声誉损失。因此，需要早期检测或去除受冷害的水果。本研究展示了一种新的双激光扫描系统，该系统有可能发展成为用于检测猕猴桃冷害的快速在线系统。中华'Zesy002' 奇异果。该系统由两个波长为 730 和 880 nm 的激光模块、一个扫描机构和两个部分 (90°) 和全 (180°) 透光的检测器组成。使用 231 个猕猴桃样本来证明这一概念，其中包括 80 个健全的水果和 151 个受冻害的三种不同严重程度（轻度、中度和重度）的水果。主成分分析——反向传播神经网络用于通过 5 折交叉验证对水果进行分类。与用于使用相同建模算法对相同水果进行分类的标准可见-近红外 (Vis-NIR) 相互作用光谱进行了比较。双激光扫描系统显示出比可见近红外光谱略高的二元分类精度，区分声音和冷害水果的平均准确率为 95%。严重受损果实的分类错误率为0%。这些实验结果证明了这种双激光扫描系统在检测冷害水果方面的潜力。该设置仅使用两个波长，其独特的扫描操作和灵活的系统布局使其实用且对未来在高速水果分级机上的应用具有吸引力。