The early detection of fruit disorders is crucial to maintaining a consistent, high-quality kiwifruit product. Chilling injury is a physiological disorder found in kiwifruit that can be challenging to identify until it reaches a severe stage or the fruit is cut and opened. Considering this, Raman spectroscopy combined with chemometrics was investigated for sound and chill-damaged ‘Zesy002’ kiwifruit. We carried out spectral analysis on fruit harvested in 2018 and 2019. Damaged and sound fruit samples were separated based on spectral signatures from phenyl propanoids and sugars. Furthermore, the 2018 fruit sample set was used to construct, validate, and test models using support vector machine, principal component analysis–linear discriminant analysis, and partial least squares–discriminant analysis. Additionally, the robustness of the model was assessed using the 2019 fruit sample set considering test set accuracy, sensitivity, and specificity. Support vector machine models were developed and resulted in a 93% accuracy, 85% sensitivity, and 100% specificity to differentiate the test set fruit (2018 season). Principal component analysis–linear discriminant analysis models and partial least squares–discriminant analysis model built with the same data set gave >83% and 93% test accuracy, respectively. Models showed robustness with samples from the 2019 season. This study provides insights into the potential of using Raman spectroscopy for identifying chilling injury in kiwifruit.

中文翻译：

---

使用拉曼光谱和化学计量学对冷害猕猴桃与健康猕猴桃进行鉴定和分类

早期发现水果病害对于保持猕猴桃产品始终如一的高品质至关重要。冷害是猕猴桃中发现的一种生理疾病，在其达到严重阶段或果实被切开之前很难识别。考虑到这一点，拉曼光谱与化学计量学相结合，对完好和冷害的“Zesy002”猕猴桃进行了研究。我们对 2018 年和 2019 年收获的水果进行了光谱分析。根据苯丙素和糖的光谱特征分离受损和完好的水果样品。此外，还使用 ​​2018 年水果样本集使用支持向量机、主成分分析-线性判别分析和偏最小二乘-判别分析来构建、验证和测试模型。此外，考虑到测试集的准确性、敏感性和特异性，使用 2019 年水果样本集评估了模型的稳健性。开发了支持向量机模型，区分测试集水果（2018 年季节）的准确度为 93%，灵敏度为 85%，特异性为 100%。使用相同数据集构建的主成分分析-线性判别分析模型和偏最小二乘-判别分析模型分别给出了>83%和93%的测试精度。模型对 2019 年季节的样本表现出稳健性。这项研究为利用拉曼光谱识别猕猴桃冷害的潜力提供了见解。