The early non-destructive detection of chilling injury (CI) in aubergine fruit was investigated using spectroscopy. CI is a physiological disorder that occurs when the fruit is subjected to temperatures lower than 12 °C. Reference measurements of CI were acquired by visual appearance analysis, measuring electrolyte leakage (EL), mass loss and firmness evaluations which demonstrated that even before three days of storage at 2 °C, the CI process was initiated. An ANOVA-simultaneous component analysis (ASCA) was used to investigate the effect of temperature and storage time on the Fourier transform – near infra-red (FT-NIR) spectral fingerprints. The ASCA model demonstrated that temperature, duration of storage, and their interaction had a significant effect on the spectra. In addition, it was possible to highlight the main variations in the experimental results with reference to the effects of the main factors, and with respect to storage time, to discover any major monotonic trends with time. Partial least squares-discriminant analysis (PLS-DA) was used as a supervised classification method to discriminate between fruit based on chilling and safe temperatures. In this case, only significant spectral wavebands which were significantly influenced by the effect of temperature based on ASCA were utilised. PLS-DA prediction accuracy was 87.4 ± 2.7% as estimated by a repeated double-cross-validation procedure (50 runs) and the significance of the observed discrimination was verified by means of permutation tests. The outcomes of this study indicate a promising potential for near infra-red spectroscopy (NIRS) to provide non-invasive, rapid and reliable detection of CI in aubergine fruit.

中文翻译：

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使用化学计量学表征和解开冷藏时间和温度对茄子果实冷害引起的近红外光谱变化

使用光谱学研究了茄子果实冷害 (CI) 的早期无损检测。CI 是一种生理紊乱，当水果受到低于 12 °C 的温度时会发生。CI 的参考测量值是通过视觉外观分析、测量电解质泄漏 (EL)、质量损失和坚固性评估获得的，这表明即使在 2°C 下储存三天之前，CI 过程就开始了。方差分析同时分量分析 (ASCA) 用于研究温度和存储时间对傅立叶变换 - 近红外 (FT-NIR) 光谱指纹的影响。ASCA 模型表明温度、储存时间及其相互作用对光谱有显着影响。此外，可以参考主要因素的影响和存储时间来突出实验结果中的主要变化，以发现随时间变化的任何主要单调趋势。偏最小二乘判别分析 (PLS-DA) 被用作一种监督分类方法，以根据冷藏温度和安全温度来区分水果。在这种情况下，仅使用了受基于 ASCA 的温度影响显着影响的重要光谱波段。PLS-DA 预测准确度为 87.4 ± 2.7%，通过重复的双重交叉验证程序（50 次运行）估计，并且通过置换测试验证了观察到的区分的显着性。这项研究的结果表明近红外光谱 (NIRS) 具有提供非侵入性、