Using chemometrics to characterise and unravel the near infra-red spectral changes induced in aubergine fruit by chilling injury as influenced by storage time and temperature
Using chemometrics to characterise and unravel the near infra-red spectral changes induced in aubergine fruit by chilling injury as influenced by storage time and temperature
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DOI：
10.1016/j.biosystemseng.2020.08.008
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发表时间：
2020-10-01
影响因子：
5.1
通讯作者：
Colelli, Giancarlo
中科院分区：
文献类型：
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作者：
Babellahi, Farahmand;Amodio, Maria L.;Colelli, Giancarlo
利用光谱技术对茄子果实冷害的早期无损检测进行了研究。CI是一种生理障碍，当水果受到低于12摄氏度的温度时发生。通过视觉外观分析、测量电解质渗漏（EL）、质量损失和硬度评估获得CI的参考测量值，其证明甚至在2 ° C下储存三天之前，CI过程就开始了。采用方差分析-同时组分分析（ASCA）方法研究了温度和储存时间对近红外光谱指纹图谱的影响。ASCA模型表明，温度，储存时间，以及它们的相互作用对光谱有显着的影响。此外，可以突出实验结果中的主要变化，参考主要因素的影响，并相对于储存时间，以发现随时间的任何主要单调趋势。偏最小二乘判别分析（PLSDA）被用来作为监督分类方法来区分水果的基础上冷却和安全温度。在这种情况下，仅利用基于ASCA的受温度影响显著的显著光谱波段。PLS-DA预测准确度为87.4 +/- 2.7%，通过重复的双交叉验证程序（50次运行）估计，观察到的歧视的显着性通过排列测试进行验证。这项研究的结果表明，近红外光谱（NIRS）提供了一个很有前途的潜力，非侵入性，快速和可靠的检测茄子水果中的CI。(c)2020由Elsevier Ltd代表IAgrE发布。
The early non-destructive detection of chilling injury (CI) in aubergine fruit was investi-gated using spectroscopy. CI is a physiological disorder that occurs when the fruit is subjected to temperatures lower than 12 degrees 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 degrees 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 (PLSDA) 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. (c) 2020 Published by Elsevier Ltd on behalf of IAgrE.