利用拉曼光谱和化学计量学对冷害猕猴桃和完好猕猴桃进行识别和分类

IF 2.4 3区 化学 Q2 SPECTROSCOPY
Garagoda Arachchige P. Samanali, David J. Burritt, Jeremy N. Burdon, Chelsea Kerr, Sara J. Fraser-Miller, Keith C. Gordon
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引用次数: 0

摘要

及早发现果实病害对于保持猕猴桃产品的品质稳定至关重要。冷害是猕猴桃中的一种生理性病害,在病害发展到严重阶段或果实被切开之前很难识别。有鉴于此,我们对拉曼光谱与化学计量学相结合的方法进行了研究,以确定 "Zesy002 "猕猴桃是否受到冷害。我们对 2018 年和 2019 年收获的果实进行了光谱分析。根据苯丙酮和糖类的光谱特征,将受损果实样本和健全果实样本区分开来。此外,我们还利用 2018 年的水果样本集,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、灵敏度和特异性,使用 2019 年水果样本集评估了模型的稳健性。开发的支持向量机模型在区分测试集水果(2018 年季节)方面的准确率为 93%,灵敏度为 85%,特异性为 100%。使用相同数据集建立的主成分分析-线性判别分析模型和偏最小二乘-判别分析模型的测试准确率分别为 83% 和 93%。模型在使用 2019 年的样本时显示出稳健性。这项研究为利用拉曼光谱识别猕猴桃冷害的潜力提供了启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Identification and classification of chill-damaged versus sound kiwifruit using Raman spectroscopy and chemometrics

Identification and classification of chill-damaged versus sound kiwifruit using Raman spectroscopy and chemometrics

Identification and classification of chill-damaged versus sound kiwifruit using Raman spectroscopy and chemometrics

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.

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来源期刊
CiteScore
5.40
自引率
8.00%
发文量
185
审稿时长
3.0 months
期刊介绍: The Journal of Raman Spectroscopy is an international journal dedicated to the publication of original research at the cutting edge of all areas of science and technology related to Raman spectroscopy. The journal seeks to be the central forum for documenting the evolution of the broadly-defined field of Raman spectroscopy that includes an increasing number of rapidly developing techniques and an ever-widening array of interdisciplinary applications. Such topics include time-resolved, coherent and non-linear Raman spectroscopies, nanostructure-based surface-enhanced and tip-enhanced Raman spectroscopies of molecules, resonance Raman to investigate the structure-function relationships and dynamics of biological molecules, linear and nonlinear Raman imaging and microscopy, biomedical applications of Raman, theoretical formalism and advances in quantum computational methodology of all forms of Raman scattering, Raman spectroscopy in archaeology and art, advances in remote Raman sensing and industrial applications, and Raman optical activity of all classes of chiral molecules.
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