Garagoda Arachchige P. Samanali, David J. Burritt, Jeremy N. Burdon, Chelsea Kerr, Sara J. Fraser-Miller, Keith C. Gordon
{"title":"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","authors":"Garagoda Arachchige P. Samanali, David J. Burritt, Jeremy N. Burdon, Chelsea Kerr, Sara J. Fraser-Miller, Keith C. Gordon","doi":"10.1002/jrs.6623","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":16926,"journal":{"name":"Journal of Raman Spectroscopy","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jrs.6623","citationCount":"0","resultStr":"{\"title\":\"Identification and classification of chill-damaged versus sound kiwifruit using Raman spectroscopy and chemometrics\",\"authors\":\"Garagoda Arachchige P. Samanali, David J. Burritt, Jeremy N. Burdon, Chelsea Kerr, Sara J. Fraser-Miller, Keith C. Gordon\",\"doi\":\"10.1002/jrs.6623\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":16926,\"journal\":{\"name\":\"Journal of Raman Spectroscopy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jrs.6623\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Raman Spectroscopy\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jrs.6623\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"SPECTROSCOPY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Raman Spectroscopy","FirstCategoryId":"92","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jrs.6623","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SPECTROSCOPY","Score":null,"Total":0}
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.
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.