{"title":"Dynamic changes and spectrometric quantitative analysis of antioxidant enzyme activity of TYLCV-infected tomato plants","authors":"Jiheng Ni, Yawen Xue, Jialin Liao","doi":"10.1016/j.compag.2025.110109","DOIUrl":null,"url":null,"abstract":"<div><div>To investigate the dynamic changes in superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities in <em>Tomato yellow leaf</em> curl <em>virus</em> (TYLCV)-infected tomato leaves is essential for monitoring tomato growth, selecting disease-resistant varieties and disease control. Utilizing hyperspectral technique offers a rapid and non-destructive method to estimate antioxidant enzyme activity in tomato leaves. This study focuses on one a TYLCV-susceptible tomato variety (Hezuo 908) and two TYLCV-resistant varieties (Dingyanfen No. 3 and No. 5) to analyze the changes in photosynthetic characteristics and antioxidant enzyme activity following TYLCV infection. Hyperspectral data were used to quantify the correlation between antioxidant enzyme activity and spectral features under different pretreatments, as well as the relationship between enzyme activity and classical spectral indices. Several optimized indices were developed by iterating on the condition of R, forming a combined index. Considering efficiency and complexity, the support vector machine regression algorithm was employed to evaluate the predictive performance of the models. The results showed a decline in photosynthetic rate and relative chlorophyll content, while stomatal conductance initially decreased and then increased. The activities of the three antioxidant enzymes increased. activities of the three antioxidant enzymes post-TYLCV infection, with POD activity correlating with tomato variety resistance—a potential auxiliary index for antiviral variety identification. Among the models, the CAT prediction model performed the best, with a test set determination coefficient (R<sup>2</sup>) of 0.82, followed by POD with an R<sup>2</sup> of 0.67, and SOD R<sup>2</sup> of 0.43. These findings demonstrate that spectra enable rapid and non-destructive estimation of antioxidant enzyme activity in tomatoes under viral stress. This study provides valuable insights for antiviral variety breeding and the early warning of viral diseases.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 110109"},"PeriodicalIF":7.7000,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169925002157","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
To investigate the dynamic changes in superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities in Tomato yellow leaf curl virus (TYLCV)-infected tomato leaves is essential for monitoring tomato growth, selecting disease-resistant varieties and disease control. Utilizing hyperspectral technique offers a rapid and non-destructive method to estimate antioxidant enzyme activity in tomato leaves. This study focuses on one a TYLCV-susceptible tomato variety (Hezuo 908) and two TYLCV-resistant varieties (Dingyanfen No. 3 and No. 5) to analyze the changes in photosynthetic characteristics and antioxidant enzyme activity following TYLCV infection. Hyperspectral data were used to quantify the correlation between antioxidant enzyme activity and spectral features under different pretreatments, as well as the relationship between enzyme activity and classical spectral indices. Several optimized indices were developed by iterating on the condition of R, forming a combined index. Considering efficiency and complexity, the support vector machine regression algorithm was employed to evaluate the predictive performance of the models. The results showed a decline in photosynthetic rate and relative chlorophyll content, while stomatal conductance initially decreased and then increased. The activities of the three antioxidant enzymes increased. activities of the three antioxidant enzymes post-TYLCV infection, with POD activity correlating with tomato variety resistance—a potential auxiliary index for antiviral variety identification. Among the models, the CAT prediction model performed the best, with a test set determination coefficient (R2) of 0.82, followed by POD with an R2 of 0.67, and SOD R2 of 0.43. These findings demonstrate that spectra enable rapid and non-destructive estimation of antioxidant enzyme activity in tomatoes under viral stress. This study provides valuable insights for antiviral variety breeding and the early warning of viral diseases.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.