{"title":"近红外光谱法检测芒果收获后早期炭疽病的能力","authors":"Pimjai Seehanam, Katthareeya Sonthiya, Phonkrit Maniwara, Parichat Theanjumpol, Onuma Ruangwong, Kazuhiro Nakano, Shintaroh Ohashi, Somsak Kramchote, Patcharaporn Suwor","doi":"10.1007/s13580-023-00590-3","DOIUrl":null,"url":null,"abstract":"<p>Determining anthracnose-infested mango can involve laborious and time-consuming assays, resulting in delayed postharvest management and decreased fruit marketability. Near infrared spectroscopy (NIRS) is proposed to detect the fungus in fully matured ‘Namdokmai Sithong’ mango. Inoculation of <i>Colletotrichum gloeosporioides</i> (1 × 10<sup>6</sup> conidia/mL) was artificially made onto one side of the fruit’s peel at the center of mango fruit while the other side was left intact. Interactance measurements were conducted at both inoculated and intact locations for 104 mango samples every 24 h until anthracnose symptoms visibly appeared. The classification approaches included a partial least squares discriminant analysis (PLS-DA) and a conventional artificial neural network (ANN). Results of our study revealed increased absorbance values corresponding with days after inoculation. Relatively high classification accuracies were obtained from all chemometrics approaches (˃ 89%). In the early hours after inoculation (24 h), the best classification result was obtained from the ANN model (98.1%), confirming that early detection was possible. Applications of PLS-DA and ANN are discussed.</p>","PeriodicalId":13123,"journal":{"name":"Horticulture Environment and Biotechnology","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ability of near infrared spectroscopy to detect anthracnose disease early in mango after harvest\",\"authors\":\"Pimjai Seehanam, Katthareeya Sonthiya, Phonkrit Maniwara, Parichat Theanjumpol, Onuma Ruangwong, Kazuhiro Nakano, Shintaroh Ohashi, Somsak Kramchote, Patcharaporn Suwor\",\"doi\":\"10.1007/s13580-023-00590-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Determining anthracnose-infested mango can involve laborious and time-consuming assays, resulting in delayed postharvest management and decreased fruit marketability. Near infrared spectroscopy (NIRS) is proposed to detect the fungus in fully matured ‘Namdokmai Sithong’ mango. Inoculation of <i>Colletotrichum gloeosporioides</i> (1 × 10<sup>6</sup> conidia/mL) was artificially made onto one side of the fruit’s peel at the center of mango fruit while the other side was left intact. Interactance measurements were conducted at both inoculated and intact locations for 104 mango samples every 24 h until anthracnose symptoms visibly appeared. The classification approaches included a partial least squares discriminant analysis (PLS-DA) and a conventional artificial neural network (ANN). Results of our study revealed increased absorbance values corresponding with days after inoculation. Relatively high classification accuracies were obtained from all chemometrics approaches (˃ 89%). In the early hours after inoculation (24 h), the best classification result was obtained from the ANN model (98.1%), confirming that early detection was possible. Applications of PLS-DA and ANN are discussed.</p>\",\"PeriodicalId\":13123,\"journal\":{\"name\":\"Horticulture Environment and Biotechnology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-01-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Horticulture Environment and Biotechnology\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1007/s13580-023-00590-3\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Agricultural and Biological Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Horticulture Environment and Biotechnology","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s13580-023-00590-3","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
Ability of near infrared spectroscopy to detect anthracnose disease early in mango after harvest
Determining anthracnose-infested mango can involve laborious and time-consuming assays, resulting in delayed postharvest management and decreased fruit marketability. Near infrared spectroscopy (NIRS) is proposed to detect the fungus in fully matured ‘Namdokmai Sithong’ mango. Inoculation of Colletotrichum gloeosporioides (1 × 106 conidia/mL) was artificially made onto one side of the fruit’s peel at the center of mango fruit while the other side was left intact. Interactance measurements were conducted at both inoculated and intact locations for 104 mango samples every 24 h until anthracnose symptoms visibly appeared. The classification approaches included a partial least squares discriminant analysis (PLS-DA) and a conventional artificial neural network (ANN). Results of our study revealed increased absorbance values corresponding with days after inoculation. Relatively high classification accuracies were obtained from all chemometrics approaches (˃ 89%). In the early hours after inoculation (24 h), the best classification result was obtained from the ANN model (98.1%), confirming that early detection was possible. Applications of PLS-DA and ANN are discussed.
期刊介绍:
Horticulture, Environment, and Biotechnology (HEB) is the official journal of the Korean Society for Horticultural Science, was launched in 1965 as the "Journal of Korean Society for Horticultural Science".
HEB is an international journal, published in English, bimonthly on the last day of even number months, and indexed in Biosys Preview, SCIE, and CABI.
The journal is devoted for the publication of original research papers and review articles related to vegetables, fruits, ornamental and herbal plants, and covers all aspects of physiology, molecular biology, biotechnology, protected cultivation, postharvest technology, and research in plants related to environment.