{"title":"利用电子鼻和SPAD法测定番石榴叶片黄酮和氮含量","authors":"Bambang Marhaenanto, Putri Wahyulian Aningtyas, Bayu Taruna Widjaja Putraa, Dedy Wirawan Soedibyo, Wahyu Nurkholis Hadi Syahputra","doi":"10.1007/s40003-025-00849-4","DOIUrl":null,"url":null,"abstract":"<div><p>Flavonoids are a type of antioxidants widely used to treat various human diseases. Apart from guava fruits, flavonoids are also found in the leaves. Determining the leaf flavonoid content generally involves chemical analysis, which is time-consuming and costly. This study aimed to estimate quickly the flavonoid and nitrogen contents in fresh and extracted leaves using two different phenomena: (gas and vision) captured by an e-nose (equipped with nine different MQ sensors) and a SPAD-502 chlorophyll meter. This study also determined the relationship between flavonoids and nitrogen in guava plants and the selection of leaf variations, which contained the maximum levels of these compounds. A total of nine machine learning algorithms were applied to evaluate the data obtained from both the e-nose and SPAD-502 m as ten input features. The results showed that: (1) using ten input features obtained from fresh leaf samples provided better accuracy in classifying and estimating both flavonoid and nitrogen contents rather than using feature important ranking and extracted guava leaves. An artificial neural network—multilayer perceptron (ANN MLP) is a machine learning algorithm that provided maximum accuracy in classifying and estimating flavonoid and nitrogen contents with coefficient determination (<i>R</i><sup>2</sup>) ranging between 0.76 and 0.96; (2) laboratory analysis did not indicate a positive relationship between flavonoid and nitrogen contents in guava leaves; and (3) selection of leaf numbers 1–5 was appropriate to ascertain flavonoid content in the optimum–high range, while the leaf number 3 from shoots that have opened completely to estimate the N status can be selected.</p></div>","PeriodicalId":7553,"journal":{"name":"Agricultural Research","volume":"14 3","pages":"414 - 427"},"PeriodicalIF":1.1000,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating Flavonoid and Nitrogen Status of Guava Leaves Using E-Nose and SPAD Meter\",\"authors\":\"Bambang Marhaenanto, Putri Wahyulian Aningtyas, Bayu Taruna Widjaja Putraa, Dedy Wirawan Soedibyo, Wahyu Nurkholis Hadi Syahputra\",\"doi\":\"10.1007/s40003-025-00849-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Flavonoids are a type of antioxidants widely used to treat various human diseases. Apart from guava fruits, flavonoids are also found in the leaves. Determining the leaf flavonoid content generally involves chemical analysis, which is time-consuming and costly. This study aimed to estimate quickly the flavonoid and nitrogen contents in fresh and extracted leaves using two different phenomena: (gas and vision) captured by an e-nose (equipped with nine different MQ sensors) and a SPAD-502 chlorophyll meter. This study also determined the relationship between flavonoids and nitrogen in guava plants and the selection of leaf variations, which contained the maximum levels of these compounds. A total of nine machine learning algorithms were applied to evaluate the data obtained from both the e-nose and SPAD-502 m as ten input features. The results showed that: (1) using ten input features obtained from fresh leaf samples provided better accuracy in classifying and estimating both flavonoid and nitrogen contents rather than using feature important ranking and extracted guava leaves. An artificial neural network—multilayer perceptron (ANN MLP) is a machine learning algorithm that provided maximum accuracy in classifying and estimating flavonoid and nitrogen contents with coefficient determination (<i>R</i><sup>2</sup>) ranging between 0.76 and 0.96; (2) laboratory analysis did not indicate a positive relationship between flavonoid and nitrogen contents in guava leaves; and (3) selection of leaf numbers 1–5 was appropriate to ascertain flavonoid content in the optimum–high range, while the leaf number 3 from shoots that have opened completely to estimate the N status can be selected.</p></div>\",\"PeriodicalId\":7553,\"journal\":{\"name\":\"Agricultural Research\",\"volume\":\"14 3\",\"pages\":\"414 - 427\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2025-02-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Agricultural Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s40003-025-00849-4\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agricultural Research","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s40003-025-00849-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AGRONOMY","Score":null,"Total":0}
Estimating Flavonoid and Nitrogen Status of Guava Leaves Using E-Nose and SPAD Meter
Flavonoids are a type of antioxidants widely used to treat various human diseases. Apart from guava fruits, flavonoids are also found in the leaves. Determining the leaf flavonoid content generally involves chemical analysis, which is time-consuming and costly. This study aimed to estimate quickly the flavonoid and nitrogen contents in fresh and extracted leaves using two different phenomena: (gas and vision) captured by an e-nose (equipped with nine different MQ sensors) and a SPAD-502 chlorophyll meter. This study also determined the relationship between flavonoids and nitrogen in guava plants and the selection of leaf variations, which contained the maximum levels of these compounds. A total of nine machine learning algorithms were applied to evaluate the data obtained from both the e-nose and SPAD-502 m as ten input features. The results showed that: (1) using ten input features obtained from fresh leaf samples provided better accuracy in classifying and estimating both flavonoid and nitrogen contents rather than using feature important ranking and extracted guava leaves. An artificial neural network—multilayer perceptron (ANN MLP) is a machine learning algorithm that provided maximum accuracy in classifying and estimating flavonoid and nitrogen contents with coefficient determination (R2) ranging between 0.76 and 0.96; (2) laboratory analysis did not indicate a positive relationship between flavonoid and nitrogen contents in guava leaves; and (3) selection of leaf numbers 1–5 was appropriate to ascertain flavonoid content in the optimum–high range, while the leaf number 3 from shoots that have opened completely to estimate the N status can be selected.
期刊介绍:
The main objective of this initiative is to promote agricultural research and development. The journal will publish high quality original research papers and critical reviews on emerging fields and concepts for providing future directions. The publications will include both applied and basic research covering the following disciplines of agricultural sciences: Genetic resources, genetics and breeding, biotechnology, physiology, biochemistry, management of biotic and abiotic stresses, and nutrition of field crops, horticultural crops, livestock and fishes; agricultural meteorology, environmental sciences, forestry and agro forestry, agronomy, soils and soil management, microbiology, water management, agricultural engineering and technology, agricultural policy, agricultural economics, food nutrition, agricultural statistics, and extension research; impact of climate change and the emerging technologies on agriculture, and the role of agricultural research and innovation for development.