{"title":"用于辣椒辣椒素含量估算的近红外光谱预测模型:用于特异性种质筛选的快速挖掘工具","authors":"Deepika D.D. , Vedprakash Sharma , Manisha Mangal , Arpita Srivastava , Chithra Pandey , Himani Mehta , G.J. Abhishek , Racheal John , Hemlata Bharti , Rakesh Bharadwaj , R.K. Gautam , J.C. Rana , Gyanendra Pratap Singh , Vinod K. Sharma","doi":"10.1016/j.jfca.2024.106915","DOIUrl":null,"url":null,"abstract":"<div><div>Chilli is a widely produced crop, highly valued for its capsaicin content, a key economic trait. Traditional wet chemistry methods for estimating capsaicin are time-taking and laborious, while non-destructive methods like NIRS coupled with chemometrics, offer efficient alternatives, simplifying and accelerating biochemical assessments. This study is the first to develop and validate and tested for applicability of NIRS-based prediction model for capsaicin content in Indian chilli germplasm using MPLS regression. Various mathematical treatments were performed, and the most suited model was selected based on high RSQ<sub>external</sub>, RPD and lower SEP values in the external validation set, indicating strong prediction accuracy and minimal error. The model achieved high RSQ<sub>external</sub> value of 0.808, RPD value of 2.088 and low SEP value of 3.415 for capsaicin content, demonstrating excellent prediction performance. A paired sample <em>t</em>-test <em>p</em>-value of 0.757 (<em>p</em> > 0.05) showed non-significant difference between wet lab and predicted values, confirming the model’s accuracy. The applicability of the model was validated on fresh harvest germplasm the following year, showing a higher reliability score of 0.949, further confirming model’s reliability. This model would aid in high-throughput, accurate screening of chilli germplasm for capsaicin, accelerating chilli crop improvement programs and the development of new high-capsaicin varieties.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"137 ","pages":"Article 106915"},"PeriodicalIF":4.0000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"NIR spectroscopy prediction model for capsaicin content estimation in chilli: A rapid mining tool for trait-specific germplasm screening\",\"authors\":\"Deepika D.D. , Vedprakash Sharma , Manisha Mangal , Arpita Srivastava , Chithra Pandey , Himani Mehta , G.J. Abhishek , Racheal John , Hemlata Bharti , Rakesh Bharadwaj , R.K. Gautam , J.C. Rana , Gyanendra Pratap Singh , Vinod K. Sharma\",\"doi\":\"10.1016/j.jfca.2024.106915\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Chilli is a widely produced crop, highly valued for its capsaicin content, a key economic trait. Traditional wet chemistry methods for estimating capsaicin are time-taking and laborious, while non-destructive methods like NIRS coupled with chemometrics, offer efficient alternatives, simplifying and accelerating biochemical assessments. This study is the first to develop and validate and tested for applicability of NIRS-based prediction model for capsaicin content in Indian chilli germplasm using MPLS regression. Various mathematical treatments were performed, and the most suited model was selected based on high RSQ<sub>external</sub>, RPD and lower SEP values in the external validation set, indicating strong prediction accuracy and minimal error. The model achieved high RSQ<sub>external</sub> value of 0.808, RPD value of 2.088 and low SEP value of 3.415 for capsaicin content, demonstrating excellent prediction performance. A paired sample <em>t</em>-test <em>p</em>-value of 0.757 (<em>p</em> > 0.05) showed non-significant difference between wet lab and predicted values, confirming the model’s accuracy. The applicability of the model was validated on fresh harvest germplasm the following year, showing a higher reliability score of 0.949, further confirming model’s reliability. This model would aid in high-throughput, accurate screening of chilli germplasm for capsaicin, accelerating chilli crop improvement programs and the development of new high-capsaicin varieties.</div></div>\",\"PeriodicalId\":15867,\"journal\":{\"name\":\"Journal of Food Composition and Analysis\",\"volume\":\"137 \",\"pages\":\"Article 106915\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Food Composition and Analysis\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0889157524009499\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Composition and Analysis","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0889157524009499","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
NIR spectroscopy prediction model for capsaicin content estimation in chilli: A rapid mining tool for trait-specific germplasm screening
Chilli is a widely produced crop, highly valued for its capsaicin content, a key economic trait. Traditional wet chemistry methods for estimating capsaicin are time-taking and laborious, while non-destructive methods like NIRS coupled with chemometrics, offer efficient alternatives, simplifying and accelerating biochemical assessments. This study is the first to develop and validate and tested for applicability of NIRS-based prediction model for capsaicin content in Indian chilli germplasm using MPLS regression. Various mathematical treatments were performed, and the most suited model was selected based on high RSQexternal, RPD and lower SEP values in the external validation set, indicating strong prediction accuracy and minimal error. The model achieved high RSQexternal value of 0.808, RPD value of 2.088 and low SEP value of 3.415 for capsaicin content, demonstrating excellent prediction performance. A paired sample t-test p-value of 0.757 (p > 0.05) showed non-significant difference between wet lab and predicted values, confirming the model’s accuracy. The applicability of the model was validated on fresh harvest germplasm the following year, showing a higher reliability score of 0.949, further confirming model’s reliability. This model would aid in high-throughput, accurate screening of chilli germplasm for capsaicin, accelerating chilli crop improvement programs and the development of new high-capsaicin varieties.
期刊介绍:
The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects.
The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.