Non‐destructive characterization of pearl millet [Pennisetum glaucum (L.) R. Br.] composition using single‐kernel NIR spectroscopy

IF 2 3区 农林科学 Q2 AGRONOMY Crop Science Pub Date : 2024-10-09 DOI:10.1002/csc2.21375
Princess Tiffany D. Mendoza, Paul R. Armstrong, Kaliramesh Siliveru, Manoj Kumar Pulivarthi, Ajay Prasanth Ramalingam, P. V. Vara Prasad, Ramasamy Perumal
{"title":"Non‐destructive characterization of pearl millet [Pennisetum glaucum (L.) R. Br.] composition using single‐kernel NIR spectroscopy","authors":"Princess Tiffany D. Mendoza, Paul R. Armstrong, Kaliramesh Siliveru, Manoj Kumar Pulivarthi, Ajay Prasanth Ramalingam, P. V. Vara Prasad, Ramasamy Perumal","doi":"10.1002/csc2.21375","DOIUrl":null,"url":null,"abstract":"As a gluten‐free cereal with high nutritional properties, pearl millet [<jats:italic>Pennisetum glaucum</jats:italic> (L.) R. Br.] has been increasingly regarded as an alternative dryland resilient food crop with enriched grain nutritional value. This paper explores the potential of single‐kernel near‐infrared (SKNIR) spectroscopy combined with multivariate analysis for rapid and non‐destructive evaluation of protein, moisture, fat, fiber, and ash contents of pearl millet grains. Samples harvested from two consecutive years (2021 and 2022) were evaluated under dryland and irrigated conditions in Kansas State University, Agricultural Research Center, Hays (ARCH), KS and were analyzed using SKNIR and conventional laboratory methods. Model calibrations were developed using partial least squares regression. Results showed satisfactory performance of models with standard errors cross‐validation of 1.04%, 0.17%, 0.39%, 0.21%, and 0.16%, respectively, for protein, moisture, fat, fiber, and ash content. The findings suggest that SKNIR can be a potential tool for high‐throughput pearl millet composition screening efficiently, which will assist breeders and grain processors to optimize grain properties and enhance the grain quality and products.","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Crop Science","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1002/csc2.21375","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0

Abstract

As a gluten‐free cereal with high nutritional properties, pearl millet [Pennisetum glaucum (L.) R. Br.] has been increasingly regarded as an alternative dryland resilient food crop with enriched grain nutritional value. This paper explores the potential of single‐kernel near‐infrared (SKNIR) spectroscopy combined with multivariate analysis for rapid and non‐destructive evaluation of protein, moisture, fat, fiber, and ash contents of pearl millet grains. Samples harvested from two consecutive years (2021 and 2022) were evaluated under dryland and irrigated conditions in Kansas State University, Agricultural Research Center, Hays (ARCH), KS and were analyzed using SKNIR and conventional laboratory methods. Model calibrations were developed using partial least squares regression. Results showed satisfactory performance of models with standard errors cross‐validation of 1.04%, 0.17%, 0.39%, 0.21%, and 0.16%, respectively, for protein, moisture, fat, fiber, and ash content. The findings suggest that SKNIR can be a potential tool for high‐throughput pearl millet composition screening efficiently, which will assist breeders and grain processors to optimize grain properties and enhance the grain quality and products.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用单核近红外光谱对珍珠粟[Pennisetum glaucum (L.) R. Br.]成分进行非破坏性鉴定
珍珠粟[Pennisetum glaucum (L.) R. Br.]是一种无麸质谷物,具有很高的营养价值,越来越多的人将其视为具有丰富谷物营养价值的旱地抗逆粮食作物。本文探讨了单核近红外光谱(SKNIR)与多元分析相结合,快速、无损地评估珍珠粟谷物蛋白质、水分、脂肪、纤维和灰分含量的潜力。连续两年(2021 年和 2022 年)收获的样品在堪萨斯州海斯市堪萨斯州立大学农业研究中心(ARCH)的旱地和灌溉条件下进行了评估,并使用 SKNIR 和传统实验室方法进行了分析。使用偏最小二乘回归法进行了模型校准。结果表明,模型的性能令人满意,蛋白质、水分、脂肪、纤维和灰分含量的标准误差交叉验证分别为 1.04%、0.17%、0.39%、0.21% 和 0.16%。研究结果表明,SKNIR 可以作为一种潜在的工具,有效地进行高通量珍珠米成分筛选,从而帮助育种者和谷物加工者优化谷物特性,提高谷物品质和产品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Crop Science
Crop Science 农林科学-农艺学
CiteScore
4.50
自引率
8.70%
发文量
197
审稿时长
3 months
期刊介绍: Articles in Crop Science are of interest to researchers, policy makers, educators, and practitioners. The scope of articles in Crop Science includes crop breeding and genetics; crop physiology and metabolism; crop ecology, production, and management; seed physiology, production, and technology; turfgrass science; forage and grazing land ecology and management; genomics, molecular genetics, and biotechnology; germplasm collections and their use; and biomedical, health beneficial, and nutritionally enhanced plants. Crop Science publishes thematic collections of articles across its scope and includes topical Review and Interpretation, and Perspectives articles.
期刊最新文献
A finite mixture distribution to model genetic architecture of image‐based oat grain morphology Plant‐based protein crops and their improvement: Current status and future perspectives Shade, height of cut, and plant growth regulator effects on bermudagrass and zoysiagrass putting greens Impact of flooding at the early reproductive growth stage on soybean yield and seed composition Characterization of a gene conferring resistance to US Russian wheat aphid biotypes in the Iranian wheat landrace PI 625139
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1