Test of a light emitting diode fully integrated pre-prototype spectrometer for rapid evaluation of table tomato (Solanum lycopersicum L., Marinda F1) quality

IF 1.9 4区 化学 Q3 CHEMISTRY, APPLIED Journal of Near Infrared Spectroscopy Pub Date : 2022-10-01 DOI:10.1177/09670335221119721
A. Tugnolo, A. Pampuri, V. Giovenzana, A. Casson, R. Guidetti, R. Beghi
{"title":"Test of a light emitting diode fully integrated pre-prototype spectrometer for rapid evaluation of table tomato (Solanum lycopersicum L., Marinda F1) quality","authors":"A. Tugnolo, A. Pampuri, V. Giovenzana, A. Casson, R. Guidetti, R. Beghi","doi":"10.1177/09670335221119721","DOIUrl":null,"url":null,"abstract":"The present research aims to evaluate the performance of an optical pre-prototype based on light emitting diode, (450–860 nm) to quantify table tomatoes’ quality features in a rapid and non-destructive way (Solanum lycopersicum L., Marinda F1). A total of 200 samples were analysed. Calibration of the pure near infrared (NIR, 960–1650 nm) and visible/near infrared (VIS/NIR, 400–1000 nm) commercial spectrometers to estimate the main tomato quality parameters, i.e. moisture content (MC) and total soluble solids (TSS), was performed by using PLS regression. Since no substantial differences were highlighted between the two commercial devices, to reduce the complexity while keeping the performance of the model using the whole spectra (1647 variables for VIS/NIR), a cost-effective pre-prototype was designed and built by using 12 bands in the VIS/NIR optical range. The pre-prototype shows slightly lower performance, resulting in RMSEP values of 2% and 1.45 °Brix for MC and TSS respectively, compared to RMSEP values of 1% and 1.19 °Brix for the VIS/NIR device (using the entire spectrum). Moreover, no significant differences at 95% were highlighted by using Passing-Bablok regression. In conclusion, the pre-prototype performance can be considered sufficiently accurate to allow an initial field screening of the trend of the analysed parameters (MC and TSS) using a new generation of simplified optical sensors.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"279 - 287"},"PeriodicalIF":1.9000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Near Infrared Spectroscopy","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1177/09670335221119721","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
引用次数: 0

Abstract

The present research aims to evaluate the performance of an optical pre-prototype based on light emitting diode, (450–860 nm) to quantify table tomatoes’ quality features in a rapid and non-destructive way (Solanum lycopersicum L., Marinda F1). A total of 200 samples were analysed. Calibration of the pure near infrared (NIR, 960–1650 nm) and visible/near infrared (VIS/NIR, 400–1000 nm) commercial spectrometers to estimate the main tomato quality parameters, i.e. moisture content (MC) and total soluble solids (TSS), was performed by using PLS regression. Since no substantial differences were highlighted between the two commercial devices, to reduce the complexity while keeping the performance of the model using the whole spectra (1647 variables for VIS/NIR), a cost-effective pre-prototype was designed and built by using 12 bands in the VIS/NIR optical range. The pre-prototype shows slightly lower performance, resulting in RMSEP values of 2% and 1.45 °Brix for MC and TSS respectively, compared to RMSEP values of 1% and 1.19 °Brix for the VIS/NIR device (using the entire spectrum). Moreover, no significant differences at 95% were highlighted by using Passing-Bablok regression. In conclusion, the pre-prototype performance can be considered sufficiently accurate to allow an initial field screening of the trend of the analysed parameters (MC and TSS) using a new generation of simplified optical sensors.
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
用于快速评估食用番茄(Solanum lycopersicum L.,Marinda F1)质量的发光二极管全集成预原型光谱仪的测试
本研究旨在评估基于发光二极管(450–860 nm)的光学预原型的性能,以快速、无损的方式量化番茄的质量特征(Solanum lycopersicum L.,Marinda F1)。共分析了200个样本。通过使用PLS回归对纯近红外(NIR,960–1650 nm)和可见光/近红外(VIS/NIR,400–1000 nm)商用光谱仪进行校准,以估计番茄的主要质量参数,即水分含量(MC)和总可溶性固形物(TSS)。由于两种商业设备之间没有显著差异,为了降低复杂性,同时保持使用全光谱(1647个VIS/NIR变量)的模型性能,通过使用VIS/NIR光学范围内的12个波段设计和构建了一个具有成本效益的预原型。预原型显示出略低的性能,导致MC和TSS的RMSEP值分别为2%和1.45°Brix,而VIS/NIR设备(使用整个光谱)的RMSEP值分别为1%和1.19°Brix。此外,通过Passing Bablok回归,在95%时没有显著差异。总之,可以认为原型前的性能足够准确,可以使用新一代简化的光学传感器对分析参数(MC和TSS)的趋势进行初步现场筛选。
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
CiteScore
3.30
自引率
5.60%
发文量
35
审稿时长
6 months
期刊介绍: JNIRS — Journal of Near Infrared Spectroscopy is a peer reviewed journal, publishing original research papers, short communications, review articles and letters concerned with near infrared spectroscopy and technology, its application, new instrumentation and the use of chemometric and data handling techniques within NIR.
期刊最新文献
Non-linear machine learning coupled near infrared spectroscopy enhanced model performance and insights for coffee origin traceability Using visible and near infrared spectroscopy and machine learning for estimating total petroleum hydrocarbons in contaminated soils Detection and classification of spongy tissue disorder in mango fruit during ripening by using visible-near infrared spectroscopy and multivariate analysis A method to standardize the temperature for near infrared spectra of the indigo pigment in non-dairy cream based on symbolic regression Moisture content of Panax notoginseng taproot predicted using near infrared spectroscopy
×
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:604180095
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