用于植物油质量评价的香气和光学吸收光谱法

F. A. Rashid, H. Maamor, N. Yusuf, N. Z. I. Zakaria, S. Ismail, K. Adnan, A. Zakaria, L. Kamarudin, A. Shakaff
{"title":"用于植物油质量评价的香气和光学吸收光谱法","authors":"F. A. Rashid, H. Maamor, N. Yusuf, N. Z. I. Zakaria, S. Ismail, K. Adnan, A. Zakaria, L. Kamarudin, A. Shakaff","doi":"10.1109/ICED.2014.7015826","DOIUrl":null,"url":null,"abstract":"Vegetable oils from different type of sources may have a distinctive aroma and flavour. This work explored the ability of combining PEN3 and UV-Vis Spectrophotometer for aroma and volatiles analysis. Nine types of vegetable oils were characterized and classified into three categories based on aroma and volatiles absorption characteristics which are fresh, heated and used cooking oil. The results of PCA analysis showed a good separation among three groups of vegetable cooking oil. Data set from both PEN3 (e-nose) and UV-Vis Spectroscopy was subjected to Linear Discriminant Analysis. Our results propose that discriminant analysis provides a rapid, efficient and accurate study for multi-class classification difficulties. LDA is capable to provide 100.0% correct classification of original grouped cases. However, only 85.4% of un-known grouped cases are correctly classified.","PeriodicalId":143806,"journal":{"name":"2014 2nd International Conference on Electronic Design (ICED)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Aroma and optical absorption spectroscopy for quality assessment of vegetable cooking oils\",\"authors\":\"F. A. Rashid, H. Maamor, N. Yusuf, N. Z. I. Zakaria, S. Ismail, K. Adnan, A. Zakaria, L. Kamarudin, A. Shakaff\",\"doi\":\"10.1109/ICED.2014.7015826\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Vegetable oils from different type of sources may have a distinctive aroma and flavour. This work explored the ability of combining PEN3 and UV-Vis Spectrophotometer for aroma and volatiles analysis. Nine types of vegetable oils were characterized and classified into three categories based on aroma and volatiles absorption characteristics which are fresh, heated and used cooking oil. The results of PCA analysis showed a good separation among three groups of vegetable cooking oil. Data set from both PEN3 (e-nose) and UV-Vis Spectroscopy was subjected to Linear Discriminant Analysis. Our results propose that discriminant analysis provides a rapid, efficient and accurate study for multi-class classification difficulties. LDA is capable to provide 100.0% correct classification of original grouped cases. However, only 85.4% of un-known grouped cases are correctly classified.\",\"PeriodicalId\":143806,\"journal\":{\"name\":\"2014 2nd International Conference on Electronic Design (ICED)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 2nd International Conference on Electronic Design (ICED)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICED.2014.7015826\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 2nd International Conference on Electronic Design (ICED)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICED.2014.7015826","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

不同来源的植物油可能有不同的香气和风味。本工作探讨了PEN3与紫外可见分光光度计相结合用于香气和挥发物分析的能力。对9种植物油的香气和挥发物吸收特性进行了表征,并将其分为鲜油、加热油和废油3类。主成分分析结果表明,三组植物油具有较好的分离性。PEN3(电子鼻)和UV-Vis光谱数据集进行线性判别分析。我们的研究结果表明,判别分析为多类分类难题提供了快速、高效和准确的研究。LDA能够对原始分组病例提供100.0%的正确分类。然而,只有85.4%的未知分组病例被正确分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Aroma and optical absorption spectroscopy for quality assessment of vegetable cooking oils
Vegetable oils from different type of sources may have a distinctive aroma and flavour. This work explored the ability of combining PEN3 and UV-Vis Spectrophotometer for aroma and volatiles analysis. Nine types of vegetable oils were characterized and classified into three categories based on aroma and volatiles absorption characteristics which are fresh, heated and used cooking oil. The results of PCA analysis showed a good separation among three groups of vegetable cooking oil. Data set from both PEN3 (e-nose) and UV-Vis Spectroscopy was subjected to Linear Discriminant Analysis. Our results propose that discriminant analysis provides a rapid, efficient and accurate study for multi-class classification difficulties. LDA is capable to provide 100.0% correct classification of original grouped cases. However, only 85.4% of un-known grouped cases are correctly classified.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A new trimming approach for shunt resistors used in metering applications Taxanomy and overview on cooperative MAC for vehicular ad hoc networks Electrical characterization of USB2 multiplexers/BC1.2 power switches/charging modules for accurate channel simulation Experimental studies of the correlation between fingers bending angle with voltage outputted from GloveMAP Comparison on TiO2 and TaO2 based bipolar resistive switching devices
×
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