Geographical identification of Italian extra virgin olive oil by the combination of near infrared and Raman spectroscopy: A feasibility study

偏最小二乘回归 线性判别分析 拉曼光谱 橄榄油 光谱学 分析化学(期刊) 近红外光谱 融合 模式识别(心理学) 交叉验证 化学计量学 灵敏度(控制系统) 数学 人工智能 化学 材料科学
作者
Marco Bragolusi,Andrea Massaro,Carmela Zacometti,Alessandra Tata,Roberto Piro
出处
期刊:Journal of Near Infrared Spectroscopy [SAGE]
卷期号:: 096703352110515-096703352110515 被引量:1
标识
DOI:10.1177/09670335211051575
摘要

The potential of the combination of near infrared (NIR) spectroscopy and Raman spectroscopy to differentiate Italian and Greek extra virgin olive oil (EVOO) by geographical origin was evaluated. Near infrared spectroscopy and Raman fingerprints of both study groups (extra virgin olive oil from the two countries) were pre-processed, merged by low-level and mid-level data fusion strategies and submitted to partial least-squares discriminant analysis. The classification models were cross-validated. After low-level data fusion, the partial least-squares discriminant analysis correctly predicted the geographical origins of extra virgin olive oils in cross-validation with 93.9% accuracy, while sensitivity and specificity were 77.8% and 100%, respectively. After mid-level data fusion, the partial least-squares discriminant analysis correctly predicted the geographical origins of extra virgin olive oils in cross-validation with 97.0% accuracy, while sensitivity and specificity were 88.9% and 100%, respectively. In this preliminary study, improved discrimination of Italian extra virgin olive oils was achieved by the synergism of near infrared spectroscopy and Raman spectroscopy as compared to the discrimination obtained by the separate laboratory techniques. This pilot study shows encouraging results that could open a new avenue for the authentication of Italian extra virgin olive oil.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
sqw完成签到,获得积分10
刚刚
郑雅柔完成签到 ,获得积分10
2秒前
小二郎应助悦悦采纳,获得10
3秒前
棉袄完成签到 ,获得积分10
3秒前
5秒前
宁少爷完成签到,获得积分0
6秒前
livra1058完成签到,获得积分10
6秒前
乐观紫霜发布了新的文献求助10
6秒前
会飞的鱼完成签到,获得积分10
7秒前
cocopepsi完成签到,获得积分10
7秒前
ECT完成签到,获得积分10
8秒前
友好的导师关注了科研通微信公众号
8秒前
耶啵8825完成签到,获得积分10
8秒前
涵de暴躁小地雷完成签到 ,获得积分10
9秒前
贪玩鸵鸟完成签到,获得积分10
9秒前
南栀完成签到,获得积分10
10秒前
11秒前
CodeCraft应助轩辕一笑采纳,获得10
11秒前
drd完成签到,获得积分10
11秒前
愉快的真应助温眸采纳,获得30
11秒前
破晓之照完成签到,获得积分10
12秒前
73Jennie123完成签到,获得积分10
12秒前
Z01完成签到,获得积分10
12秒前
13秒前
regina完成签到,获得积分10
14秒前
兑润泽发布了新的文献求助10
16秒前
sugar完成签到,获得积分10
16秒前
Rainbow完成签到,获得积分10
16秒前
沉静紫安发布了新的文献求助10
17秒前
瑾玉完成签到,获得积分10
17秒前
17秒前
Ling完成签到,获得积分10
18秒前
Mindray完成签到,获得积分10
20秒前
20秒前
114555完成签到,获得积分10
21秒前
yinhe完成签到 ,获得积分10
23秒前
yaolei完成签到,获得积分10
24秒前
文章大发发布了新的文献求助10
24秒前
狗子完成签到,获得积分10
25秒前
26秒前
高分求助中
Rock-Forming Minerals, Volume 3C, Sheet Silicates: Clay Minerals 2000
The late Devonian Standard Conodont Zonation 2000
Nickel superalloy market size, share, growth, trends, and forecast 2023-2030 2000
The Lali Section: An Excellent Reference Section for Upper - Devonian in South China 1500
Very-high-order BVD Schemes Using β-variable THINC Method 930
The Healthy Socialist Life in Maoist China 600
The Vladimirov Diaries [by Peter Vladimirov] 600
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3265756
求助须知:如何正确求助?哪些是违规求助? 2905690
关于积分的说明 8334507
捐赠科研通 2575964
什么是DOI,文献DOI怎么找? 1400298
科研通“疑难数据库(出版商)”最低求助积分说明 654712
邀请新用户注册赠送积分活动 633554