JIA-2022-0063 基于拉曼光谱和多元数据分析方法的花生油产地鉴别

doi:10.1016/j.jia.2022.07.026

Journal of Integrative Agriculture ›› 2022, Vol. 21 ›› Issue (9): 2777-2785.DOI: 10.1016/j.jia.2022.07.026

所属专题：食品科学合辑Food Science

• • 上一篇

JIA-2022-0063 基于拉曼光谱和多元数据分析方法的花生油产地鉴别

收稿日期:2022-01-13 接受日期:2022-03-20 出版日期:2022-09-01 发布日期:2022-03-20

Identification of peanut oil origins based on Raman spectroscopy combined with multivariate data analysis methods

ZHU Peng-fei, YANG Qing-li, ZHAO Hai-yan

College of Food Science and Engineering, Qingdao Agricultural University, Qingdao 266109, P.R.China

Received:2022-01-13 Accepted:2022-03-20 Online:2022-09-01 Published:2022-03-20
About author:ZHU Peng-fei, E-mail: fei935765952@163.com; Correspondence ZHAO Hai-yan, Tel/Fax: +86-532-58957771, E-mail: xinyuyuanyin@163.com
Supported by:
This work was supported by the Natural Science Foundation of Shandong Province, China (ZR2019BC033).

摘要/Abstract

摘要：

本研究旨在利用拉曼光谱学方法鉴别花生油产地，并建立稳健的鉴别模型，进一步筛选出与产地密切相关的特征光谱。对来自不同省份和同一省份不同城市的159个花生油样品进行拉曼光谱测定，获得的数据进行了逐步线性判别分析分析(SLDA)，k-最近邻分析(k-NN)和多因素方差分析等。结果表明，基于全光谱的样本识别率达到90%以上。花生油的产地、品种及其互作对拉曼光谱影响显著，筛选出1400-1500 cm^-1和1600-1700 cm^-1为受品种影响较小的产地特征光谱。结合产地特征光谱建立的SLDA最佳分类模型能够快速、准确地识别花生油的产地。

Abstract:

This study aimed to use Raman spectroscopy to identify the producing areas of peanut oil and build a robust discriminant model to further screen out the characteristic spectra closely related to the origin. Raman spectra of 159 peanut oil samples from different provinces and different cities of the same province were collected. The obtained data were analyzed by stepwise linear discriminant analysis (SLDA), k-nearest neighbor analysis (k-NN), support vector machine (SVM) and multi-way analysis of variance. The results showed that the overall recognition rate of samples based on full spectra was higher than 90%. The producing origin, variety and their interaction influenced Raman spectra of peanut oil significantly, and 1 400–1 500 cm^–1 and 1 600–1 700 cm^–1were selected as the characteristic spectra of origin and less affected by variety. The best classification model established by SLDA combined with characteristic spectra could rapidly and accurately identify peanut oil’s origin.

Key words: Raman spectroscopy , peanut oil , large-scale district , small-scale district , variety , chemometrics

. JIA-2022-0063 基于拉曼光谱和多元数据分析方法的花生油产地鉴别[J]. Journal of Integrative Agriculture, 2022, 21(9): 2777-2785.

ZHU Peng-fei, YANG Qing-li, ZHAO Hai-yan. Identification of peanut oil origins based on Raman spectroscopy combined with multivariate data analysis methods[J]. Journal of Integrative Agriculture, 2022, 21(9): 2777-2785.

参考文献

Acciani C, De Boni A, Bozzo F, Roma R. 2020. Pulses for healthy and sustainable food systems: The effect of origin on market price. Sustainability, 13, 185–199.
Aykas D P, Shotts M L, Rodriguez-Saona L E. 2020. Authentication of commercial honeys based on Raman fingerprinting and pattern recognition analysis. Food Control, 117, 107346.
Berghian-Grosan C, Magdas D A. 2020a. Application of Raman spectroscopy and machine learning algorithms for fruit distillates discrimination. Scientific Reports, 10, 21152–21160.
Berghian-Grosan C, Magdas D A. 2020b. Raman spectroscopy and machine-learning for edible oils evaluation. Talanta, 218, 121176.
Corvucci F, Nobili L, Melucci D, Grillenzoni F V. 2015. The discrimination of honey origin using melissopalynology and Raman spectroscopy techniques coupled with multivariate analysis. Food Chemistry, 169, 297–304.
Genis D O, Sezer B, Durna S, Boyaci I H. 2021. Determination of milk fat authenticity in ultra-filtered white cheese by using Raman spectroscopy with multivariate data analysis. Food Chemistry, 336, 127699.
He S X, Liu X H, Zhang W, Xie W Y, Zhang H, Fu W L, Liu H, Liu X L, Xu Y J, Yang D J, Gao Y M. 2015. Discrimination of the coptis chinensis geographic origins with surface enhanced Raman scattering spectroscopy. Chemometrics and Intelligent Laboratory Systems, 146, 472–477.
Jiang F, Yuan L Y, Shu N X, Wang W L, Liu Y F, Xu Y J. 2020. Foodomics revealed the effects of extract methods on the composition and nutrition of peanut oil. Journal of Agricultural and Food Chemistry, 68, 1147–1156.
de Lima T K, Musso M, Bertoldo Menezes D. 2020. Using Raman spectroscopy and an exponential equation approach to detect adulteration of olive oil with rapeseed and corn oil. Food Chemistry, 333, 127454.
Magdas D A, Guyon F, Berghian-Grosan C, Molnar C M. 2021. Challenges and a step forward in honey classification based on Raman spectroscopy. Food Control, 123, 107769.
Mandrile L, Zeppa G, Giovannozzi A M, Rossi A M. 2016. Controlling protected designation of origin of wine by Raman spectroscopy. Food Chemistry, 211, 260–267.
Matthaus B, Musazcan Ozcan M. 2015. Oil content, fatty acid composition and distributions of vitamin-E-active compounds of some fruit seed oils. Antioxidants (Basel), 4, 124–133.
Oroian M, Ropciuc S. 2017. Botanical authentication of honeys based on Raman spectra. Journal of Food Measurement and Characterization, 12, 545–554.
Petersen M, Yu Z, Lu X. 2021. Application of Raman spectroscopic methods in food safety: A review. Biosensors (Basel), 11, 187–208.
Richter B, Rurik M, Gurk S, Kohlbacher O, Fischer M. 2019. Food monitoring: Screening of the geographical origin of white asparagus using FT-NIR and machine learning. Food Control, 104, 318–325.
Sánchez-López E, Sánchez-Rodríguez M I, Marinas A, Marinas J M, Urbano F J, Caridad J M, Moalem M. 2016. Chemometric study of Andalusian extra virgin olive oils Raman spectra: Qualitative and quantitative information. Talanta, 156–157, 180–190.
Sun S N, Wang X P, Zhang Y. 2017. Sustainable traceability in the food supply chain: The impact of consumer willingness to pay. Sustainability, 9, 999–1017.
Tian F M, Tan F, Li H. 2020. An rapid nondestructive testing method for distinguishing rice producing areas based on Raman spectroscopy and support vector machine. Vibrational Spectroscopy, 107, 103017.
Toomer O T. 2018. Nutritional chemistry of the peanut (Arachis hypogaea). Critical Reviews in Food Science and Nutrition, 58, 3042–3053.
USDA (United States Department of Agriculture). 2020. Foreign Agricultural Service. [2020-12-9]. https://apps.fas.usda.gov/psdonline/app/index.html#/app/compositeViz
Vassiliou E K, Gonzalez A, Garcia C, Tadros J H, Chakraborty G, Toney J H. 2009. Oleic acid and peanut oil high in oleic acid reverse the inhibitory effect of insulin production of the inflammatory cytokine TNF-alpha both in vitro and in vivo systems. Lipids in Health and Disease, 8, 25–34.
Wang H P, Xin Y J, Ma H Z, Fang P P, Li C H, Wan X, He Z P, Jia J J, Ling Z C. 2021. Rapid detection of Chinese-specific peony seed oil by using confocal Raman spectroscopy and chemometrics. Food Chemistry, 362, 130041.
Wang L, Yang Q L, Zhao H Y. 2021. Sub-regional identification of peanuts from Shandong Province of China based on fourier transform infrared (FT-IR) spectroscopy. Food Control, 124, 107879.
Wang Y X, Tan F. 2021. Extraction and classification of origin characteristic peaks from rice Raman spectra by principal component analysis. Vibrational Spectroscopy, 114, 103249.
Wu Q J, Dong Q H, Sun W J, Huang Y, Wang Q Q, Zhou W L. 2014. Discrimination of Chinese teas with different fermentation degrees by stepwise linear discriminant analysis (S-LDA) of the chemical compounds. Journal of Agricultural and Food Chemistry, 62, 9336–9344.
Xu Y, Zhong P, Jiang A M, Shen X, Li X M, Xu Z L, Shen Y D, Sun Y M, Lei H T. 2020. Raman spectroscopy coupled with chemometrics for food authentication: A review. Trends in Analytical Chemistry, 131, 116017.
Yaseen T, Sun D, Cheng J. 2017. Raman imaging for food quality and safety evaluation: Fundamentals and applications. Trends in Food Science & Technology, 62, 177–189.
Zhang X, Sun J L, Li P P, Zeng F Y, Wang H H. 2021. Hyperspectral detection of salted sea cucumber adulteration using different spectral preprocessing techniques and SVM method. LWT-Food Science and Technology, 152, 112295.
Zhao H Y, Guo B L, Wei Y M, Zhang B. 2013. Near infrared reflectance spectroscopy for determination of the geographical origin of wheat. Food Chemistry, 138, 1902–1907.
Zhao H Y, Wang F, Yang Q L. 2020. Origin traceability of peanut kernels based on multi-element fingerprinting combined with multivariate data analysis. Journal of the Science of Food and Agriculture, 100, 4040–4048.
Zhao H Y, Yu C D, Li M. 2017. Effects of geographical origin, variety, season and their interactions on minerals in tea for traceability. Journal of Food Composition and Analysis, 63, 15–20.
Zhu L, Sun J, Wu G C, Wang Y N, Zhang H, Wang L, Qian H F, Qi X G. 2018. Identification of rice varieties and determination of their geographical origin in China using Raman spectroscopy. Journal of Cereal Science, 82, 175–182.

JIA-2022-0063 基于拉曼光谱和多元数据分析方法的花生油产地鉴别

Identification of peanut oil origins based on Raman spectroscopy combined with multivariate data analysis methods

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