A Combined Classification Modeling Strategy for Detection and Identification of Extra Virgin Olive Oil Adulteration Using Raman Spectroscopy

A Combined Classification Modeling Strategy for Detection and Identification of Extra Virgin Olive Oil Adulteration Using Raman Spectroscopy Publisher

Vali Zade S¹ ; Forooghi E¹ ; Jannat B² ; Hashempourbaltork F¹ ; Abdollahi H³

Show Affiliations

1. Halal Research Center of IRI, Food and Drug Administration, Ministry of Health and Medical Education, Tehran, Iran
2. Department of Food and Drug Control, School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
3. Department of Chemistry, Institute for Advanced Studies in Basic Sciences, P.O. Box 45195-1159, Zanjan, Iran

Source: Chemometrics and Intelligent Laboratory Systems Published:2023

Abstract

Identifying and discriminating of pure edible oils as well as detecting and identifying the type of adulteration, are essential issues in the field of food-related research. Extra virgin olive, hazelnut, canola, soya, and sunflower oils were investigated by Raman spectroscopy to answer two main questions: How can it be recognized that an unknown sample of edible oil belongs to one of these five types of pure oil? Also, if the unknown sample is adulterated extra virgin olive oil, how can the type and composition of that sample be recognized? The problem-based research was planned by proposing a combined classification strategy for achieving the proper solutions. In this approach, class modeling was combined with discriminant analysis to benefit from the advantages of both methods. In the first step, the Raman spectra of five pure edible oil classes were used to model the target objects by DD-SIMCA to exclude adulterated samples. Then PLS-DA was used for the discrimination of five edible oils classes. This strategy was successfully applied for several binary classes, including different types of extra virgin olive oil adulterations with different compositions. Raman spectroscopy, accompanied by the proposed combined classification strategy, showed good power in solving the pattern recognition problems in food adulteration subjects. The proposed combined classification strategy allowed the discrimination between pure edible oil samples with up to 100% of correct classifications. The developed method showed also high performances based on the figures of merit (up to 100% sensitivity and specificity) for the detection and identification the type of adulteration in extra virgin olive oil. © 2023 Elsevier B.V.

Related Docs

1. Recent Methods in Detection of Olive Oil Adulteration: State-Of- The-Art, Journal of Agriculture and Food Research (2024)

2. Authentication and Discrimination of Tissue Origin of Bovine Gelatin Using Combined Supervised Pattern Recognition Strategies, Microchemical Journal (2023)

3. Multivariate Analysis of Food Fraud: A Review of Nir Based Instruments in Tandem With Chemometrics, Journal of Food Composition and Analysis (2022)

4. Detection of Sheep Butter Adulteration With Cow Butter and Margarine by Employing Raman Spectroscopy and Multivariate Data Analysis, International Dairy Journal (2024)

Experts (# of related papers)

Mohammad Reza Khoshayand (1)

Nafiseh Sadeghi (1)

Style	Citing Format
MLA	Vali Zade S, et al.. "A Combined Classification Modeling Strategy for Detection and Identification of Extra Virgin Olive Oil Adulteration Using Raman Spectroscopy." Chemometrics and Intelligent Laboratory Systems, vol. 240, no. , 2023, pp. -.
APA	Vali Zade S, Forooghi E, Jannat B, Hashempourbaltork F, Abdollahi H (2023). A Combined Classification Modeling Strategy for Detection and Identification of Extra Virgin Olive Oil Adulteration Using Raman Spectroscopy. Chemometrics and Intelligent Laboratory Systems, 240(), -.
Chicago	Vali Zade S, Forooghi E, Jannat B, Hashempourbaltork F, Abdollahi H. "A Combined Classification Modeling Strategy for Detection and Identification of Extra Virgin Olive Oil Adulteration Using Raman Spectroscopy." Chemometrics and Intelligent Laboratory Systems 240, no. (2023): -.
Harvard	Vali Zade S et al. (2023) 'A Combined Classification Modeling Strategy for Detection and Identification of Extra Virgin Olive Oil Adulteration Using Raman Spectroscopy', Chemometrics and Intelligent Laboratory Systems, 240(), pp. -.
Vancouver	Vali Zade S, Forooghi E, Jannat B, Hashempourbaltork F, Abdollahi H. A Combined Classification Modeling Strategy for Detection and Identification of Extra Virgin Olive Oil Adulteration Using Raman Spectroscopy. Chemometrics and Intelligent Laboratory Systems. 2023;240():-.
BibTex	@article{ author = {Vali Zade S and Forooghi E and Jannat B and Hashempourbaltork F and Abdollahi H}, title = {A Combined Classification Modeling Strategy for Detection and Identification of Extra Virgin Olive Oil Adulteration Using Raman Spectroscopy}, journal = {Chemometrics and Intelligent Laboratory Systems}, volume = {240}, number = {}, pages = {-}, year = {2023} }
RIS	TY - JOUR AU - Vali Zade S AU - Forooghi E AU - Jannat B AU - Hashempourbaltork F AU - Abdollahi H TI - A Combined Classification Modeling Strategy for Detection and Identification of Extra Virgin Olive Oil Adulteration Using Raman Spectroscopy JO - Chemometrics and Intelligent Laboratory Systems VL - 240 IS - SP - EP - PY - 2023 ER -

Science Communicator Platform

Authors

Authors Affiliations

Abstract