Detection and quantification of adulteration of sesame oils with vegetable oils using gas chromatography and multivariate data analysis

Peng, Dan; Bi, Yanlan; Ren, Xiaona; Yang, Guolong; Sun, Shangde; Wang, Xuede

doi:10.1016/j.foodchem.2015.05.001

Cited by 63 publications

(21 citation statements)

References 20 publications

(14 reference statements)

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“…The final dataset tested was a combination of the two datasets previously mentioned, resulting in five classes; CPRO, RRO, RSO, CPRO:RRO, and CPRO:RSO. Ternary oil mixtures were not investigated because classification accuracy decreases with an increasing number of adulterants and in fact, binary adulteration is a more common form of adulteration. As a result of the increased class number in this scenario, the accuracy of the models decreased, compared with the previous three class datasets (scenario 1 and 2).…”

Section: Resultsmentioning

confidence: 99%

Detection of Refined Sunflower and Rapeseed Oil Addition in Cold Pressed Rapeseed Oil Using Mid Infrared and Raman Spectroscopy

McDowell

Osório

Elliott

et al. 2018

Euro J Lipid Sci & Tech

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In this study, a new screening technique for the detection of two types of oil adulterants in cold pressed rapeseed oil is investigated. The calibration models built with four different multivariate classifiers (SIMCA, PLS‐DA, LDA‐KNN, and LDA‐SVM) are based on spectral fingerprints from either FT‐IR and Raman instruments from authentic pure oils and in‐house admixtures of the oils involved. When refined sunflower oil is the adulterant, both FT‐IR and Raman produce effective models with high sensitivity of 86% and 93% respectively. When refined rapeseed oil is the adulterant the sensitivity decreases. This is explained by the chemical differences of the two adulterants. PLS‐R quantification analysis estimates minimum detection levels of 15% (Raman) and 9% (FT‐IR) when refined sunflower oil is the adulterant, and 22% (Raman) and 64% (FT‐IR) when refined rapeseed oil is the adulterant. This initial study shows the potential of Raman spectroscopy to be utilized for the screening of cold pressed rapeseed oil authenticity. Practical Applications: It has been well documented in the past that high‐value edible oils can be easily adulterated with lower cost oils for economic gain. Although the cold pressed rapeseed oil industry has not experienced such fraud, it would be prudent to have analytical techniques available to authenticate genuine oils quickly. This would further strengthen cold pressed rapeseed oil's reputation as a product free from substitutional fraud. These calibration models can tool the industry and regulatory bodies with a screening method to detect authenticity in realistic levels. A feasibility study regarding cold pressed rapeseed oil adulteration is investigated. Cold pressed rapeseed oil is adulterated with either refined rapeseed oil or refined sunflower oil and spectra is acquired using either Raman or FT‐IR spectroscopy. A library of spectra is gathered which includes pure cold pressed rapeseed oil and cold pressed rapeseed oil admixtures of varying concentrations. Chemometric analysis is carried out on the dataset with both classification and quantification techniques. The optimum instrument‐chemometric pairings for cold pressed rapeseed oil authentication are discussed.

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Section: Resultsmentioning

confidence: 99%

Detection of Refined Sunflower and Rapeseed Oil Addition in Cold Pressed Rapeseed Oil Using Mid Infrared and Raman Spectroscopy

McDowell

Osório

Elliott

et al. 2018

Euro J Lipid Sci & Tech

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show abstract

“…For instance, several nonseparation/nondestructive techniques (e.g., isotope ratio mass spectrometry, infrared spectroscopy, and nuclear magnetic resonance spectroscopy) have been applied for the detection of adulterated sesame oils [1,4,6]. Previous studies have also employed techniques such as gas chromatography (GC) coupled with a flame ionization detector [7] or mass spectrometer [8,9], high performance liquid chromatography with a refractive index detector [10], an evaporative light scattering detector [5,11] or a fluorescence detector [12], an electronic nose [13], and realtime PCR [14]. Despite the recent advances in the analytical 2 Journal of Chemistry methods available for the detection of sesame oil adulteration, the minimum adulteration detection levels remain relatively high.…”

Section: Introductionmentioning

confidence: 99%

Determination of 2-Propenal Using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography–Time-of-Flight Mass Spectrometry as a Marker for Authentication of Unrefined Sesame Oil

Mansur

Nam

Jang

et al. 2017

Journal of Chemistry

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Ascertaining the authenticity of the unrefined sesame oil presents an ongoing challenge. Here, the determination of 2-propenal was performed by headspace solid-phase microextraction (HS-SPME) under mild temperature coupled to gas chromatography with time-of-flight mass spectrometry, enabling the detection of adulteration of unrefined sesame oil with refined corn or soybean oil. Employing this coupled technique, 2-propenal was detected in all tested refined corn and soybean oils but not in any of the tested unrefined sesame oil samples. Using response surface methodology, the optimum extraction temperature, equilibrium time, and extraction time for the HS-SPME analysis of 2-propenal using carboxen/polydimethylsiloxane fiber were determined to be 55 ∘ C, 15 min, and 15 min, respectively, for refined corn oil and 55 ∘ C, 25 min, and 15 min, respectively, for refined soybean oil. Under these optimized conditions, the adulteration of unrefined sesame oil with refined corn or soybean oils (1-5%) was successfully detected. The detection and quantification limits of 2-propenal were found to be in the range of 0.008-0.010 and 0.023-0.031 g mL −1 , respectively. The overall results demonstrate the potential of this novel method for the authentication of unrefined sesame oil.

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“…With the development of methods based on spectral technology (Wu, Wu, Sun, & Li, 2014), hyper-spectral imaging (HSI) (Bauer, Stefan, & Le on, 2015;Huang, Zhao, Wang, & Zhu, 2015) has gained increasing attention for rapid detection of anomalies on food products in recent years such as for quality inspection of onions (Wang, 2015), revealing food adulteration with independent components analysis (Puneet et al, 2016), detection of cold injury in peaches by artificial neural network (Pan et al, 2016), visualizing moisture distribution of mango slices during microwavevacuum drying (Pu & Sun, 2015), and discrimination of rice varieties using LS-SVM classification algorithms . Compared to the traditional methods (Zhu, Shen, Chen, Yang, & Hou, 2015) (Gas Chromatography, GC; Gas Chromatography Mass Spectrometer, GC-MS; Liquid Chromatograph Mass Spectrometer, LC-MS; High Performance Liquid Chromatography, HPLC, et al) for chemical detection of pesticide residues, HSI is a rapid, accurate and nondestructive method (Han, Zeng, Lu, & Zhang, 2015;Julio Cesare & Jairo Arturo, 2015;Peng et al, 2015;Xu, Hu, Wang, Wan, & Bao, 2015). Although traditional methods have high accuracy, the detection process is more complex, which belongs to the damage detection being conducive to be promoted.…”

Section: Introductionmentioning

confidence: 99%

Visualizing distribution of pesticide residues in mulberry leaves using NIR hyperspectral imaging

Jiang

Sun

Zhou

et al. 2016

J Food Process Engineering

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Nowadays, hyperspectral imaging technique has been abroad and successfully used for quality inspection of food products. In this study, the microstructural changes of mulberry were investigated by scanning electron microscope (SEM). Furthermore, NIR hyperspectral imaging system was used to predict the distribution of Pesticide residues in mulberry leaves with the aid of Gas Chromatography. It is primary to extract the characteristic wavelengths in the prediction and visualization of Pesticide residues in mulberry leaves. Successive projections algorithm (SPA), Stepwise regression(SR) and Regression coefficients (RC) were utilized. MLR was applied to build prediction models based on spectral feature in characteristic wavelengths. The best model SPA‐MLR ( Rp=0.859 and RMSEP = 38.789) was implemented into the pesticide residues visualization procedure. This study demonstrated that pesticide residues distribution map clearly showed the distribution of Pesticide residues in mulberry leaves. Practical applications Well understanding the effect of Pesticide residues to biological structure is very important for revelation of novel biological function and mechanism of action of the protein. To facilitate more quickly and effectively detect the types of pesticide residues on the surface of mulberry leaves, the microstructural changes of mulberry were investigated by scanning electron microscope (SEM). Furthermore, NIR hyperspectral imaging system was used to predict the distribution of Pesticide residues in mulberry leaves with the aid of Gas Chromatography.

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Detection and quantification of adulteration of sesame oils with vegetable oils using gas chromatography and multivariate data analysis

Cited by 63 publications

References 20 publications

Detection of Refined Sunflower and Rapeseed Oil Addition in Cold Pressed Rapeseed Oil Using Mid Infrared and Raman Spectroscopy

Detection of Refined Sunflower and Rapeseed Oil Addition in Cold Pressed Rapeseed Oil Using Mid Infrared and Raman Spectroscopy

Determination of 2-Propenal Using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography–Time-of-Flight Mass Spectrometry as a Marker for Authentication of Unrefined Sesame Oil

Visualizing distribution of pesticide residues in mulberry leaves using NIR hyperspectral imaging

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