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Construction and Adulteration Detection Based on Fingerprint of Volatile Components in Hazelnut Oil

Qunxing Zhou, Jingjing Han, Chunmao Lyu , Xianjun Meng, Jinlong Tian, Hui Tan
Journal of Food and Nutrition Research. 2022, 10(2), 164-174. DOI: 10.12691/jfnr-10-2-10
Received January 03, 2022; Revised February 05, 2022; Accepted February 11, 2022

Abstract

The volatile components of hazelnut oil are one of the important characteristics of hazelnut oil quality. To evaluate and identify the adulteration quality of hazelnut oil, this experiment adopts the HS – SPME/GC - MS technique combined with similarity analysis and cluster analysis and builds the standard fingerprint of volatile components in 9 samples of hazelnut oil. hazelnut oil samples are obtained from 3 kinds of hazelnut (flat hazelnut, European hazelnut, Flat-European hazelnut) by pressing method, water enzymatic method and leaching method, respectively. And on this basis, the fit of the good hazelnut oil and peanut oil adulteration model were established. The results showed that the volatile components of hazelnut oil obtained by different varieties combined with different oil preparation methods were similar. However, for flat hazelnut varieties, different preparation methods had a certain influence on the volatile components of flat hazelnut oil, and there was a certain difference among samples. When the amount of adulteration is larger, the relative error is smaller, and the proportion model of adulteration is more accurate. When the proportion of peanut oil adulteration is in the range of 20%~100%, the average relative error is 2.427%, and the relative errors are all less than 10%. In this case, model of hazelnut oil mixed with peanut oil is the most reliable model.

1. Introduction

Volatile components of hazelnut oil are important for quality evaluation of hazelnut oil and can be used to identify the purity of hazelnut oil 1. However, the quality of hazelnut oil is difficult to assess due to the differences between hazelnut raw material varieties, growing environment, and hazelnut oil preparation methods 2. In addition, domestic and foreign scholars' studies on volatile components of vegetable oil mainly focus on sunflower oil, rapeseed oil, sesame oil, peanut oil and other vegetable oils. There are few reports on the fingerprint of volatile components of hazelnut oil. In recent years, with the development of medicinal diets, TCM fingerprint technology has been gradually extended to the quality evaluation of functional food raw materials and the control of product processing technology, showing a good application prospect 3, 4. Gas chromatography is internationally recognized as one of the most effective methods for detecting and controlling material quality. GC data can reflect the types and contents of the intrinsic components of edible oil, and further reflect the quality of edible oil. GC fingerprint is a quality control mode to evaluate the authenticity, stability and consistency of edible oil quality at present. Therefore, in this research, the fingerprint of volatile components in hazelnut oil prepared by different varieties combined with different oil preparation methods was studied. The standard fingerprint of volatile components and the adulteration model was established. These results can not only effectively evaluate the differences among hazelnut oils, provide scientific basis and methods for quality evaluation, quality control and adulteration identification of hazelnut oils, but also have certain scientific and practical significance for clarifying the material basis of hazelnut oil and the selection of hazelnut varieties.

2. Materials and Methods

2.1. Materials and Reagents

Material. European hazelnuts were obtained from Grohe, Turkey, while flat hazelnuts were obtained from Hazelnut Street, Kaiyuan and Liaoning. Flat-European hazelnuts were purchased in Benxi County Sanyang big fruit hazelnut professional production cooperative, while peanut oil was obtained from Shandong Longda Vegetable Oil Co., Ltd. The different varieties of hazelnuts were Flat-European hybrid hazelnuts, European hazelnuts and flat hazelnuts.

Reagents. Alkaline protease: from Beijing Aobo Star Biotechnology Co. Ltd. Ether, ethanol are produced from Shenyang Sinopharm Group Chemical Reagent Co. Ltd.

2.2. Equipment

Oil press: made in Zhuhai Hangxing Electric Appliance Co. Ltd. Guangdong Province; Grinder: Model BJ-150, produced from Shanghai Baijie Industrial Co. Ltd. Rotating evaporator: from Shanghai Yarong Biochemical Instrument Factory; Digital display constant temperature water bath: model is HH-6, produced from Guohua Electric Appliance Co.Ltd; Solid phase microextraction device: extraction needle is 50/30 μm DVB/CAR/PDMS, Supelco, USA; GC-MS Gas Chromatography-Mass Spectrometer: 7800-5975 Gas Chromatography-Mass Spectrometer, Agilent Corporation, USA;

2.3. Preparation and Adulteration of Hazelnut Oil

In this study, hazelnut varieties for preparing hazelnut oil include flat hazelnut, European hazelnut, Flat-European hazelnut. Hazelnuts are shelled, peeled and crushed after processing, respectively using the press method, water enzymatic method and leaching method to get a total of 9 oil samples. 10ml of the treated hazelnut oil were taken out and put into a headspace bottle. The oil was extracted for 35min at a water bath temperature of 55°C and analyzed by GC-MS for 4min. Press method: the pretreated hazelnut is pressed in an oil press machine and filtered by a filter net to get pressed hazelnut oil.Water enzymatic method: the hazelnut raw materials in the appropriate temperature for fermentation, and then for centrifugation, suction out of the upper layer of the centrifuge oil which is hazelnut oil. Leaching method: crushed after the hazelnut powder, leaching oil with solvent. The golden yellow transparent hazelnut oil was obtained by rotating vacuum distillation to remove the solvent.

Hazelnut oil and peanut oil were mixed in the ratios of 9:1, 8:2, 7:3, 6:4, 5:5, 4:6, 3:7, 2:8, 1:9, and 0:10. The oil mixture was extracted in a 20ml transparent headspace bottle at a water bath temperature of 55°C. After the temperature stabilized, the SPME needle was passed through the silica gel bottle pad on the cap of the headspace bottle, and the fiber extraction head was extended without touching the sample. After 35min of headspace solid-phase extraction, GC-MS analysis was performed. The sample was analyzed at the injector for 4min to analyze the experimental results.

2.4. GC-MS Conditions

The volatile components were analyzed by an Agilent 7980A-5975C GC-MS instrument with an auto-sample injector. A DB-WAX column (30 m × 250 μm × 0.25 μm) was used for separation. The chromatographic conditions were as follows: inlet temperature, 250°C; oven temperature program, 40°C for 1 min, ramp at 8°C/min to 75°C, hold for 1 min, ramp at 4°C/min to 100°C, ramp at 3°C/min to 148°C, ramp at 4°C/min to 185°C, ramp at 5°C/min to 270°C, and hold for 8 min; injection volume, 10 μL; carrier gas, He at 1.0 mL/min; and injection mode, splitless. The mass spectrometric conditions were as follows: ion source temperature, 200°C; quadrupole temperature, 150°C; interface temperature, 280°C; and quality scan range, 35-350 m/z; electron bombardment ion source, transmission line temperature 250C, multiplication voltage 1200eV.

2.5. Establishment of Standard Fingerprint of Volatile Components

The common peaks were calibrated by calculating the relative retention time α and relative peak area Sr of the volatile components of hazelnut oil from different varieties and different oil preparation methods. Based on relative retention time and relative peak area of chromatographic peak, for common peak calibration, "Chinese medicine chromatographic fingerprint similarity evaluation system for 2004 A version" (national pharmacopoeia committee, A version for the study of edition, mainly for the scientific research work, has generated reference map function) of the common peak matching was used to select reference spectrum. Common peaks were matched and control maps were generated. The RSD value of the peak area corresponding to the selected common peak should be less than 40%. At the same time, the establishment of the fingerprint map must also meet the requirement that the total area of the selected fingerprint characteristic peak can account for more than 70% of the total peak area of the chromatogram. After that, similarity evaluation was conducted on the fingerprint of traditional Chinese medicine to verify the established standard fingerprint 5. In other words, the correlation coefficient method was adopted, and the peak area of the chromatographic peak was used as the vector to calculate the correlation coefficient. Equation (3) was used to calculate the similarity between each sample and the standard fingerprint 6.

The relative peak area calculation formula (1) and relative retention time calculation formula (2) are as follows:

(1)
(2)

where Sri is the absolute peak area of the peak to be measured, Srs is the absolute peak area of the reference peak, Tri is absolute retention time of peak to be measured, and trs is absolute retention time of reference peak.

(3)
2.6. Application of Standard Fingerprint of Volatile Components

In order to identify the adulterated hazelnut oil more intuitively and accurately, the adulteration models of hazelnut oil with different proportions of peanut oil were established. The mixed oil was analyzed by GC-MS. According to the analysis results, the peak area of the chromatographic peak which was positively correlated with the content of hazelnut oil in the model was selected as the vector, and the cosine of the included Angle was calculated 7, 8, 9. The following formula (4) was used to calculate the similarity between hazelnut oil and hazelnut oil with different proportions of peanut oil.

(4)
2.7. Data Analysis

The Similarity Evaluation System of Chromatographic Fingerprint software (National Pharmacopoeia Commission) was used for similarity analysis. SPSS software (SPSS for Windows 19.0, SPSS Corporation, USA) was used for clustering analysis according to the tree diagram. Nonlinear fitting analysis was performed using Origin 8.0 statistical software (Origin Lab Corporation, Northampton, USA).

3. Results and Discussion

3.1. Evaluationof Fingerprint Analysis Methods

The results of the precision, stability and reproducibility tests (Table 1 ~ Table 6). According to the “Technical Requirements of Traditional Chinese Medicine Injection Fingerprint Study (provisional)”, the RSD of the relative retention time and relative peak area of the precision, stability and reproducibility of fatty acids and volatile components was lower than 1.0 % and 5.0%, respectively (n=3), which was in line with the establishment of fingerprint requirements.

3.2. Fingerprint Analysis of Hazelnut Oil by GC-MS

Figure 1 shows the GC-MS ion chromatogram of the volatile components of 9 hazelnut oil samples. It can be seen from Figure 1 that the volatile components of hazelnut oil produced by different varieties and different extraction methods are generally the same. Nevertheless, the types and contents of the volatile components of hazelnut oil are different due to different hazelnut raw materials and different extraction methods of hazelnut oil. The volatile components of hazelnut oil prepared by different varieties and methods (shown in Table 7). Seventy-one aromatic compounds, including esters (17), alcohols (19), alkanes (9), aldehydes (15), olefins (9), acids (5), aldehydes (5), amines (3), azoles (3) and furans (1), were identified from flat hazelnut, European hazelnut and Flat-European hazelnut.

3.3. Establishment and Analysis of Standard Fingerprint of Volatile Components

By analyzing the data, it can be concluded that in different varieties, different extraction methods of hazelnut oil chromatogram, nonyl aldehyde by chromatographic peak not only accounts for all the chromatographic peak, but good separating degree and peak strength is also very stable (Figure 1). Therefore, nonyl aldehyde is chosen as reference of the chromatographic peak and the Sr > l % of the total of the peak is selected on the basis of the calculation. Common peaks of volatile substances in hazelnut oil were selected (Table 8).

7 kinds of acids, 5 kinds of aldehydes, 3 kinds of esters, 2 kinds of alkenes, 2 kinds of alcohols, 2 kinds of alkanes, 1 kind of siloxane and 1 kind of furanones were selected as the common peaks of volatile substances in hazelnut oil. According to the common peak of volatile substances in hazelnut oil, the reference spectrum was selected to establish the standard fingerprint of volatile substances in hazelnut oil (Figure 3) 10. The specific information of standard fingerprint of volatile substances in hazelnut oil (Table 9).

To determine the applicability of standard fingerprints, Table 10 shows the similarity between volatile components in different hazelnut oil varieties and standard fingerprints. The similarity between the volatile components of different varieties of hazelnut oil and the standard fingerprint was greater than 0.9. It indicates that the hazelnut oil samples of different varieties and different extraction methods were basically stable. In each batch of hazelnut oil samples, the volatiles of hazelnut oil were basically consistent with the standard fingerprint of hazelnut oil volatiles established previously. This is basically consistent with the results of previous GC-MS analysis. Moreover, it also met the similarity evaluation requirements of fingerprint, so the established standard fingerprint of volatile substances in hazelnut oil was established. Figure 4 shows the cluster tree diagram of volatile components of hazelnut oil prepared by different varieties and different methods. As shown in the figure, nine oil samples can be divided into two categories at the point where the scale line is 5. The hazelnut oil prepared by the organic solvent method was classified as one class, and the other eight were classified as another class. The volatiles in hazelnut oil prepared by flat hazelnuts water enzymatic, European hazelnut water enzymatic, Flat-European hazelnut water enzymatic, flat hazelnut organic solvent, Flat-European hazelnut organic solvent, European hazelnut squeeze and flat hazelnut squeeze were the most similar, which was consistent with the similarity results of standard fingerprint of the volatiles in hazelnut oil.

At the same time, the similarity evaluation and cluster analysis showed that the volatile components of hazelnut oil prepared by different methods of flat hazelnut and European hazelnut were generally similar, while the volatile components of oil prepared by different methods of Flat-European Hazelnut were different. The results indicated that different preparation methods of hazelnut oil had certain influence on the volatiles of hazelnut oil of Flat-European hazelnut. However, the volatile components of different varieties of hazelnut oil prepared by water enzymatic method were similar and could be classified into the same category. The results showed that the volatile components of hazelnut oil extracted by water enzymatic method were similar even in different varieties.

3.4. Application of Standard Fingerprint of Volatile Components

The similarities between different proportions of pure hazelnut oil and mixed hazelnut oil and peanut oil (Table 11). The similarity of mixed hazelnut oil decreased with the increase of peanut oil content, which means that the similarity was negatively correlated with peanut oil content. Fitting curve between peanut oil adulteration model and peanut oil adulteration rate (Figure 5). According to the fitting curve of peanut oil adulteration rate, the nonlinear equation of Y= 12818X3-31286X2 + 23678X-5615.9 was established, where Y represents the adulteration rate of peanut oil and X represents the cosine value (cosθ). The correlation coefficient (R2) of the nonlinear fitting equation is 0.996, which indicates that the hazelnut oil-peanut oil adulteration model is effective 11, 12, 13, 14. Adulteration model was used to calculate the adulteration rate of hazelnut oil samples. Then, the accuracy of the adulteration model was verified by comparing the adulteration amount with the actual amount of peanut oil and calculating the relative error. The average relative error of adulteration was 3.924%. However, when the adulteration rate is between 20% and 100%, the mean relative error decreases to 2.427% (Table 11). In addition, the relative error within the range of the adulteration rate is less than 10%.

At the same time, when the amount of adulteration is larger, the relative error is less than the relative error in the above range, and the model is more accurate. The results showed that the model could be used to determine the adulteration rate of peanut oil in hazelnut oil. The model is the most reliable when the adulteration rate is 20% ~ 100%. An accuracy of 20% ~ 100% adulteration seems to be sufficient, as the commercial profit that illegal traders can make from an adulteration rate below 20% is very small and would not normally operate in this way. After several years of development, a number of advanced detection technologies have been put into use, such as electronic nose, nuclear magnetic resonance and Fourier transform Raman spectroscopy, etc. Although mid-infrared and Raman spectroscopy can also be used to identify adulteration of edible oil 15, these technologies provide more options for vegetable oil adulteration. They are not as good as chromatography at identifying foreign molecules, which complements targeted technologies. In addition, volatile components are more representative than spectral data, which is one of the important indicators for hazelnut oil quality evaluation. Therefore, it is more reliable and reasonable to determine the adulteration of hazelnut oil by chromatogram analysis of volatile components.

4. Conclusion

This experiment based on the analysis of three kinds of hazelnut (flat hazelnut, European hazelnut and Flat-European hazelnuts) combined with three methods for making oil (squeezing method, water enzymatic and organic solvent method) get a total of nine hazelnut oil sample of GC - MS fingerprint. On the basis, a volatile component standard fingerprint was established, and it was proved to be true by similarity evaluation. Moreover, a well-fitting hazelnut oil-peanut oil adulteration model was successfully established based on the standard fingerprint of hazelnut oil volatile components. The results provided an effective scientific basis for identification of hazelnut oil adulteration by volatile component fingerprint.

Acknowledgements

This work was supported by Liaoning Province key R & D project "Research and demonstration of key Technologies for Deep processing and Comprehensive Utilization of Northeast hazelnut" [grant number: 2020JH2/10200037]; Liaoning Provincial Department of Education serves the local project "demonstration and Popularization of New Deep processing Technology for Comprehensive Utilization of Northeast hazelnut" [grant number: LSNFW201903]; Horizontal project "demonstration and Popularization of key Technologies for Transformation and Deep processing of Wild Corylus Forest in Northwest Liaoning" [grant number: H2019388].

References

[1]  Dabbou, S., Issaoui, M., Brahmi, F., Nakbi, A. Changes in volatile compounds during processing of tunisian-style table olives. Journal of the American Oil Chemists Society, 89(2), 347-354. (2011).
In article      View Article
 
[2]  Calvano, C. D., Aresta, A., Zambonin, C. G. Detection of hazelnut oil in extra-virgin olive oil by analysis of polar components by micro-solid phase extraction based on hydrophilic liquid chromatography and MALDI-ToF mass spectrometry. Journal of Mass Spectrometry, 45(9), 981-988. (2010).
In article      View Article  PubMed
 
[3]  Borse, B. B., Jagan Mohan Rao, L., Nagalakshmi, S., Krishnamurthy, N. Fringerprint of black teas from India: identification of the regio-specific characteristics. Food Chemistry, 79, 419-424. (2002).
In article      View Article
 
[4]  Zheng, Y., Xin, Y., Guo, Y. Study on the fingerprint profile of Monascus products with HPLC-FD, PAD and MS. Food Chemistry, 113, 705-711. (2009).
In article      View Article
 
[5]  Wei J, & Ma, Y. Similarity between Excel and chromatographic fingerprint of traditional Chinese medicine. Journal of Kunming Teachers College, (04):110-112 (2007).
In article      
 
[6]  Shi, G, OuYang., Preliminary Study on Tea Aroma Fingerprint and Feature Recognition. Shandong Agricultural University (2011).
In article      
 
[7]  García-González, D. L., Mannina, L., D’Imperio, M., Segre, A. L., Aparicio R. Using 1H and 13C NMR techniques and artificial neural networks to detect the adulteration of olive oil with hazelnut oil. European Food Research & Technology, 219(5), 545-548. (2004).
In article      View Article
 
[8]  Azadmarddamirchi, S. Review of the use of phytosterols as a detection tool for adulteration of olive oil with hazelnut oil. Food Additives & Contaminants, 27(1), 1-10. (2010).
In article      View Article  PubMed
 
[9]  Vlahov, G. Quantitative 13C NMR method using the DEPT pulse sequence for the detection of olive oil adulteration with soybean oil. Magnetic Resonance in Chemistry, 35(13), S8-S12. (2015).
In article      View Article
 
[10]  Benincasa C,Russo A, Romano E. Chemical and sensory analysis of some egyptian virgin olive oils [J].International Journal of Food Sciences and Nutrition,2011 (01): 1427-1436.
In article      View Article
 
[11]  Jabeur, H., Zribi, A., Makni, J., Rebai, A., Abdelhedi, R., Bouaziz, M. Detection of Chemlali Extra-Virgin Olive Oil Adulteration Mixed with Soybean Oil, Corn Oil, and Sunflower Oil by Using GC and HPLC. Journal of Agricultural and Food Chemistry, 62(21), 4893-4904. (2014).
In article      View Article  PubMed
 
[12]  Paraschos, S., Magiatis, P., Gikas, E., Smyrnioudis, L., Skaltsounis, A. Quality profile determination of Chios mastic gum essential oil and detection of adulteration in mastic oil products with the application of chiral and non-chiral GC-MS analysis. Fitoterapia, 114(12), 12-17. (2016).
In article      View Article  PubMed
 
[13]  Zhang, L., Shuai, Q., Li, P., Zhang, Q., Ma, F., Zhang, W., Ding, X. Ion mobility spectrometry fingerprints: A rapid detection technology for adulteration of sesame oil. Food Chemistry, 192(17), 60-66. (2016).
In article      View Article  PubMed
 
[14]  Garrido-Delgado, R., Munoz-Perez, M., Arce, L. Detection of adulteration in extra virgin olive oils by using UV-IMS and chemometric analysis. Food Control, 85(9), 292-299. (2018).
In article      View Article
 
[15]  Georgouli, K., Martinez Del Rincon, J., Koidis, A. Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data. Food Chemistry, 217, 735-742. (2017).
In article      View Article  PubMed
 

Published with license by Science and Education Publishing, Copyright © 2022 Qunxing Zhou, Jingjing Han, Chunmao Lyu, Xianjun Meng, Jinlong Tian and Hui Tan

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Cite this article:

Normal Style
Qunxing Zhou, Jingjing Han, Chunmao Lyu, Xianjun Meng, Jinlong Tian, Hui Tan. Construction and Adulteration Detection Based on Fingerprint of Volatile Components in Hazelnut Oil. Journal of Food and Nutrition Research. Vol. 10, No. 2, 2022, pp 164-174. http://pubs.sciepub.com/jfnr/10/2/10
MLA Style
Zhou, Qunxing, et al. "Construction and Adulteration Detection Based on Fingerprint of Volatile Components in Hazelnut Oil." Journal of Food and Nutrition Research 10.2 (2022): 164-174.
APA Style
Zhou, Q. , Han, J. , Lyu, C. , Meng, X. , Tian, J. , & Tan, H. (2022). Construction and Adulteration Detection Based on Fingerprint of Volatile Components in Hazelnut Oil. Journal of Food and Nutrition Research, 10(2), 164-174.
Chicago Style
Zhou, Qunxing, Jingjing Han, Chunmao Lyu, Xianjun Meng, Jinlong Tian, and Hui Tan. "Construction and Adulteration Detection Based on Fingerprint of Volatile Components in Hazelnut Oil." Journal of Food and Nutrition Research 10, no. 2 (2022): 164-174.
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  • Figure 2. GC-MS total ion chromatograms of volatile compounds of hazelnut oil with different proportions of adulteration (In the figure, the No.1 chromatographic peak is octanol, the No.2 chromatographic peak is nonanal, the No.3 chromatographic peak is decanal, the No.4 chromatographic peak is nonanal, and the 5th chromatographic peak is oleic acid)
  • Table 11. The hazelnut oil mixed with peanut oil volatile components fingerprint similarity and relative error
[1]  Dabbou, S., Issaoui, M., Brahmi, F., Nakbi, A. Changes in volatile compounds during processing of tunisian-style table olives. Journal of the American Oil Chemists Society, 89(2), 347-354. (2011).
In article      View Article
 
[2]  Calvano, C. D., Aresta, A., Zambonin, C. G. Detection of hazelnut oil in extra-virgin olive oil by analysis of polar components by micro-solid phase extraction based on hydrophilic liquid chromatography and MALDI-ToF mass spectrometry. Journal of Mass Spectrometry, 45(9), 981-988. (2010).
In article      View Article  PubMed
 
[3]  Borse, B. B., Jagan Mohan Rao, L., Nagalakshmi, S., Krishnamurthy, N. Fringerprint of black teas from India: identification of the regio-specific characteristics. Food Chemistry, 79, 419-424. (2002).
In article      View Article
 
[4]  Zheng, Y., Xin, Y., Guo, Y. Study on the fingerprint profile of Monascus products with HPLC-FD, PAD and MS. Food Chemistry, 113, 705-711. (2009).
In article      View Article
 
[5]  Wei J, & Ma, Y. Similarity between Excel and chromatographic fingerprint of traditional Chinese medicine. Journal of Kunming Teachers College, (04):110-112 (2007).
In article      
 
[6]  Shi, G, OuYang., Preliminary Study on Tea Aroma Fingerprint and Feature Recognition. Shandong Agricultural University (2011).
In article      
 
[7]  García-González, D. L., Mannina, L., D’Imperio, M., Segre, A. L., Aparicio R. Using 1H and 13C NMR techniques and artificial neural networks to detect the adulteration of olive oil with hazelnut oil. European Food Research & Technology, 219(5), 545-548. (2004).
In article      View Article
 
[8]  Azadmarddamirchi, S. Review of the use of phytosterols as a detection tool for adulteration of olive oil with hazelnut oil. Food Additives & Contaminants, 27(1), 1-10. (2010).
In article      View Article  PubMed
 
[9]  Vlahov, G. Quantitative 13C NMR method using the DEPT pulse sequence for the detection of olive oil adulteration with soybean oil. Magnetic Resonance in Chemistry, 35(13), S8-S12. (2015).
In article      View Article
 
[10]  Benincasa C,Russo A, Romano E. Chemical and sensory analysis of some egyptian virgin olive oils [J].International Journal of Food Sciences and Nutrition,2011 (01): 1427-1436.
In article      View Article
 
[11]  Jabeur, H., Zribi, A., Makni, J., Rebai, A., Abdelhedi, R., Bouaziz, M. Detection of Chemlali Extra-Virgin Olive Oil Adulteration Mixed with Soybean Oil, Corn Oil, and Sunflower Oil by Using GC and HPLC. Journal of Agricultural and Food Chemistry, 62(21), 4893-4904. (2014).
In article      View Article  PubMed
 
[12]  Paraschos, S., Magiatis, P., Gikas, E., Smyrnioudis, L., Skaltsounis, A. Quality profile determination of Chios mastic gum essential oil and detection of adulteration in mastic oil products with the application of chiral and non-chiral GC-MS analysis. Fitoterapia, 114(12), 12-17. (2016).
In article      View Article  PubMed
 
[13]  Zhang, L., Shuai, Q., Li, P., Zhang, Q., Ma, F., Zhang, W., Ding, X. Ion mobility spectrometry fingerprints: A rapid detection technology for adulteration of sesame oil. Food Chemistry, 192(17), 60-66. (2016).
In article      View Article  PubMed
 
[14]  Garrido-Delgado, R., Munoz-Perez, M., Arce, L. Detection of adulteration in extra virgin olive oils by using UV-IMS and chemometric analysis. Food Control, 85(9), 292-299. (2018).
In article      View Article
 
[15]  Georgouli, K., Martinez Del Rincon, J., Koidis, A. Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data. Food Chemistry, 217, 735-742. (2017).
In article      View Article  PubMed