Next Article in Journal
Broad-Spectrum Legionaminic Acid-Specific Antibodies in Pooled Human IgGs Revealed by Glycan Microarrays with Chemoenzymatically Synthesized Nonulosonosides
Previous Article in Journal
Investigating the Discoloration of Leaves of Dioscorea polystachya Using Developed Atomic Absorption Spectrometry Methods for Manganese and Molybdenum
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Molecules
Volume 29
Issue 16
10.3390/molecules29163979
molecules-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Waste Point Identification of Frying Oil Based on Gas Chromatography–Ion Mobility Spectrometry (GC-IMS)

by
Lin Ye
1,2,
Lijun Song
3,
Li Zhang
3 and
Ruiguo Cui
3,*
1
College of Food Science and Engineering, Tarim University, Alar 843300, China
2
Construction Corps Key Laboratory of Special Agricultural Products Further Processing in Southern Xinjiang, Alar 843300, China
3
School of Food Science and Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066600, China
*
Author to whom correspondence should be addressed.
Molecules 2024, 29(16), 3979; https://doi.org/10.3390/molecules29163979
Submission received: 19 July 2024 / Revised: 16 August 2024 / Accepted: 21 August 2024 / Published: 22 August 2024
Browse Figures
Versions Notes

Abstract

:
This study described the quality detection and rapid identification of frying oil waste points based on gas chromatography–ion mobility spectrometry (GC-IMS). A total of 48 volatile substances were identified, among which the levels of 11 components, including 2-pentylfuran, 2-butylfuran, and 2-hexanone, increased with prolonged frying time after 40 h in cottonseed oil. Conversely, the levels of hexanal, heptanal, and E,E-2,4-heptadienal decreased as frying time extended. Correlation analysis revealed a significant association between volatile substances of the oil and acid value (p < 0.05) and polar components with volatile substances (p < 0.05). Furthermore, significant differences in the types and contents of flavor substances were observed in cottonseed oil at different frying times (including before and after reaching the discard point) (p < 0.05). Subsequently, principal component analysis (PCA) results clearly showed that the cottonseed oil samples at different frying times were well distinguished by the volatile compounds; moreover, discriminant model analysis indicated a model accuracy rate of 100%. These results showed the potential of GC-IMS-based approaches in discriminating the waste points of frying oil.

1. Introduction

Edible oil is commonly utilized for frying [1]. The high temperature required during the frying process results in oxidation in the oil and a series of changes in some composition of frying oil with the extension of frying time, leading to the decline in oil quality [2,3]. Consequently, the quality of fried food also deteriorates. Due to the long-term high-temperature heating of oils and fats, a large number of fatty acids and volatile substances such as aldehydes, ketones, lactones, and others are produced, which pose a serious threat to individuals’ physical and mental well-beings [4,5]. Therefore, robust stability and anoxidation are crucial for edible oil. Furthermore, the distinctive flavor found in fried food originates from unsaturated fatty acids present in frying oil. These unsaturated fatty acids are prone to oxidation at high temperatures. The organic compounds produced through the oxidation of unsaturated fatty acids contribute to this flavor profile by generating ketones and aldehydes. Husked cottonseed contains approximately 40% oil content [6]. As one of the daily edible oils, cottonseed oil plays an important role in the food industry. And its composition of fatty acid and glyceryl ester in cottonseed oil have good crisp performance and natural antioxidant capacity. Therefore, cottonseed oil possesses substantial development potential and serves as a notable choice for frying purposes [7,8]. Therefore, cottonseed oil was used as a representative research object matter in this study.
Recently, various strategies have been developed to distinguish the waste point of oil, including electronic nose and electronic tongue technology [9,10,11], dielectric constant method [12], LF-NMR (low-field nuclear magnetic resonance) [13], TLC (thin-layer chromatography) [14], and IR (infrared spectra) [15,16,17,18]. These conventional analytical methods have high sensitivity and good repeatability. However, they require cumbersome sample processing and relatively expensive reagents, which limit their application for the rapid determination of the waste point of oil. In recent years, with the increasing consumption of fried food in Xinjiang, China, the reuse of frying oil has become a common phenomenon in the market. This has raised concerns about food safety among consumers. Due to numerous and scattered processing enterprises and vendors, market supervision is extremely difficult as there are limited numbers of inspectors available. Therefore, there is a need for rapid determination methods for waste index and the identification of waste points to ensure quality supervision and food safety control.
Gas chromatography-ion mobility spectrometry (GC-IMS) is an emerging technique. Its application in food quality and safety has increased in recent years due to its multiple advantages (e.g., comparatively simple system setups and robustness, rapid identification, sensitiveness, and convenience) over other conventional analytical techniques [19]. It is used to determine volatile compounds in a sample for rapid sample quality detection [20]. Currently, the analysis and detection of GC-IMS in the adulteration and doping of edible oil are at a preliminary stage, with few studies on the judgment of the waste index of frying oil [18,21]. Garrido-Delgado et al. [21] used GC-IMS to establish a discriminant model for olive oil and obtained accurate results. A method of adulteration detection and predictive analysis model with PLS was established by GC-IMS in canola oil. These studies provide the basis for the application of GC-IMS in food analysis and detection. To the best of our knowledge, no attempt has been made to examine the potential use of GC-IMS to detect waste-point oil by detecting the volatile components of frying oil at different frying times.
The present study aimed to establish an emerging method for rapidly identifying waste points in cottonseed oil. Deep-fried noodle cake was used as a frying food and fried at 180 °C for 80 h in cottonseed oil. The changes in the content and composition of volatile substances in cottonseed oil at different frying times were analyzed using GC-IMS, and a fingerprint spectrum database of cottonseed oil was established to provide technical support for the quality detection and rapid identification of waste point in frying oil.

2. Results and Discussion

The GC-IMS instrument automatically generated 3D topographic plots (Figure 1A) and 2D topographic plots (Figure 1B), illustrating the volatile compound profiles in cottonseed oil during frying. Although the topographic plots exhibited a tendency of volatile compounds, accurately discerning the dense material on the map (Figure 1B,C) proved to be challenging.

2.1. Volatile Mass Spectrogram and Fingerprinting of Volatile Compounds in Cottonseed Oil during Frying

The complex composition of volatile compounds is indicated by the 3D topographic plots and 2D topographic plots of GC-IMS spectra shown in Figure 1. The vertical coordinate represented the retention time of gas chromatography(s), while the horizontal coordinate represented the ion migration time (a.u.). The red vertical line denotes the ion reaction peak (RIP peak), with each point on both sides representing a volatile compound [22]. From analyzing the 3D topographic plots, it can be observed that different constituents of volatile compounds are present in cottonseed oil at different frying times. Figure 1B provides a top view of the 3D topographic projected onto a 2D topographic plane. By examining the color intensity of the RIP peak, differences in composition and the concentration difference between volatile compounds in cottonseed oil at different frying times can be intuitively displayed through the 2D topographic (Figure 1B) plots.
The composition and content (Figure 1C) of volatile substances in cottonseed oil were compared at different frying durations. The red color indicated a higher concentration of volatile compounds in the oil sample during frying compared to the reference sample, while blue indicated a lower concentration [23]. It is evident that the red color is obviously darker and gradually increased, with the extension of frying time, indicating an augmentation in both the composition and content of volatile compounds. When the frying duration exceeded 40 h, the concentration of volatile substances showed significant changes, and some volatile flavor substances showed a gradual increase. Therefore, disparities in the composition and content of volatile substances can serve as indicators for determining changes in oil quality.
The results of the qualitative analysis of volatile compounds in cottonseed oil at different frying times are listed in Table 1. A total of 48 monomers and partial dimers were identified from the GC × IMS library, including 16 aldehydes, 7 ketones, 3 acids, 2 alcohols, 2 esters, 2 furans, 1 thiazole, and 1 sulfur-containing compound. Aldehydes were abundant, followed by ketones and acids. The drift time and retention time of volatile compounds were qualitatively analyzed for cottonseed oil at different frying times.
Utilizing fingerprints provided an effective approach to address this issue [24,25]. The horizontal coordinate in the figure (Figure 1D) represented the detected volatile substances, while the numbers indicated undetected substances. The vertical coordinate denoted different frying times for oil samples. Each cell represented the content of a substance at a specific frying time. Colors represented the contents of volatile compounds: the brighter colors indicated higher content [26]. The results revealed that both changes in volatile substances and composition of volatile flavor substances were associated with color brightness. With the extension of frying time, both the variety and concentration of volatile flavor substances in cottonseed oil increased, which may be attributed to the oxidation and cracking of oil during frying. As shown in the red box, after 40 h of frying, significant increases were observed in concentrations of 11 identified components, including 2-methyl-2-acrolein, 2-amylfuran, 2-butylfuran, 2-hexanone, 1-propenylmethyldisulfide, butyraldehyde, 2-butanone, E-2-nonenal, E-2-pentenal monomer, E-2-pentenal dimer, and γ-butyrolactone. As shown in the green box, the concentration of hexal, heptadienal, E, e-2, 4-heptadienal decreased.
In short, the characteristic volatiles fingerprints of cottonseed oil at different frying times were successfully established through GC-IMS, and the changing regular of volatile compounds in cottonseed oil was observed.

2.2. Principal Component Analysis (PCA) of Various Cottonseed Oil Samples at Different Frying Times

The PCA is a multivariate statistical analysis method that reveals differences between various samples, with similar samples indicating minimal disparities. Conversely, greater distances on the diagram indicate significant variations in composition [27]. Generally, when the cumulative contribution rate reaches 60%, the PCA model is selected as the separation model [28]. The results of PCA clearly demonstrated that the accumulative variance contribution rate of the first PC (75%) and second PC (12%) amounts to 87%. It is evident that PCA retains the primary information of volatile substances and the characteristics of volatile flavor in each sample. There was a substantial distance between the principal component of cottonseed oil sample at frying 0 h and after frying, indicating significant differences in volatile components before and after frying. From observation, parallel samples cluster closely while non-parallel samples exhibit a certain distance larger than that between parallel ones.
In Figure 2, a distinct separation trend in cottonseed oil samples in volatile components at different frying times can be observed. The cottonseed oil samples fried for 40 h and 48 h exhibit closer proximity, indicating similarity in the volatile compounds during these two periods. On the axis, a large distance was found between the cottonseed oil sample at frying 48 h and 56 h. The samples fried for 56 h and 64 h are easily distinguishable, suggesting a notable difference in the composition and concentration of volatile substances. This implies that the quality of cottonseed oil further deteriorated after frying for 48 h. The results demonstrate that GC-IMS technology can effectively identify and differentiate oils with varying frying times, which aligns with the conclusions drawn in other researchers’ studies. Su [29] conducted principal component analysis on flavor substances during brown rice storage using GC-IMS technology. The results revealed an intensified deterioration in brown rice quality within 70 days at 35 °C and within 28 days at 45 °C, providing a methodological basis for rapid monitoring of brown rice quality. Dirinck [30] analyzed the characteristic flavor components of ginger from three different origins using gas-phase ion migration spectrometry technology. The findings indicated significant differentiation among gingers from various origins, demonstrating that GC-IMS technology can be utilized for origin differentiation, traceability, and quality evaluation.

2.3. Correlation Analysis of Waste Index and Volatile Substances in Cottonseed Oil during Frying

Acid value refers to the amount of potassium hydroxide required to neutralize 1 g of free fatty acids [31,32], which is a crucial indicator for measuring the rancidity and deterioration of oils and fats. During frying, oil undergoes hydrolysis, resulting in the formation of free fatty acids that contribute to an increase in acid value [33]. The acid value of cottonseed oil undergoes changes during the frying process of three different types of food at a temperature of 180 °C. As depicted in Figure S1, it can be observed that the acid values of the oils from all three fried foods exhibit a gradual increase with prolonged frying time. Notably, the oil’s acid value shows the most rapid rise during the frying process and reaches 5.64 mg KOH/g at 64 h, indicating that it becomes unfit for further use between 56 and 64 h. Additionally, unsaturated fatty acids are oxidized and hydrolyzed, leading to the decomposition of hydroperoxides and the production of aldehydes in an unstable manner. These aldehydes are subsequently oxidized, causing an elevation in acid value due to acid production [34,35]. Toth [36] discovered that the rise in acid value was mainly attributed totriglyceride oxidation into free fatty acids. Lin [37] demonstrated that the acid value of palm oil increased with prolonged frying time. The correlation between acid value and volatile substances (Table 2) during frying was observed. Notably, there was a significant positive correlation between the acid value and the contents of some volatile substances (p < 0.05), such as isobutenol, 2-amylfuran, 2-butylfuran, 2-hexanone, 1-propenyl methyl disulfide, butanal, butanone, E-2-nonenal, E-2-pentenal dimer. These findings suggest that volatile components present in cottonseed oil can serve as a rapid determination method for assessing its acid value.
Polar compounds (PCs) were the result of greater polarity in compounds than triglycerides, caused by the oxidation, polymerization, and pyrolysis of oils fried at high temperatures [38]. The detection of polar components is important as it contains almost all products resulting from the oxidation, polymerization, hydrolysis, and pyrolysis of frying oil [39]. The content of the polar components is positively correlated with frying time and the increase in frying temperature for different oils [40]. As shown in Figure 1D, with the extension of frying time, both the variety and concentration of volatile flavor substances in cottonseed oil increased, which may be attributed to the oxidation and cracking of oil during frying. PCs and volatile substances (Table 3) showed correlation during frying. There was a significant positive correlation between the PC and the contents of some volatile substances (p < 0.05), such as isobutenol, 2-amylfuran, 2-butylfuran, 2-hexanone, 1-propenyl methyl disulfide, butanal, butanone, E-2-nonenal, E-2-pentenal dimer. The results indicated that volatile components in cottonseed oil can be used for the rapid determination of PC.

2.4. Support Vector Machine (SVM) Model Analysis Diagram

The volatile compounds of naan during frying were predicted using a support vector machine (SVM) in Matlab 2018a, (version 3.23) and a discriminative model for distinguishing cottonseed oil was established. Cottonseed oil samples before and after discarding were classified as class1 and class2 (Figure 3). The findings indicated significant differences in the volatile compounds of oil before and after waste treatment. The model achieved an accuracy rate of 100%, indicating a clear differentiation between the volatile compounds of cottonseed oil before and after discarding. Tata [41] utilized GC-IMS technology to detect soft-refined oils in extra virgin olive oil, establishing an SVM mode with an accuracy rate of 100.0%. This study also verified that GC-IMS technology can rapidly identify the point at which frying oil becomes waste.

3. Materials and Methods

3.1. Materials

Deep-fried noodle cake used in the experiment was obtained from our own laboratory. Samples (100 mL) of cottonseed oil were withdrawn at selected times during the deep frying of the deep-fried noodles at 180 °C. Cottonseed oil was provided by Alar Jinkouxiang Oil Co., Ltd. (Alar, Xinjiang, China). Isopropyl alcohol, ethyl ether, and ethanol were acquired from Shanghai Yuanye Bio-Technology Co., Ltd. (Shanghai, China).

3.2. GC-IMS and Acid Value Analysis Method

For GC-IMS analysis, a GC-IMS system (Flavourspec®, G.A.S, Dortmund, Germany) equipped with an MXT-5 capillary column (15 m × 0.28 mm ID, 0.25 μm film thickness) was employed in this study, which was used in conjunction with the CTC-PAL·3 Static Headspace Automatic Injection device from CTC Analytics AG in Zwingen, Switzerland. Data processing was performed using the vOCa software (version 0.4.03) also developed by G.A.S.
The oil sample was mixed with 1 mL of n-hexane by vortex, and then filtered through membranes (0.22 μm). Then, 1 mL mixtures were placed in a 20 mL headspace glass sampling vial. Subsequently, these samples were incubated at 80 °C for 15 min with speed agitation of 500 rpm. Subsequently, 500 μL of headspace was automatically injected into the splitless injector (80 °C) by means of a heated syringe at 85 °C. The carrier gas (nitrogen of 99.999% purity) passed through the GC-IMS injector transferring the sample into the capillary column at 2 mL min−1 over the first 2 min, and then it increased up to 100 mL min−1 for 20 min, and the drift gas flowrate in drift tube was 150 mL/min. The resulting ions were driven to the drift tube which operated on a constant temperature (45 °C).
Acid-base titration is a classical technique employed to determine the acid value of edible oils. In this method, the oil sample is initially dissolved in a non-polar organic solvent. Then, an acidic indicator, such as phenol phthalide, is added to facilitate the neutralization reaction between the sodium hydroxide solution and free fatty acids present in the oil. The neutralization point of the acid-base reaction is determined through titration, allowing for the calculation of the acid value.
The acid value is determined by the following calculation:
X A V = ( V − V 0 ) × c × 56.1 m
XAV—acid value, mg/g;
V—the amount of the standard titration solution consumed by the sample is quantified, mL;
V0—the determination of the corresponding blank for the consumed volume of the standard titration solution, mL;
c—the concentration of the titration solution in terms of molarity, mol/L;

3.3. Data Processing and Statistical Analysis

The qualitative analysis of the flavor substances in oil sample was performed using the NIST and IMS databases. Reporter and Gallery Plot was applied to record the fingerprints and difference profiles of volatile components. Principal component analysis was performed by Dynamic PCA. The discriminant model was established by matlab2018a software. Experimental data were analyzed statistically using SPSS 25.0, with mean separation at p < 0.05 level of significance.

4. Conclusions

In this study, 48 volatile compounds were identified via GC-IMS in cottonseed oil at different frying times. Among the identified substances, 11 volatile substances, including 2-amylfuran, 2-butylfuran, and 2-hexanone in cottonseed oil increased significantly with the extension of frying time, while hexanal, heptanal, E, E-2, 4-heptadienal decreased significantly. In addition, the results of PCA also clearly showed that the different cottonseed oil samples at different frying times were in a relatively independent space and were well distinguished. The correlation analysis showed that the acid value of oil was significantly correlated with volatile substances and polar components with volatile substances (p < 0.05). The statistical analysis and difference analysis showed that there were significant differences in volatile substances in frying oil before and after waste. The analysis of discriminant model showed that the accuracy of the established model was 100%, which could distinguish waste point of cottonseed oil. Therefore, this study established a rapid method to distinguish waste point of cottonseed oil by GC-IMS. Moreover, according to the differences in the key volatile compounds in cottonseed oil at different frying times, further refinement research can be combined with the relevant processing technology to produce a specific flavor for further fine research in order to find the best way to distinguish the waste point of cottonseed oil.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules29163979/s1, Figure S1: The acid value variation diagram of cottonseed oil during food frying at different temperatures.

Author Contributions

This work was carried out in collaboration with all authors. L.Y. performed the experimental investigation, the data curation, and the analysis. L.Z. wrote the first draft of the manuscript. L.S. was responsible for English editing and performed the data curation and the review and editing. R.C. performed the project administration and the funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Regional Science Fund Project, China (31960487).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Astwood, J. Edible pongamia oil. In Proceedings of the INFORM: International News on Fats, Oils and Related Materials, Shenzhen, China, 2–4 July 2022; Volume 33, pp. 24–26. [Google Scholar]
  2. Li, P.; Liu, Z.; Li, M.; Zhao, Y.; Li, X.; Wan, S.; Ma, Y.; He, Y. Investigation on the low temperature oxidation of light oil for safely enhancing oil recovery at high temperatures and pressures. Energy 2020, 200, 117546. [Google Scholar] [CrossRef]
  3. Kumar, M.; Ranga, C.; Chandel, A.K.; Mishra, A.K. Thermal evaluation and oxidation stability of high temperature alternative solid dielectrics of power transformers in mixed oil. Int. Conf. Adv. Energy 2018, 508, 141–149. [Google Scholar]
  4. Kumar, D.K.; Maanas, S.; Ajita, T. Heat and mass transfer modeling and quality changes during deep fat frying: A comprehensive review. J. Food Process Eng. 2022, 45, e13999. [Google Scholar]
  5. Pawar, D.P.; Boomathi, S.; Hathwar, S.C.; Rai, A.K.; Modi, V.K. Effect of conventional and pressure frying on lipids and f atty acid composition of fried chicken and oil. J. Food Sci. Technol. 2013, 50, 381–386. [Google Scholar] [CrossRef] [PubMed]
  6. Mark, A. Oil Crops Outlook: February 2012; Economic Research Service, USDA: Washington, DC, USA, 2012.
  7. Yang, A.; Zhang, C.; Zhang, B.; Wang, Z.; Zhu, L.; Mu, Y.; Wang, S.; Qi, D. Effects of dietary cottonseed oil and cottonseed meal supplementation on Liver Lipid Content, Fatty acid profile and hepatic function in laying hens. Animals 2021, 11, 78. [Google Scholar] [CrossRef]
  8. Awais, K.; Muhammad, N.; Muhammad, L.; Anjum, K. Impact of winterization on fatty acids’ composition, isomers, and oxidative stability of conjugated linoleic acids produced from selected vegetable oils. J. Food Process. Preserv. 2021, 45, e15254. [Google Scholar]
  9. Xu, L.; Yu, X.; Liu, L.; Zhang, R. A novel method for qualitative analysis of edible oil oxidation using an electronic nose-ScienceDirec. Food Chem. 2016, 202, 229–235. [Google Scholar] [CrossRef] [PubMed]
  10. Aparicio, R.; Rocha, S.M.; Delgadillo, I.; Morales, M.T. Detection of rancid defect in virgin olive oil by the electronic nose. J. Agric. Food Chem. 2000, 48, 853–860. [Google Scholar] [CrossRef]
  11. Yang, M.Y.; Han, K.Y. Analysis of lipid oxidation ofsoybean oil using the portable electron nose. Food Sci. Biotechnol. 2019, 9, 146–150. [Google Scholar]
  12. Fritsch, C.W. Measurements of frying fat deterioration: A brief review. J. Am. Oil Chem. Soc. 1981, 58, 272–274. [Google Scholar] [CrossRef]
  13. Sun, Y.; Zhang, M.; Fan, D. Effect of ultrasonic on deterioration of oil in microwave vacuum frying and prediction of frying oil quality based on low field nuclear magnetic resonance (LF-NMR). Ultrason.-Sonochem. 2019, 51, 77–89. [Google Scholar] [CrossRef] [PubMed]
  14. Correia, A.C.; Dubreucq, E.; Ferreira-Dias, S.; Lecomte, J. Rapid quantification of polar compounds in thermo-oxidized oils by HPTLC-densitometry. Eur. J. Lipid Sci. Technol. 2015, 117, 311–319. [Google Scholar] [CrossRef]
  15. Chen, J.; Zhang, H.; Ma, J.; Tuchiya, T.; Miao, Y. Determination of the degree of degradation of frying rapeseed oil using fourier-Transform infrared spectroscopy combined with partial least-squares regression. Int. J. Anal. Chem. 2015, 2015, 185367. [Google Scholar] [CrossRef] [PubMed]
  16. Farhoosh, R.; Moosavi, S. Carbonyl value in monitoring of the quality of used frying oils. Anal. Chim. Acta. 2008, 617, 18–21. [Google Scholar] [CrossRef]
  17. Rao, Y.; Xiang, B.; Zhou, X.; Wang, Z.; Xie, S.; Xu, J. Quantitative and qualitative determination of acid value of peanut oil using near-infrared spectrometry. J. Food Eng. 2009, 93, 249–252. [Google Scholar] [CrossRef]
  18. Armenta, S.; Garrigues, S.; Guardia, M. Determination of edible oil parameters by near infrared spectrometry. Anal. Chim. Acta. 2007, 596, 330–337. [Google Scholar] [CrossRef]
  19. Arroyo-Manzanares, N.; Martin-Gomez, A.; Jurado-Campos, N.; Garrido-Delgado, R.; Arce, C.; Arce, L. Target vs spectral fingerprint data analysis of Iberian hamsamples for avoiding labelling fraud using headspace-gas chromatography-ion mobility spectrometry. Food Chem. 2018, 246, 65–73. [Google Scholar] [CrossRef]
  20. Arroyo-Manzanares, N.; Garcia-Nicolas, M.; Castell, A.; Campillo, N.; Vinas, P.; LopezGarcia, I.; Hernandez-Cordoba, M. Untargeted headspace gas chromatography-Ion mobility spectrometry analysis for detection of adulterated honey. Talanta 2019, 205, 120123. [Google Scholar] [CrossRef]
  21. Garrido-Delgado, R.; Mercader-Trejo, F.; Sielemann, S.; Bruyn, W.; Arce, L.; Valcárcel, M. Direct classification of olive oils by using two types of ion mobility spectrometers. Anal. Chim. Acta. 2011, 696, 108–115. [Google Scholar] [CrossRef] [PubMed]
  22. Xu, X.; Sun, C.; Liu, B.; Zhou, Q.; Xu, P.; Liu, M.; Wang, A.; Tian, H.; Luo, W.; Jiang, Q. Flesh flavor of red swamp crayfish (Procambarus clarkii Girard, 1852) processing by GS-IMS and electronic tongue is changed by dietary animal and plant protein. Food Chem. 2022, 373, 131453. [Google Scholar] [CrossRef]
  23. Li, W. GC×GC-ToF-MS and GC-IMS based volatile profile characterization of the Chinese dry-cured hams from different regions. Food Res. Int. 2021, 142, 110222. [Google Scholar] [CrossRef]
  24. Yang, L.; Liu, J.; Wang, X.; Wang, R.; Ren, F.; Zhang, Q.; Shan, Y.; Ding, S. Characterization of volatile component changes in Jujube fruits during cold storage by ising headspace-gas chromatography-ion mobility spectrometry. Molecules 2019, 24, 3904. [Google Scholar] [CrossRef]
  25. Gao, Q.; Moschytz, G.S. Fingerprint Feature Extraction Using CNNs. In Proceedings of the European Conference on Circuit Theory and Design, Espoo, Finland, 28–31 August 2001; pp. 97–100. [Google Scholar]
  26. Yivlialin, R.; Galli, A.; Raimondo, L.; Martini, M.; Sassella, A. Detecting the NIR fingerprint of colors: The characteristic response of modern blue pigments. Heritage 2019, 2, 2255–2261. [Google Scholar] [CrossRef]
  27. Chelouche, S.; Chelouche, D.; Benlemir, R.; Soudani, A. Principal component analysis of FTIR data to accurately assess the Real/Equivalent In-Service-Times of homogenous solid propellant. Propellants Explos. Pyrotech. 2022, 6, e202100352. [Google Scholar] [CrossRef]
  28. Wu, Z.; Chen, L.; Wu, L.; Xue, X.; Zhao, J.; Li, Y.; Ye, Z.; Lin, G. Classification of Chinese honeys according to their floral origins using elemental and stable isotopic compositions. J. Agric. Food Chem. 2015, 63, 5388–5394. [Google Scholar] [CrossRef]
  29. Sun, Z.; Lyu, Q.; Chen, L.; Zhuang, K.; Wang, G.; Ding, W.; Wang, Y.; Chen, X. An HS-GC-IMS analysis of volatile flavor compounds in brown rice flour and brown rice noodles produced using different methods. LWT-Food Sci. Technol. 2022, 161, 113358. [Google Scholar] [CrossRef]
  30. Dirinck, I.; Van Leuven, I.; Dirinck, P. Dirinck. SDE-GC-MS profiling of green and roasted coffee beans from different origin. Czech J. Food Sci.-UZPI (Czech Repub.) 2000, 18, 50–51. [Google Scholar]
  31. Hsiao, M.C.; Liao, P.H.; Lan, N.V.; Hou, S.-S. Enhancement of Biodiesel Production from High-Acid-Value Waste Cooking Oil via a Microwave Reactor Using a Homogeneous Alkaline Catalyst. Energies 2021, 14, 437. [Google Scholar] [CrossRef]
  32. Nguyen, L.P.; Tran, T.; Tran, T.; Ngo, P.; Ha, Q.; Luong, T.N.; Tran, T.; Phan, T. High-efficient production of biofuels using spent fluid catalytic cracking (FCC) catalysts and high acid value waste cooking oils. Renew. Energy 2021, 168, 57–63. [Google Scholar]
  33. Qazuini, M.; Saloko, S. The influence of heating time of coconut milk on the influence of heating time of coconut milk on free fatty acids formation free fatty acids formation. J. Teknol. Pertan. 2012, 168, 59–63. [Google Scholar]
  34. Fujimoto, K. The effect of oxygen concentration on oxidative deterioration in frying oil. Food Sci. Biotechnol. 2003, 12, 694–699. [Google Scholar]
  35. Abe, Y.; Toba, M.; Mochizuki, T.; Yoshimura, Y. Oxidative degradation behavior of fatty acid methyl ester in fish oil biodiesel and improvement of oxidation stability by partial hydrogenation. Bull. Jpn. Pet. Inst. 2009, 52, 307–315. [Google Scholar] [CrossRef]
  36. Toth, P.P. Triglycerides and Atherosclerosis. J. Am. Coll. Cardiol. 2021, 77, 3042–3045. [Google Scholar] [CrossRef]
  37. Lin, C.; Rong, L.I.; Tao, L.; Zi-Tao, J. Effects of Cold Pressing and Hot Pressing on Acidic Value and Unsaturated Fatty Acid Contents of Perilla Oil. Food Sci. 2014, 35, 279–282. [Google Scholar]
  38. Noce, A.; Marrone, G.; Urciuoli, S.; Di Daniele, F.; Di Lauro, M.; Zaitseva, A.P.; Di Daniele, N.; Romani, A. Usefulness of Extra Virgin Olive Oil Minor Polar Compounds in the Management of Chronic Kidney Disease Patients. Nutrients 2021, 13, 581. [Google Scholar] [CrossRef]
  39. Liu, Y.; Wang, Y.; Zhang, Z.; Yang, S.b. Rapid detection of the polar component content of frying oil by conductivity method. J. Chin. Cereals Oils Assoc. 2015, 30, 53–56. [Google Scholar]
  40. Gil, B.; Cho, Y.J.; Yoon, S.H. Rapid determination of polar compounds in frying fats and oils using image analysis. LWT-Food Sci. Technol. 2004, 37, 657–661. [Google Scholar] [CrossRef]
  41. Tata, A.; Massaro, A.; Damiani, T.; Piro, R.; Dall’Asta, C.; Suman, M. Detection of soft-refined oils in extra virgin olive oil using data fusion approaches for LC-MS, GC-IMS and FGC-Enose techniques: The winning synergy of GC-IMS and FGC-Enose. Food Control 2022, 133, 108645. [Google Scholar] [CrossRef]
Molecules 29 03979 g001aMolecules 29 03979 g001b
Figure 1. The abbreviations MS0, MS8, MS16, MS24, MS32, MS40, MS48, MS56, MS64, MS72, and MS80 represent 0, 8, 16, 24, 32, 40, 48, 56, 64, 72, and 80 h. (A) Three-dimensional topographic plots of volatile compounds in cottonseed oil during frying. (B) Two-dimensional topographic plots of volatile compounds during frying in cottonseed oil (top view). (C) Comparison and difference spectrum of volatile substances in cottonseed oil during frying. (D) Fingerprint of volatile compounds in cottonseed oil during frying.
Figure 1. The abbreviations MS0, MS8, MS16, MS24, MS32, MS40, MS48, MS56, MS64, MS72, and MS80 represent 0, 8, 16, 24, 32, 40, 48, 56, 64, 72, and 80 h. (A) Three-dimensional topographic plots of volatile compounds in cottonseed oil during frying. (B) Two-dimensional topographic plots of volatile compounds during frying in cottonseed oil (top view). (C) Comparison and difference spectrum of volatile substances in cottonseed oil during frying. (D) Fingerprint of volatile compounds in cottonseed oil during frying.
Molecules 29 03979 g001aMolecules 29 03979 g001b
Molecules 29 03979 g002
Figure 2. PCA of volatile compounds in cottonseed oil during frying.
Figure 2. PCA of volatile compounds in cottonseed oil during frying.
Molecules 29 03979 g002
Molecules 29 03979 g003
Figure 3. Support vector machine model analysis diagram of naan before and after being discarded in the process of frying in cottonseed oil; class1 and class2 present naan before and after being discarded in the process of frying in cottonseed oil.
Figure 3. Support vector machine model analysis diagram of naan before and after being discarded in the process of frying in cottonseed oil; class1 and class2 present naan before and after being discarded in the process of frying in cottonseed oil.
Molecules 29 03979 g003
Table 1. Volatile substances identified during frying in cottonseed oil.
Table 1. Volatile substances identified during frying in cottonseed oil.
No.CompoundsFormulaRIRt (s)Dt (RIP Relative)Comment
1(E)-non-2-enalC9H16O11936291.4138
2Nonyl aldehydeC9H18O11125121.4750Monomer
3Nonyl aldehydeC9H18O11115111.9491Dimer
4Trans-2-Octen-1-alC8H14O10584351.3391Monomer
5Trans-2-Octen-1-alC8H14O10584341.8250Dimer
6Octyl aldehydeC8H16O10083621.4036Monomer
7Octyl aldehydeC8H16O10073611.8284Dimer
82-pentylfuranC9H14O9973461.2560
9Trans-2-HeptenalC7H12O9563111.2575Monomer
10Trans-2-HeptenalC7H12O9593141.6738Dimer
11HeptaldehydeC7H14O9012641.3286Monomer
12HeptaldehydeC7H14O9022651.7013Dimer
132-HeptanoneC7H14O8942581.2632Monomer
142-HeptanoneC7H14O8932581.6372Dimer
15Trans-2-hexenalC6H10O8492341.1847Monomer
16Trans-2-hexenalC6H10O8522351.5234Dimer
172-FuraldehydeC5H4O28322241.0866Monomer
182-FuraldehydeC5H4O28292231.3361Dimer
19HexanalC6H12O7952051.2574Monomer
20HexanalC6H12O7952051.5651Dimer
211-PentanolC5H12O7691931.2556Monomer
221-PentanolC5H12O7661921.5138Dimer
231-Octen-3-olC8H16O9853361.1616
24Trans,trans-2,4-HeptadienalC7H10O10143711.1914
25Tetrahydrothiophen-3-oneC4H6OS9433001.1802
26(E)-pent-2-en-1-alC5H8O7551871.1076Monomer
27(E)-pent-2-en-1-alC5H8O7551871.3636Dimer
28Trans,trans-2,4-HeptadienalC7H10O10133691.6262
29CyclohexanoneC6H10O8982621.1523
30Hexa-2,4-dienalC6H8O9132741.1118Dimer
312-HexanoneC6H12O7872001.5056
32Ethyl acetoacetateC6H10O39172781.1479
33Allylmethyl disulphideC4H8S29372951.1441
34ValeraldehydeC5H10O6961631.1830
35ValeraldehydeC5H10O6991641.4278
36CrotonaldehydeC4H6O6551511.0363
37CrotonaldehydeC4H6O6501501.2045
382-MethacrylaldehydeC4H6O5831311.2210
39Ethyl vinyl ketoneC5H8O6821581.3167
402-ButanoneC4H8O5921341.2495
412-AcetylthiazoleC5H5NOS10063601.1301
42Gamma-butyrolactoneC4H6O29182791.3050
43n-Butyric acidC4H8O28172161.1620
44Propanoic acidC3H6O26841591.2641
452-n-ButylfuranC8H12O8942581.1835
462-PentanoneC5H10O6931621.3728
47ButyraldehydeC4H8O5971351.2911
48Acetic acidC2H4O2593.21341.1543
RI: the retention index, Rt: the retention time, Dt: the drift time.
Table 2. Correlation analysis between acid value and volatile substances of deep-fried noodle cake during frying in cottonseed oil.
Table 2. Correlation analysis between acid value and volatile substances of deep-fried noodle cake during frying in cottonseed oil.
Acid Value2-Methyl-2-propen-1-olEmpirical Formula2-n-Butylfuran2-ButanoneAllylmethyl
Disulphide		Butyraldehyde2-Butanone(E)-Non-2-enal(E)-Pent-2-en-1-al
Monomer		(E)-Pent-2-en-1-al
Dimer
Acid value1
2-Methyl-2-propen-1-ol0.793 **1
Empirical Formula0.891 **0.964 **1
2-n-Butylfuran0.858 **0.962 **0.970 **1
2-Butanone0.954 **0.771 **0.891 **0.817 **1
Allylmethyl disulphide0.912 **0.909 **0.945 **0.927 **0.882 **1
Allylmethyl disulphide0.937 **0.720 *0.812 **0.808 **0.889 **0.879 **1
2-Butanone0.753 **0.4190.5410.4780.771 **0.612 *0.5911
(E)-non-2-enal0.843 **0.709 *0.801 **0.706 *0.889 **0.717 *0.641 *0.808 **1
(E)-pent-2-en-1-al
Monomer		−0.374−0.099−0.104−0.138−0.184−0.202−0.26−0.535−0.3211
(E)-pent-2-en-1-al
Dimer		0.676 *0.680 *0.770 **0.711 *0.792 **0.705 *0.5330.632 *0.759 **0.1871
Results are presented as mean ± SEM with the level of significance, * p < 0.05, ** p < 0.01.
Table 3. Correlation analysis between polar components and volatile substances of naan during frying in cottonseed oil.
Table 3. Correlation analysis between polar components and volatile substances of naan during frying in cottonseed oil.
Polar Components2-Methyl-2-propen-1-olEmpirical Formula2-n-Butylfuran2-ButanoneAllylmethyl
Disulphide		Butyraldehyde2-Butanone(E)-Non-2-enal(E)-Pent-2-en-1-al
Monomer		(E)-Pent-2-en-1-al
Dimer
Polar components1
2-Methyl-2-propen-1-ol0.849 **1
Empirical Formula0.939 **0.964 **1
2-n-Butylfuran0.872 **0.962 **0.970 **1
2-Butanone0.959 **0.771 **0.891 **0.817 **1
Allylmethyl disulphide0.936 **0.909 **0.945 **0.927 **0.882 **1
Allylmethyl disulphide0.850 **0.720 *0.812 **0.808 **0.889 **0.879 **1
2-Butanone0.741 **0.4190.5410.4780.771 **0.612 *0.5911
(E)-non-2-enal0.880 **0.709 *0.801 **0.706 *0.889 **0.717 *0.641 *0.808 **1
(E)-pent-2-en-1-al
Monomer		−0.249−0.099−0.104−0.138−0.184−0.2020−0.26−0.535−0.3211
(E)-pent-2-en-1-al
Dimer		0.797 **0.680 *0.770 **0.711 *0.792 **0.705 *0.5330.632 *0.759 **0.1871
Results are presented as mean ± SEM with the level of significance, * p < 0.05, ** p < 0.01.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ye, L.; Song, L.; Zhang, L.; Cui, R. Waste Point Identification of Frying Oil Based on Gas Chromatography–Ion Mobility Spectrometry (GC-IMS). Molecules 2024, 29, 3979. https://doi.org/10.3390/molecules29163979

AMA Style

Ye L, Song L, Zhang L, Cui R. Waste Point Identification of Frying Oil Based on Gas Chromatography–Ion Mobility Spectrometry (GC-IMS). Molecules. 2024; 29(16):3979. https://doi.org/10.3390/molecules29163979

Chicago/Turabian Style

Ye, Lin, Lijun Song, Li Zhang, and Ruiguo Cui. 2024. "Waste Point Identification of Frying Oil Based on Gas Chromatography–Ion Mobility Spectrometry (GC-IMS)" Molecules 29, no. 16: 3979. https://doi.org/10.3390/molecules29163979

APA Style

Ye, L., Song, L., Zhang, L., & Cui, R. (2024). Waste Point Identification of Frying Oil Based on Gas Chromatography–Ion Mobility Spectrometry (GC-IMS). Molecules, 29(16), 3979. https://doi.org/10.3390/molecules29163979

Article Metrics

Back to TopTop