Characterization of donkey-meat flavor profiles by GC–IMS and multivariate analysis

Abstract

The distinctive flavor compounds of donkey meat are unknown. Accordingly, in the present study, the volatile compounds (VOCs) in the meat from SanFen (SF) and WuTou (WT) donkeys were comprehensively analyzed by gas chromatography–ion mobility spectrometry (GC-IMS) combined with multivariate analysis. A total of 38 VOCs, of which 33.33% were ketones, 28.89% were alcohols, 20.00% were aldehydes, and 2.22% were heterocycles, were identified. Ketones and alcohols were significantly more abundant for SF than for WT, whereas aldehydes showed the opposite trend. The donkey meats from the two strains were well differentiated using topographic plots, VOC fingerprinting, and multivariate analysis. A total of 17 different VOCs were identified as potential markers for distinguishing the different strains, including hexanal-m, 3-octenal, oct-1-en-3-ol, and pentanal-d. These results indicate that GC–IMS combined with multivariate analysis is a convenient and powerful method for characterizing and discriminating donkey meat.

1. Introduction

Donkey husbandry is an important industry in China and has become instrumental in optimizing animal farming practices, enriching the number and types of animal products available to the consumer, meeting diversified consumption demands, increasing farmers’ incomes, and implementing rural revitalization (1). Donkey meat has gained increasing popularity because of its nutritional value and distinctive flavor. It is a tender, low-calorie meat with high protein and polyunsaturated fatty acid contents. Accordingly, it has become accepted as a high-quality meat, and it accounts for more than 80% of the economic benefits of donkey breeding and production (2–4).

Flavor is one of the most important factors of meat quality. Meat flavor is the result of the combination of volatile compounds (VOCs), which are sensed by smell and taste (5). Meat flavor directly affects sensory characteristics, food quality, and consumers’ purchase intention, and it is affected by the source, type, and processing of the meat (6, 7). More than 1,000 VOCs belonging to nine categories, including alcohols, aldehydes, esters, ketones, and acids, have been identified in meat and meat products (8). VOCs contribute to the characteristic flavor of meats and are therefore closely related to consumer preference (9). For instance, a large number of VOCs, including aldehydes, ketones, alcohols, carboxylic acids, and esters, have been detected in mutton, with 4-methyl octanoic acid and 4-ethyl octanoic acid playing major roles in mutton smell (10); while aldehydes are the main VOCs in donkey meat, as revealed by solid-phase microextraction–gas chromatography–mass spectrometry (SPME–GC–MS), with hexanal the most abundant volatile flavor compound (3, 11). Other SPME-GC–MS studies have shown that maltotriose, L-glutamate, and L-proline are the main contributors to the unique taste of donkey meat (12). GC–MS or gas chromatography–olfactometry–mass spectrometry (GC–O–MS) are commonly used to identify the VOCs in meat and meat products (13). However, these methods require complex sample pre-treatments (e.g., heating, distillation, and extraction) and long detection times, leading to untimely and inaccurate determination results (14).

Gas chromatography–ion mobility spectroscopy (GC–IMS), which has the advantages of rapid detection, stable results, and convenient operation without sample pre-treatment, is a new method to detect the VOCs of meat and meat products (15, 16). Using GC–IMS in combination with principal component analysis (PCA), beef, mutton, and chicken were distinguished with a classification accuracy of 98.3% (17). Furthermore, GC–IMS and statistical methods have been used to identify significant differences in the types and contents of VOCs in bacon from different pork breeds (18). In addition, the VOCs of Jinhua ham aged for different times and dry-cured pork with different salt contents have been analyzed based on GC–IMS and chemometrics analysis (19, 20). However, GC–IMS of VOCs combined with multivariate analysis has not been applied to establishing a methodology for the identification and analysis of donkey meat.

Accordingly, in the present study, the VOC profiles of donkey meat from SanFen (SF) and WuTou (WT) donkeys were comprehensively analyzed and compared by GC–IMS combined with multivariate analysis. Ultimately, our results facilitate a better understanding of the characteristic VOCs of donkey meat and provide a novel strategy for its authentication.

2. Materials and methods

2.1. Sample collection

A total of 12 healthy two-year-old Dezhou donkeys including 6 SF donkeys and 6 WT donkeys, with both containing 3 males and 3 females, which were obtained from a local farm in Liaocheng (Shandong, China). All donkeys were fed same diet and raised under the similar condition. Donkeys were transported to a local slaughter house (Shandong Dong’a Tianlong Food Co., Ltd., China) and killed according international standards (CAC/RCP 41-1993 and ISO/TS 34700: 2016) after starvation for 12 h. After slaughter, the longissimus dorsi (Between the 17th and 18th ribs) was acquired. The muscle was immediately frozen in liquid nitrogen and stored at −80°C in a refrigerator for GC–IMS analysis. Animal experiments were approved by the Animal Care and Use Committee of Liaocheng University (2023022706).

2.2. GC–IMS

The VOCs in the donkey meat samples were analyzed using a FlavourSpec® (Gesellschaft für Analytische Sensorysteme GmbH, G.A.S., Dortmund, Germany) GC–IMS unit equipped with a capillary column (MXT-5, 15 m × 0.53 mm × 1.0 μm) and an automatic headspace sampling unit (CTC-PAL, CTC Analytics AG, Zwingen, Switzerland).

A meat sample (1.5 g) was placed into a 20-mL-headspace glass bottle and incubated at 60°C for 15 min with spinning at 500 rpm. Subsequently, 500 μL of the headspace gas was automatically injected into the apparatus. The temperature of the injector was set to 85°C. The GC column temperature was 40°C with ≥99.999% purity nitrogen used as the carrier gas. The programmed flow of the carrier gas was 0–2 min, 2 mL/min; 2–10 min, 2–20 mL/min; 10–20 min, 20–100 mL/min. The 9.8-cm long drift tube and drift temperatures of the IMS instrument were 60°C and 45°C, respectively. The voltage of the drift tube was set at 5 kV. Drift gas was ≥99.999% purity nitrogen at a flow rate of 150 mL/min. 3H ionization was performed in positive ion mode.

2.3. VOCs identification

The retention indices (RIs) of the volatiles were compared with those of C4–C9 n-ketones (Sinopharm Chemical Reagent Beijing Co., Ltd., China) obtained under the same analytical conditions. The RIs and drift times (DTs) of the standards in the NIST (National Institute of Standards and Technology, Gaithersburg, MD, United States) 2014 library and GC–IMS database (G.A.S., Dortmund, Germany) were used to identify the VOCs.

2.4. Statistical analysis

Each sample was analyzed in triplicate and the data are represented as mean ± standard error of mean (SEM). Results were analyzed using SPSS version 24.0 (SPSS Inc., Chicago, IL, United States) with one-way analysis of variance (ANOVA) and Tukey’s test to evaluate the differences among them. p < 0.05 was regarded as significant. The spectra and fingerprints were processed using the Reporter plug-in and Gallery Plot plug-in, respectively. PCA, partial least squares discriminant analysis (PLS-DA), orthogonal PLS-DA, and heatmap analysis were performed using MetaboAnalyst 5.0 online software.¹ Differential VOCs were determined according to a variable importance in projection (VIP) of >1 and p < 0.05.

3. Results

3.1. VOC profiles of donkey meats

As shown in Figure 1 and Table 1, 45 VOCs were detected (15 ketones, 13 alcohols, 9 aldehydes,1 heterocycle, and 7 unidentified, i.e., 33.33% ketones, 28.89% alcohols, 20.00% aldehydes, 2.22% heterocycles, and 15.56% unidentified), 38 of which were identified in both strains. Thus, ketones, aldehydes, and alcohols are the most abundant VOCs in donkey meat (Figure 1D). Ketones and alcohols are significantly more abundant in SF than in WT donkeys (p < 0.001; p < 0.05), whereas aldehydes show the opposite trend (p < 0.001; Figure 1E).

Figure 1

3.2. Comparison of VOCs from the two strains

As shown in Figure 2A, the results show good repeatability according to topographic plots. Significant difference was observed between the fingerprints of the two strains (Figure 2B). In addition, isopropyl alcohol, ethanol, acetone, benzaldehyde, 2-pentanone-d, 2-pentanone-m, oct-1-en-3-ol, 3-octenal, pentanal-d, pentanal-m, methyl isobutyl ketone-d, methyl isobutyl ketone-m, heptanal-d, and heptanal-m present different signals and thus represent differential components in the fingerprint regions of the donkey meats from the two strains (Figure 2C).

Figure 2

3.3. Multivariate analysis of VOCs

To better present and distinguish the differences between the donkey meats from the two strains, PCA, PLS-DA, and OPLS-DA analysis was performed (Figure 3). As shown in Figure 3A, the donkey meat samples are well differentiated according to strain using PCA, which is consistent with the results of PLS-DA and OPLS-DA (Figures 3B,C). Figure 3D shows R² and Q² intercept values are (0, 0.71) and (0, −0.26). All the Q² points are lower than the rightmost original Q² point, and the Q² regression line is less than 0 at the intersection of the vertical coordinates, indicating that the OPLS-DA model is robust, reliable, and free from over-fitting. These results indicate that the strains of donkey meat can be well differentiated according to their VOCs using multivariate analysis.

Figure 3

The VIP results for OPLS-DA are shown in Figure 4A, revealing that 17 differential VOCs were identified in donkey meats from the two strains by applying a criteria of VIP > 1. A total of 7 downregulated and 10 upregulated VOCs were identified for SF compared with WT by setting VIP > 1 and p < 0.05 (Figure 4B). These differential VOCs belong to four categories: 7 alcohols, 7 aldehydes, 1 heterocycle, and 2 ketones (Table 2). The levels of tetrahydrofurane, hexanal-m, pentan-1-ol-d, pentan-1-ol-m, 3-octenal, oct-1-en-3-ol, pentanal-d, (e)-hept-2-enal, pentanal-m, and hexanal-d are significantly lower for SF than for WT (p < 0.05), whereas 2-butanol, 2-methyl-1-propanol, benzaldehyde, ethanol, isopropyl alcohol, acetone, and 2-pentanone-m show the opposite trend (p < 0.05, Figure 4C; Table 2).

Figure 4

4. Discussion

In this study, the VOCs in donkey meat were identified and analyzed by GC–IMS with chemometrics analysis. Accordingly, 38 VOCs belonging to four categories were identified in raw donkey meat. This is significantly lower than the number of VOCs previously identified by GC–MS in cooked donkey meat and cooked horse meat (11, 21). It is well known that VOCs are more abundant in cooked meat and that they are generated by the Maillard reaction between amino compounds and reducing sugars, lipid degradation, and lipid-Maillard interactions during heating (22). The present study has shown that ketones, alcohols, and aldehydes are the most abundant VOCs in uncooked donkey meat and that aldehydes are the characteristic VOCs in WT donkey meat. This is consistent with previous GC–MS results for Xiaohei donkey meat (3). The levels of ketones, alcohols, and aldehydes in SF and WT donkey meats are significantly different, with aldehydes being more abundant in WT donkey meat. Previous studies have demonstrated that the concentrations of different types of VOCs differ between breeds of pork and chicken (23, 24).

The GC-IMS spectra and fingerprints visually represent the results of flavor measurement between samples, where the fingerprint can be used to intuitively and quantitatively compare the differences in VOC profiles between samples (15). In the present study, significant differences were observed in the spectra of SF and WT donkey meats. These results were confirmed by their VOC fingerprints, where ethanol and hexanal-d show very clear differences between the two strains. This indicates that differences between SF and WT donkey meat samples can be quickly identified by GC–IMS analysis through their VOC profiles, which is in agreement with previous studies on pork from different pig breeds (18). Multivariate analysis, including unsupervised pattern recognition (UPR) and supervised pattern recognition (SPR), is used to analyze omics data from different experimental groups (25). UPR (PCA and hierarchical cluster analysis) is used to analyze data without grouping samples, which ignores random errors in the group (26). SPR includes PLS-DA and OPLS-DA analysis, which eliminates the deficiency of UPR, i.e., easy overfitting (27). Permutation validation is applied in OPLS-DA analysis to determine the overfitting of a model (28). In the present study, multivariate analysis methods (PCA, PLS-DA, OPLS-DA, and heatmap analysis) were used to confirm the accuracy of the data and fingerprints obtained by GC–IMS. The VOCs of donkey meats from the different strains were well differentiated using PCA, PLS-DA, and OPLS-DA analysis, and OPLS-DA eliminated over-fitting in the current study. A total of 17 differential VOCs were identified between the different strains, including hexanal-m, 3-octenal, oct-1-en-3-ol, hexanal-d, ethanol, isopropyl alcohol, and acetone, which is consistent with the fingerprint results. These results demonstrate that multivariate analysis can be applied to distinguishing different samples and screening biomarkers from GC–IMS data.

Previous studies have shown that the VOCs in meat are breed-dependent (29). This is consistent with the present study, in which the VOCs in donkey meat were also found to be strain-dependent. In this study, tetrahydrofurane, hexanal-m, pentan-1-ol-d, pentan-1-ol-m, 3-octenal, oct-1-en-3-ol, pentanal-d, (e)-hept-2-enal, pentanal-m, and hexanal-d were identified as characteristic VOCs of WT donkey meat, whereas 2-butanol, 2-methyl-1-propanol, benzaldehyde, ethanol, isopropyl alcohol, acetone, and 2-pentanone-m were identified as characteristic VOCs of SF donkey meat. This is consistent with the findings reported for Xiaohei donkey meat (3). The formation of VOCs is complex and their sources are extensive, among which, lipids are the main sources of meat flavor development. Lipid degradation is the basis for the formation of unique meat flavor compounds such as aldehydes, ketones, and alcohols (30). 18:1 fatty acid is oxidized to produce octanal, nonanal, and 2-undecenal; 18:2 fatty acid is oxidized to produce hexanal, 2-nonenal, (E,E)-2,4-decadienal, and 2-pentylfuran; and 20:4 fatty acid is oxidized to produce heptanal, 1-octene-3-ol (31). Recent research has shown that the fatty acid compositions of SF and WT donkey meats are not significantly different, and 37 different lipid molecules were identified between them, mainly including glycerolipids and glycerophospholipids (32). Fatty acids at different positions of lipid molecules also affect the formation of VOCs. For example, fatty acids in glycerophospholipids at the sn-2 site show high thermal oxidative stability, whereas triglyceride is more readily hydrolyzed to free fatty acids and then oxidized to form VOCs (33, 34). In addition, VOCs can undergo further degradation into other VOCs, as observed in different types of meat (35).

5. Conclusion

In the present study, VOCs in donkey meats from different strains were comprehensively analyzed and compared using GC–IMS. Overall, 38 VOCs belonging to four different compound categories were identified. The donkey meats from the two strains can be differentiated by using their VOC fingerprints and multivariate analysis, with 17 different VOCs being identified as potential markers to distinguish different donkey meat strains. To conclude, our results may facilitate a better understanding of the characteristic VOCs of donkey meat and provide a novel strategy for its authentication. However, the characteristic VOCs of donkey meat are not clear and require further studies.

Funding

This work was supported by the Shandong Provincial Natural Science Foundation (ZR2022QE143 and ZR2022QC130), the Shandong Province Modern Agricultural Technology System Donkey Industrial Innovation Team (SDAIT-27), the Scientific Research Fund of Liaocheng University (318052019 and 318052057), the Open Project of Liaocheng University Animal Husbandry Discipline (319462207-10), the Livestock and Poultry Breeds Project of Ministry of Agriculture and Rural Affairs (19211162), the Shandong Rural Revitalization Science and Technology Innovation Action Plan (2021TZXD012), and the Innovation and Entrepreneurship Training Program for College Students (CXCY2022176 and CXCY2022381).

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

• 1.

  SeyitiSKelimuA. Donkey industry in China: current aspects, suggestions and future challenges. J Equine Vet Sci. (2021) 102:103642. doi: 10.1016/j.jevs.2021.103642

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 2.

  LiMZhangDChaiWZhuMWangYLiuYet al. Chemical and physical properties of meat from Dezhou black donkey. Food Sci Technol Res. (2022) 28:87–94. doi: 10.3136/fstr.FSTR-D-21-00149

  • CrossRef
  • Google Scholar

• 3.

  LiXAmadouIZhouGQianLZhangJWangDet al. Flavor components comparison between the neck meat of donkey, swine, bovine, and sheep. Food Sci Anim Resour. (2020) 40:527–40. doi: 10.5851/kosfa.2020.e30

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4.

  MarinoRAlbenzioMDella MalvaAMuscioASeviA. Nutritional properties and consumer evaluation of donkey bresaola and salami: comparison with conventional products. Meat Sci. (2015) 101:19–24. doi: 10.1016/j.meatsci.2014.11.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 5.

  AaslyngMDMeinertL. Meat flavour in pork and beef – from animal to meal. Meat Sci. (2017) 132:112–7. doi: 10.1016/j.meatsci.2017.04.012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6.

  LiJZhangJYangYZhuJHeWZhaoQet al. Comparative characterization of lipids and volatile compounds of Beijing Heiliu and Laiwu Chinese black pork as markers. Food Res Int. (2021) 146:110433. doi: 10.1016/j.foodres.2021.110433

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7.

  MaughanCTansawatRCornforthDWardRMartiniS. Development of a beef flavor lexicon and its application to compare the flavor profile and consumer acceptance of rib steaks from grass- or grain-fed cattle. Meat Sci. (2012) 90:116–21. doi: 10.1016/j.meatsci.2011.06.006

  • CrossRef
  • Google Scholar

• 8.

  KosowskaMMajcherMAFortunaT. Volatile compounds in meat and meat products. Food Sci Tech. (2017) 37:1–7. doi: 10.1590/1678-457X.08416

  • CrossRef
  • Google Scholar

• 9.

  LiuPXiangQChenGLiuYZhaiGLuSXet al. Consumer preference of Chinese traditional fermented fava pastes. Int J Food Prop. (2018) 21:2469–90. doi: 10.1080/10942912.2018.1528271

  • CrossRef
  • Google Scholar

• 10.

  WatkinsPJKearneyGRoseGAllenDBallAJPethickDWet al. Effect of branched-chain fatty acids, 3-methylindole and 4-methylphenol on consumer sensory scores of grilled lamb meat. Meat Sci. (2014) 96:1088–94. doi: 10.1016/j.meatsci.2012.08.011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11.

  PolidoriPSantiniGKlimanovaYZhangJJVincenzettiS. Effects of ageing on donkey meat chemical composition, fatty acid profile and volatile compounds. Foods. (2022) 11:11. doi: 10.3390/foods11060821

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12.

  LiMRenWChaiWZhuMManLZhanYet al. Comparing the profiles of raw and cooked donkey meat by metabonomics and lipidomics assessment. Front Nutr. (2022) 9:851761–1. doi: 10.3389/fnut.2022.851761

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13.

  XiaQZhouCWuZPanDCaoJ. Proposing processomics as the methodology of food quality monitoring: reconceptualization, opportunities, and challenges. Curr Opin Food Sci. (2022) 45:100823. doi: 10.1016/j.cofs.2022.100823

  • CrossRef
  • Google Scholar

• 14.

  SongHLiuJ. GC-O-MS technique and its applications in food flavor analysis. Food Res Int. (2018) 114:187–98. doi: 10.1016/j.foodres.2018.07.037

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15.

  WangSChenHSunB. Recent progress in food flavor analysis using gas chromatography–ion mobility spectrometry (GC–IMS). Food Chem. (2020) 315:126158. doi: 10.1016/j.foodchem.2019.126158

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 16.

  ContrerasMDAparicioLArceL. Usefulness of GC-IMS for rapid quantitative analysis without sample treatment: focus on ethanol, one of the potential classification markers of olive oils. Lwt-food. Sci Technol. (2020) 120:108897. doi: 10.1016/j.lwt.2019.108897

  • CrossRef
  • Google Scholar

• 17.

  ChenTWuZWangZLuDChenB. Identification of meat species by gas chromatography-ion mobility spectrometry and chemometrics. J Chin Instit Food Sci Technol. (2019) 19:221–6. doi: 10.16429/j.1009-7848.2019.07.029

  • CrossRef
  • Google Scholar

• 18.

  DengSLiuYHuangFLiuJHanDZhangCet al. Evaluation of volatile flavor compounds in bacon made by different pig breeds during storage time. Food Chem. (2021) 357:129765. doi: 10.1016/j.foodchem.2021.129765

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 19.

  LiuDBaiLFengXChenYPZhangDYaoWet al. Characterization of Jinhua ham aroma profiles in specific to aging time by gas chromatography-ion mobility spectrometry (GC-IMS). Meat Sci. (2020) 168:108178. doi: 10.1016/j.meatsci.2020.108178

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 20.

  TianXLiZJChaoYZWuZQZhouMXXiaoSTet al. Evaluation by electronic tongue and headspace-GC-IMS analyses of the flavor compounds in dry-cured pork with different salt content. Food Res Int. (2020) 137:109456. doi: 10.1016/j.foodres.2020.109456

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21.

  BeldarrainLRMoránLSentandreuMÁBarronLJRAldaiN. Effect of ageing time on the volatile compounds from cooked horse meat. Meat Sci. (2022) 184:108692. doi: 10.1016/j.meatsci.2021.108692

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 22.

  SohailAAl-DalaliSWangJXieJShakoorAAsimiSet al. Aroma compounds identified in cooked meat: a review. Food Res Int. (2022) 157:111385. doi: 10.1016/j.foodres.2022.111385

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 23.

  HanDZhangCFauconnierMMiS. Characterization and differentiation of boiled pork from Tibetan, Sanmenxia and Duroc × (Landrac × Yorkshire) pigs by volatiles profiling and chemometrics analysis. Food Res Int. (2020) 130:108910. doi: 10.1016/j.foodres.2019.108910

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 24.

  WeiQCuiHHuYLiJYueSTangCet al. Comparative characterization of Taihe silky chicken and cobb chicken using LC/MS-based lipidomics and GC/MS-based volatilomics. LWT-Food Sci Technol. (2022) 163:113554. doi: 10.1016/j.lwt.2022.113554

  • CrossRef
  • Google Scholar

• 25.

  SongYCaiCSongYSunXLiuBXuePet al. A comprehensive review of lipidomics and its application to assess food obtained from farm animals. Food Sci Anim Resour. (2022) 42:1–17. doi: 10.5851/kosfa.2021.e59

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 26.

  HeJWuXZhouYChenJ. Effects of different preheat treatments on volatile compounds of camellia (Camellia oleifera Abel.) seed oil and formation mechanism of key aroma compounds. J Food Biochem. (2021) 45:e13649. doi: 10.1111/jfbc.13649

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 27.

  YiLDongNYunYDengBRenDLiuSet al. Chemometric methods in data processing of mass spectrometry-based metabolomics: a review. Anal Chim Acta. (2016) 914:17–34. doi: 10.1016/j.aca.2016.02.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 28.

  BoccardJRutledgeDN. A consensus orthogonal partial least squares discriminant analysis (OPLS-DA) strategy for multiblock omics data fusion. Anal Chim Acta. (2013) 769:30–9. doi: 10.1016/j.aca.2013.01.022

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29.

  WuWZhanJTangXLiTDuanS. Characterization and identification of pork flavor compounds and their precursors in Chinese indigenous pig breeds by volatile profiling and multivariate analysis. Food Chem. (2022) 385:132543. doi: 10.1016/j.foodchem.2022.132543

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 30.

  BassamSMNoleto-DiasCFaragMA. Dissecting grilled red and white meat flavor: its characteristics, production mechanisms, influencing factors and chemical hazards. Food Chem. (2022) 371:131139. doi: 10.1016/j.foodchem.2021.131139

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 31.

  DuWBZhaoMYZhenDWTanJWangTZXieJC. Key aroma compounds in Chinese fried food of youtiao. Flavour Fragr J. (2020) 35:88–98. doi: 10.1002/ffj.3539

  • CrossRef
  • Google Scholar

• 32.

  LiMSongYLiuBXuePRenWChaiWet al. Lipid profiles of meat from donkeys in Dezhou analyzed by liquid chromatography-mass spectrometry-based lipidomics. Food Sci. (2022) 43:249–55. doi: 10.7506/spkx1002-6630-20210816-200

  • CrossRef
  • Google Scholar

• 33.

  FrankDKaczmarskaKPatersonJPiyasiriUWarnerR. Effect of marbling on volatile generation, oral breakdown and in mouth flavor release of grilled beef. Meat Sci. (2017) 133:61–8. doi: 10.1016/j.meatsci.2017.06.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34.

  ZhouLZhaoMBindlerFMarchioniE. Comparison of the volatiles formed by oxidation of phosphatidylcholine to triglyceride in model systems. J Agric Food Chem. (2014) 62:8295–301. doi: 10.1021/jf501934w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 35.

  CasaburiAPiombinoPNychasGVillaniFErcoliniD. Bacterial populations and the volatilome associated to meat spoilage. Food Microbiol. (2015) 45:83–102. doi: 10.1016/j.fm.2014.02.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar