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ISSN : 1598-5504(Print)
ISSN : 2383-8272(Online)
Journal of Agriculture & Life Science Vol.56 No.2 pp.85-95
DOI : https://doi.org/10.14397/jals.2022.56.2.85

Changes of Volatile and Non-volatile Metabolites in Beef M. Longissimus Lumborum under Aerobic Storage Condition

Jun-young Park1, Jeong-Uk Eom2, Rashida Parvin3, Jin-Kyun Seo4, Hyun-Wook Kim5, Han-Sul Yang6*
1Master's degree, Division of Applied Life Science (BK21 plus), Gyeongsang National University, 501 Jinju-daero, Jinju-si, Gyeongsangnam-do, 52828, Republic of Korea
2Doctor's course, Division of Applied Life Science (BK21 plus), Gyeongsang National University, 501 Jinju-daero, Jinju-si, Gyeongsangnam-do, 52828, Republic of Korea
3Doctor's course, Division of Applied Life Science (BK21 plus), Gyeongsang National University, 501 Jinju-daero, Jinju-si, Gyeongsangnam-do, 52828, Republic of Korea
4Doctor's degree, Division of Applied Life Science (BK21 plus), Gyeongsang National University, 501 Jinju-daero, Jinju-si, Gyeongsangnam-do, 52828, Republic of Korea
5Professor, Department of Animal Science & Biotechnology, Gyeongsang National University, 33 Dongjinro, Jinju-si, Gyeongsangnam-do, 52725, Republic of Korea
6Professor, Division of Applied Life Science (BK21 plus), Institute of Agriculture and Life Science, Gyeongsang National University, 501 Jinju-daero, Jinju-si, Gyeongsangnam-do, 52828, Republic of Korea
* Corresponding author: Han-Sul Yang (Tel.) +82-55-772-1948 (E-mail) hsyang@gnu.ac.kr
November 24, 2021 February 24, 2022 April 5, 2022

Abstract


Meat affects color and quality by metabolite concentrations. Meat produces metabolites, and metabolites are caused by a variety of causes. Meat also produces metabolites by oxidation, which is an inevitable chemical process that meat undergoes which is resulting information of various chemical compounds. Thus, the aim of this study was to profiling the change of metabolites of M. longissimus lumborum during the storage at 4°C. Instrumental color measurements were showed decreasing chroma value, redness and yellowness (P<0.05) during storage, while non-significance (P>0.05) changes found in lightness value. Above all, hue angle was highest at 21 d of storage (P<0.05). The lipid and protein oxidation of muscles was measured by TBARS value significantly increased (P<0.05), thiol and carbonyl groups were also increased significantly (P<0.05) during the display. Total 19 of 60 identified compounds appeared to have a significant difference by storage time (P<0.05). Hue angle had a significant correlation with specific metabolites such as carbon disulfide, 3-methyl-1-butanol, 2-ethyl-1-hexanol, lactic acid and palmitic acid (P<0.05). Results of the current study provide the conversion of volatile and non-volatile metabolites and their correlation with oxidative indicators for changes in meat quality during aerobic storage.



초록


    Introduction

    The high protein content of meat and meat product plays a major role in the human daily diet. Meat has a variety of processes from production to sales, such as breed, breeding, and slaughter. Although meat is safely produced, if spoilage occurs during the sale process, it is impossible to supply safe protein. Meat preservation techniques and methods are developed and have been improving because meat oxidation and deterioration take place as a problem since its consumption began. Initially, from adding salts to meat for preservation, it reached specific packaging methods and materials such as modified-atmosphere packaging by science and research developed. In the past few decades, many types of researches were carried out regarding meat oxidation. For example, Min & Ahn (2005), reported that meat oxidation starts with oxygen anion changes into reactive oxygen species. Faustman & Cassens (1990) reviewed and suggest the possibility of interaction between lipid oxidation and myoglobin condition. Lund et al. (2011) also reviewed the mechanism of protein oxidation including its interaction with lipid oxidation, the effect on foods, and a possible strategy to extended shelf-life. To find out these results, a lot of experiments were conducted, bases on species, breed, or muscle types to effects of slaughtering, chilling, temperature, and packaging.

    Components of the muscle such as lipids or proteins are degraded through intricate oxidative processes at the end and produce amino acids, peptides, saccharides, inorganic salts, and inorganic acids. In addition, the decomposition of organized muscles into smaller size molecules occurs slowly. Metabolites analysis allows to detection and access of multiple reactive substances, like acids, alcohols, aldehydes, or ketones (MW < 1 kDa). Metabolic profiling through GC-MS based can detect low molecular weight components such as amino acids at the same time. The main element of fats and fatty acids includes phospholipids and to a lesser extent triacylglycerol plays a significant role in oxidizing flavor to particular meat species. For example, Ahn & Nam (2004) reported that metabolite changes of ascorbic acid and other antioxidants could occur in irradiated beef. Similarly, Ma et al. (2017) reported the effects of aging and muscle type on metabolites. Abraham et al. (2017) emphasize metabolites related to eating quality and color stability. Metabolic profiling is gradually and widely used in meat science, but few studies focused on its chemical oxidative changes. So, the understanding of the basic mechanism of oxidative stability of beef muscles is a crucial step to develop a practical storage condition to minimize oxidation-related quality defects while maintaining the beneficial impact of metabolites on meat quality attributes.

    Oxygen-permeable PVC (Poly Vinyl Chloride) wrapping is the most common packaging method in the retail sector, causes gradual oxidation of meat color and flavor. Consumers deeply rely on the visual appearance of meat color while purchasing. Discoloration of meat by peroxidation and the oxidation of myoglobin may result in refusal of meat procuring. The changes in the volatile components of vacuum-packed or wet-aged meat at different storage periods and condition’s for different species had already been reported in many studies.

    Therefore, changes in volatile and nonvolatile metabolite concentrations in meat with different storage periods can provide useful information to clarify the effect on meat color and meat quality. Thus, the study aimed to estimate the changes of the volatile and non-volatile metabolites of M. longissimus lumborum under the aerobic condition to provide basic information.

    Material and Methods

    1. Preparation of samples

    A total of four cattle (carcass weight 435 to 465 kg) were randomly selected at a slaughter plant. The muscle of beef Longissimus lumborum was dissected from each carcass 96 h postmortem. L. lumborum was divided into 4 pieces and randomized, and divided by storage period. The pH of beef was 5.69-5.75, respectively. All visible fats and connective tissue were removed from the fresh muscles. The crude fat content was determined on fresh muscles from each treatment according to Folch et al. (1957) procedure. The L. lumborum muscles were cut into 2.54 cm thick steak. All steaks were placed with a soaking pad on a foam tray, wrapped using an 0.013 mm thick oxygen-permeable polyvinylchloride film, and stored at 4℃ in a refrigerator for 28 d. One steak from each carcass was used as an initial sample (control) and labeled to 4 d standardized by slaughter date. The color, lipid oxidation, and protein oxidation of the steak samples were determined at 7, 14, 21, and 28 d of the storage period, and the samples for metabolite analysis were collected at 4 d and 28 d.

    2. Analysis methods

    2.1 Instrumental color measurement

    On each day for analysis, color values of beef steak were measured using CR-300 colorimeter (Minolta, Tokyo, Japan) after 30 min of blooming. Color measurements were performed with 5 replicates per steak. Standardization was accomplished using a standard white tile with Commission international d’Eclairage (CIE) standard illuminant C (Y=81.2; x=0.3214; y=0.3391) prior to the color measurement and CIE L*(lightness), a* (redness), b* (yellowness) were obtained, and hue angle [tan-1(b*/a*)] and chroma value [(a*2+b*2)1/2] were automatically calculated (Hunt et al., 1991).

    2.2 Lipid oxidation

    2-Thiobarbituric acid reactive substances (TBARS) measurement for evaluating lipid oxidation was conducted using the modified methods of Salih et al. (1987). Briefly, three grams of samples were homogenized with 27 mL of 3.86% perchloric acid at 14,000 rpm for 30 s. After keeping in the dark cold room for 1 h, samples were filtered through Whatman No.1. Two milliliters of 20 mM TBA solution were added to 2 mL of sample and allowed to stay for 15 h at room temperature. Then, the absorbance of the resulting supernatant solution was determined at 531 nm using a spectrophotometer (Agilent Co., CA. USA) against a blank containing 1 mL of deionized distilled water and 2 mL of TBA/TCA solution. TBARS values were calculated by multiplying 5.2 at absorbance and expressed as mg of malondialdehyde (MDA) per Kg sample.

    2.3 Protein oxidation

    Thiol contents in protein were measured based on Vossen & De Smet (2015) with some modifications. Briefly, 2 g of sample was homogenized with 50 mL of 5% sodium dodecyl sulfate (SDS) in 0.1 M Tris buffer (pH 8.0). After 1 h of incubation in the water bath at 80℃, samples were centrifuged at 7,000 g for 15 min. The upper layer was undergone suction for removing lipids. Remains were filtered through Whatman No.1 filter paper. One-hundred sixty μL of 0.1 M Tris buffer (pH 8.0) and 40 μL of 10 mM 5,5’-dithiobis (2-nitrobenzoic acid) (DTNB) in 0.1 M Tris buffer were added to 40 μL samples and stayed at dark cold room for 30 min. After the reaction, the absorbance was measured at 412 nM to determine thiol groups with reagent blank (40 μL of 5% SDS in Tris buffer (pH 8.0), 40 μL of 10 mM DTNB in Tris buffer (pH 8.0), and 160 μL of 0.1 M Tris buffer (pH 8.0) using a spectrophotometer (Epoch, Biotek Instruments, Inc., Winooski, VT, USA). To determine the protein content of samples, 200 μL of 0.1 M Tris buffer was added to 40 μL of samples, and BSA standard curve was applied to calculate the protein content at 280 nm. Thiol content was calculated by the following equation: [OD Ⅹ 25 (dilution factor) / 0.014 nM-1cm-1 (molecular extinction coefficient)] / protein concentration (mg).

    The formation of carbonyl content in protein was measured using the method of Vossen & De Smet (2015). Three grams of samples were homogenized with 30 mL of the 0.6 M NaCl in 20 mM phosphate buffer (pH 6.5). Four aliquots of 0.2 mL from each sample were taken, and 1 mL of ice-cold 10 % TCA solution was added to the samples. Then, samples were kept in the ice bath for 15 min. Mixed samples were centrifuged at 2,000 g for 30 min and the supernatant was discarded carefully. Then, 1 mL of ice-cold 10% TCA was added and the aforementioned procedure was repeated. After discarding the supernatant, 0.5 mL of 10 mM DNPH (2,4-dinitrophenylhydrazine) solutions were added to two aliquots for carbonyl content and 0.5 mL of 2.0 M HCl was added to the others as blank. After centrifugation and careful removal, 1 mL of ethanol: ethyl acetate (1:1; v/v) was added and centrifuged at 4,500 g for 15 min at 4℃ to wash the remaining DNPH 3 times. To evaporate the washing solution, samples were kept on the hood for 15 min. Dried samples were dissolved in 1 mL of 6 M guanidine hydrochloride on the shaker at 360 rpm until it was completely dissolved. Carbonyl contents have an optimal wavelength at 280~370 nM and are determined by the following equation with an appropriate blank.

    JALS-56-2-85_EQ1.gif

    2.4 Metabolites analysis

    The sample are randomized by grinding Longissimus lumborum and mixing them sufficiently. Three replicates were measured per sample.

    For volatile metabolite analysis, ground meat samples (1 g) with 2-methyl-1-pentanol as an internal standard were placed into a vial with a septum cap and heated at 30℃ for 30 min. After heating, a solid-phase microextraction (SPME) fiber (50/30 μM DVB/CAR/PDMS Stableflex, Sigma-Aldrich, Saint Louis, MI, USA) was inserted into the vial cap to absorb volatile compounds in the headspace at 30℃ for 10 min. The absorbed volatile compounds were then analyzed using a Shimadzu GC-2010 plus (Shimadzu, Tokyo, Japan). The absorbed volatile metabolites in the SPME fiber were injected into a DB-WAX capillary column (30 m Ⅹ 0.25 mm i.d. Ⅹ 0.25 μM film thickness, Restek, PA, USA) with splitless. The injector temperature was set to 200℃, the oven temperature program was initiated at 40℃ for 3 min, increased at a rate of 7 min-1 to 80℃, again increased at a rate of 25℃ min-1 to 240℃, and held for 5 min. Helium was used as the carrier gas at 1 mL/min. The GC column effluent was detected using a Shimadzu GCMS-TQ 8030 mass spectrometer. A 70 eV was used for ionization. The ion source and interface temperatures were 230℃ and 250℃, respectively. Data monitoring was performed in the full scan mode (m/z 45-550). The scan event time and velocity were 0.3 sec and 2000 amu/sec, respectively. Data sets obtained using a GC/MS were aligned with a retention time window of 0.1 ± 0.05 min and normalized with the internal standard. Retention indices (RIs) of metabolites were calculated using a series of C8-C40 n-alkanes. Metabolites were identified using calculated Ris, GC/MS databases (NIST 11 and Wiley 9 mass spectral libraries), and authentic standards.

    For non-volatile metabolite analysis, a ground meat sample (0.02 g) was extracted with 80% methanol including dicyclohexyl phthalate as an internal standard. Ten microliters of the extract were completely dried in a vacuum concentrator (CentriVap; Labconco Corp., Kansas City, MO, USA) at 60℃. For derivatization, the dried extract was methoximated with 70 UI of methoxyamine hydrochloride in pyridine (20 mg/mL), at 70℃ for 90 min, and then silylated with 70 μL of N,O-bis(trimethylsilyl) trifluoracetamide containing 1% trimethylchlorosilane at 37℃ for 30 min. The derivatized metabolites were then analyzed using a Shimadzu GC-2010 plus (Shimadzu, Tokyo, Japan). The non-volatile metabolites were injected into a DB-5 capillary column (30 m Ⅹ 0.25 mm i.d. Ⅹ 0.25 μM film thickness, Restek, PA, U.S.A.) with split 50 (1:1). The injector temperature was set to 200℃, the oven temperature program was initiated at 70℃ for 2 min, increased at a rate of 7 min-1 to 210℃, increased at a rate of 10℃ min-1 to 320℃, and held for 7 min. Helium was used as the carrier gas at 1 mL/min. The GC column effluent was detected using a Shimadzu GCMS-TQ 8030 mass spectrometer. A 70 eV was used for ionization. The ion source and interface temperatures were 230℃ and 280℃, respectively. Data monitoring was performed in the full scan mode (m/z 45-800). The scan event time and velocity were 0.3 sec and 3333 amu/sec, respectively. Data sets obtained using a GC/MS were aligned with a retention time window of 0.1 ± 0.05 min and normalized with the internal standard. Retention indices (RIs) of metabolites were calculated using a series of C8-C40 n-alkanes. Metabolites were identified using calculated RIs, GC/MS databases (NIST 11 and Wiley 9 mass spectral libraries), and authentic standards.

    3. Statistical analysis

    All conducted experiments were triplicated and the data were presented as means ± SE. The statistical analysis was performed using the analysis of variance (ANOVA) procedure of SAS program (9.4, SAS Institute, USA). Duncan’s multiple range tests were used for identifying the substantial difference of means amongst the storage time.

    Processed GC/MS data sets were analyzed with multivariate statistics using SIMCA-P+ version 12.0.1 (Umetrics, Umea, Sweden). Comparison of volatile/non-volatile profiling data between meat samples was visualized using Partial least squares discriminant analysis (PLS-DA). The quality of PLS-DA models was evaluated using two explainable variation parameters (R2X and R2Y) and predictable variation parameters (Q2), validated by permutation tests (n=200). Normalized GC/MS intensities of metabolites were statistically analyzed using one-way analysis of variance (ANOVA) with independent sample Duncan (P<0.05) using SPSS 21.0.0.0 (IBM, NY, USA). Identified metabolites with significant differences (P<0.05) were also visualized in a box plot.

    Linear correlations between metabolites and oxidative indicators were determined through Pearson’s coefficients calculated by PROC CORR using the SAS program. The significance of the correlation was accepted at P<0.05.

    Results and Discussion

    1. Color evaluation

    The color of fresh meat is a significant quality attribute that helps a consumer deciding to buy or not to buy meat at retail. Changes in instrumental color parameters during the storage are shown in Fig. 1. During storage, CIE L* (lightness) values were trended to slightly decrease but not significantly change among the treatments during 28 d of storage. Mostly, the desirable color of meat and meat products, relatively brighter and redder, is easily accepted by customers. But, the CIE a* and b* values were significantly decreased due to the brownish color of metmyoglobin begins to appear on the surface of steaks after 14 and 21 d of storage (P<0.05). The significant reduction of a* values indicated the dropping of meat red color during storage due to oxidation which produces free radicals and initiates oxymyoglobin oxidation to metmyoglobin and ferryl myoglobin in muscle foods (O’Grady et al., 2001). Chroma value which means color intensity was observed to be lower during the refrigerated storage at 4°C. Hue angle, correlated with meat discoloration, showed a significant difference (P<0.05) in 21 d of the storage. The hue angle is an important criterion for determining color stability in muscle-based foods, and an in- crease in hue angle is generally observed due to the progress of meat discoloration (Bázan-Lugo et al., 2012).

    2. Crude fat contents and lipid oxidation

    Fat contents are an important component in imparting a definite flavor to particular meat species. The crude fat contents of beef steaks varied from 6.89% to 8.02%. All steak samples did not show significant differences (P>0.05) (data are not shown).

    The TBARS values, the most representative lipid oxidation indicator in meat during storage, are shown in Fig. 2. Generally, lipid peroxidation is known to be initiated by the abstraction of an unstable hydrogen atom in lipid molecules from any species which has enough reactivity and keeps changing to other molecules called a free radical chain reaction (Min & Ahn, 2005). The values resulted from M. longissimus lumborum were significantly influenced by storage time (P<0.05). The rancid odor was first perceived, in between 0.5 and 1.0 mg malonaldehyde/kg meat, reported by Maria et al. (2010). TBARS values from later storage samples (21 and 28 days) were higher than early days of storage and, exceeded the values of 0.5 mg MDA/kg meat (P<0.05) after 21 d of storage.

    3. Protein oxidation

    Oxidative changes of protein were determined by thiol content and carbonyl content in beef steaks under aerobic storage and are shown in Fig. 2. Protein oxidation generally leads to a lower level of thiol content, which was recognized as a disulfide bond. Losses of the free thiol group quantified by DTNB were greatly affected by storage time in our study. Carbonyl derivatives are also widely used because of their chemical stability, which is useful to detect protein oxidation and maintain meat protein quality (Dalle-Donne et al., 2003). Both indicators showed a positive and significant (P<0.05) correlation to storage days, indicating that protein oxidation increased with the extension of storage time. Interestingly, a lower value of carbonyl contents for 28 d of storage was observed (P<0.05) compared to 21 d, which shows a similar trend to an earlier study on myofibrillar protein (Morzel et al., 2006). They suggested that the remaining carbonyl groups of protein after a certain chemical oxidation point, change to a smaller molecule or other derivatives.

    4. Metabolite changes

    Total 60 of non-volatile and volatile compounds were detected by GC-MS. The results were statistically analyzed by PLS-DA scores scatter plot (Fig. 3) and the model quality parameters for fitness and predictability indicated that the PLS-DA plots were statistically acceptable (R2X=0.686, R2Y=0.977 and Q2=0.911). Both PLS-DA of volatiles and non-volatiles compounds showed distinct separation in accordance with the storage (P=0.0097, 0.021; respectively). The changes of compounds during the storage were confirmed and compared at 4 and 28 d under aerobic conditions.

    Figures (Fig. 4 and 5) represent significant changes (P<0.05) of volatile and non-volatile compounds. Nitric oxide (NO) which has been mostly studied in muscle-based foods or cured meat due to the use of nitrites, was significantly increased at 28 d from no detection level at 4 d (P<0.05). Previously, opposite results were reported by Cook et al. (1998), that the nitric oxide synthase inhibitor distributed in skeletal muscle significantly (P<0.05) reduced the NO concentration and the nitric oxide synthase enhancer synthesized nitric oxide in muscles during 8 d storage. These differences possibly occurred by different storage conditions (existence of oxygen or not).

    Hexane and heptane from the alkane family were significantly (P<0.05) increased with the storage time. In our study, hexane and heptane showed a lower level or no detection (respectively) at initial storage time compared to 28 d (P<0.05). On the other hand, Kim et al. (2002) detected a small amount of those compounds at 0 d of the storage. Similar nature of increasing hexane and heptane during the storage was found in a previous study (Kim et al., 2002). Detection of carbon disulfide was possible at 4 d, but not at 28 d (P<0.05). Probably, the consumption of carbon disulfide tends to zero after reaction with glutathione, which is a well-known endogenous antioxidant in meat. Glutathione formed trithiocarbonate (TTCs) with carbon disulfide and TTCs are decomposed to glutamate, glycine and thiazolidine- 2-thione-4 carboxylic acid (as known as TTCA), not cysteine (Decker & Hultin, 1992).

    The formation of free amino acids and peptide carnosine were recognized as indicators of meat proteolysis (Hanagasaki and Asato, 2018). Only 2-propanone, 2-pentanone and 2-heptanone showed significant differences (P<0.05) between the storage time of 4 and 28 d, among ketone bodies. Alcohols are derived from amino acid metabolism and proteolysis increased which have significant differences (P<0.05) except for 2-heptanol during storage. 3-methyl-1-butanol was reported to have a significant correlation with TBARS (R=0.773) in aerobic- packaged pork belly by Park et al. (2008).

    Lactic acid is naturally found in raw and cooked meat and formed through LAB metabolic activity which has a great contribution to beef flavor. In the case of lactic acid, it showed a lower level at 28 d compared to 4 d, which shows a completely different result from a previous study that reported a constantly increasing trend of lactic acid in ground beef during storage for 20 d (Nassos et al., 1983). A similar result was reported by Graham et al. (2010) in aged beef during 21 d of storage. Muroya et al. (2019) reported that, however, a different result of lactic acid content was found in Wagyu beef during the 14 d of storage as hanging carcasses. Generally, organic compounds are changed to small compounds (e.g. protein to amino acid and carbohydrates to sugars) by extracellular enzymes and these derivatives are decomposed to smaller compounds through acidogenesis. Therefore, it seems to assume that the extent of hydrolysis for acidogenesis was exceeded in this time difference.

    Except for lactic acid, other compounds were significantly increased during the storage (P<0.05). Isoleucine, proline, serine, stearic acid, phenylalanine, and tyrosine showed the same result of increasing similar to previous studies in different storage conditions, while lactic acid showed different results following publications (Graham et al., 2010;Abraham et al., 2017;Ma et al., 2017). Among increased metabolites, isoleucine is known as it can produce 2-methylbutanal (known as meat odorant) by reacting with dicarbonyl compounds through the Maillard reaction (Koutsidis et al., 2008). Ma et al. (2017) reported the highest level of phenylalanine and tyrosine at 16 and 23 d of aging, respectively.

    5. Correlations

    Results of significantly correlated key metabolites and conducted oxidative parameters including color attributes are presented in Table 1. The aroma volatiles of meat, as well as other non-volatile components, directly originate from free amino acid precursors during thermal processing which improves meat flavor and color intensity. With increased storage time, an overall consistent increase in the concentration of most volatiles was exhibited over time. In the color characteristics, nearly all volatile and non-volatile compounds showed significant correlations with redness, yellowness, and chroma value (P<0.05) except lightness. The volatile metabolites like carbon disulfide, 2-heptanol, and lactic acid as non-volatile components were positively correlated with CIE a*, CIE b*, and chroma value during storage. The rest of the metabolites showed a negative correlation meaning that the deterioration of color attributes occurred during storage which signifies that positive hue angle (palmitic acid, 3-methyl-1-butanol, and 2-ethyl-1-hexanol) represents discoloration of meat. Though, only the presented compounds and indicators showed significant differences (P<0.05) during the storage amongst the detected metabolites. For example, the identified compounds, interestingly, lactic acid and carbon disulfide were showed a negative correlation with hue angle (P<0.05). In the study of Ma et al. (2017), the positive correlation between phenylalanine, tyrosine, and hue angle is not the same as the current study (P=0.1116 and 0.1456; respectively). This difference can be obtained for different storage conditions. Thiol contents and TBARS results were showed significant variation with selected compounds (P<0.05). Consistently, the correlation between two volatile metabolites like carbon disulfide, 2-heptanol, and one non-volatile precursor-like lactic acid, and TBARS were significantly negative (P<0.05). But for thiol contents, a positively significant correlation (P<0.05) was found between carbon disulfide, 2-heptanol, and lactic acid, and the reverse result was observed only for carbon disulfide in the case of carbonyl content. 2-pentanone, 2-heptanone, 2-heptanol, lactic acid, isoleucine and proline had no significant interaction (P>0.05) with the oxidation indicators. It can be happened due to decreased carbonyl contents at 28 d. As mentioned before, detailed profiling of metabolites during the storage should be needed. All non-volatile metabolites except lactic acid were showed a significantly negative correlation (P<0.05) to the color attributes (redness, yellowness and chroma values), and oxidation- related chemical attributes like thiol content. Overall, the correlation data indicated that aerobic storage at 4℃ had a notice- able impact on changing of volatile and non-volatile metabolites by the influence of oxidative indicators.

    Summing up the gained results, the general changes of oxidative indicators, non-volatile and volatile metabolites during the aerobic storage were confirmed. Our study result clarifies ten volatile and nine non-volatile compounds were shown significant differences at undergone ordinary display storage. The significant interaction between oxidative indicators and metabolites for oxidation and color deterioration of M. longissimus lumborum at 4℃ storage were evaluated. Consequently, the current study provides the conversion of volatile and non-volatile metabolites and their correlation with oxidative indicators for changes in meat quality during aerobic storage.

    Acknowledgments

    This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (Project No. 2017R1D1A1B0403564414).

    Figure

    JALS-56-2-85_F1.gif

    Changes of instrumental color parameters of beef steaks from M. Longissimus lumborum during the storage.

    (a): CIE L*, (b): CIE a*, (c): CIE b*, (d): Chroma, (e): Hue angle. A-DMeans with sharing the same letter are not significantly different.

    JALS-56-2-85_F2.gif

    Changes of 2-thiobarbituric acid reactive substances (TBARS), thiol groups and carbonyl contents of beef steak from M. Longissimus lumborum during the storage. (a): TBARS, (b): Thiol group, (c): Carbonyl compounds. A-EMeans with sharing the same letter are not significantly different.

    JALS-56-2-85_F3.gif

    PLS-DA scores scatter plot of volatile and non-volatile compounds from beef M. Longissimus lumborum during the storage.

    JALS-56-2-85_F4.gif

    Significantly changed volatile compounds from beef M. Longissimus lumborum between 4 days and 28 days of storage.

    JALS-56-2-85_F5.gif

    Significantly changed non-volatile compounds from beef M. Longissimus lumborum between 4 days and 28 days of storage.

    Table

    Correlation value between volatile and non-volatile compounds and oxidative indicators

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