
==== Front
Food Chem X
Food Chem X
Food Chemistry: X
2590-1575
Elsevier

S2590-1575(24)00601-1
10.1016/j.fochx.2024.101713
101713
Research Article
Effect of cooking methods on flavor profiles of Xuanwei Ham: Analytical insights into aromatic composition and sensory attributes
Yang Jing ab
Shi Shu e
Wang Ping a
Li Gui Peng a
Wang Huai Yao c
Wu Wen Liang d
Luo Zhang a
Gao Qian Yang a
Liu Zhen Dong liu304418091@126.com
ab⁎
a Food Science College, Tibet Agriculture & Animal Husbandry University, Nyingchi, Tibet 860000, China
b Key Laboratory of Tibetan Medicine Resources Conservation and Utilization of Tibet Autonomous Region, Xizang Agriculture and Animal Husbandry University, Nyingchi of Xizang 860000, China
c Yunnan Yiji Food Co., Wuhua Kunming, Yunnan 650000, China
d School of Information Engineering, Northwest A&F University, Shaanxi, Yangling 712100, China
e Chongqing Vocational Institute of Tourism, Chongqing 409000, China
⁎ Corresponding author at: School of Information Engineering, Northwest A&F University, Shaanxi, Yangling, 712100 China. liu304418091@126.com
11 8 2024
30 10 2024
11 8 2024
23 10171321 5 2024
17 7 2024
1 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
To examine flavor variations in Xuanwei ham due to different cooking methods, we selected one-year cured Xuanwei ham and applied four techniques: dry frying (DF), baking (BA), steaming (ST), and boiling (BO). Organoleptic evaluation revealed ST received the highest overall sensory score. High-performance liquid chromatography (HPLC) revealed that the total nucleotide content was significantly different (P < 0.05), lactic acid predominated the measured organic acids. Solid-phase microextraction-gas chromatography–mass spectrometry (SPME-GC–MS) and chromatography-electronic nose (GC-E-Nose) data indicated that ST resulting in significantly higher total volatile compounds than the other methods (P < 0.05). SPME-GC–MS detected 55 volatile compounds, and 12 characteristic flavor substances were identified using orthogonal partial least squares discriminant analysis (OPLS-DA) (VIP > 1). This study aimed to support comprehensive research on the flavor characteristics of cooked Xuanwei ham and guide the selection of appropriate processing methods.

Highlights

• The sensory score of Xuanwei ham treated by steaming is the highest.

• The content of flavor substances in Xuanwei ham processed by steaming is the highest.

• The total nucleotide content of Xuanwei ham baked is the highest.

• The organic acid content of Xuanwei ham boiled is the lowest.

Keywords

Xuanwei ham
Cooking method
Nucleotides
Organic acids
Volatile flavor compounds
==== Body
pmc1 Introduction

Xuanwei Ham, a renowned specialty of Yunnan, China, is appreciated by consumers for its rich nutrition, vibrant color, delightful taste, and potent aroma. This ham is primarily produced from Wujin pigs in the Xuanwei area, and its unique flavor and texture are achieved through trimming, salting, and air-drying in the distinct geographical and natural conditions of the Wumeng Mountain area in the Yunnan-Guizhou Plateau. The unique fermentation conditions and dry-curing process contribute significantly to the distinctive characteristics of Xuanwei Ham.

The current research landscape predominantly concentrates on the curing and maturation process of fresh ham to understand the quality and flavor of Xuanwei ham. For instance, Li et al. (2024) used GC–MS and high-throughput sequencing to analyze the trends of physicochemical properties, free fatty acids, and microbial communities in Xuanwei hams of different ages. Their findings indicated that Xuanwei hams cured for three years exhibited the best overall flavor quality. In a different study, Wang et al. (2024) compared the volatile flavors between normal and spoiled hams, revealing higher aldehyde and alcohol content in spoiled hams. Furthermore, Ding et al. (2021) discovered that substituting 30% or 40% of NaCl with KCl during the curing process enhanced the flavor quality of Xuanwei ham.

Xuanwei ham, a distinctive local food with special flavor, can be cooked in various ways, such as stir-frying, baking, steaming, and boiling, with each method revealing different flavor qualities (Utama et al., 2018). Currently, there is limited research on the cooking flavor analysis of Xuanwei ham. However, some studies have explored the effects of different cooking methods on the flavor characteristics of other types of fermented meat. For instance, Zhou et al. (2022) found that prolonged high-temperature and repeated brining can produce a large number of heterocyclic amines in fermented beef, which negatively impacts the flavor quality of fermented beef in soy sauce. Meanwhile, Chen et al. (2024) investigated the flavor characteristics of fermented beef in soy sauce by using an electronic nose, gas chromatography-ion mobility mass spectrometry (GC-IMS), and solid-phase microextraction gas chromatography–mass spectrometry (SPME-GC–MS) to analyze the differences in volatile compounds of wet-marinated fermented golden pomfret after boiling, steaming, microwaving, air frying, and baking. The results showed that the flavor compounds produced by baking and air frying were relatively similar.

This study aimed to investigate the effects of different cooking methods on the flavor quality of Xuanwei ham. Four methods were selected for treatment: dry frying (DF), baking (BA), steaming (ST), and boiling (BO). Sensory evaluation was conducted, followed by analysis and identification of nucleotides, organic acids, and volatile compounds using HPLC, GC-E-Nose, and SPME-GC–MS. The data obtained were analyzed using OPLS-DA and other multivariate statistical methods to explore differences in flavor quality among the variously cooked Xuanwei ham samples. Therefore, this study thus provides reference data for further examination of flavor differences in Xuanwei ham resulting from different cooking treatments.

2 Materials and methods

2.1 Test materials

The curing process of Xuanwei ham crown part (Yunnan Yiji Food Co., Ltd.) from December 2022 to December 2023 (Fig. 1) involved six stages: trimming and shaping, salt curing, stacking and turning pressure, washing and sunshine shaping, hanging air-drying, and fermentation and maturation. Fresh, full, and scar-free legs of Wujin pig were selected for this process.Fig. 1 Select the unplaning drawing of Xuanwei ham crown.

Fig. 1

2.2 Reagents and instruments

All reagents used, including methanol 10 and 5% perchloric acid solutions, 10 M potassium hydroxide solution, perchloric acid‑potassium hydroxide neutral solution, 20 mM citric acid-40 mM triethylamine −0.1% glacial acetic acid (pH 4.8), and standard materials (adenosine triphosphate, adenosine diphosphate, adenosine acid, inosine acid, hypoxanthine nucleoside, hypoxanthine, guanosine acid, purity ≥99%), were of analytical purity. Other reagents included 0.1% aqueous phosphoric acid solution, methanol, and 2,4,6-trimethylpyridine (0.05 mg/mL).

Equipment used in the analysis included an analytical balance (FA2004), homogenizer, freezing centrifuge, acid meter, HPLC (Agilent 1100 with DAD detector), constant temperature magnetic stirrer (08-2 T, Shanghai Meiyingpu Instrument Manufacturing Co., Ltd.), solid-phase microextraction device (57330-U, Supelco), gas chromatography and mass spectrometry coupled (Agilent 6890 N-5973 GC–MS), electrically heated blast drying oven (TGF-9140 A), and electronic nose (cNose-10, BaoSheng Technology), along with a 20 mL headspace injection bottle.

2.3 Cooking method

The crown part of a one-year-old Jinwu pig's Xuanwei ham, stripped of excess fat and fascia, is cut into 0.5 mm thick square slices. These slices are then divided into four experimental groups, each subjected to a different cooking method: DF, BA, ST, and BO, following a five-minute soak in warm water. Post-cooling, the samples are stored in sealed bags. To prevent other factors affecting the experimental results, no seasonings or additives were incorporated during cooking.

2.3.1 Dry frying

The processed meat samples undergo blanching in hot water, subsequently drained and transferred to a preheated induction cooker (120 °C). Direct dry frying ensues until the meat is fully cooked. Upon removal, absorbent paper is utilized to eliminate surface oil from the meat samples.

2.3.2 Baking

The meat samples are laid flat in a baking pan, and a constant temperature drying oven is preheated to 150 °C. The samples are then placed in the oven and dried for 20 min, which the oven temperature is reduced to 70 °C and drying continues for 3 h. Upon cooling of the oven, the meat samples are removed and excess surface oil is absorbed using absorbent paper.

2.3.3 Steaming

Water in a household steamer is boiled to a half-boiled state, and the meat samples are placed on a cage drawer before being steamed at 200 °C for 25 min. Upon removal, surface water is absorbed using absorbent paper.

2.3.4 Boiling

A household boil pot is used to boil water halfway, and the meat samples are then placed in the pot and boiled at 200 °C for 30 min. The soup is discarded, water is reintroduced, and boiling continues for an additional 10 min. Subsequently, the samples were removed and surface moisture was absorbed using absorbent paper.

2.4 Experimental methods

2.4.1 Sensory evaluation

The sensory indexes of GB/T 18357–2008 “Xuanwei Ham, Geographical Indication Product” are referred to, and an evaluation team of 10 healthy food majors with normal senses is randomly assembled. A sensory evaluation is conducted on the appearance, color, tissue state, aroma, and taste of Xuanwei ham processed in four cooking modes, and sensory evaluation standards are established (Table 1).Table 1 Sensory evaluation table for different cooking methods of Xuanwei ham.

Table 1Item	Scoring Criteria	Scoring	
Aroma	Strong aroma inherent to Xuanwei Ham	23–30	
Some inherent aroma of Xuanwei Ham	15–22	
Faint aroma of Xuanwei ham	0–15	
Color and luster	Slices of rosy red or peach color, normal color	16–20	
Slices are light pink, normal color	10–15	
The surface of the slices is dark, poor color	0–10	
Texture	Dense tissue on the cut surface, elastic	16–20	
Dense organization of cut surface, poor elasticity	10–15	
Cutting surface organization is soft, very poor elasticity	0–10	
Taste	moderately salty, tasty, tender meat	23–30	
Moderately salty, average flavor, average meat quality	15–22	
Salty or bland, strange taste, hard meat	0–15	

2.4.2 Nucleotide analysis

2.4.2.1 Sample pretreatment

After the sample has been homogenized, it was centrifuged at 4000 rpm for 5 min. The supernatant was then be collected, filtered through a 0.22 μm membrane, and loaded onto the machine. Analytically pure reagents were utilized, and the test water complied with GB/T 6682 first-grade water standards.

The preparation of the standard solution involves the accurate weighing of 0.1102 g of adenosine triphosphate (ATP) standard, 0.0854 g of adenosine diphosphate (ADP) standard, 0.0735 g of adenosine monophosphate (AMP) standard, 0.0696 g of inosine monophosphate (IMP) standard, 0.0536 g of hypoxanthine nucleoside (HXR) standard, 0.0272 g of hypoxanthine (HX) standard, and 0.0391 g of guanosine monophosphate (GMP) standard. These standards should be dissolved in water and adjusted to a final volume of 10 mL, resulting in a concentration of 20 mM for each standard, and subsequently stored at −30 °C. Additionally, standard working solutions with concentrations ranging from 0.005 mM to 1.00 mM should be prepared by diluting the standard solutions with water. These working solutions are stored at 4 °C and kept ready for use.

2.4.2.2 Liquid chromatography conditions

Chromatographic conditions comprised a C18 column (4.6 mm × 250 mm, 5 μm particle size), a column temperature of 40 °C, a flow rate of 1.0 mL/min, a detection wavelength of 260 nm, and an injection volume of 10 μL. The mobile phase comprised Liquid A (20 mM citric acid-40 mM triethylamine −0.1% glacial acetic acid, pH 4.8 and Liquid B (methanol), employing gradient elution (Table 2).Table 2 Nucleotide gradient elution procedure.

Table 2T/min	Liquid A (%)	Liquid B (%)	
0	100	0	
2	100	0	
14	95	5	
20	93	7	
21	60	40	
26	60	40	
26.5	100	0	
32	100	0	

The sample extract was placed in the injector of the liquid chromatograph and analyzed chromatographically under the above-mentioned conditions. The peak area was recorded, with response values falling within the instrument's detected linear range. The retention time was characterized by comparing it to the standard's retention time, followed by quantification using the external standard method.

2.4.2.3 Calculation

ATP and its decomposition correlates were extracted from the sample using perchloric acid. The pH was adjusted with a potassium hydroxide solution to precipitate perchlorate, impurities were removed, and separation was performed on a C18 column with an ultraviolet detector. Quantification was done using the external standard method, and the content of ATP and decomposition associates was calculated.

The ATP, ADP, AMP, IMP, HXR, HX, and GMP contents of the samples were calculated using the following formula:(1) Xn=A×C×VAs×M

Xn - the amount of 7 nucleotides in the sample in micrograms per kilogram (μmoL/g); A - peak area of the seven nucleotides in the sample; C - concentration of the seven nucleotides in the standard working solution in micromoles per milliliter (μmoL/mL); V - the volume of the volume of fixation in milliliters (mL); As - peak area of the 7 nucleotides in the standard working solution; M - sample mass in grams (g). Calculations were retained to three decimal places.

2.4.3 Organic acid analysis

The analysis was performed as per “GB/5009. 157-2016 Determination of organic acids in food”. The sample was placed in a 10 mL plastic centrifuge tube, and 4 mL of water was added. The mixture was shaken thoroughly and centrifuged at 4000 rpm for 5 min. The upper extract layer was transferred to a 15 mL centrifuge tube, and the extraction was repeated with 4 mL of water. The extracts were combined in the same centrifuge tube, and the volume was adjusted to 10 mL with water, and subsequently filtered by 0.22 μm aqueous phase filter membrane.

Chromatographic conditions included an Agilent ZORBAX SB-Aq column (250 mm × 4.6 mm, 5 μm particle size), mobile phases A (0.1% aqueous phosphoric acid) and B (methanol), a detection wavelength of 210 nm, a column temperature of 40 °C, a flow rate of 1 mL/min, and an injection volume of 10 μL (Table 3).Table 3 Organic acid gradient elution procedure.

Table 3T/min	Liquid A (%)	Liquid B (%)	
0	97.5	2.5	
7	97.5	2.5	
9	10	90	
12	10	90	
13	97.5	2.5	
20	97.5	2.5	

2.4.4 SPME-GC–MS analysis of volatile flavor compounds

SPME coupled with GC–MS was used to determine the volatile compounds (Domínguez et al., 2019). A 5 g sample in a 20 mL vial was mixed with 100 μL of 2, 4, 6-trimethylpyridine (0.05 mg/mL) as the internal standard. After sealing, the vial was placed in an 85 °C water bath with magnetic stirring at 500 rpm for 20 min. Subsequently, 50/30 um DVB/CAR/PDMS solid-phase microextraction needles was used to extract the compounds for 30 min, repeating this process thrice for each sample. Prior to use, the extraction needle underwent activation in the gas injection port for 20 min at 250 °C. GC conditions included an injection port temperature of 250 °C, a gas interface temperature of 250 °C, a carrier gas flow rate of 1.5 mL/min, and no shunt injection. The heating program initiated at 40 °C for 5 min, heated to 250 °C at 5 °C/min, and maintained this temperature for 10 min. MS conditions comprised an ion source temperature of 230 °C, a quadrupole temperature of 150 °C, EI ionization at 70 eV, and a full scan of 35–550 da. The column employed was an HP-INNOWax (60 m × 250 μm × 0.25 μm).

2.4.5 GC-E-nose analysis of volatile flavor substances

Four 5 g portions (precision value: 0.001 g) of the treated samples were weighed and placed into 20 mL headspace injection bottles. The bottles were sealed tightly and equilibrated in an oven at 40 °C for 30 min until the upper gas layer stabilized. Subsequently, the electronic nose detected the samples, with each sample analyzed four times. The cleaning time was 120 s, detection time was 90 s, gas flow rate was 1.0 L/min, cleaning flow rate was 6.0 L/min, and detection temperature was 30 °C. Table 4 outlines the electronic nose sensor's performance.Table 4 Electronic nose sensor corresponding substance.

Table 4Sensor number	Response substance	Category substance	
s1	Alkanes, fumes	Propane, natural gas, fumes	
s2	Alcohols, aldehydes, short-chain alkanes	Alcohol, fumes, isobutane, formaldehyde	
s3	Ozone		
s4	Sulfur compounds	Hydrogen sulfide	
s5	organic amine	Ammonia, methylamine, ethanolamine	
s6	Organic gases, phenyl ketones, alcohols and aldehydes, aromatic compounds	Toluene, acetone, ethanol, hydrogen, other organic vapors	
s7	Short-chain alkanes	Methane, natural gas, biogas	
s8	Aromatic compounds, alcohols and aldehydes	Toluene, formaldehyde, benzene, alcohol, acetone	
s9	hydrogen-containing gas	hydrogen (gas)	
s10	Flammable gases	methane CH4	

2.5 Data processing and analysis

Data statistics were performed using Microsoft Office Excel 2016, while IBM SPSS Statistics 27 was employed for one-way ANOVA and Duncan's test. GC-E-Nose software was used to build radar charts, and OriginPro 2021 was used to generate stacked histogram models and clustered heat maps. SIMCA Version 14.1 was utilized for OPLS-DA and VIP discriminant analysis.

3 Results

3.1 Analysis of sensory evaluation results of different cooking and processing methods

The four cooking methods exhibited significant differences in aroma and texture (P < 0.05) (Table 5Fig. 2). Notably, DF produced a significantly superior aroma compared to the other methods, yet its taste was compromised, being salty due to rapid water loss and bland due to BO. In contrast, BA and ST displayed more vibrant color and luster than DF and BO, with ST boasting the best flavor. BA presented the hardest texture, a change primarily attributed to collagen's structural transformation and protein hydrolysis reactions. Heat treatment denatures proteins, causing them to adhere and form a tighter structure, with BA's prolonged thermal processing resulting in a firmer and harder ham (Dominguez-Hernandez et al., 2018). Analyzing the sensory differences post-processing of Xuanwei ham, distinct variations among the four cooking methods are evident.Table 5 Sensory score of Xuanwei ham in different cooking methods.

Table 5group	Aroma	Color and luster	texture	taste	
DF	25.63 ± 0.7a	11.3 ± 0.66b	10.53 ± 0.35c	13.63 ± 0.21c	
BA	23.7 ± 0.4b	15.8 ± 0.6a	9.13 ± 0.47d	17.27 ± 0.76b	
ST	20.93 ± 0.35c	15.2 ± 0.56a	16.03 ± 0.32a	22 ± 0.76a	
BO	16.97 ± 1.2d	10.4 ± 0.62b	14.4 ± 0.62b	14.27 ± 0.4c	
Note: Different letters in the same column indicate significant differences (P < 0.05), and data are expressed as mean ± SD (standard deviation), below.

Fig. 2 Samples of Xuanwei ham cooked in different ways(A:Dry stir-frying;B:Baking;C:Steaming;D:stew).

Fig. 2

3.2 Analysis of nucleotide differences in different cooking and processing methods

Nucleotides, crucial flavor compounds in meat products (Ivankin et al., 2020), undergo sequential transformations. ATP hydrolysis yields ADP, which is subsequently degraded by phosphate kinase to form AMP. This is further dehydrogenated to produce IMP, a potential determinant of meat flavor (Zhang et al., 2018). Among nucleotides, GMP and IMP exhibit the most intense flavors in fresh meat (Li et al., 2022), commonly found in daily life as sodium salts and often mixed in a 1:1 ratio. This combination synergistically enhances freshness, surpassing the effects of individual usage (Delompre et al., 2020). Additionally, a fraction of IMP is catalyzed by phosphokinase to form inosine, which subsequently hydrolyzes to form HX.

The total nucleotide content was highest in BA, surpassing the other three cooking methods. Unlike BA, GMP was undetectable in the other three methods. ATP and HXR were absent in all Xuanwei hams, regardless of the cooking method employed. The rapid hydrolysis of ATP during cooking, which yields AMP, Ribose, and phosphoric acid, may account for the absence of ATP, given that this process releases energy at a rate exceeding that of synthesis (Timofeev et al., 2022). HXR undergoes phosphorylation by nucleoside phosphorylase (NP), resulting in free HX and ribulose-1-phosphate (R1P). Subsequent breakdown and oxidation of HX by xanthine oxidase (XOD) produces xanthine, and ultimately, uric acid. Both stages of this reaction are catalyzed by XOD, yielding H2O2 as a byproduct Table 6.Table 6 The content of 7 nucleotides in four cooking methods.

Table 6Cooking style	Nucleotide content/(mg/kg)	
HX	GMP	IMP	ADP	HXR	AMP	ATP	
DF	570.25 ± 0.33c	ND	4.94 ± 0.16d	1219.98 ± 4.06b	ND	42.27 ± 1.3d	ND	
BA	825.82 ± 1.59a	2.24 ± 0.16a	15.07 ± 0.35a	1293.69 ± 23.49a	ND	61.25 ± 0.52c	ND	
ST	621.69 ± 1.36b	ND	13.77 ± 0.6b	13.77 ± 0.45c	ND	1040.07 ± 5.53a	ND	
BO	283.32 ± 2.06d	ND	5.89 ± 0.11c	5.89 ± 0.52c	ND	525.26 ± 0.83b	ND	
ND: not detected.The same as below.

3.3 Analysis of the differences in organic acids in different cooking and processing methods

From Fig. 3, it is evident that formic acid, acetic acid, citric acid, and succinic acid are present in low amounts in Xuanwei ham across all cooking methods, with lactic acid showing notably higher levels compared to these acids. During the processing of Xuanwei ham, glucose is converted to lactic acid via the glycolytic pathway, which continues throughout processing and maturation, resulting in gradual lactic acid accumulation (Terlouw et al., 2021). Heating during cooking can alter the organic acid composition of hams; while some acids may decompose or volatilize, lactic acid remains relatively stable and maintains higher levels during cooking. Consequently, cooked ham often retains elevated levels of lactic acid due to ongoing production during processing. However, in BO, lactic acid consumption is significantly higher compared to the other three methods, likely because lactic acid readily dissolves in water during boiling, leading to increased lactic acid consumption.Fig. 3 Organic acid content of different cooking methods.

Fig. 3

3.4 Analysis of differences in SPME-GC–MS results of volatile compounds in different cooking and processing methods

The curing and processing of Xuanwei ham involve the oxidative decomposition of proteins and fats, yielding a substantial quantity of volatile compounds (Xu et al., 2023). During cooking, the Meladic reaction generates an array of flavor substances, including aldehydes, ketones, alcohols, esters, etc. (Wu et al., 2014). Therefore, the volatile compounds in Xuanwei ham are diverse and intricate, contributing to its distinctive flavor profile (L. Li, Belloch, & Flores, 2021). An analysis of the relative content and differences in volatile compound components of hams subjected to four cooking methods was conducted using the SPME-GC–MS technique.

Table 7 reveals that DF, ST, and BA each contained 39, 39, and 42 volatile compounds, respectively, while BO had only 36. DF comprised 6 alkanes, 2 alcohols, 12 aldehydes, 6 olefins, 10 esters, 1 ketone, 1 phenol, and 1 pyridine. In contrast, BA comprised 6 alkanes, 4 alcohols, 13 aldehydes, 8 olefins, 8 esters, 1 phenol, and 1 pyridine. ST had 6 alkanes, 3 alcohols, 12 aldehydes, 6 olefins, 8 esters, 1 ketone, 2 phenols, and 1 pyridine. BO contained 6 alkanes, 1 alcohol, 14 aldehydes, 2 olefins, 6 esters, 1 ketone, 2 phenols, 1 pyridine, and 3 other compounds. Dimethyl sulfoxide, artemisia herbaceous brain, and 2,6-methyl piperazine were absent in all cooking methods except BO. Fig. 4 indicates that aldehydes, esters, and olefins had the highest relative substance contents. Notably, ST exhibited significantly higher volatile compounds than the other three cooking modes, except for 2,4,6-trimethylpyridine (P < 0.05). The levels of 2, 4, 6-trimethylpyridine were comparable across all four cooking methods. Alcohol content was highest in DF, while phenols and ketones peaked in BA.Table 7 The content of volatile substances in four different cooking methods.

Table 7number	Volatile compound composition	CAS	content/(μg/kg)	VIP	RT/min	
			DF	BA	ST	BO	
1	2-Methylbutyraldehyde	000096-17-3	615.46 ± 0.10a	10.11 ± 0.06c	373.04 ± 0.14b	6.54 ± 0.12d	2.52	6.4	
2	Isovaleraldehyde	000590-86-3	14.42 ± 0.05d	25.71 ± 0.04b	716.56 ± 0.06a	21.86 ± 0.08c	1.48	6.49	
3	n-Hexanal	000066-25-1	2.04 ± 0.12d	6.15 ± 0.08c	278.13 ± 1.65a	13.31 ± 0.03b	0.93	11.03	
4	Benzaldehyde	000100-52-7	36.85 ± 0.10c	23.47 ± 0.12d	1247.46 ± 0.14a	106.57 ± 0.04b	1.99	24.21	
5	Benzeneacetaldehyde	000122-78-1	26.36 ± 0.45c	23.24 ± 0.08d	680.16 ± 0.05a	30.34 ± 0.04b	1.43	27.1	
6	2-Undecenal	002463-77-6	16.54 ± 0.05a	ND	ND	8.92 ± 0.04b	0.60	29.61	
7	Myristaldehyde	000124-25-4	8.15 ± 0.10b	4.13 ± 0.08c	52.69 ± 0.08a	ND	0.43	33.21	
8	Pentadecanal	002765-11-9	7.82 ± 0.31c	6.40 ± 0.08d	235.14 ± 0.09a	20.22 ± 0.06b	0.86	35.35	
9	Hexadecanal	000629-80-1	244.74 ± 0.05b	160.45 ± 0.14d	200.90 ± 0.03c	250.85 ± 0.08a	2.31	37.4	
10	Heptadecanal	1,000,376-70-0	13.27 ± 0.10b	11.12 ± 0.07c	155.38 ± 0.05a	10.99 ± 0.05c	0.67	39.35	
11	Octadecanal	000638-66-4	33.40 ± 0.09c	22.12 ± 0.06d	118.38 ± 0.06a	33.75 ± 0.10b	0.59	41.23	
12	(Z)-13-Octadecenal	058594-45-9	21.09 ± 0.05c	15.63 ± 0.07d	1489.44 ± 0.10a	27.42 ± 0.02b	2.15	41.66	
13	Propanal, 2-methyl-	000078-84-2	ND	0.64 ± 0.03a	ND	ND	0.19	6.49	
14	Nonanal	000124-19-6	ND	10.19 ± 0.05b	ND	29.37 ± 0.05a	0.97	20.59	
15	2-phenyl-2-butenal	004411-89-6	ND	ND	482.92 ± 0.17a	2.55 ± 0.03b	1.23	33.48	
16	n-Octanal	000124-13-0	ND	ND	ND	2.86 ± 0.04a	0.41	17.53	
Aldehyde class(16 species)	1040.14 ± 0.61b	319.34 ± 0.62d	6030.20 ± 1.82a	565.54 ± 0.23c			
17	1-Octen-3-ol	003391-86-4	244.85 ± 0.08a	2.45 ± 0.10d	76.24 ± 0.09b	7.02 ± 0.13c	1.60	21.96	
18	n-Dodecanol	000112-53-8	205.76 ± 0.11a	1.55 ± 0.07c	64.69 ± 0.10b	ND	1.48	33.92	
19	Ethanol	000064-17-5	ND	6.53 ± 0.08a	ND	ND	0.60	6.92	
20	Benzeneacetaldehyde	000122-78-1	ND	2.14 ± 0.06b	51.6 ± 0.01a	ND	0.41	32.9	
Alcohols(4 species)	450.60 ± 0.18a	12.68 ± 0.30c	192.53 ± 0.05b	7.02 ± 0.13d			
21	Hexane	000110-54-3	0.32 ± 0.01c	0.33 ± 0.005c	10.30 ± 0.01a	0.67 ± 0.06b	0.18	3.69	
22	Heptane	000142-82-5	0.15 ± 0.01c	0.31 ± 0.04b	3.73 ± 0.08a	0.29 ± 0.02b	0.11	3.99	
23	Cyclohexane	000110-82-7	0.24 ± 0.01d	0.41 ± 0.01c	6.67 ± 0.03a	0.46 ± 0.004b	0.14	4.08	
24	Pentadecane	000629-62-9	0.06 ± 0.01bc	0.05 ± 0.004c	1.08 ± 0.01a	0.07 ± 0.004b	0.06	23.57	
25	Hexeadecane	000544-76-3	0.10 ± 0.01bc	0.08 ± 0.01c	0.54 ± 0.02a	0.11 ± 0.02b	0.04	26.65	
26	Heptadecane	000629-78-7	0.05 ± 0.01b	0.04 ± 0.01b	1.62 ± 0.01a	0.05 ± 0.01b	0.07	28.42	
Alkanes(6 species)	0.92 ± 0.03d	1.21 ± 0.05c	23.94 ± 0.05a	1.65 ± 0.08b			
27	trans-Caryophyllene	000087-44-5	3.49 ± 0.01b	3.81 ± 0.01a	ND	ND	0.45	28.9	
28	alpha-himachalene	003853-83-6	4.30 ± 0.02c	5.62 ± 0.21b	115.09 ± 0.02a	ND	0.61	28.98	
29	Germacrene D	023986-74-5	3.16 ± 0.01c	3.47 ± 0.03b	315.39 ± 0.06a	ND	1.00	29.7	
30	α-curcumene	000644-30-4	4.42 ± 0.01d	5.09 ± 0.02c	81.31 ± 0.004a	5.19 ± 0.02b	0.49	29.92	
31	Cuparene	016982-00-6	2.32 ± 0.01b	3.87 ± 0.05a	ND	ND	0.47	31.13	
32	Calamenene	000483-77-2	4.58 ± 0.04b	4.50 ± 0.01c	190.16 ± 0.01a	ND	0.77	31.3	
33	Styrene	000100-42-5	ND	5.71 ± 0.02a	ND	ND	0.56	16.59	
34	delta-Cadinene	000483-76-1	14.25 ± 0.06b	ND	224.55 ± 0.07a	12.25 ± 0.06c	0.84	29.59	
35	d-Limonene	005989-27-5	ND	ND	145.78 ± 0.02a	ND	0.68	14	
Olefins(9 species)	36.51 ± 0.11b	32.08 ± 0.12c	1072.28 ± 0.02a	17.44 ± 0.08d			
36	Boric acid-trimethyl ester	000121-43-7	84.73 ± 0.07b	32.49 ± 0.15d	2530.20 ± 0.16a	62.60 ± 0.02c	2.78	6.23	
37	Methyl caproate	000106-70-7	4.69 ± 0.06a	ND	ND	ND	0.22	14.12	
38	Methyl caprate	000110-42-9	1.77 ± 0.005b	1.69 ± 0.01c	ND	1.94 ± 0.03a	0.09	25.76	
39	Methyl myristate	000124-10-7	5.05 ± 0.06b	2.73 ± 0.03c	185.54 ± 0.23a	ND	0.76	34.89	
40	Methyl palmitate	000112-39-0	14.57 ± 0.07c	18.62 ± 0.02b	123.79 ± 0.13a	14.43 ± 0.05c	0.59	38.85	
41	Hexamethylene diacrylate	013048-33-4	25.55 ± 0.10b	7.20 ± 0.03c	670.64 ± 0.05a	ND	1.45	38.93	
42	Dimethyl phthalate	000131-11-3	3.50 ± 0.01b	ND	97.61 ± 0.02a	ND	0.55	40.41	
43	Methyl stearate	000112-61-8	8.25 ± 0.05b	6.67 ± 0.04d	46.86 ± 0.04a	7.14 ± 0.07c	0.35	42.5	
44	Methyl oleate	000112-62-9	19.87 ± 0.17c	20.35 ± 0.09b	220.12 ± 0.07a	16.29 ± 0.03d	0.80	42.86	
45	Methyl linoleate	000112-63-0	8.70 ± 0.02c	6.39 ± 0.04d	22.94 ± 0.07a	9.05 ± 0.06b	0.26	43.66	
Esters(10 species)	176.67 ± 0.32b	96.14 ± 0.16d	3897.70 ± 0.21a	111.45 ± 0.05c			
46	Phenol	000108-95-2	5.70 ± 0.2a	ND	ND	3.01 ± 0.01b	0.35	34.73	
47	Thymol	000089-83-8	ND	6.63 ± 0.08a	ND	ND	0.60	38.04	
48	2,6-Di-tert-butyl-4-methylphenol	000128-37-0	ND	ND	164.61 ± 0.08a	ND	0.72	32.84	
49	2,4-Di-tert-butylphenol	000096-76-4	ND	ND	299.67 ± 0.56a	4.37 ± 0.04b	0.97	40.27	
Phenols(4 species)	5.70 ± 0.20d	6.63 ± 0.08c	464.28 ± 0.63a	7.38 ± 0.05b			
50	2-Pentadecanone	002345-28-0	2.60 ± 0.03c	ND	169.93 ± 0.07a	6.69 ± 0.09b	0.73	35.19	
Ketone(1 specie)	2.60 ± 0.03c	ND	169.93 ± 0.07a	6.69 ± 0.09b			
51	2,4,6-Trimethylpyridine	000108-75-8	100.04 ± 0.05a	100.01 ± 0.01a	100.04 ± 0.05a	100.04 ± 0.05a	0.03	20.33	
Pyridines(1 specie)	100.04 ± 0.05a	100.01 ± 0.01a	100.04 ± 0.05a	100.04 ± 0.05a			
52	2-Pentylfuran	003777-69-3	ND	2.20 ± 0.06a	ND	ND	0.35	15.62	
53	Dimethyl sulfoxide	000067-68-5	ND	ND	ND	8.90 ± 0.03a	0.72	25.99	
54	Estragole	000140-67-0	ND	ND	ND	3.59 ± 0.02a	0.46	31.29	
55	2,6-Dimethylpyrazine	000108-50-9	ND	ND	ND	2.97 ± 0.04a	0.42	14.27	
Other classes(4 species)	ND	2.20 ± 0.06b	ND	15.46 ± 0.09a			
Note: RT: Retention time.

Fig. 4 Content of volatile compounds under different cooking methods based on SPME-GC–MS(A: Histogram B: Clustered heat map).

Fig. 4

3.4.1 OPLS-DA

To better understand the data distribution post-treatment with the four cooking methods, the processed volatile flavor compounds data were plotted on the OPLS-DA (Fig. 5). Steaming predominantly occupies the left X-axis half, while dry stir-frying is situated in the upper part of the right half (Fig. 6A). Meanwhile, boiling and baking reside in the lower X-axis half, exhibiting the most significant similarities. The model boasts an independent variable fitting index (R2x) of 0.998, a dependent variable fitting index (R2y) of 0.944, and a model prediction index (Q2) of 0.999. Both R2 and Q2 surpass 0.5, indicating acceptable model fit and demonstrating the model's robust discriminatory and predictive capabilities (Yun et al., 2021). Permutation test (n = 200) intercepts for R2 and Q2 were 0.0406 and − 0.788, respectively, with the Q2 regression line intercept <0, confirming no model overfitting (Fig. 6B). Thus, the model serves as a reliable tool for discriminating among hams treated with the four cooking methods.Fig. 5 Volatile compound OPLS-DA based on SPME-GC–MS(A: score diagram B:Permutation test diagram).

Fig. 5

Fig. 6 VIP value distribution map.

Fig. 6

3.4.2 Variable importance projection (VIP)

The value of VIP corresponds to the significance of the difference between variables in the various cooking methods of Xuanwei ham. According to Fig. 6, 13 variables with VIP values >1 were chosen as volatile characteristic markers. These include seven aldehydes (2-methylbutanal, isovaleraldehyde, benzaldehyde, phenylacetaldehyde, hexadecanal, (Z)-13-octadecadienal, and 2-phenylcrotonaldehyde), two alcohols (mushroom alcohol and lauryl alcohol), one olefin (daikonocoumarol D), and two esters (trimethyl borate, 1, 6-hexanediol di acrylate) (Pérez-Santaescolástica et al., 2018). Particularly noteworthy are the variables 2-methylbutyraldehyde, hexadecanal, trimethyl borate, and (Z)-13-octadecadienal, each having VIP > 2, which indicates the significant role these four variables play in the discriminant analysis.

3.5 Differential analysis of GC-E-Nose results of volatile compounds in different cooking and processing methods

3.5.1 Differential analysis of GC-E-Nose response values

Upon analysis of the electronic nose odor radar chart (Fig. 7A) and clustered heat map (Fig. 7B) generated from the response intensity value data, it is evident that the sensor's maximum response value should exceed 0.5 for the sample being tested. In this experiment, all samples fulfilled this detection criterion (Wang, Zhang, et al., 2021). The figure showcases the variability in response values from the 10 sensors when exposed to volatile aroma components from four different cooking methods. Eight sensors (s1, s2, s4, s5, s6, s7, s8, and s10) exhibited response values >1, which can be utilized as primary indicators for determining volatile odor characteristics and establishing the OPLS-OD (Calvini & Pigani, 2022). The s6 and s8 sensors exhibit sensitivity to aroma compounds, primarily benzene ring aromatic compounds produced during the cooking process due to the Maillard reaction (Ahmad Kamal et al., 2018). The response values of these sensors to the four cooking methods were significant, with ST showing a higher response value than the other three methods. In contrast, BA had a lower response value than the other three methods, likely due to the prolonged cooking time and consistent temperature of ∼70 °C, resulting in insufficient fat decomposition and evaporation of some decomposition products. The prolonged BA time and maintained temperature of ∼70 °C may have contributed to insufficient fat decomposition and volatilization of some decomposition products.Fig. 7 Volatile flavor compounds content based on GC-E-Nose (A:Radar plot B:Clustered heat map).

Fig. 7

3.5.2 OPLS-DA

Utilizing the volatile compounds detected by the electronic nose, an OPLS-DA model was established to analyze the aroma components of different cooking styles (Fig. 8). The distributions of the four cooking styles significantly differed (Fig. A), with ST, DF, BA, and BO predominantly distributed in the right, lower, and left half-axes of the X-axis, respectively. BA and BO exhibited the closest trends. Visual differences were observed among the groups, with the independent variable fitting index (R2x) = 0.995, the dependent variable fitting index (R2y) = 0.975, and the model prediction index (Q2) = 0.93. The acceptable model fitting results (R2 and Q2 > 0.5) aligned with SPME-GC–MS findings, thus confirming data credibility.Fig. 8 Based on GC-E-Nose volatile compound OPLS-DA (A:Score chart B: Permutation test diagram).

Fig. 8

Subsequent 200 substitution tests yielded R2 = 0.319 and Q2 = −0.924, with the Q2 regression line's Y-axis intercept below 0, indicating no model overfitting. These electronic nose test results can effectively identify and analyze the four different cooking methods, thereby corroborating the SPME-GC–MS data analysis.

4 Conclusion

In this paper, we utilized multivariate analysis based on sensory evaluation to examine the flavor variability of Xuanwei ham processed using different cooking methods. The HPLC data revealed that baking resulted in a higher total nucleotide content compared to the other three methods, with GMP being the compound indicative of freshness. Lactic acid, which continually accumulates during curing and exhibits high thermal stability, was significantly higher after cooking than other organic acids. However, lactic acid consumption was significantly greater during boiling than in the other methods. The data from GC-E-Nose and SPME-GC–MS indicated a substantial presence of aromatic and aldehydic compounds in the cooked hams. Aldehydes comprised abundant aromatic and aldehydic compounds, which significantly affect the flavor of meat products due to their high relative content and low odor threshold (Wang et al., 2018). Steaming exhibited the highest overall volatile compound content, suggesting its superiority in producing Xuanwei ham's distinctive flavor. The identification data from GC-E-Nose and SPME-GC–MS showed considerable consistency, thereby validating the reliability of the results. The OPLS-DA model discriminant analysis revealed discernible differences among the four cooking methods, although the volatile flavor data for baking and boiling were similar. However, baking demonstrated higher nucleotide and organic acid content than boiling, offering a basis for differentiation between these two methods. Twelve characteristic flavor compounds, including 2-methylbutyraldehyde, isovaleraldehyde, benzaldehyde, phenylacetaldehyde, hexadecanal, (Z)-13-octadecadienal, 2-phenylcrotonaldehyde, mushroom alcohol, lauryl alcohol, daikatsugaele D, trimethyl borate, and 1,6-hexanediol diacrylate (VIP > 1), were identified. This study offers fresh insights into Xuanwei ham flavor research and provides a scientific foundation for optimal ham cooking in daily life.

5 Discussion

Previous culinary research has established that diverse food processing methods decompose and act upon various food compounds differently, influencing both chemical composition and overall food quality and flavor (Gomez et al., 2019; J. Li et al., 2023; Ozsarac et al., 2019; Suleman et al., 2020). Despite this knowledge, comprehensive flavor analysis of Xuanwei ham cooked via different methods remains lacking. The multifaceted effects of cooking methods on Xuanwei ham's flavor may be attributed to complex heat transfer mechanisms, temperature fluctuations during heating, and varying heating temperatures (Dominguez-Hernandez et al., 2018). The Melad reaction is not the primary cause of aroma changes in cooked meat products. Instead, the reaction of small molecule volatile compounds, released from lipid decomposition during heat processing, with other substances such as proteins and carbohydrates, produces a unique and complex flavor (Sohail et al., 2022). Aldehydes, the main products of fat degradation, and acetaldehyde, an oxidized product of sulfite oxidation and the main oxidation product in ham, are significant contributors to this flavor (Qian Chen et al., 2015). Additionally, esters, which are generated through microbial metabolism or acid-alcohol reactions, are the next most abundant compounds and play a key role in the flavor of Xuanwei ham (Olivares et al., 2011). Flavor precursors in ham undergo varying degrees of thermal degradation during different cooking processes, including caramelization, amino acid degradation, enzymatic oxidation of fats and fatty acids, and the Maillard reaction. These processes generate aldehydes, alcohols, and other small molecules that interact to produce a richer flavor profile (W. Wang, Dong, et al., 2021). Compared to other cooking methods, baking results in relatively low aldehyde and ester content, likely due to prolonged cooking time and significant water loss, which limit compound reactions. Additionally, some alcohols and aldehydes produced during boiling dissolve in the boiling water, causing loss. Traditional cooking methods can generate undesirable hazards, such as prolonged baking, which can result in the loss of essential nutrients like vitamins and minerals. This loss not only impacts flavor but also introduces harmful compounds, notably nitrites, which are potentially detrimental to human health upon long-term accumulation (H. Li, Tang, et al., 2021; Lobefaro et al., 2021). Therefore, it is beneficial to investigate a cooking method that enhances Xuanwei ham's flavor and quality while preserving its original nutritional value. This study focuses on examining the variability in Xuanwei ham's flavor quality under different cooking treatments, aiming to provide foundational data for future comprehensive flavor studies.

Funding

Major Science and Technology Projects of the Tibet Autonomous Region [XZ202101ZD0005N ]; Tibet College of Agriculture and Animal Husbandry Graduate Education Innovation Programme Project [YJS2024–54 ].

CRediT authorship contribution statement

Shu Shi: Resources. Ping Wang: Formal analysis, Data curation. Gui Peng Li: Visualization, Funding acquisition. Huai Yao Wang: Resources. Wen Liang Wu: Validation. Zhang Luo: Supervision, Project administration. Qian Yang Gao: Investigation. Zhen Dong Liu: Writing – review & editing, Project administration, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.
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