Study of Consumer Liking of Six Chinese Vinegar Products and the Correlation between These Likings and the Volatile Profile

As the aroma of Chinese vinegar is a key quality trait that influences consumer liking, a combination of sensory data and instrumental measurements were performed to help understand the aroma differences of six types of Chinese vinegar. A total of 52 volatile compounds, mostly ethyl acetate, acetic acid, and phenethyl alcohol, were detected in six types of Chinese vinegar using solid-phase microextraction coupled with gas chromatography–mass spectrometry (SPME-GC–MS). Combined with open-ended questions, the correlation between consumer liking and the volatile profile of the vinegar was further investigated. More consumers preferred the potato vinegar (B6) described as “having a sweet aroma and fruity vinegar aroma”. The Heng-shun Jinyou balsamic vinegar (B5) was not favored by consumers with its exhibition of “too pungent vinegar aroma”. Based on their preference patterns, consumers were grouped into three clusters by k-means clustering and principal component analysis (PCA). Using partial least squares regression (PLSR), the most important volatile compounds that drove consumer liking in the three clusters were obtained, among which 14 compounds such as 1-methylpyrrole-2-carboxaldehyde, ethyl acetate, and acetylfuran had the greatest impact on consumer liking, which could guide manufacturers to improve product quality and customer satisfaction.


Introduction
Vinegar is a popular seasoning and cooking ingredient that contains acetic acid and other flavor components [1]. The acetic acid in vinegar is mainly produced from ethanol via acetic acid fermentation [2]. Vinegar has attracted increasing attention since various studies have suggested that its consumption can improve human health [3]. For instance, vinegar reportedly contains many nutritional ingredients, including amino acids, minerals, organic acids, and phenolic compounds, displaying anti-microbial and anti-oxidant properties that can prevent hypertension, cardiovascular disease, and cancer [3][4][5][6][7][8].
Since Chinese vinegar is used extensively, the country produces 26 million hectoliters of vinegar annually, and its quality is essentially determined by its appearance, aroma, and nutritional components [9]. Volatile compounds play a vital role in determining the overall aroma of Chinese vinegar and are mainly formed from the source material (grain and cereal) via the fermentation process [9][10][11]. It has been reported that different acetic acid bacterial strains possess different preferences on metabolizing nutrient components during fermentation, which could result in different aromatic features for Chinese vinegar [12]. After the fermentation process, Chinese vinegar is aged to enhance its sensory attributes and nutritional quality while improving the aromatic complexity [13][14][15].
Solid-phase microextraction (SPME) represents a well-established sampling method, which can be used to extract many volatile compounds from a large variety of foods. It has been frequently combined with gas chromatography-mass spectrometry (GC-MS), which is widely used to identify volatile compounds [16,17]. Chung et al. (2017) [18] reported that SPME-GC-MS helped to distinguish the aroma profiles of rice vinegars of different producer origin, reflecting the important role of SPME-GC-MS in the extraction and identification of volatile compounds.
Regarding sensory analysis, a descriptive assessment by a trained panel represents the typical method used in the food industry to develop and control the sensory quality of products [19][20][21]. However, creating and maintaining well-trained, calibrated sensory panels can be economically challenging and time-consuming, particularly when dealing with a complex product, such as wine [22]. Moreover, due to extensive training, highly trained assessors can perceive wine aroma differently from consumers, who display a unified and holistic impression of the product. Some studies have indicated that the perception of a trained panel does not reflect the sensory impression of consumers [19,21]. Considering the high competitiveness of the current market, companies must base their decisions on consumer preferences to increase the success of their products [23]. Openended questions are a fast sensory descriptive analysis method. Recent studies have employed it to evaluate 3D printed cookies and coffee [24,25]. Applying it to sensory food evaluation not only complements the quantitative results provided by the sensory panels and helps to explore the similarities and differences between products but also provides considerable information for product developers and designers.
To better understand vinegar aroma perception, finding a correlation between the sensory data and instrumental measurements is necessary [26]. The combination of sensory data and instrumental measurements helped facilitate marketing and quality control. Yu et al. (2021) [27] revealed the aroma characteristics of traditional Huangjiu produced around the winter solstice via sensory evaluation, GC-MS, and gas chromatography-ion mobility spectrometry (GC-IMS). The results suggested that the traditional Huangjiu produced around the winter solstice contained more aroma volatile compounds and had better aroma quality than those produced during other periods. It proved that the combination of sensory data and instrumental measurements could guide product optimization effectively. The present study selected six different types of commercially available Chinese vinegar for volatile compounds extraction and analysis using SPME-GC-MS while exploring the association between consumer perception and volatile composition. This study aims to characterize the aromatic features of these vinegar samples to help understand the relationship between volatile compounds and sensory attributes, and guide manufacturers to improve the quality and consumer liking of vinegar.

Chemicals
The external standards with a purity of at least 95% included ethyl acetate, diethyl succinate, isoamyl acetate, benzaldehyde, isovaleric acid, caproic acid, octanoic acid, propionic acid, phenylethyl alcohol, and were purchased from Sigma-Aldrich (St. Louis, MO, USA). Furthermore, 2-methyl-3-heptanone with a purity of 99% represented the internal standard and was also obtained from Sigma-Aldrich (St. Louis, MO, USA).

Chinese Vinegar Samples
In this study, six representatives of different types of vinegar produced in various regions of China were selected, including ten-year aged Qian-he cellar vinegar (B1), Ninghua-mansion old vinegar (B2), East-lake health vinegar (B3), Qian-he glutinous rice vinegar (B4), Heng-shun Jinyou balsamic vinegar (B5), and potato vinegar (B6). Among them, potato vinegar (B6) was selected because potato (one of the principal raw materials) is widely cultivated around the world, is rich in nutrients, and has enormous development potential. All the above samples were purchased from a local supermarket (Beijing, China). Detailed information regarding these Chinese vinegar samples is listed in Table 1.

Volatile Compounds Extraction
The volatile compounds were extracted from the Chinese vinegar samples using SPME according to a published method with minor modifications [28]. Briefly, each Chinese vinegar sample (5 mL) was mixed with 1 µL of 0.816 µg/µL 2-methyl-3-heptanone and 1 g sodium chloride in a 15-mL vial containing a magnetic stirrer. The vial was immediately capped with a PTFE-silicone septum and equilibrated in a 55 • C water bath under agitation for 20 min. Next, a DVB/CAR/PDMS fiber was inserted into the headspace of the vial to adsorb the volatile compounds for 40 min at the same temperature with the same agitation (55 • C water bath under agitation). After SPME, the fiber was removed from the headspace of the vial and immediately inserted into the injection port of the gas chromatograph; it was then left for 5 min at 250 • C to desorb the volatile compounds into the GC column. All the samples were analyzed in triplicate.

GC-MS Analysis
An Agilent 7890A GC coupled with an Agilent 7000B mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) was used to analyze the volatile compounds in the Chinese vinegar samples according to a previously described method [28]. An Agilent DB-WAX capillary column (30 m × 0.32 mm, 0.25 µm film thickness, Agilent Technologies, Santa Clara, CA, USA) was employed to separate the volatile compounds using a carrier gas (helium) at a flow rate of 1 mL/min. The temperature of the oven was programmed as follows: The temperature was increased from 40 • C to 250 • C at 5 • C/min, and maintained at 250 • C for 3 min. A 5:1 split mode was used under an electron impact mode of 70 eV, with a mass spectrometer interface temperature of 280 • C, and an ion source temperature of 230 • C. A selective ion mode was used for the mass scan, ranging from m/z 20 to m/z 450. A C6-C24 n-alkane series (Supelco, Bellefonte, PA, USA) was used in the same chromatographic conditions to calculate the retention indices. Volatile compounds with available reference standards were identified by comparing their mass spectra and retention indices with the standard, while volatile compounds without available standards were tentatively identified by comparing their mass spectra and retention indices with the Standard NIST11 library and reference literature [29]. The stock solution (5 mL) was mixed with 5 mL distilled water and then consecutively diluted to six concentration level standards, which were extracted and analyzed employing the same procedure used for the Chinese vinegar samples. A quantitative analysis was carried out through the standard curve. In addition, volatile compounds without a standard curve were quantified using standards sharing similar structures or carbon atom numbers.

Odor Activity Value (OAV)
The OAVs of volatile compounds reflected their importance in contributing aroma notes to the overall aroma of the sample, and were calculated by comparing their concentrations in the sample with their perception threshold [30,31]. Odor thresholds were taken from the literature [32]. A volatile compound OAV higher than 1 indicated that its aroma features significantly contributed to the overall aroma of the sample.

Sensory Evaluation
This survey was conducted in January 2018 and featured 86 healthy participants (67% women and 33% men, aged 18 to 40) from the Beijing Forestry University. The inclusion criteria were regular vinegar consumption, as well as sufficient interest and time to participate in the study. The respondents were asked to complete an online questionnaire before evaluating the samples, which consisted of 32 questions divided into four sections: (1) Basic Information of Consumers. This section included eight questions regarding name, gender, age, permanent residence in the last ten years, occupation, vinegar consumption frequency, experimental participation time, and contact information. (2) Preference for Vinegar. This section investigated the attitude of the respondents towards vinegar via ten statement questions, which were scored on a 7-point scale. The 1-7 scale represented the responses, "strongly disagree", "disagree", "disagree slightly", "indifferent", "agree slightly", "agree", and "strongly agree", respectively. The respondents were required to rate each statement question according to their actual situations. (3) General Health Interest. This section also investigated the attitude of the respondents towards vinegar using ten statement questions scored on a 7-point scale. The 1-7 scale represented the responses, "strongly disagree", "disagree", "disagree slightly", "indifferent", "agree slightly", "agree", and "strongly agree", respectively. The respondents were required to rate each statement question according to their actual situations. (4) Consumer Purchasing Behavior and Preferences. This section consisted of four questions regarding understanding the vinegar aroma, the factors valued by the participants when choosing vinegar, the difference in vinegar quality, and the type of vinegar generally purchased.
This study evaluated six vinegar samples during a single experiment lasting for approximately 15-30 min. Here, 5 mL of each sample was placed in separate 30 mL brown PET plastic vials at room temperature and labeled with random three-digit numbers. The samples were placed on separate tables under artificial white light. The participants were required to smell each sample, with a 3 min resting period between samples to remove the residual odor of the previous sample. After entering the evaluation room, the respondents smelled six different vinegar samples successively to determine the difference between the vinegar aromas. Then, the respondents scored each sample within 30 s according to their personal liking and provided comments for 2 min. The vinegar samples were subjected to a sequential blind test, while the smelling order was rotated for each respondent to avoid the bias caused by the smelling sequence. This test was repeated six times. A 7-point scale was used, with points 1-7 representing "particularly dislike", "dislike", "dislike slightly", "just so, so", "like slightly, "like", and "particularly like", respectively. After the evaluation, the respondents were asked to reply to an additional two questions. (1) "Why do you like this sample?" → "Is there any other reason?" (2) "Why don't you like this sample?" → "Is there any other reason?" The tests were conducted in controlled conditions in accordance with the ISO8589:2007 standard. All respondents have consented to participation in the study. In the consumer stage, there were 86 participants, while only 76 participants completed the sensory evaluation. The data in this study were the data of the 76 participants

Statistical Analysis
The data were expressed as the mean ± standard deviation of triplicate tests. An analysis of variance (ANOVA) was performed to compare the significant differences between the means using Duncan's range test and SPSS version 23.0 (Chicago, IL, USA) with a significant level of 0.05. In addition, the Kruskal-Wallis test was used to analyze the consumer liking score. Principal component analysis (PCA) was used to evaluate the similarities and differences between the Chinese vinegar samples regarding their volatile compositions and aromatic properties. All statistical analyses of the sensory data were conducted in the R language and employed packages, such as ggplot2, reshape2, FactoMineR, pheatmap, and PLSR. PCA and k-means clustering were used to draw a consumer preference map using the relevant product data. Partial least squares regression (PLSR) was used to investigate the relationship between the volatile compound concentrations and product liking of each consumer cluster. The PLSR data were scaled and centered according to the volatile compound structures.

Volatile Compounds Detection Using GC-MS
A total of 52 volatile compounds were detected in vinegar thanks to GC-MS analysis, including eleven esters, seven aldehydes, seven acids, four phenols, three alcohols, three ketones, eight furans, three pyrazines, and six others ( Table 2). The relevant information of the standard curve for vinegar compounds is provided in Table 3. The GC-MS total ion chromatograms of six kinds of vinegar are in Figure S1.

Acids
Studies had shown that vinegar contains an abundance of acid compounds, primarily acetic acid, which was consistent with the findings of this paper. Acetic acid is produced via the alcoholic fermentation of wine yeast and by acetobacter acting on alcohol. Other acids may be the products of amino acid degradation via oxidation, or the reduction or the oxidation and degradation of saturated fatty acids [35]. Seven acids were present in all the samples, including acetic acid, propionic acid, butyric acid, isovaleric acid, 2-methylbutyric acid, caproic acid, and octanoic acid. Acid compounds are vital for providing vinegar with is bold aromas and include strong, acidic, pungent, spicy, cheesy, and chemical notes [36]. These compounds significantly contribute to the overall aroma of the vinegar and lay the foundation for its sour taste [37,38]

Furans
The furans in vinegar are mainly produced by sugar degradation via heating [9].

Aromatic Features of the Chinese Vinegar Samples
The overall aromatic features in the traditional Chinese vinegar samples were assessed according to nine aroma elements, including fruity, floral, herbaceous, nutty, caramel, earthy, chemical, fatty, and roasted. The overall aroma was rated according to the OAVs of each volatile compound that significantly contributed to each aromatic category (OAV above 1). The B1 sample presented sour, green, floral, and sweet scents, while sour, green, fruity, sweet, and roasted aromas were evident in the B2 sample ( Table 6). The B3 and B5 samples presented strong sour, green, fruity, and sweet notes, while B4 displayed sour, green, floral, and sweet aromatic notes. The B6 sample presented strong sour, fragrant, green, fruity, and sweet aromas. Besides, the aromatic features of the Chinese vinegar are similar to the other vinegar such as Shanxi aged-vinegar [49], strawberry vinegar [50], and cordyceps vinegar [51]. Table 6. Aromatic features of six kinds of vinegar.

Produce Name Aroma Description
Ten-year aged Qian-he cellar vinegar Sour, green, floral, and sweet scents Ning-hua-mansion old vinegar Sour, green, fruity, sweet, and roasted aromas East-lake health vinegar Sour, green, fruity, and sweet notes Qian-he glutinous rice vinegar Sour, green, floral, and sweet aromatic notes Heng-shun Jinyou balsamic vinegar Sour, green, fruity, and sweet notes Potato vinegar Sour, fragrant, green, fruity, and sweet aromas

Aromatic Features of the Chinese Vinegar Samples
The characteristic aromas of different vinegar varieties were analyzed according to the qualitative and quantitative aroma substance results. Figure 1, where 86.9% of the variance is in the first two components, reflects most of the sample information. The results showed that the samples were divided into four groups, with B1 and B4 concentrated in quadrant 2, B2 and B6 concentrated quadrant 3, B5 in quadrant 1, and B3 in quadrant 4.

Aromatic Features of the Chinese Vinegar Samples
The characteristic aromas of different vinegar varieties were analyzed according to the qualitative and quantitative aroma substance results. Figure 1, where 86.9% of the variance is in the first two components, reflects most of the sample information. The results showed that the samples were divided into four groups, with B1 and B4 concentrated in quadrant 2, B2 and B6 concentrated quadrant 3, B5 in quadrant 1, and B3 in quadrant 4.
As shown in Figure 1, acetic acid, caproic acid, butyric acid, and diethyl succinate were located on the positive side of PC1, whereas 3-hydroxy-2-butanone, 4-ethyl-2-methoxyphenol and benzaldehyde were located on the negative side of PC1. Phenethyl alcohol, ethyl benzoate, and 2-methylbutyric acid were located on the positive side of PC2, whereas ethyl acetate, ethyl propionate, and 1-methypyrrole-2-carboxaldehyde were located on the negative side of PC2.
Since the acetic acid, caproic acid, and butyric acid levels in B3 and B5 were high, a positive direction distribution was evident in PC1. Similarly, B4 and B6 displayed distribution in a negative direction in PC1 due to the high 3-hydroxy-2-butanone and benzaldehyde concentrations. Therefore, variation was evident in the characteristic volatile composition of the different vinegar samples.    Table 1. The number of compounds corresponds with  Table 2. As shown in Figure 1, acetic acid, caproic acid, butyric acid, and diethyl succinate were located on the positive side of PC1, whereas 3-hydroxy-2-butanone, 4-ethyl-2-methoxyphenol and benzaldehyde were located on the negative side of PC1. Phenethyl alcohol, ethyl benzoate, and 2-methylbutyric acid were located on the positive side of PC2, whereas ethyl acetate, ethyl propionate, and 1-methypyrrole-2-carboxaldehyde were located on the negative side of PC2.
Since the acetic acid, caproic acid, and butyric acid levels in B3 and B5 were high, a positive direction distribution was evident in PC1. Similarly, B4 and B6 displayed distribution in a negative direction in PC1 due to the high 3-hydroxy-2-butanone and benzaldehyde concentrations. Therefore, variation was evident in the characteristic volatile composition of the different vinegar samples.

Overall Consumer Liking
This study collected the liking data of 76 qualified consumers. A larger sample size could make the results more accurate and instructive, although our sample size (76) was appropriate for consumer liking, as many studies show. Berna et al. [52], Yanxin et al. [53], and Varela et al. [54] studied tomatoes, Chinese bog bilberry wines, and coffee with a sample size of 54, 93, and 96 respectively. On average, B6 was preferred, receiving a score of 3.60 on a scale of 1 to 7, followed by B2, indicating that the most preferred vinegar was still not liked much (Figure 2). A previous study showed that the satisfaction level of consumers of vinegar products was low at this stage [55]. B1 scored the lowest in liking with a value of 3.18. The liking score range of all the tested products was 0.43, suggesting that respondents provided scores in a relatively limited range. The Kruskal-Wallis test calculated that there was no significant difference between the six products at a significance level of 0.05. This could be attributed to the significant segmentation in the liking results of the respondents, as discussed subsequently. Zamora and Guirao (2004) [56] mentioned that experts gave a more consistent description of attributes than the trained panelists for different wine product brands. was 0.43, suggesting that respondents provided scores in a relatively limited range. The Kruskal-Wallis test calculated that there was no significant difference between the six products at a significance level of 0.05. This could be attributed to the significant segmentation in the liking results of the respondents, as discussed subsequently. Zamora and Guirao (2004) [56] mentioned that experts gave a more consistent description of attributes than the trained panelists for different wine product brands.

The Association between the Geographical Location of Consumers for the Past Ten
Years and Their Likings Figure 3 shows a heatmap representing the geographical origins of the respondents and their likings. The results yielded two distinct clusters, one containing B1, B3, and B5, while the other comprising B2, B4, and B6. Respondents from Chongqing, Hunan, and Sichuan generally showed a marked liking for B1, B3, and B5, while participants from Heilongjiang, Henan, Shaanxi, and Inner Mongolia preferred B2, B4, and B6. Respondents from Beijing, Hebei, and Shanxi exhibited lower liking differences regarding the tested products. Moreover, respondents from Hubei, Anhui, and Zhejiang displayed a less positive attitude toward most of the tested samples. Obviously, consumers from different geographical locations had different likings [57].
It should be noted that the respondents were not evenly distributed in this study, denoting an area that could be improved in further research. Here, 48% of the respondents were from Beijing or had lived in Beijing in recent years ( Figure S2). This observation may not remain the same when the sample size increases, representing an interesting phenom-  Figure 3 shows a heatmap representing the geographical origins of the respondents and their likings. The results yielded two distinct clusters, one containing B1, B3, and B5, while the other comprising B2, B4, and B6. Respondents from Chongqing, Hunan, and Sichuan generally showed a marked liking for B1, B3, and B5, while participants from Heilongjiang, Henan, Shaanxi, and Inner Mongolia preferred B2, B4, and B6. Respondents from Beijing, Hebei, and Shanxi exhibited lower liking differences regarding the tested products. Moreover, respondents from Hubei, Anhui, and Zhejiang displayed a less positive attitude toward most of the tested samples. Obviously, consumers from different geographical locations had different likings [57].

The Association between the Geographical Location of Consumers for the Past Ten Years and Their Likings
itive attitude toward most of the tested samples. Obviously, consumers from different geographical locations had different likings [57].
It should be noted that the respondents were not evenly distributed in this study, denoting an area that could be improved in further research. Here, 48% of the respondents were from Beijing or had lived in Beijing in recent years ( Figure S2). This observation may not remain the same when the sample size increases, representing an interesting phenomenon derived from this dataset.  Table 1. These regions are explained in the supplementary data ( Figure S2).  Table 1. These regions are explained in the supplementary data ( Figure S2).
It should be noted that the respondents were not evenly distributed in this study, denoting an area that could be improved in further research. Here, 48% of the respondents were from Beijing or had lived in Beijing in recent years ( Figure S2). This observation may not remain the same when the sample size increases, representing an interesting phenomenon derived from this dataset.

Open Comments from Consumers
The open comments are encompassed in Figure 4a. When describing their liking for the vinegar samples, no significant variation or specific frequency was detected in the verbiage used by the consumers, and included terms, such as "rich vinegar aroma", "medium vinegar aroma", "fruity vinegar aroma", and "sweet aroma." Consumers also mentioned terms like "too pungent vinegar aroma", "not rich vinegar aroma", "alcohol aroma", and "smelly aroma." Furthermore, the heatmap showed the differences between the six samples as per the participants.

Open Comments from Consumers
The open comments are encompassed in Figure 4a. When describing their liking for the vinegar samples, no significant variation or specific frequency was detected in the verbiage used by the consumers, and included terms, such as "rich vinegar aroma", "medium vinegar aroma", "fruity vinegar aroma", and "sweet aroma." Consumers also mentioned terms like "too pungent vinegar aroma", "not rich vinegar aroma", "alcohol aroma", and "smelly aroma." Furthermore, the heatmap showed the differences between the six samples as per the participants.
Regarding liking, B1, B3, and B4 were more often described as presenting a "medium vinegar aroma" than the remaining samples, while more participants ascribed a "rich vinegar aroma" to B2. More consumers described B6 as "having a sweet aroma and fruity vinegar aroma" than the other samples.
Many consumers (32 out of 76) described B5 as exhibiting a "too pungent vinegar aroma", while significantly fewer participants ascribed this characteristic to B3 and B6, when asked what they disliked about the product. Comments indicated B6 as "having an alcohol aroma" for 15 consumers out of 76, while fewer than five participants ascribe this attribute to the other samples. B3 was attributed a "Chinese medicine aroma" by 15 out 76 consumers, who disliked the characteristics of this sample, representing the highest percentage of the six products.   Table 1. The left 18 columns were the terms that consumers liked, and the right 18 columns were the terms that consumers disliked.

Overall Liking by Clusters
The consumers were clustered according to their preference patterns using k-means clustering and PCA, and the results were visualized in a 2-D map ( Figure 5). The average likings of the three identified consumer clusters were displayed in the bar plot shown in Figure 6. The clusters contained 30, 18, and 28 participants, respectively. The consumers in cluster 1 generally provided lower liking scores for all the products, with average values below 4. Cluster 1 consumers favored B4 and B6 over the other samples. Cluster 2 consumers preferred B2 and B3, while cluster 3 participants favored the B1, B3, and B6 samples, with liking scores over 4.  Table 1. The left 18 columns were the terms that consumers liked, and the right 18 columns were the terms that consumers disliked.
Regarding liking, B1, B3, and B4 were more often described as presenting a "medium vinegar aroma" than the remaining samples, while more participants ascribed a "rich vinegar aroma" to B2. More consumers described B6 as "having a sweet aroma and fruity vinegar aroma" than the other samples.
Many consumers (32 out of 76) described B5 as exhibiting a "too pungent vinegar aroma", while significantly fewer participants ascribed this characteristic to B3 and B6, when asked what they disliked about the product. Comments indicated B6 as "having an alcohol aroma" for 15 consumers out of 76, while fewer than five participants ascribe this attribute to the other samples. B3 was attributed a "Chinese medicine aroma" by 15 out 76 consumers, who disliked the characteristics of this sample, representing the highest percentage of the six products.

Overall Liking by Clusters
The consumers were clustered according to their preference patterns using k-means clustering and PCA, and the results were visualized in a 2-D map ( Figure 5). The average likings of the three identified consumer clusters were displayed in the bar plot shown in Figure 6. The clusters contained 30, 18, and 28 participants, respectively. The consumers in cluster 1 generally provided lower liking scores for all the products, with average values below 4. Cluster 1 consumers favored B4 and B6 over the other samples. Cluster 2 consumers preferred B2 and B3, while cluster 3 participants favored the B1, B3, and B6 samples, with liking scores over 4.

Cluster Differences in Demographics, Usage, and Attitude
The three consumer clusters displayed differences in attitude towards purchasing and using vinegar (Figure 7). A higher percentage of cluster 2 consumers considered the product brand essential when purchasing vinegar products than the other two clusters. Cluster 3 consumers often used vinegar as a condiment in daily life when having noodles, while cluster 1 and 2 consumers agreed to a lesser extent in this regard, indicating different vinegar utilization habits. A higher percentage of cluster 1 consumers used rice vinegar than the other clusters, and attached less importance to acidity when purchasing vinegar. A previous study showed that consumers differ in their usage and attitudes towards balsamic vinegar. Italians would pair balsamic vinegar mainly with vegetables, fruits, and cheese, while Koreans would combine balsamic vinegar preferably with bread, vegeta-

Cluster Differences in Demographics, Usage, and Attitude
The three consumer clusters displayed differences in attitude towards purchasing and using vinegar (Figure 7). A higher percentage of cluster 2 consumers considered the product brand essential when purchasing vinegar products than the other two clusters. Cluster 3 consumers often used vinegar as a condiment in daily life when having noodles, while cluster 1 and 2 consumers agreed to a lesser extent in this regard, indicating different vinegar utilization habits. A higher percentage of cluster 1 consumers used rice vinegar than the other clusters, and attached less importance to acidity when purchasing vinegar. A previous study showed that consumers differ in their usage and attitudes towards balsamic vinegar. Italians would pair balsamic vinegar mainly with vegetables, fruits, and cheese, while Koreans would combine balsamic vinegar preferably with bread, vegeta-

Cluster Differences in Demographics, Usage, and Attitude
The three consumer clusters displayed differences in attitude towards purchasing and using vinegar (Figure 7). A higher percentage of cluster 2 consumers considered the product brand essential when purchasing vinegar products than the other two clusters. Cluster 3 consumers often used vinegar as a condiment in daily life when having noodles, while cluster 1 and 2 consumers agreed to a lesser extent in this regard, indicating different vinegar utilization habits. A higher percentage of cluster 1 consumers used rice vinegar than the other clusters, and attached less importance to acidity when purchasing vinegar. A previous study showed that consumers differ in their usage and attitudes towards balsamic vinegar. Italians would pair balsamic vinegar mainly with vegetables, fruits, and cheese, while Koreans would combine balsamic vinegar preferably with bread, vegetables, and meat [58]. In this study, a lot of consumers used vinegar when eating dumplings and noodles.

Open Comments by Clusters
The open comment elicitation rates of each consumer cluster were visualized in a heatmap (Figure 4b). The elicitation rate pattern of the comments showed minimal differences between the clusters, which was validated by the correlation among the groups (results not shown). This consistency suggested that the consumers displayed a limited capability to distinguish and describe the aroma of the vinegar and to express their likes and dislikes.

The Correlation between Consumer Liking and the Volatile Profiles of the Vinegar
PLSR was applied to investigate the correlation between the volatile profiles of the vinegar and the likings of the consumers in each cluster, to reveal the volatile chemicals responsible for favorable aroma scores. The biplot for each cluster is shown in Figure 8. The relative importance of a specific compound was calculated by its percentage of the absolute value of the coefficient in the sum of the absolute value of all coefficients. The top five most important volatiles are listed in Table 7, displaying the different volatile compounds that may drive consumer likings in the clusters. Therefore, 1-methylpyrrole-2-carboxaldehyde, ethyl acetate, acetylfuran, 1H-Pyrrole-2-carbaldehyde, and 2,3,5-trimethylpyrazine played a crucial role in the likings of cluster 1 consumers. Cluster 2 consumers were partial to benzaldehyde, phenylethanal, 3-methyl-1-butanol, 3-hydroxy-2-butanone, and ethyl acetate, while cluster 3 consumers favored products containing (2-methoxy-4-vinyl-phenyl)-acetate, 1,2-propanediol,2-acetate, isobutyl acetate, methylbutyric acid, and isoamyl acetate. Detailed regression coefficients are provided in Table S1. Jo et al. (2013) [59] reported that the highest score was observed for vinegar with moderate acidity. Cejudo- Bastante et al. (2018) [60] confirmed that for the majority of volatile compounds, higher contents were observed for the submerged culture acetification process, and this was also reflected in the sensory analysis, presenting higher scores for the different descriptors.

Open Comments by Clusters
The open comment elicitation rates of each consumer cluster were visualized in a heatmap (Figure 4b). The elicitation rate pattern of the comments showed minimal differences between the clusters, which was validated by the correlation among the groups (results not shown). This consistency suggested that the consumers displayed a limited capability to distinguish and describe the aroma of the vinegar and to express their likes and dislikes.

The Correlation between Consumer Liking and the Volatile Profiles of the Vinegar
PLSR was applied to investigate the correlation between the volatile profiles of the vinegar and the likings of the consumers in each cluster, to reveal the volatile chemicals responsible for favorable aroma scores. The biplot for each cluster is shown in Figure 8. The relative importance of a specific compound was calculated by its percentage of the absolute value of the coefficient in the sum of the absolute value of all coefficients. The top five most important volatiles are listed in Table 7, displaying the different volatile compounds that may drive consumer likings in the clusters. Therefore, 1-methylpyrrole-2-carboxaldehyde, ethyl acetate, acetylfuran, 1H-Pyrrole-2-carbaldehyde, and 2,3,5-trimethylpyrazine played a crucial role in the likings of cluster 1 consumers. Cluster 2 consumers were partial to benzaldehyde, phenylethanal, 3-methyl-1-butanol, 3-hydroxy-2-butanone, and ethyl acetate, while cluster 3 consumers favored products containing (2-methoxy-4-vinyl-phenyl)acetate, 1,2-propanediol,2-acetate, isobutyl acetate, methylbutyric acid, and isoamyl acetate. Detailed regression coefficients are provided in Table S1. Jo et al. (2013) [59] reported that the highest score was observed for vinegar with moderate acidity. Cejudo- Bastante et al. (2018) [60] confirmed that for the majority of volatile compounds, higher contents were observed for the submerged culture acetification process, and this was also reflected in the sensory analysis, presenting higher scores for the different descriptors.  Table 1. The number of compounds corresponds with Table 2.

Conclusions
In this study, we used SPME-GC-MS combined with sensory evaluation to examine the association between consumer perception and volatile compounds of six types of Chinese vinegar. The results showed that 52 volatile compounds were detected by GC-MS in six types of Chinese vinegar. High concentrations of ethyl acetate, acetic acid, and phenethyl alcohol were found in all the vinegar samples. Combined with sensory evaluation, it was found that some specific volatile compounds affected consumer liking for Chinese vinegar significantly. The potato vinegar (B6) was preferred; more consumers described B6 as "having a sweet aroma and fruity vinegar aroma" than the other samples, and many consumers described the Heng-shun Jinyou balsamic vinegar (B5) as exhibiting a "too pungent vinegar aroma" when asked what they disliked about the product. For PLSR, the most important volatile compounds in the three clusters that drove consumer liking confirmed the importance of 1-methylpyrrole-2-carboxaldehyde, ethyl acetate, acetylfuran, 1H-Pyrrole-2-carbaldehyde, 2,3,5-trimethylpyrazine, benzaldehyde, phenylethanal, 3-methyl-1-butanol, 3-hydroxy-2-butanone, (2-methoxy-4-vinyl-phenyl)-acetate, 1,2-propanediol,2-acetate, isobutyl acetate, methylbutyric acid, and isoamyl acetate in Chinese vinegar. Manufacturers should pay attention to the changes in these 14 compounds and the content of the end product in the production process, and at the same time accumulate data about the correlation between compounds and consumer liking. They should subsequently change the production process using the data to improve the quality and consumer preference of vinegar.  Table 1. The number of compounds corresponds with Table 2.

Conclusions
In this study, we used SPME-GC-MS combined with sensory evaluation to examine the association between consumer perception and volatile compounds of six types of Chinese vinegar. The results showed that 52 volatile compounds were detected by GC-MS in six types of Chinese vinegar. High concentrations of ethyl acetate, acetic acid, and phenethyl alcohol were found in all the vinegar samples. Combined with sensory evaluation, it was found that some specific volatile compounds affected consumer liking for Chinese vinegar significantly. The potato vinegar (B6) was preferred; more consumers described B6 as "having a sweet aroma and fruity vinegar aroma" than the other samples, and many consumers described the Heng-shun Jinyou balsamic vinegar (B5) as exhibiting a "too pungent vinegar aroma" when asked what they disliked about the product. For PLSR, the most important volatile compounds in the three clusters that drove consumer liking confirmed the importance of 1-methylpyrrole-2-carboxaldehyde, ethyl acetate, acetylfuran, 1H-Pyrrole-2-carbaldehyde, 2,3,5-trimethylpyrazine, benzaldehyde, phenylethanal, 3-methyl-1-butanol, 3-hydroxy-2-butanone, (2-methoxy-4-vinyl-phenyl)acetate, 1,2-propanediol,2-acetate, isobutyl acetate, methylbutyric acid, and isoamyl acetate in Chinese vinegar. Manufacturers should pay attention to the changes in these 14 compounds and the content of the end product in the production process, and at the same time accumulate data about the correlation between compounds and consumer liking. They should subsequently change the production process using the data to improve the quality and consumer preference of vinegar.