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. 2026 Sep 18;39:104443. doi: 10.1016/j.fochx.2026.104443

Regional differences and round-by-round dynamics of aldehydes in sauce-flavor baijiu from the Chishui River Basin

Xiangui Wang a, Dan Yang a, Xinyu Yuan a, Dan Zeng b, Deguang Wu a, Huajie Xu a,⁎, Tianzhu Shi a,⁎
PMCID: PMC13626780  PMID: 42820234

Abstract

Sauce-flavor baijiu derives its characteristic aroma from the unique geography of the Chishui River Basin and a multi-round fermentation process. Aldehydes are key odorants contributing fruity, roasted, and nutty notes, yet their regional distribution and evolution across the seven production rounds remain poorly characterized. We developed a GC-FID method to quantify aldehydes in eight commercial baijiu samples from seven production regions and monitored acetaldehyde, acetal, and furfural across the seven rounds of baijiu. The method showed excellent linearity (R2 ≥ 0.996), low detection limits (0.02–0.30 mg/L) and high precision (RSD ≤ 3.12%). Regional aldehyde profiles differed significantly (P < 0.05). Multivariate analysis clearly separated samples by origin and identified the three compounds as high-VIP concentration variables (VIP > 1). Across rounds, acetaldehyde and acetal exhibited a rise-fall-rise pattern, while furfural increased steadily. Acetaldehyde was more sensitive to region and processing conditions, furfural showed cross-regional consistency, and acetal was intermediate. These findings provide a chemical basis for origin authentication, round-based grading and process optimization in sauce-flavor baijiu production.

Keywords: Sauce-flavor baijiu, Aldehydes, Regional differences, Round-by-round dynamics

1. Introduction

The sauce-flavor baijiu, with its unique brewing process of “nine distillations, eight fermentations, and seven rounds of distillation”, has become one of the most important traditional distilled spirits in China (Li, Zhang, Li, et al., 2024; Liu, Mu, et al., 2025). Its flavor depends closely on high-temperature Daqu, solid-state fermentation, and repeated rounds of fermentation and distillation (Wu et al., 2023; Xu et al., 2023). The Chishui River Basin spans Guizhou and Sichuan Provinces and is widely recognized as the core production region of sauce-flavor baijiu. Its unique geology, climate, and microbial ecology provide the foundation for the development of regional flavor characteristics (Ma et al., 2026; Niu et al., 2025). These factors also give the baijiu strong geographic specificity.

Among the many volatile compounds in sauce-flavor baijiu, aldehydes play a dual role (Li, Yang, et al., 2025). They are important contributors to aroma, but when present at excessive or unbalanced levels, they may also contribute to pungency (Cai et al., 2022; Guan et al., 2022; He et al., 2022). Previous studies have shown that acetaldehyde, acetal, and furfural are closely associated with fruity notes, aged character, mouthfeel fullness, and overall flavor harmony (Dong et al., 2024; Mou et al., 2025). They are therefore important chemical components affecting baijiu quality. Although aldehydes in baijiu have been investigated both qualitatively and quantitatively, most previous studies have focused on finished baijiu or on a limited number of production regions (Ding et al., 2024; Li, Han, et al., 2025). Consequently, current evidence is largely static and does not adequately capture the dynamics that occur during the multi-round brewing process (Chen et al., 2024). In particular, systematic comparisons of aldehydes in sauce-flavor baijiu from different regions of the Chishui River Basin, as well as the dynamic behavior of key aldehydes across the seven rounds of distillation, are still limited (Aliya et al., 2025; Ma et al., 2026).

Although progress has been made in understanding the flavor formation of sauce-flavor baijiu, most studies focus on specific production regions or individual processes (Li, Zhang, Li, et al., 2024; Shi et al., 2024). A comprehensive study addressing different production regions and process stages is still lacking. In fact, the flavor of sauce-flavor baijiu is not only determined by raw materials, processes, and brewing environments but is also closely related to the changes in chemical substances at various stages (Li, Zhang, Li, et al., 2024; Li, Zhang, Wang, et al., 2024). Particularly, aldehydes and their transformation during different rounds of production play a crucial role in shaping the final flavor and quality of the baijiu (Wu et al., 2023). Therefore, a systematic exploration of the differences in aldehyde composition among production regions and their dynamics across different production rounds will help further reveal the sources and evolution of the flavor components in sauce-flavor baijiu (Ma et al., 2026).

Accordingly, this study investigated aldehyde variation in sauce-flavor baijiu from both regional and process perspectives. Specifically, it aimed to (1) compare aldehyde compositions in finished baijiu from seven major production regions, (2) characterize the dynamics of acetaldehyde, acetal, and furfural in base baijiu across rounds 1 to 7, and (3) identify key aldehyde with potential for regional discrimination. The results may support origin identification, quality evaluation of round baijiu, and process optimization in sauce-flavor baijiu production.

2. Materials and methods

2.1. Materials and reagents

As shown in Fig. 1, eight representative sauce-flavor baijiu samples from seven major production regions in the Chishui River Basin were selected. Finished baijiu samples included two from Renhuai, Guizhou (RH-1 and RH-2; RH-1 from a large distillery and RH-2 from a medium-sized distillery), and one each from Xishui, Guizhou (XS), Gulin, Sichuan (GL), Chishui, Guizhou (CS), Jinsha, Guizhou (JS), Zunyi, Guizhou (ZY), and Guiyang, Guizhou (GY). The round baijiu samples were the corresponding baijiu from rounds 1 to 7 for each of these eight sample sets. Except for Renhuai, each of the other investigated regions has only one large sauce-flavor baijiu distillery; therefore, a representative finished product from that distillery was selected for analysis. These regions included Xishui(XS), Gulin(GL), Chishui(CS), Jinsha(JS), Zunyi(ZY), and Guiyang(GY). Renhuai has multiple large sauce-flavor baijiu distilleries; therefore, two representative distilleries were selected from this region (RH-1 and RH-2) to provide additional information on intra-regional variation. For the round-by-round analysis, baijiu samples from rounds 1–7 were collected from the corresponding distilleries within the same 2024–2025 production cycle. Detailed information on sample origin, production year, collection year, acquisition, and sample status is provided in Table S5. All samples were obtained either from enterprises or through retail purchase, and were selected to represent the major production regions included in this study.

Fig. 1.

Fig. 1

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Geographical distribution and sampling locations of sauce-flavor baijiu in the Chishui River Basin.

Acetaldehyde, propionaldehyde, isobutyraldehyde, acetal, 2-methylbutyraldehyde, 3-methylbutyraldehyde, furfural, benzaldehyde, and phenylacetaldehyde standards were purchased from Tianjin Xima Technology Co., Ltd. All standards had purities of ≥99.5%. Absolute ethanol (chromatographic grade) was purchased from Shanghai Aladdin Biochemical Technology Co., Ltd.

2.2. Instruments and equipment

A Nexis-2030 gas chromatograph (Shimadzu, Japan). An ME204E electronic balance (Mettler Toledo, Shanghai, China), pipettes (Brand, Germany), and 2 mL sample vials (ANPEL Laboratory Technologies, Shanghai, China) were used.

2.3. Methods

2.3.1. Determination of flavor compounds

Target aldehydes were determined by GC-FID (Gas Chromatography-Flame Ionization Detector), and each sample was analyzed in triplicate (Gao et al., 2024; Li et al., 2023). The chromatographic column was DB-Wax (30 m × 0.25 mm × 0.25 μm). The oven program was as follows: initial temperature 28 °C for 12 min; ramped to 50 °C at 7.0 °C/min; ramped to 95 °C at 3.5 °C/min; ramped to 125 °C at 3.0 °C/min and held for 8 min; ramped to 145 °C at 1.5 °C/min; then ramped to 240 °C at 8.0 °C/min and held for 15 min. The injector temperature was 250 °C, and the flame ionization detector (FID) temperature was also 250 °C. Hydrogen and air were supplied to the FID at 32.0 and 200.0 mL/min, respectively. Nitrogen was used as the carrier gas at 0.5 mL/min in constant-flow mode, and the make-up gas flow rate was 24.0 mL/min. Samples were injected in split mode with a split ratio of 45:1, and the injection volume was 1 μL (Huang et al., 2023; Wang et al., 2023).

2.3.2. Qualitative and quantitative analysis

Target compounds were identified by comparing their retention times with those of authentic standards. Quantification was performed by the external standard method(Ma et al., 2026). A series of calibration solutions was prepared using a simulated sauce-flavor baijiu matrix, and the calibration ranges covered the actual concentrations of each target compound in the samples. All concentrations were expressed as mg/L.

2.3.3. Odor activity value analysis

Odor activity values (OAVs) were calculated as OAV = C/T, where C is the measured concentration and T is the published odor threshold expressed in the same units. Thresholds were selected preferentially from baijiu studies using ethanol-water matrices. The thresholds used for the nine target compounds and the resulting OAVs are provided in Supplementary Tables S6. Because odor thresholds depend on ethanol concentration and matrix composition, these OAVs are interpreted as screening estimates of potential sensory relevance rather than direct sensory proof (Guan et al., 2022; Xu et al., 2024).

2.3.4. Statistical analysis

SPSS Statistics 25.0 was used for one-way analysis of variance (ANOVA). Homogeneity of variance was tested before group comparison. The least significant difference (LSD) test and Duncan's multiple range test were applied for multiple comparisons, with P < 0.05 considered significant. Origin 2021 was used for data visualization. SIMCA 14.1 was used for principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Pearson correlation heatmaps were generated using ChiPlot (Lian et al., 2025; Liu, Lian, et al., 2025; Wan et al., 2024).

3. Results

3.1. Method validation for aldehyde analysis

As shown in Table 1, the gas chromatographic method established in this study showed demonstrated satisfactory analytical performance for the nine aldehydes in sauce-flavor baijiu. All compounds exhibited good linearity within their respective concentration ranges, with correlation coefficients (R2) of 0.996–0.999. The limits of detection ranged from 0.02 to 0.30 mg/L, and the limits of quantification ranged from 0.06 to 0.90 mg/L. Recoveries ranged from 96.34% to 98.56%, and relative standard deviations (RSDs) ranged from 1.89% to 3.12%. These results indicate that the method has satisfactory linearity, sensitivity, recovery, and repeatability, and is suitable for quantitative analysis of aldehydes in sauce-flavor baijiu.

Table 1.

Validation parameters of the gas chromatographic method for aldehyde analysis.

Compounds Retention time (min) Linear range (mg/L) R2 LOD (mg/L) LOQ (mg/L) Recovery (%) RSD
(%)
Acetaldehyde 8.286 10–1000 0.998 0.20 0.60 98.56 1.89
Propionaldehyde 9.839 1–100 0.997 0.10 0.30 96.34 3.12
Isobutyraldehyde 10.441 1–100 0.998 0.10 0.30 96.78 3.04
Acetal 13.970 10–1000 0.999 0.30 0.90 98.42 1.92
2-Methylbutyraldehyde 14.836 1–500 0.996 0.05 0.15 97.25 2.86
3-Methylbutyraldehyde 15.111 1–500 0.996 0.05 0.15 96.93 3.01
Furfural 44.108 10–1000 0.999 0.02 0.06 98.21 1.97
Benzaldehyde 47.079 1–100 0.998 0.02 0.06 98.05 2.04
Phenylacetaldehyde 52.857 0.2–100 0.998 0.05 0.15 97.86 2.15
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3.2. Overall distribution of aldehydes in finished baijiu from different regions

The concentrations of nine aldehydes in eight finished sauce-flavor baijiu samples from the Chishui River Basin are shown in Fig. 2. Overall, aldehyde concentrations differed significantly among production regions (P < 0.05), indicating pronounced regional variation in aldehyde composition. Aldehydes with different biosynthetic or thermal origins displayed different accumulation patterns across regions. In terms of total aldehyde content, the GL region had the highest overall level, followed by GY, JS, and CS. XS, RH-1, and RH-2 were intermediate, whereas ZY showed the lowest overall level.

Fig. 2.

Fig. 2

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Concentrations of aldehydes in finished baijiu from different production regions. Note: Different lowercase letters indicate significant differences between groups (P < 0.05).

Among the key aldehydes, GL had the highest acetaldehyde content (945.42 mg/L), followed by GY, JS, and CS, while ZY had the lowest level (295.00 mg/L), with significant differences among groups (P < 0.05). GL had the highest acetaldehyde content (945.42 mg/L), followed by GY, JS, and CS, while ZY had the lowest level (295.00 mg/L), with significant differences among groups (P < 0.05). Notably, GL represents the Sichuan production region, whereas the other investigated production regions are located in Guizhou. Therefore, the co-elevation of acetaldehyde and acetal in GL may be more closely associated with differences in production practices between the Sichuan and Guizhou production areas, particularly differences in distillation process control and liquor-cutting criteria, rather than geography alone. Fermentation intensity, storage, and blending practices may also contribute to this pattern. Acetal showed a trend similar to that of acetaldehyde, and GL also had the highest level (540.86 mg/L). In contrast, furfural was higher in CS (296.93 mg/L) and JS (293.49 mg/L), but relatively lower in GL (148.16 mg/L), indicating that furfural formation did not fully follow the same pattern as acetaldehyde and acetal.

Branched-chain amino acid-derived aldehydes also varied markedly among regions. JS showed the highest levels of isobutyraldehyde, 2-methylbutyraldehyde, and 3-methylbutyraldehyde, indicating strong accumulation of branched aldehydes; for example, 3-methylbutyraldehyde in JS was about 3.1 times that in RH-1. Among aromatic metabolism-related aldehydes, phenylacetaldehyde was much higher in the Renhuai samples than in all other regions, reaching 9.63 mg/L in RH-1 and 9.62 mg/L in RH-2, whereas all other regions were below 1.70 mg/L. Benzaldehyde was highest in JS, followed by RH-2 and CS. Taken together, the different regions showed distinct region-specific profiles in acetaldehyde, acetal, furfural, and aromatic aldehydes.

3.3. Regional clustering features and intra-regional differences within Renhuai

Based on the combined distribution of the nine aldehydes, the samples showed clear regional clustering. GL was characterized mainly by high acetaldehyde and acetal levels. JS showed marked advantages in branched-chain amino acid-related aldehydes and benzaldehyde, while also maintaining relatively high levels of furfural and acetal. GY and CS were characterized by elevated levels of acetaldehyde, propionaldehyde, and 3-methylbutyraldehyde, and in furfural and acetal, respectively. XS, RH-1, RH-2, and ZY were generally at low to moderate levels, although the Renhuai samples were notable for their high phenylacetaldehyde content.

RH-1 and RH-2 exhibited highly similar aldehyde profiles. Their phenylacetaldehyde levels were almost identical and were both significantly higher than those of the other regions. They also showed similar levels of 2-methylbutyraldehyde, isobutyraldehyde, and benzaldehyde. Although RH-2 had slightly higher acetaldehyde, acetal, and furfural than RH-1, the differences were limited. These results suggest that samples from different distilleries within the same region still share strong common flavor-chemical features, although some variation in key aldehydes remains.

3.4. Discriminant analysis based on aldehyde profiles

Fig. 3A shows the relative abundances of the nine aldehydes in sauce-flavor baijiu samples from different regions. Clear differences were observed among regions. RH-1 and RH-2 formed a similar subgroup, which suggests strong within-region consistency. As shown in Fig. 3B, the first two principal components explained 92.6% of the total variance (R2X[1] = 0.753, R2X[2] = 0.173), indicating that the model captured most of the overall variation among samples. GL and JS were clearly separated from the others. CS and GY showed a certain clustering tendency, whereas RH-1, RH-2, XS, and ZY were relatively close but still showed subtle differences. The loading plot (Fig. 3C) indicated that acetaldehyde, acetal, furfural, and 3-methylbutyraldehyde were the main variables driving sample classification.

Fig. 3.

Fig. 3

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Multivariate statistical analysis of aldehydes in finished baijiu from different production regions: A. Heat map of relative abundance; B. PCA score scatter plot; C. Loading scatter plot; D. OPLS-DA score scatter plot; E. Permutation test (200 permutations); F. VIP predictive.

Compared with PCA, OPLS-DA further improved the discrimination of samples from different regions. As shown in Fig. 3D, GL and JS were the most clearly separated samples in the score plot. The clustering of CS and GY also became more distinct, while RH-1, RH-2, and ZY were tightly grouped but still separable from XS to some extent. The 200-permutation test (Fig. 3E) showed that the original model had markedly higher R2 and Q2 values than the permuted models, and the Q2 intercept was negative, indicating that the model was robust and not overfitted, and could therefore be used for key marker screening and sample discrimination (Liu et al., 2022; Xu et al., 2024; Zhao et al., 2026; Zheng et al., 2025). VIP analysis (Fig. 3F) further identified acetaldehyde (VIP = 2.03), acetal (VIP = 1.54), and furfural (VIP = 1.22) as the key discriminatory aldehydes. 3-Methylbutyraldehyde also contributed to sample separation.

Although phenylacetaldehyde did not rank among the highest VIP variables, its concentration showed a distinctive Renhuai-associated pattern. RH-1 and RH-2 contained 9.63 and 9.62 mg/L phenylacetaldehyde, respectively, whereas the concentrations in the other samples were markedly lower. This indicates that phenylacetaldehyde may provide specific discriminatory information for the Renhuai samples, although its contribution to the overall OPLS-DA model was lower than that of acetaldehyde, acetal, and furfural.

3.5. Odor activity values of aldehydes in finished baijiu

To further evaluate the potential sensory relevance of the quantified aldehydes, odor activity values (OAVs) were calculated for all nine target compounds using their measured concentrations and literature-reported odor thresholds shown in Table 2. Most aldehydes showed OAVs greater than 1 in the finished baijiu samples, indicating that they were potentially aroma-active under the threshold conditions used in this study. Among the three key aldehydes identified by multivariate analysis, acetaldehyde showed OAVs of 246–788, acetal 70–259, and furfural 3–7 across the investigated products. Thus, all three compounds had OAVs above 1 in every sample, supporting their potential sensory relevance. Clear product-origin-associated differences were also observed: GL showed the highest OAVs of acetaldehyde (788) and acetal (259), whereas furfural showed relatively higher OAVs in CS and JS. In addition, propionaldehyde, isobutyraldehyde, 2-methylbutyraldehyde, and 3-methylbutyraldehyde also exhibited OAVs well above 1. Phenylacetaldehyde showed particularly high OAVs in RH-1 and RH-2 (both 37), markedly exceeding those in the other samples (2–6), whereas benzaldehyde remained below or close to 1 in most products.

Table 2.

Odor activity values (OAVs) of nine target compounds in finished baijiu.

Region Acetaldehyde Propionaldehyde Isobutyraldehyde Acetal 2-Methylbutyraldehyde 3-Methylbutyraldehyde Furfural Benzaldehyde Phenylacetaldehyde
Odor
Threshold
(μg/L)		 1200(Guan et al., 2022) 2(Mu et al., 2021) 34.69(Guan et al., 2022) 2090(Wang et al., 2020) 17(Guan et al., 2022) 980(Guan et al., 2022) 44,000(Wang et al., 2020) 4200(Wang et al., 2020) 262(Wang et al., 2020)
RH-1 303 1710 447 70 893 46 4 <1 37
RH-2 330 2350 501 93 977 57 5 1 37
XS 349 2700 491 101 949 64 5 <1 6
GL 788 3465 300 259 375 38 3 <1 6
CS 510 1765 309 167 535 68 7 <1 2
JS 519 4075 654 168 1262 142 7 2 3
ZY 246 3755 329 103 353 78 4 <1 5
GY 571 4250 309 141 327 88 5 <1 2
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Overall, the OAV results confirmed that several aldehydes, including acetaldehyde, acetal, and furfural, were present at concentrations above their reported odor thresholds and therefore had the potential to contribute to the aroma characteristics of the investigated baijiu products. The magnitude of their OAVs varied among production regions, indicating differences in their relative potential sensory importance.

3.6. Spatiotemporal changes of key aldehydes across the seven production rounds

Based on the VIP results, acetaldehyde, acetal, and furfural were selected for further analysis of their changes in seven rounds baijiu (Fig. 4). Overall, the three aldehydes showed clearly different patterns across rounds, and these patterns also varied by region.

Fig. 4.

Fig. 4

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Dynamics of acetaldehyde, acetal and furfural in seven rounds baijiu across different production regions. Note: Different lowercase letters indicate significant differences among different production regions within the same round (P < 0.05).

Acetaldehyde generally showed a rise–fall–rise pattern across the seven rounds. Its level increased from rounds 1 to 4, dropped briefly at round 5, and increased again in rounds 6 and 7, reaching a high level at round 7. This general pattern was partly shared among regions, but the round at which the peak occurred and the corresponding concentration varied significantly (P < 0.05). GY remained at relatively high levels in rounds 1, 3, 4, and 5, with a peak at round 5. JS was most prominent in round 6, and GL reached its peak in round 7. RH-1 and RH-2 showed some differences in acetaldehyde trends, with RH-2 higher than RH-1 in most rounds. XS showed a marked drop at round 5 followed by a rapid rebound at round 6, indicating strong fluctuation, whereas ZY remained low across rounds.

Acetal showed a broadly similar round-dependent pattern to acetaldehyde, with a rise–fall–rise trajectory, but the fluctuation amplitude was larger and obvious high values appeared around rounds 3 and 6. Its distribution among regions also showed clear round specificity. ZY had significantly higher acetal than other regions in rounds 1 and 2. RH-1 reached a high level in round 3. JS remained high from rounds 5 to 7 and peaked at round 6. GL showed relatively high values in rounds 4 and 7. In contrast, GY declined sharply after round 2 and remained at a low level, suggesting regional differences in the acetalization process involving acetaldehyde and ethanol.

Furfural exhibited a pattern distinct from those of the other two compounds. Its concentration generally increased with production round number (Liu et al., 2023; Wu et al., 2023). The increase was moderate from rounds 1 to 3, but became more obvious after round 4, and the highest level appeared at round 7. This trend was evident across regions, although the rate of increase and the peak level differed. XS maintained relatively high furfural in the first four rounds. JS was prominent in rounds 4 to 6 and peaked at round 6. GL was relatively high in rounds 5 and 7, whereas CS showed a more gradual increase. RH-1 and RH-2 also differed in later-round furfural changes, indicating that even within the same region, different samples may not share the same furfural accumulation capacity in later rounds.

3.7. Correlation analysis of round-by-round regional patterns

Fig. 5 presents the Pearson correlation analysis of the three key aldehydes across regions based on round baijiu profiles. Acetaldehyde showed generally weak correlations among regions, indicating that its round-by-round variation was strongly affected by regional and process conditions, and that the pattern was relatively independent across regions (Li, Zhang, Li, et al., 2024; Liu, Lian, et al., 2025). Acetal showed a more divergent pattern: some regional pairs showed similar variation patterns, whereas others showed weak correlations, suggesting that the stability of its formation process differed among regions. In contrast, furfural showed stronger correlations among most regions, which indicates a more consistent accumulation pattern across production regions. Overall, acetaldehyde was the most sensitive to regional environment and process conditions, furfural showed the highest cross-regional stability, and acetal was intermediate.

Fig. 5.

Fig. 5

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Pearson correlation heatmap of key aldehydes in round baijiu from different production regions. Note: (*P < 0.05, **P < 0.01) indicate different levels of statistical significance.

4. Discussion

4.1. Influence of regional factors on aldehyde composition

The results show clear differences in aldehyde composition among sauce-flavor baijiu samples from different regions of the Chishui River Basin. This suggests that local environment, process characteristics, and blending preferences may jointly affect aldehyde formation and accumulation. GL was characterized by high acetaldehyde and acetal. JS was characterized by elevated levels of branched-chain amino acid-derived aldehydes. The Renhuai samples (RH-1 and RH-2) were distinguished by high phenylacetaldehyde levels. These differences imply that each region may have its own features in raw material utilization, fermentation metabolism, and process control (Li, Zhang, Li, et al., 2024; Ma et al., 2026; Wu et al., 2024). The comparison between RH-1 and RH-2 also indicates that even within the same region, process differences among distilleries can still lead to variation in key aldehydes in finished baijiu.

4.2. Potential of aldehydes for regional traceability

Both PCA and OPLS-DA showed that aldehyde profiles provided good discrimination among samples from different regions, and the separation in OPLS-DA was more distinct. Acetaldehyde, acetal, and furfural all had high contributions in the VIP analysis, which indicates strong discriminatory value among regional samples. Combined with their regional distribution patterns, these aldehydes not only reflect chemical differences among samples but are also closely linked to regional flavor characteristics (Luo et al., 2026). They may therefore serve as key aldehydes for origin identification of sauce-flavor baijiu. However, the number of samples in this study was still limited, and larger datasets are needed to further verify the stability and general applicability of these key aldehydes.

It should be noted that phenylacetaldehyde showed a particularly distinctive pattern in the Renhuai samples, with concentrations approximately 6–20 times higher than those in most other products. However, VIP values reflect the contribution of each variable to the overall discrimination of all samples in the OPLS-DA model rather than the magnitude of a difference within a specific subgroup. The high phenylacetaldehyde concentrations were mainly confined to RH-1 and RH-2, whereas acetaldehyde, acetal, and furfural showed broader concentration variation across multiple production regions. Therefore, the latter compounds contributed more strongly to the overall model separation. Nevertheless, phenylacetaldehyde may represent a potentially useful Renhuai-associated discriminatory feature, and its robustness should be further evaluated using larger independent sample sets.

4.3. Possible reasons for round-dependent differences in key aldehydes

The different round-dependent patterns of the three key aldehydes suggest that their formation pathways and controlling factors are not the same. Acetaldehyde fluctuated markedly across regions and production rounds, which implies that its generation is highly sensitive to regional environment and process conditions. Acetal showed a degree of co-variation with acetaldehyde, which is consistent with its close relationship to the conversion between acetaldehyde and ethanol (Dai et al., 2025; Li, Yang, et al., 2025). However, its larger fluctuation among regions suggests that the related process may be more strongly influenced by local process conditions. Furfural showed a continuous accumulation trend in most regions, which suggests a stronger stage-dependent and more stable formation process. It should be noted that the specific effects of microbial community, fermented grain acidity, temperature, and baijiu-cutting practice on the formation of key aldehydes are inferred here mainly from the observed results. These inferences should be further validated by microbiome analyses and detailed process monitoring.

4.4. Implications of key aldehydes for flavor and quality

Acetaldehyde, acetal, and furfural contribute differently to the flavor of sauce-flavor baijiu (Guan et al., 2022; Wu et al., 2023). Acetaldehyde is mainly associated with fruity notes, but excessive levels may also contribute to pungency (Dai et al., 2025; He et al., 2022). Excessive levels may cause harsh and may produce a harsh and irritating sensory impression, whereas moderate levels may enhance freshness. Acetal has a softer fruity and sweet aroma. It may partly moderate the irritation caused by acetaldehyde and is also associated with flavor harmony and aging character (Dai et al., 2025). Furfural is closely related to caramel-like, roasted, baked, and almond-like notes, and plays an important role in the roasted aroma and fullness of the baijiu. The combined variation of these three compounds contributes substantially to the characteristic sensory style of sauce-flavor baijiu (Wu et al., 2023; Yang et al., 2025).

From a process perspective, the fluctuation patterns of acetaldehyde and acetal may provide references for round-baijiu blending and evaluation of aging potential (Dai et al., 2025; Du et al., 2025). The progressive accumulation of furfural in the later rounds may provide a useful basis for round-based grading and blending. The round-specific advantages observed in regions such as JS, GL, and XS also provide a basis for the strategic blending of round baijiu from different regions and for targeted flavor modulation.

4.5. Limitations and future perspectives

This study used a targeted analytical strategy to quantify nine aldehydes and focused on the changes of three key aldehydes across the seven production rounds. The results provide a systematic characterization of regional differences and round-dependent dynamics of aldehydes in sauce-flavor baijiu from the Chishui River Basin. However, several limitations remain. First, the study focused on selected target aldehydes and did not cover more potential flavor-active aldehydes or unknown metabolites. Second, the sample size was still limited and cannot fully represent all regions or all process types. Third, the present work focused mainly on chemical composition and did not integrate microbiome analysis, non-targeted flavoromics, or sensory evaluation. Future studies may combine GC × GC-TOFMS, microbiome approaches, and sensory analysis to further clarify the formation mechanisms and flavor contributions of key aldehydes.

5. Conclusions

This study established a gas chromatographic method for aldehyde analysis in sauce-flavor baijiu and systematically investigated the distribution of aldehydes in eight representative samples from seven major production regions in the Chishui River Basin. The results showed significant regional differences in aldehyde composition in finished baijiu, and acetaldehyde, acetal, and furfural were the key variables driving sample separation. PCA and OPLS-DA further confirmed that aldehyde profiles showed strong discriminative ability among samples from different regions. OAV analysis further showed that acetaldehyde, acetal, and furfural had OAVs greater than 1 in all finished baijiu samples, supporting their potential sensory relevance, while the magnitude of their OAVs varied among production regions. Analysis of round baijiu showed that acetaldehyde and acetal generally followed a rise–fall–rise pattern from rounds 1 to 7, whereas furfural accumulated continuously with increasing round number. Correlation analysis further indicated that acetaldehyde was more sensitive to regional and process factors, furfural showed stronger cross-regional consistency, and acetal was intermediate. Overall, this study elucidates the regional variation and round-by-round dynamics of key aldehydes in sauce-flavor baijiu, providing a basis for origin identification, quality evaluation of base baijiu from different rounds, and process optimization.

Author contribution

Dr. SHI designed the study, conducted the experiments, analyzed the data, and wrote the manuscript. All authors have read and approved the final manuscript.

Generative AI statement

No generative AI was used in the preparation of this manuscript.

CRediT authorship contribution statement

Xiangui Wang: Writing – original draft, Conceptualization. Dan Yang: Investigation. Xinyu Yuan: Resources, Project administration. Dan Zeng: Software. Deguang Wu: Funding acquisition. Huajie Xu: Supervision. Tianzhu Shi: Writing – review & editing.

Consent for publication

Not applicable.

Funding

This work was supported by Zunyi City Science and Technology Bureau and Moutai Institute Joint Municipal-University Science and Technology Research and Development Project (Grant No. Zunshi Kehe HZ [2023]116); Research Foundation for Scientific Scholars of Moutai Institute (Grant No. mygccrc[2022]020); A Project on Characteristic Key Laboratory of Guizhou Ordinary Colleges and Universities by Department of Education of Guizhou Province (Grant No: Qian Jiao He KY Zi [2018] 003); Modern Baijiu Brewing Technology Engineering Research Center of Guizhou Universities (Qianjiaoji [2023] No. 028). Guizhou Province Technology Innovation Center for Jiangxiangxing Baijiu, Qiankehe Platform JSZX (2025) 002.Data availability.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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.

Acknowledgements

This work was supported by Zunyi City Science and Technology Bureau and Moutai Institute Joint Municipal-University Science and Technology Research and Development Project (Grant No. Zunshi Kehe HZ [2023]116); Research Foundation for Scientific Scholars of Moutai Institute (Grant No. mygccrc[2022]020); A Project on Characteristic Key Laboratory of Guizhou Ordinary Colleges and Universities by Department of Education of Guizhou Province (Grant No: Qian Jiao He KY Zi [2018] 003); Modern Baijiu Brewing Technology Engineering Research Center of Guizhou Universities (Qianjiaoji [2023] No. 028). Guizhou Province Technology Innovation Center for Jiangxiangxing Baijiu, Qiankehe Platform JSZX (2025) 002.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.fochx.2026.104443.

Contributor Information

Huajie Xu, Email: xuhuajie@mtxy.edu.cn.

Tianzhu Shi, Email: shitianzhu@mtxy.edu.cn.

Appendix A. Supplementary data

Supplementary material.

mmc1.docx (27.5KB, docx)

Data availability

No data was used for the research described in the article.

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Associated Data

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Supplementary Materials

Supplementary material.

mmc1.docx (27.5KB, docx)

Data Availability Statement

No data was used for the research described in the article.

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