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. 2024 Nov 15;13(22):3644.
doi: 10.3390/foods13223644.

Aroma Identification and Traceability of the Core Sub-Producing Area in the Helan Mountain Eastern Foothills Using Two-Dimensional Gas Chromatography and Time-of-Flight Mass Spectrometry and Chemometrics

Affiliations

Aroma Identification and Traceability of the Core Sub-Producing Area in the Helan Mountain Eastern Foothills Using Two-Dimensional Gas Chromatography and Time-of-Flight Mass Spectrometry and Chemometrics

Yuanke Zhang et al. Foods. .

Abstract

The combination of volatile compounds endows wines with unique aromatic characteristics and is closely related to their geographical origins. In the pursuit of origin identification and the subdivision of homogeneous production areas, clarifying the characteristics of production areas is of great significance for improving wine quality and commercial value. In this study, GC×GC-TOFMS technology was used to analyze the aroma characteristics of "Cabernet Sauvignon" wines from 26 wineries in the Helan (HL), Yinchuan (YC), Yongning (YN), Qingtongxia (QTX), and Hongsibu (HSP) sub-producing areas in the eastern foothills of Helan Mountain in Ningxia, China. The results indicate a gradual increase in relative humidity from the southern part of Ningxia, with the YN sub-region showing optimal fruit development and the QTX region having the highest maturity. A total of 184 volatile compounds were identified, with 36 compounds with an OAV > 1, crucial for the aroma profiles of primarily fermentation-derived alcohols and esters. An aromatic vector analysis revealed that "floral" and "fruity" notes are the primary characteristics of Cabernet Sauvignon wines from the Helan Mountain East region, with lower maturity aiding in the retention of these aromas. By constructing a reliable OPLS-DA model, it was determined that 15 substances (VIP > 1) played a crucial role in identifying production areas, among which phenylethyl alcohol and isoamyl alcohol were the main contributors. In addition, a Pearson correlation analysis showed a negative correlation between sunlight duration during the growing season and benzyl alcohol accumulation, while a significant positive correlation was observed during the ripening period. Due to the critical role of phenyl ethanol in identifying producing areas, this further demonstrates that sunshine conditions may be a key factor contributing to the differences in wine flavor across regions. This study offers a theoretical foundation for understanding the relationship between climatic factors and flavor characteristics, addressing the issue of wine homogenization in small production areas, clarifying typical style characteristics, and establishing a traceability technology system based on characteristic aroma.

Keywords: GC×GC-TOFMS; aroma; eastern foothills of Helan Mountain in Ningxia; producing area traceability; wine.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Basic physical and chemical parameters of wine grapes in each producing area: (A) 100-grain weight (g); (B) Total Soluble Solids (°Brix); (C) reducing sugar (g/L); (D) Titratable Acid (g/L); (E) pH value; (F) TSS-TA ratio. Different letters indicate that there are significant differences between different producing areas (p < 0.05).
Figure 2
Figure 2
An analysis of the volatile compounds in wines from different producing areas. (A) The proportion of 9 volatile compounds. (B) A Wayne diagram of volatile compounds; (C) The contents and percentages of volatile compounds. Different letters indicate significant differences (p < 0.05) between different producing areas.
Figure 3
Figure 3
Analysis of volatile compounds based on OAV > 1. (A) Heat map. (B) Intensity of different aroma categories. Detailed explanations of aroma category attributes can be found in Table 6. Different letters indicate that there are significant differences between different producing areas (p < 0.05).
Figure 4
Figure 4
Multivariate statistical analysis of volatile compounds obtained by GC×GC-TOFMS. (A) OPLS-DA score plot. (B) Cross-validation plot through 200 permutation tests. (C) Substance with VIP > 1. (D) Heat map based on VIP > 1.
Figure 5
Figure 5
A correlation heat map between the substances of key compounds (VIP > 1) and meteorological factors. * is shown when the correlation is significant at p < 0.05.

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