*3.2. Identification of Wine Metabolites from Helan Mountain in Ningxia*

Figure 6 shows the 1H-NMR spectra of Cabernet Sauvignon dry red wine, Chardonnay and Italian Riesling dry white wine of Helan Mountain in Ningxia. The metabolite

displacement information in wine is combined with Figure 6 and references the relevant literature [47,48]. The results are shown in Table 1.

**Figure 6.** 1H-NMR spectra of wine metabolites from Helan Mountain in Ningxia.

**Table 1.** 1H-NMR assignment of metabolites in wines.


The characters in brackets refer to peak information: s, singlet; d, doublet; t, triplet; q, quartet; dd, doublet of doublets; m, multiple.

Because 1H-NMR detection hardly needs sample pre-treatment, the inherent properties of the sample are well preserved. Using pattern recognition analysis combined with the chemical shift of nuclear magnetic spectrum, the characteristic variables with a large contribution to the difference between samples can be obtained, so as to identify the metabolites causing the difference between samples. The metabolites in the 1H-NMR spectra of wine were identified, and these substances mainly included amino acids, organic acids, sugars, phenols and so on. It can be found that the composition of metabolites in these three wines is basically the same, which means that the composition of metabolites in wines is relatively stable, but there are differences in the content of metabolites between different types of wines. Each wine has its own fingerprint, and different types of wine metabolic profiles describe their physiological and biochemical states, which need to be processed to find their markers [49]. The positions of the characteristic peaks in the NMR spectra correspond to different types of metabolites in wine, and the peak intensity (such as area) represents the relative content of the corresponding metabolites.

### *3.3. Differences of Metabolites in Different Wine Varieties from Helan Mountain*

In this experiment, the metabolite map data of three wines in the Helan Mountain production area of Ningxia were compared with the PLS-DA model to determine the main metabolites causing the difference between wine varieties. Firstly, Chardonnay and Italian Riesling dry white wine were analyzed by PLS-DA. Then, the pair PLS-DA comparison of dry white and dry red wine was carried out to determine the metabolites causing the difference between dry red and dry white wine varieties.

### 3.3.1. Analysis of Metabolites of Chardonnay and Italian Riesling Dry White

Two pairs of PLS-DA of dry white wine were compared to determine the main metabolites causing the difference between dry white wine varieties. The PLS-DA model of the 2020 Chardonnay and Italian Riesling Dry white wine is shown in Figure 7. In the score graph, Chardonnay and Italian Riesling dry white wine are clearly distinguished on the PC1 axis, and the cumulative contribution rate is R2X = 0.965, R2Y = 0.994 and Q2 = 0.955, indicating that this model is effective. The validation diagram of the model in the permutation experiment further demonstrates the reliability and predictability of the model. It can be seen from the load diagram that compared with Chardonnay dry white wine, the 2,3-butanediol, lactic acid, succinic acid, glycerin, choline, tartaric acid, D-sucrose, and γaminobutyric acid content is relatively high in Italian Riesling Dry white wine, while the content of gallic acid, ethyl acetate, proline, malic acid, alanine, α-glucose and β-gluconic acid is relatively low.

In order to ensure the unique taste of dry white wine, the fermentation process of malic acid and lactic acid is properly controlled during the brewing of dry white wine. Malic acid plays an important physiological role in the human body. It can effectively improve the body's exercise ability, resist fatigue, accelerate the metabolism of carboxylate, protect the heart, improve memory, etc. [50]. From the perspective of organic acids, it can be considered that dry white wine has a relatively high protective effect on the body.

3.3.2. Analysis of Differences of Metabolites between Cabernet Sauvignon Dry Red Wine and Italian Riesling, Chardonnay Dry White Wine

Pairwise comparison PLS-DA were compared between dry white and dry red wines to determine the metabolites that caused the difference between the two wine varieties. Figure 8 shows the PLS-DA model of Cabernet Sauvignon dry red wine and Italian Riesling Dry white wine in 2020. In the score chart, the two wines are clearly distinguished on the PC1 axis, where the cumulative contribution rate is R2X = 0.76, R2Y = 0.987 and Q<sup>2</sup> = 0.969, indicating that the quality of this model is good. The validation diagram of the permutation experiment of this model once again shows the reliability and predictability of this model. It can be seen from the load diagram that compared with Cabernet Sauvignon dry white wine, Italian Riesling dry white wine has higher alanine and malic acid content, while 2,3-butanediol, glycerol, choline, lactic acid, valine, proline, ethyl acetate, succinic acid, tartaric acid, gallic acid, α-D-Glucuronic acid is low.

**Figure 7.** (**a**) PLS-DA model derived from the 1H-NMR spectra of Chardonnay and Italian Riesling dry white wine. PLS-DA scores plot; (**b**) PLS-DA cross-validation plot; (**c**) PLS-DA loading plot.

**Figure 8.** (**a**) PLS-DA model derived from the 1H-NMR spectra of Cabernet Sauvignon dry red wine and Italian Riesling dry white wine. PLS-DA scores plot; (**b**) PLS-DA cross-validation plot; (**c**) PLS-DA loading plot.

The metabolites of amino acid characteristic differences among wines detected in this experiment are valine, alanine and proline. Among them, valine and alanine contribute less to the difference between wines, while proline contributes more to the difference between different varieties of wines. Song et al. [51] also recognized that the content of proline in wine is affected by environmental factors and different varieties of wine grape berries.

The PLS-DA model of 2020 Cabernet Sauvignon and Chardonnay is shown in Figure 9. In the score graph, the two wines are significantly different on the PC1 axis, where the cumulative contribution rate R2X = 0.798, R2Y = 0.987 and Q2 = 0.979 are relatively high, which also indicates that the model established is effective. The validation diagram of the permutation experiment further demonstrates the reliability and predictability of the model. As can be seen from the load diagram, Chardonnay dry white wine compared with Cabernet sauvignon dry red wine, Chardonnay dry white wine of alanine, malic acid content is higher, and 2,3-butanediol, valine, choline, glycerin, tartaric acid, lactic acid, valine, proline, ethyl acetate, succinic acid, gallic acid, α-D-Glucuronic acid content is low.

**Figure 9.** (**a**) PLS-DA model derived from the 1H-NMR spectra of Cabernet Sauvignon dry red wine and Chardonnay dry white wine. PLS-DA scores plot; (**b**) PLS-DA cross-validation plot; (**c**) PLS-DA loading plot.

As an important flavor substance of wine, organic acids not only determine the quality of wine [52], but also regulate the acid-base balance in the body, enabling the physiological activities of enzymes to be realized [53]. In this experiment, the different metabolites of organic acids were tartaric acid, malic acid, succinic acid and lactic acid. Tartaric acid and malic acid are derived from grape berries, while lactic acid and succinic acid are derived from wine fermentation [54].

Based on PLS-DA analysis of Cabernet Sauvignon, Chardonnay and Italian Riesling of the Helan Mountain region of Ningxia in 2020, it was found that the components of metabolites of different varieties of wine had little difference, but the content of metabolites had great difference. The contents of 2,3-butanediol, ethyl acetate, proline, succinic acid, tartaric acid, lactic acid, glycerin, gallic acid, choline, valine and α-D-Glucuronic acid in Cabernet Sauvignon dry red wine were higher than dry white wine. Ethyl acetate is an aromatic substance abundant in wine, which brings rich aroma to wine. Its content is also related to fermentation technology, grape varieties, fermentation temperature, etc. [55]. Ethyl acetate for wine varieties in this experiment and region identification provides a larger contribution, three different varieties of wine, the ethyl acetate content of Cabernet sauvignon is highest. The contents of malic acid, alanine and γ-aminobutyric acid in Chardonnay and Italian Riesling Dry white wine are higher than Cabernet Sauvignon dry red wine, and the contents of other nutritional metabolites are relatively low, indicating that there are significant differences in nutritional metabolites among different grape varieties. Thus, a quantitative analysis of the main metabolites was carried out, as shown in Table 2. The content of metabolites can be converted by the ratio of the peak area caused by protons on a specified group of the substance to be measured in the 1H-NMR spectrum to the peak area caused by protons on the specified group of the added internal standard DSS. The results were consistent with the results of PLS-DA analysis by comparing the contents of major metabolites by means of multiple samples (SNK method). The highest content of gallic acid was found in Cabernet Sauvignon. The content of alanine and malic acid is the highest in Chardonnay wine, which makes the wine made with different styles, guiding consumers to choose the appropriate wine according to their own needs and improving their health.


**Table 2.** Content of main metabolites in Cabernet Sauvignon dry red wine, Chardonnay, Italian Riesling dry white wine (g/L).

Means followed with different letters are statistically different at the 0.05 probability level with an AVOVAprotected SNK 0.05 test.

### **4. Conclusions**

In this study, lignosulfonate (an underutilized renewable biomass resource) was added to chitosan solution, and a separate 2% chitosan film (CH), chitosan—1% sodium lignosulfonate film (1% CH/LS) and chitosan—2% sodium lignosulfonate film (2% CH/LS) biomass composite films were developed by the classical casting method, to further study the preservation effect of 2% CH/LS film and CH film on grapes. From the test results, it can be concluded that 2% CH/LS films showed the best preservation performance. Compared with the control, CH film and 2% CH/LS coating film packaging not only effectively inhibit the evaporation of water and slow down the weight loss rate of grapes during storage, effectively alleviating the hardness, soluble solids, and titratable acid of grapes and causing the decrease of other nutrients, but they also effectively extend the shelf life of grapes. Therefore, compared with ordinary plastic packaging, 2% CH/LS film packaging is one of the promising strategies for preservation, as well as the Chitosan-lignosulfonate composite coating film. Both will be further investigated for various fruit and vegetable preservation. At the same time, this paper, based on the method of 1H-NMR combined with pattern

recognition analysis, analyzed the differences in metabolites in Cabernet Sauvignon dry red wine, Chardonnay and Italian Riesling dry white wine of Helan Mountain in Ningxia. As far as the research results are concerned, the types of metabolites of the three wines are similar, but their content is significantly different. Among them, Chardonnay wine has a more refreshing and delicate taste and more complete flavor, while Cabernet Sauvignon wine has the highest biological activity and health function. This provides theoretical and technical support for the quality control and evaluation of the origin of wine, the identification and protection of varieties, and provides a scientific guide for consumers to aid in the prevention of diseases and the maintenance of human health.

**Author Contributions:** Writing—original draft preparation, L.L.; writing—review and editing, B.H. and X.Z.; designed research, Y.F. and L.L.; validation, M.Z.; analyzed data, M.Z. and L.L.; supervision, X.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Natural Science Foundation of China "Wine Metabolomics and NMR Fingerprint Stud, grant number NO. 31271857".

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All raw data are available at the corresponding author.

**Acknowledgments:** We acknowledge financial support by the National Natural Science Foundation of China and President Zhou's Laboratory for its help.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

