Cheng Mingchuan, Li Chen, Lu Chen, Jiang Yan, Thermo Fisher Scientific (China) Co., Ltd., Shanghai, China 201206
Key words
Q Exactive; electrostatic field orbitrap high resolution mass spectrometry; omics analysis; white wine; scent identification
1. Introduction <br>Liquor has a long history of production in China. It is an excellent and precious national heritage in China. It is listed as the world's six largest distilled spirits with brandy, whiskey, vodka, rum and gin. There are many factors affecting the quality of solid-state fermented liquor in China, and the quality of the wine produced in each production batch is inconsistent. In order to unify the various trace components inherent in the product and the appropriate ratio between them, it is necessary to blend. The blended finished liquor has its fixed chemical composition and the fixed amount ratio between these components, thus forming different flavors and flavors [1]. At present, the analysis of the aroma components of white wine is mostly carried out by GC or GC/MS [2]. For the identification of scent type, it mainly relies on the sensory identification of the sommelier. The method is simple and quick, but it is more demanding and more subjective. There is also the use of electronic tongue technology to distinguish between different types of white wine [3], but the electronic tongue technology has yet to be further developed and the versatility is poor. In this experiment, ultra-high performance liquid chromatography (UHPLC) and Q Exactive benchtop mass spectrometer based on Orbitrap high-resolution mass spectrometry were combined with the group analysis software SIEVE and statistical software SIMCA to conduct a group study on different flavors of white spirits. Statistical analysis established a new method for quickly, accurately and objectively identifying the flavor of white wine, and identified the found markers.
2. Sample information and analysis process
The experiment collected the liquor samples of the three flavors of Luzhou, Sauce and Fragrance. Each fragrance contains multiple samples of different brands and different quality levels. Due to the complexity of the liquor sample, there are diversity and differences between different brands or different brands of the same brand. In order to reduce the uncertainty, the samples of the three flavors are divided into two groups of different brands and different brands of the same brand. Analyze and compare.
Different brand groups: Each fragrance contains the most representative high-end wine samples from each famous brand, and each sample is a typical sample in the corresponding fragrance type. It is sought to find out the markers of different brands according to the fragrance type through the analysis of the group. The specific sample information is shown in Table 1. 
Different grade groups: Each fragrance type contains different grades of samples from the high end to the low end of the same typical brand. The analysis of this group can identify common marker-type compounds that can be distinguished between different grades of samples, and can also verify whether typical iconic compounds found in different brand group analyses exist at all levels of the brand. In the sample. See Table 2 for specific sample information.
In order to maximize the chemical composition of the sample, the sample is only filtered using a 0.22 μm filter, that is, the data is collected by positive and negative switching scanning. Data were extracted, filtered, aligned and differentiated using SIEVE software. Multivariate statistical analysis such as PCA and OPLS-DA was performed using SIMCA, and the established mathematical model was used to discriminate and predict unknown samples. The analysis process is shown in Figure 1.
3. Experimental conditions
3.1 Liquid Chromatography Conditions <br> Chromatograph: Thermo Scientific TM Ultimate 3000 RSLC
Column: Thermo Scientific TM Accucore C8 (100×2.1, 2.6 μm)
The mobile phase composition and gradient elution conditions are shown in Table 3. 
4. Results and discussion
4.1 Selection of experimental conditions <br>To ensure that as many components as possible in the liquor sample can be effectively measured, different types of columns were examined in this experiment, including: AccucoreaQ, Accucore Hilic, Accucore C8, Syncronis C18 and Hypercarb. . The experimental results show that the Accucore C8 column can better separate and respond to the compounds in the liquor sample in positive ion mode (Figure 2).
4.2 Data Reproducibility <br> In the ensemble related experiments, the reproducibility of the method is very important, and it is necessary to ensure that the data has good reproducibility in terms of quality accuracy, retention time and response intensity. In this experiment, QC samples were interspersed in the injection sequence, and the stability of the system was investigated. The retention time (RT) deviation was <2 s throughout the injection cycle (48 hours), and the response (NL) RSD of a compound was about 10%, and the mass accuracy deviation was within 1 ppm. The experimental results show that the system has good stability, reproducibility and quality accuracy after one calibration by external standard method.
4.3 Statistical analysis
The components were extracted, filtered (P-Value < 0.05) and statistically analyzed using SIEVE software. 486 and 1246 compounds were obtained for different brand groups and different grade groups, respectively. The PCA analysis of the above compounds by SIMCA software showed that the three types of white wines in the two analysis groups were well separated, and the clustering trend was obvious. There was no obvious separation from the group and it was more obvious with other groups. Difference (Figure 3)
The SIEVE software was used to find out the compounds with large differences in the ratio of the obtained compounds, and the compounds with clear peaks in the XIC chart were used as potential markers, as shown in Figure 4 (exemplified by compound S1). The markers that were ultimately identified in the two analysis groups are shown in Tables 5 and 6. Five of these compounds are common markers for both analysis groups. Using the five common markers as indicators, the orthogonal correction partial least squares discriminant analysis (OPLS-DA) was performed using SIMCA software to maximize the difference between different groups within the model. The analysis showed that the three flavors of liquor can be well distinguished (Figure 5). 
4.4 Modeling and Pre-judgment
Based on the model of OPLS-DA analysis, a pre-judgment analysis of another typical Luzhou-flavor liquor sample (50°) was conducted to investigate the discriminating ability of the selected five markers for three flavor-type liquors. . The results show that the test sample can be well polymerized with the Luzhou-flavor sample, and the other two groups can be well distinguished, and the judgment result is correct (Fig. 6).
4.5 Marker identification
According to the accurate mass of the marker, the isotope distribution and the secondary fragment ion, the information was identified and confirmed by Xcalibur software, MassFrontier software and ChemSpider network database. The identification results are shown in Table 7.
5 Conclusion
This experiment demonstrates the workflow of omics-based analytical methods in the identification of food types. The results show that Orbitrap high-resolution mass spectrometry combined with the omics analysis software SIEVE and statistical analysis software SIMCA, can effectively identify the three flavors of aroma, sauce and fragrant liquor. Five common markers were identified in the experiment, including ethyl hexanoate, a well-characterized component of Luzhou-flavor liquor, which is consistent with previous studies and indicates that the selected markers have a high degree of confidence. Through the pre-judgment test of known samples, it is shown that the established model can be well used for the differentiation of three flavor liquors. The markers are not found in the fragrant liquor, and can be related to the three subtypes of the scented liquor itself. In addition, the accuracy of sample prediction by the established model may be further improved by the increase of the sample size. This process and method can also be used to analyze and compare the other flavors of the 12 white wine flavors and the liquor samples of different years. Use this set of workflows established in this paper, it can be extended to species identification, provenance, quality control a broader range of research areas of food groups and other research directions.
references
[1] Around, etc. Famous white liquor quality fingerprint expert identification system. Analytical Chemistry, April 2006, Vol. 32, 735-740.
[2] Liu Jun, et al. The aroma compounds in the fragrant and Luzhou-flavor liquors were compared by GC-O analysis. Brewing, May 35, 2008, 103-107.
[3] Wang Yongwei, et al. Research on the detection and differentiation of liquor based on electronic tongue. Packaging and Food Machinery, Vol. 27, No. 5, 2009, 57-61.
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