## 3 Results and Analysis
### Analysis of Key Aroma Components in 38 Plum Fruits at Different Maturation Stages Based on OPLS-DA  
As a typical multivariate calibration technique, the complexity of OPLS-DA is well recognized in the academic community [17; 18]. By focusing on maximizing the separation between predefined groups, OPLS-DA effectively aids in the detection and analysis of metabolic variations. To examine the variations in aroma compounds among different plum varieties, 138 aroma compounds served as the dependent variable. Meanwhile, these aroma compounds' density across different mature plum varieties was used as the independent variable (Figure 3). OPLS-DA was used to divide the 38 plum varieties into two groups. The first group consisted of early-maturing varieties, distributed in the first and fourth quadrants; the second group was composed of medium- and late-maturing varieties, mostly distributed in the second and third quadrant. The distribution of each variety was relatively uniform. The repeatability of the aroma at the same maturity stage was good, suggesting that there are notable differences at various maturity stages (Figure 3a). Model validation demonstrated that after 200 permutation tests, the permutation test model yielded R2X = 0.364, R2Y = 0.887, and Q2 = 0.417 (Figure 3b), underscoring the model's significance. The point where the Q2 regression line intersects the vertical axis, which was observed to be below 0, signifies that the discriminant model possesses strong explanatory power. This finding also indicates that the model did not suffer from overfitting. Consequently, the validation of the model can be considered to be effective.  
In the context of OPLS-DA, the VIP score reflects the extent to which differences in specific metabolites contribute to the classification and identification of sample groups within the model. Generally, metabolites are deemed significantly different if they exhibit VIP > 1 and \(p\) < 0.05 [19]. The OPLS-DA model was used as the outcome control criterion; 24 aroma  
Figure 2: Correlation analysis conducted between the total content of aromatic components and each aroma substance. Significance levels are denoted with an asterisk (* for \(p<\) =0.05, ** for \(p<\)= 0.01). The depth of color in the figure reflects the strength of the Pearson correlation coefficient.  
components were identified as differential, characterized by VIP > 1 and \(p\) < 0.05 (Figure 3c). Among these components, 13 were esters, seven were alcohols, two were aldehydes, one was a ketone, and one was classified as another type of compound. The model contained seven up-regulated substances: 2,2,4-trimethyl-1,3-pentanediol disobutyrate, linalo1, benzoic acid 2-ethylhexyl ester, propanoic acid butyl ester, butyl 2-methylbutanoate, acetic acid methyl ester, and (E)-2-hexenal. This indicates that the levels of these aroma substances are significantly higher in mid- to late-maturing plum germplasm compared to early-maturing ones. Additionally, there were 17 down-regulated substances: 3,4-dihydro-3,3,6,8-tetramethyl-1(2H)-naphthalenone, 2-methyl-butanoic acid ethyl ester, hexanoic acid methyl ester, 2-methyl-propanoic acid butyl ester, (Z)-2-hexen-1-ol acetate, propanoic acid hexyl ester, acetic acid 2-ethylhexyl ester, 3-methyl-2-buten-1-ol acetate, (Z)-butanoic acid 3-hexenyl ester, benzaldehyde, 2-methyl-3-buten-2-ol, L-alpha-terpineol, alpha-terpineol, 5-methyl-5-hexen-2-ol, (E)-4-hexen-1-ol, 2-ethyl-1-hexanol, and 3-methyl-4-oxo-pentanoic acid. This shows that the levels of these aroma substances are significantly lower in mid- and late-maturing plum germplasm compared to early-maturing ones. These unique aroma substances with high abundance of specific compounds can be used as one of the markers to distinguish plum germplasm of different maturity stages.  
Figure 3: _Cont._