## 2 Results
The OPLS-DA model score plot (Figure 4A,D,G) indicated significant separations among the different raw peanuts, suggesting differences in their respective metabolite profiles. The criteria, VIP \(\geq\) 1 and fold change \(\geq\)2 or \(\leq\)0.5, were utilized to screen for differentially abundant metabolites, which were visualized via volcano plots. In the ZKH1H vs. CFD comparison, 204 differentially abundant metabolites were identified (Figure 4B), of which 105 metabolites were upregulated and 99 were downregulated (Table S1). The differentially abundant metabolites were classified as follows: amino acids and peptides (24.76%); monosaccharides (15.24%); fatty acids and conjugates (11.43%); purines (8.57%); pyrimidines (6.67%); TCA acids (5.71%); benzoic acids (4.76%); disaccharides (3.81%); flavonoids (3.81%); isoprenoids (3.81%); sterols (2.86%); fatty acyls (2.86%); phenylacetic acids (1.90%); amines (1.90%); and benzamides (1.90%) (Figure 4C). In the ZKH1H vs. ZKH13H comparison, 157 significantly different nonvolatile metabolites were detected (Figure 4E), of which 77 were upregulated and 80 were downregulated (Table S2). These compounds were classified as follows: amino acids and peptides (28.40%); monosaccharides (17.28%); fatty acids and conjugates (13.58%); pyrimidines (8.64%); purines (6.17%); disaccharides (6.17%); TCA acids (4.94%); short-chain acids and derivatives (3.70%); cholines (2.47%); flavonoids (2.47%); phenylacetic acids (1.23%); benzediols (1.23%); amines (1.23%); benzamides (1.23%); and phosphate esters (Figure 4F). In the ZKH13H vs. CFD comparison, 213 differentially abundant metabolites were identified (Figure 4H), of which 116 were upregulated and 97 were downregulated (Table S3). These compounds were classified into 15 categories as follows: amino acids and peptides (29.20%); monosaccharides (15.04%); fatty acids and conjugates (14.16%); purines (8.85%); pyrimidines (5.31%); TCA acids (5.31%); disaccharides (4.42%); benzoic acids (3.54%); flavonoids (3.54%); short-chain acids and derivatives (2.65%); amines (1.77%); cholines (1.77%); fatty esters (1.77%); sterols (1.77%); and phenylacetic acids (0.88%) (Figure 4I). Figure 3: K-means clustering analysis of all nonvolatile metabolites of the three varieties of peanut samples. To identify the perturbed biological pathways, KEGG pathway analyses of the differentially abundant metabolites between the pairwise comparisons were performed via the KEGG database, and the top 25 pathways with the most significant differences in each comparison group are shown in Figure 5. In the ZKH1H vs. CFD comparison, differentially abundant metabolites were distributed to a total of 55 metabolic pathways, of which 25 had significant differences. The five most significantly different pathways were as follows: alanine, aspartate, and glutamate metabolism; citric acid cycle (TCA cycle); galactose metabolism; arginine biosynthesis; ascorbic acid; and aldolate metabolism (Figure 5A). In the ZKH1H vs. ZKH13H comparison, all differentially abundant metabolites were distributed into 51 metabolic pathways, of which 42 had significant differences (\(p<0.05\)). The five most significant differences were as follows: galactose metabolism; glycine, serine, and threonine metabolism; starch and sucrose metabolism; citric acid cycle (TCA cycle); and amino acid and nucleotide glucose metabolism (Figure 5B). In the ZKH13H and CFD comparison, differentially abundant metabolites were distributed across 55 metabolic pathways, of which 42 pathways presented significant differences (\(p<0.05\)). The five most significant differences were as follows: citric acid cycle (TCA cycle); galactose metabolism; aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylic acid metabolism; and alanine, aspartate, and glutamic acid metabolism (Figure 5C).