Abstract: Three citrus varieties, namely Newhall navel, the hybrid citrus Ehime, and Jiuyuehong navel, were examined for 21 sensory attributes, 12 nutritional components, and 73 volatile compounds. On this basis, multidimensional quality analysis was performed using multivariate statistical analysis to determine key differential compounds and establish a variety identification model. The results indicated that Newhall exhibited the highest values of longitudinal diameter, fruit navel size, peel thickness, firmness, soluble solids content (SSC), titratable acidity (TA), total flavonoids, citric acid, tartaric acid, and VC content. Ehime had the thinnest peel, lowest firmness, highest edible ratio and SSC/TA ratio, and highest malic acid content. Jiuyuehong exhibited the largest fruit transverse diameter and highest a* value, along with the highest glucose, fructose, and total carotenoid contents. Principal component analysis (PCA), correlation analysis, and cluster analysis collectively identified five core non-volatile quality indicators: peel color difference index (CI), transverse diameter, single fruit mass, SSC, and VC content. A total of 73 volatile compounds were detected across the three varieties, with terpenes being the most abundant, primarily limonene and valenene. Based on variable importance in projection (VIP) > 1 and P < 0.05, eight volatile core quality indicators were selected: valencene, limonene, (+)-nootkatone, γ-selinene, α-guaiene, γ-gurjunene, β-selinene, and β-guaiene. Using the 13 core quality indicators selected, a partial least squares discriminant analysis (PLS-DA) model was constructed. The variety identification accuracy reached 100% for both the training and test sets, demonstrating that these core quality indicators effectively retain and characterize the majority of quality differences among the three citrus varieties. These findings provide data support for citrus quality evaluation, variety classification, and consumer purchasing decision.

Key words: citrus; quality evaluation; volatile compounds; multivariate statistical analysis; partial least squares discriminant analysis