Multidimensional Fruit Quality Evaluation of Suyu Pear Relative to Its Parental Cultivars

Evaluation of a selected fruit cultivar requires identifying its distinctive combination of traits relative to relevant breeding materials. We evaluated the pear cultivar Suyu (SY) against its parents Mili (ML) and Dangshansuli (DS) using fruit quality measurements, descriptive sensory scores, texture profile analysis, electronic nose responses and headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS) volatile signals. Parent-referenced trait contrasts were combined with blockwise multivariate comparisons and sensitivity analyses of scaling and feature inclusion. In the sampled batches, SY had a mean soluble solids content of 14.10%, exceeding ML and DS by 20.4% and 11.2%, respectively, and had greater measured hardness than both parents. Its soluble sugar content was lower than ML, showing that the observed contrast was trait-dependent. Under unit-variance scaling, SY–DS to SY–ML centroid-distance ratios were 0.623 for fruit quality, 0.269 for texture, 0.837 for electronic nose responses and 0.821 for volatile profiles. Within this single-site, single-year dataset, the direction of closer resemblance to DS persisted under range scaling and single-feature omission, and after excluding specified ratio or composite traits. Of 656 distinct numerical volatile profiles, 58 met the exploratory screening criteria for SY versus ML and 40 for SY versus DS, with 17 shared. Under the examined analytical choices, descriptive resemblance to DS coexisted with distinctive soluble-solids, mechanical and volatile-signal attributes. This parent-referenced evaluation identifies traits and signals for validation in larger cultivar sets and replicated field trials; it does not estimate parental genetic contributions or establish consumer preference. Maturity was judged qualitatively, and storage duration varied with harvest date. The contrasts therefore describe the sampled fruit batches, with additional uncertainty from tree clustering and the small number of pools.