Fruit Trait Variation Among Five Ilex Taxa: Insights from Morphology, Color, and Electronic Nose-Based Volatile Profiles

Abstract

Abstract Accurate discrimination of closely related Ilex taxa is essential for effective germplasm management and taxon‑specific applications. In this study, five Ilex taxa ( Ilex rotunda Thunb., I. chinensis , I. cornuta Lindl. et Paxt., I. cornuta ‘Fortunei’, and I. latifolia Thunb.) were evaluated using an integrated framework that combines fruit morphometrics and CIELAB color parameters with electronic‑nose (E‑nose) odor fingerprints and chemometric analyses. Fruit transverse diameter, longitudinal diameter, single‑fruit weight, fruit shape index, and color parameters (L*, a*, b*, and chroma (C*)) differed significantly among taxa (one‑way ANOVA, all p I. rotunda had the smallest fruit diameters but the highest redness (a*) and chroma (C*). E‑nose data from 10 metal‑oxide semiconductor (MOS) sensors were analyzed using principal component analysis (PCA), hierarchical clustering, and supervised OPLS‑DA. PCA explained 90.21% of the variance using the first two components (PC1, 71.8%; PC2, 10.5%) and revealed taxon‑specific clustering. A targeted OPLS‑DA model separating I. rotunda from the other four taxa showed strong goodness‑of‑fit and predictive ability (R²X = 0.785, R²Y = 0.944, Q² = 0.935). Permutation testing (n = 200) supported model validity (p I. rotunda consistently exhibiting the most divergent profile, consistent with the phylogenetic framework used in this study.

Plant phenotyping relevance

果実形態・色彩および電子鼻センシングとケモメトリクスを統合してIlex分類群の観察可能な果実形質を識別する方法適用が研究の中心であり、モデル性能の検証も行っている。

Code and data availability

The article describes fruit morphometric/colorimetric measurements and E-nose chemometrics (PCA, LDA, OPLS-DA) for five Ilex taxa, but contains no data availability statement, no public phenotype dataset, no deposited sensor data or images, and no author analysis code or model checkpoints. Analysis was done in Excel 20

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