Unverified paper record
Hyperspectral imaging and metabolomics reveal color classification and anthocyanin profiles in Hydrangea macrophylla cultivars
Industrial Crops & Products. · 1 Feb 2026
Abstract
Flower color is a key criterion for evaluating Hydrangea macrophylla germplasm and breeding programs. In this study, 95 accessions were classified into six color groups (white, yellow, pink, red, purple, and blue) using the CIELAB and Munsell color systems. Partial least squares discriminant analysis (PLS-DA) of color space values (L*, a*, b*, C, and h) and pigment contents (anthocyanins, carotenoids, flavonoids, phenols) showed that the C value was crucial for the red group, while the L* value was important for the white group. A total of 83 anthocyanin derivatives were identified, with cyanidins and delphinidins being the most abundant. The proportion of delphinidin-3-O-glucoside (Dp3g) was a key factor in sepal color diversity, accounting for less than 11 % of the variation in the white and yellow groups and more than 83 % in the other groups. Dp3g was upregulated in the red, pink, purple, and blue groups compared to the yellow group, whereas malvidin-3-O-arabinoside was upregulated in the red, pink, and blue groups relative to the purple group. Hyperspectral imaging combined with partial least squares regression (PLSR) accurately predicted pigment contents (R² = 0.767–0.939, RPD = 1.946–2.636), with the best performance for anthocyanins. The spectral models for delphinidin-3,5-O-diglucoside (D3g5g) and Dp3g presented the highest correlations among differentially accumulated metabolites (|r| > 0.7), with the D3g5g model achieving the best performance (R² = 0.944, RMSE = 0.101 μg·g⁻¹ FW, RPD = 3.697). This research provides a comprehensive classification of colors and anthocyanin profiles in H. macrophylla sepals and highlights the potential of hyperspectral imaging in breeding practices.
Plant phenotyping relevance
アジサイ萼片の色および色素含量をハイパースペクトル画像から推定し、PLSRで性能評価しており、植物表現型の取得・推定手法が中心である。
abstractHyperspectral imaging combined with partial least squares regression (PLSR) accurately predicted pigment contents (R² = 0.767–0.939, RPD = 1.946–2.636), with the best performance for anthocyanins.
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