Exploring the Hyperspectral Response of Quercetin in Anoectochilus roxburghii (Wall.) Lindl. Using Standard Fingerprints and Band-Specific Feature Analysis.

Abstract

Quercetin, a key flavonoid in Anoectochilus roxburghii (Wall.) Lindl., plays an important role in determining the pharmacological value of this medicinal herb. However, traditional methods for quercetin quantification are destructive and time-consuming, limiting their application in real-time quality monitoring. This study investigates the hyperspectral response characteristics of quercetin using near-infrared hyperspectral imaging and establishes a feature-based model to explore its detectability in A. roxburghii leaves. We scanned standard quercetin solutions of known concentration under the same imaging conditions as the leaves to produce a dilution series. Feature-selection methods used included the successive projections algorithm (SPA), Pearson correlation, and competitive adaptive reweighted sampling (CARS). A 1D convolutional neural network (1D-CNN) trained on SPA-selected wavelengths yielded the best prediction performance. These key wavelengths-particularly the 923 nm band-showed strong theoretical and statistical relevance to quercetin's molecular absorption. When applied to plant leaf spectra, the standard-trained model produced continuous predicted quercetin values that effectively distinguished cultivars with varying flavonoid contents. PCA visualization and ROC-based classification confirmed spectral transferability and potential for functional evaluation. This study demonstrates a non-destructive, spatially resolved, and biochemically interpretable strategy for identifying bioactive markers in plant tissues, offering a methodological basis for future hyperspectral inversion studies and intelligent quality assessment in herbal medicine.

Plant phenotyping relevance

植物葉中のクエルセチン量を非破壊・空間分解的に推定するハイパースペクトル画像解析法と1D-CNNモデルを開発・適用しており、植物化学的形質の取得手法が中心である。

Code and data availability

The paper's hyperspectral data (leaf and quercetin standard spectra) and analysis code are not publicly deposited; the Data Availability Statement says the full dataset is available only upon reasonable request, so no public paper-specific asset qualifies.

No evidence-backed public reproduction asset is currently recorded.