Figure 4. Construction of metabolite content conversion models. (IMAGE)
Caption
(A) The pipeline for Multiple Stepwise Regression approach for predicting metabolite concentrations utilizing reflectance spectroscopy with a quintuple-phase methodology: acquisition of original spectra, spectral transformation processing, feature selection, model construction, and parameter inversion. We quantitatively assessed the fractional-order spectral characteristics of M. truncatula leaf hyperspectral data, employing a Sequential Forward Feature Selection algorithm to determine the most effective fractional differential spectral bands for metabolite sensitivity enhancement. Then the optimal band combination was selected to construct a Multiple Stepwise Regression model. A stringent screening was conducted to improve the model's accuracy, identifying content conversion models for 101 metabolites exceeding the threshold (R2 > 0.9), as indicated by the red dotted line in parameter inversion section (Tables S5–S6). (B) Heatmap showing the variations in the content of metabolites with conversion models, obtained through converting hyperspectral data. Details are shown in Table S6. 1 d, 3 d and 5 d stand for salt treatment for 1, 3 and 5 days, respectively. SS and CK represent Jemalong A17 with and without salt treatment. Variation is scaled to range from 0 to 1 and shown by different colors. (C) KEGG pathway enrichment analysis of metabolites with content conversion models. KEGG analysis was performed and shown as described in Fig. 2, Fig. 3F. The color of the columns indicates the P value as shown in Table S7.
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