VIS-NIR Modeling of Hydrangenol and Phyllodulcin Contents in Tea-Hortensia (<i>Hydrangea macrophylla</i> subsp. <i>serrata</i>)
2022
Marcel Dieter Moll | Liane Kahlert | Egon Gross | Esther-Corinna Schwarze | Maria Blings | Silke Hillebrand | Jakob Ley | Thorsten Kraska | Ralf Pude
Hyperspectral data are commonly used for the fast and inexpensive quantification of plant constituent estimation and quality control as well as in research and development applications. Based on chemical analysis, different models for dihydroisocoumarins (DHCs), namely hydrangenol (HG) and phyllodulcin (PD), were built using a partial least squares regression (PLSR). While HG is common in <i>Hydrangea macrophylla</i>, PD only occurs in cultivars of <i>Hydrangea macrophylla</i> subsp. <i>serrata</i>, also known as ‘tea-hortensia’. PD content varies significantly over the course of the growing period. For maximizing yield, a targeted estimation of PD content is needed. Nowadays, DHC contents are determined via UPLC, a time-consuming and a destructive method. In this research article we investigated PLSR-based models for HG and PD using three different spectrometers. Two separate trials were conducted to test for model quality. Measurement conditions, namely fresh or dried leaves and black or white background, did not influence model quality. While highly accurate modeling of HG and PD for single plants was not possible, the determination of the mean content on a larger scale was successful. The results of this study show that hyperspectral modeling as a decision support for farmers is feasible and provides accurate results on a field scale.
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