Quality Control of Valerianae Radix by Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) Spectroscopy

Abstract

(Acetoxy-)valerenic acid and total essential oil content are important quality attributes of pharmacy grade valerian root (Valerianae radix). Traditional analysis of these quantities is time-consuming and necessitates (harmful) solvents. Here we investigated an application of attenuated total reflection Fourier transform infrared spectroscopy for extractionless analysis of these quality attributes on a representative sample comprising 260 wild-crafted individuals covering the Central European taxonomic diversity of the L. s. l. species aggregate with its three major ploidy cytotypes (i.e., di-, tetra- and octoploid). Calibration models were built by orthogonal partial least squares regression for quantitative analysis of (acetoxy-)valerenic acid and total essential oil content. For the latter, we propose a simplistic protocol involving apolar extraction followed by gas chromatography as a reference method for multivariate calibration in order to handle the analysis of samples taken from individual plants. We found good predictive ability of chemometric models for quantification of valerenic acid, acetoxyvalerenic acid, total sesquiterpenoid acid, and essential oil content with a root mean squared error of cross-validation of 0.064, 0.043, and 0.09 and root mean squared error of prediction of 0.066, 0.057, and 0.09 (% content), respectively. Orthogonal partial least squares discriminant analysis revealed good discriminability between the most productive phenotype (i.e., the octoploid cytotype) in terms of sesquiterpenoid acids, and the less productive ones (i.e., di- and tetraploid). All in all, our results demonstrate the application of attenuated total reflection Fourier transform infrared spectroscopy for rapid, extractionless estimation of the most important quality attributes of valerian root and minimally invasive identification of the most productive phenotype in terms of sesquiterpenoid acids.

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“…While DL has been successfully employed for multivariate calibration and domain adaptation in chemometrics with the type of models usually employed in computer vision (e.g., convolutional neural networks), 72 true transfer learning with spectroscopic data will require large compilations of datasets to learn domain‐specific feature representations for data from a particular analytical platform (e.g., NIR or MIR spectroscopy). Figure 8 exemplifies this idea of transfer learning using a dataset of ATR‐FTIR spectra of dried plant leaf surfaces from 30 distinct plant families (unpublished work) 73,74 . The left plot shows non‐linear t‐SNE 75 embeddings (i.e., projections) of the activations (i.e., the scores) of the last hidden layer from a fully connected deep neuronal network fitted to a subset of this dataset.…”

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