Prediction of cyanidin 3-rutinoside content in Michelia crassipes based on near-infrared spectroscopic techniques.

Abstract

Currently the determination of cyanidin 3-rutinoside content in plant petals usually requires chemical assays or high performance liquid chromatography (HPLC), which are time-consuming and laborious. In this study, we aimed to develop a low-cost, high-throughput method to predict cyanidin 3-rutinoside content, and developed a cyanidin 3-rutinoside prediction model using near-infrared (NIR) spectroscopy combined with partial least squares regression (PLSR). We collected spectral data from Michelia crassipes (Magnoliaceae) tepals and used five different preprocessing methods and four variable selection algorithms to calibrate the PLSR model to determine the best prediction model. The results showed that (1) the PLSR model built by combining the blockScale (BS) preprocessing method and the Significance multivariate correlation (sMC) algorithm performed the best; (2) The model has a reliable prediction ability, with a coefficient of determination (R 2 ) of 0.72, a root mean square error (RMSE) of 1.04%, and a residual prediction deviation (RPD) of 2.06. The model can be effectively used to predict the cyanidin 3-rutinoside content of the perianth slices of M. crassipes , providing an efficient method for the rapid determination of cyanidin 3-rutinoside content.

Plant phenotyping relevance

NIR分光とPLSRによる花被片のアントシアニン含量推定法を開発し、前処理・変数選択と予測性能を評価しており、植物形質取得法が研究の中心である。

Code and data availability

The paper's NIR spectral data (129 spectra of Michelia crassipes tepals) and Cy3R content measurements are the paper-specific phenotyping assets, but they are not publicly deposited; the data availability statement only offers them on request. No public repository, code deposit, or authors' public URL is provided.

No evidence-backed public reproduction asset is currently recorded.