Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions

Abstract

Quantifying the phenolic compounds in plants is essential for maintaining the beneficial effects of plants on human health. Existing measurement methods are destructive and/or time consuming. To overcome these issues, research was conducted to develop a non-destructive and rapid measurement of phenolic compounds using hyperspectral imaging (HSI) and machine learning. In this study, the Arabidopsis was used since it is a model plant. They were grown in controlled and various stress conditions (LED lights and drought). Images were captured using HSI in the range of 400–1,000 nm (VIS/NIR) and 900–2,500 nm (SWIR). Initially, the plant region was segmented, and the spectra were extracted from the segmented region. These spectra were synchronized with plants’ total phenolic content reference value, which was obtained from high-performance liquid chromatography (HPLC). The partial least square regression (PLSR) model was applied for total phenolic compound prediction. The best prediction values were achieved with SWIR spectra in comparison with VIS/NIR. Hence, SWIR spectra were further used. Spectral dimensionality reduction was performed based on discrete cosine transform (DCT) coefficients and the prediction was performed. The results were better than that of obtained with original spectra. The proposed model performance yielded R2-values of 0.97 and 0.96 for calibration and validation, respectively. The lowest standard errors of predictions (SEP) were 0.05 and 0.07 mg/g. The proposed model out-performed different state-of-the-art methods. These demonstrate the efficiency of the model in quantifying the total phenolic compounds that are present in plants and opens a way to develop a rapid measurement system.

Using leaf spectroscopy and pigment estimation to monitor indoor grown lettuce dynamic response to spectral light intensity

“…this technique offers potentials for continuous monitoring of plant pigment content within the growth environment for instance by using hyperspectral cameras (Jayapal et al, 2022). The advantage of in vivo monitoring of leaf pigments is essential for detection of the dynamic plant response in adjusting the content of these compounds in response to the surrounding environment.…”

“…Parameters indicative of a rise in the plant stress level in response to light, as for instance the increasing ratio of zeaxanthin over carotenoids ( Xie et al., 2020 ) and anthocyanins over chlorophylls ( Kim et al., 2012 ; Zheng et al., 2021 ), could be used both for detecting exacerbation of stress response and increase of bioactive compounds. In contrast to strategies using portable leaf reflectance meter ( Jiménez-Lao et al., 2021 ), this technique offers potentials for continuous monitoring of plant pigment content within the growth environment for instance by using hyperspectral cameras ( Jayapal et al., 2022 ). The advantage of in vivo monitoring of leaf pigments is essential for detection of the dynamic plant response in adjusting the content of these compounds in response to the surrounding environment.…”

“…Several studies propose leaf spectroscopy to detect, with different degree of spatial resolution, e.g. leaf to canopy, based on the specific technology, content of water ( Ceccato et al., 2001 ), nitrogen ( Wang et al., 2016 ), pigments ( Blackburn, 2007 ), secondary compounds ( Jayapal et al., 2022 ) and signs of abiotic ( Grzesiak et al., 2009 ) and biotic ( Mahlein et al., 2012 ) stresses ( Jacquemoud and Ustin, 2001 ).…”

Predicting Models for Plant Metabolites Based on PLSR, AdaBoost, XGBoost, and LightGBM Algorithms Using Hyperspectral Imaging of Brassica juncea

“…The appropriate preprocessing method will depend on various factors, including the wavelength range and interval, the prediction model, the target compound, and the plant organs used, such as leaves and fruits. In previous studies, the performance of the PLSR model in detecting total phenolic content using a VIS-NIR hyperspectral imaging system was improved with the normalization method in apple fruits [26] and with the SG filter and derivative transformation in Arabidopsis leaves [27]. Derivative transforms emphasize spectral features but also emphasize the noise of data.…”

“…Yuan et al (2021) visualized the distribution of SPAD values, which indicate chlorophyll content in pepper leaves [29]. The distribution of the total phenolics has been visualized using hyperspectral imaging and modeling in Arabidopsis plants [27] and shelled cocoa beans [8]. Hence, by employing a hyperspectral imaging system and the necessary software to run the algorithm, we could non-destructively and continuously monitor the compound distribution.…”