Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery

Abstract

Phycobilisomes and chlorophyll-a ( Chla ) play important roles in the photosynthetic physiology of red macroalgae and serve as the primary light-harvesting antennae and reaction center for photosystem II. Neopyropia is an economically important red macroalga widely cultivated in East Asian countries. The contents and ratios of 3 main phycobiliproteins and Chla are visible traits to evaluate its commercial quality. The traditional analytical methods used for measuring these components have several limitations. Therefore, a high-throughput, nondestructive, optical method based on hyperspectral imaging technology was developed for phenotyping the pigments phycoerythrin (PE), phycocyanin (PC), allophycocyanin (APC), and Chla in Neopyropia thalli in this study. The average spectra from the region of interest were collected at wavelengths ranging from 400 to 1000 nm using a hyperspectral camera. Following different preprocessing methods, 2 machine learning methods, partial least squares regression (PLSR) and support vector machine regression (SVR), were performed to establish the best prediction models for PE, PC, APC, and Chla contents. The prediction results showed that the PLSR model performed the best for PE ( R Test 2 = 0.96, MAPE = 8.31%, RPD = 5.21) and the SVR model performed the best for PC ( R Test 2 = 0.94, MAPE = 7.18%, RPD = 4.16) and APC ( R Test 2 = 0.84, MAPE = 18.25%, RPD = 2.53). Two models (PLSR and SVR) performed almost the same for Chla (PLSR: R Test 2 = 0.92, MAPE = 12.77%, RPD = 3.61; SVR: R Test 2 = 0.93, MAPE = 13.51%, RPD =3.60). Further validation of the optimal models was performed using field-collected samples, and the result demonstrated satisfactory robustness and accuracy. The distribution of PE, PC, APC, and Chla contents within a thallus was visualized according to the optimal prediction models. The results showed that hyperspectral imaging technology was effective for fast, accurate, and noninvasive phenotyping of the PE, PC, APC, and Chla contents of Neopyropia in situ. This could benefit the efficiency of macroalgae breeding, phenomics research, and other related applications.

Quantifying pigment content in crustose coralline algae using hyperspectral imaging: A case study with Tethysphytum antarcticum (Ross Sea, Antarctica)

“…After comparing the absorbance peaks from our CCA samples with red algae pigment literature, our results indicated that phycobilin absorbance peaks corresponded to PE, while peaks in the nonpolar pigments were caused by Chl a . The pigment extraction absorbance spectra provided a valuable reference to evaluate its relationship with spectral reflectance and build tailored indices, or “proxies.” Further studies on coralline algae pigmentation using HSI could focus on PC (detected in low quantities in this study), APC, chlorophylls, and carotenoids (Burdett et al., 2014; Che et al., 2023; Voerman, Marsh, et al., 2022).…”

“…To improve regression algorithms, future studies could make use of additional sampling efforts to increase sample size and test multivariate analysis based on chemometrics or machine learning methods that can handle collinearity between predictors (i.e., different wavelengths from a spectrum), enabling the use of the whole spectral reflectance signature to measure different biochemical traits (Burnett et al., 2021; Che et al., 2023). Integration with multiple sensors, such as pulse amplitude modulation fluorometry (Burdett et al., 2012; Schwarz et al., 2005; Summers et al., 2023), along with measurements of different CCA response categories like calcification and primary production (Cornwall et al., 2019; Page et al., 2022), could further refine noninvasive assessments of their photo‐physiology response to different irradiance regimes.…”

“…The camera‐target distance was set at 58 cm, resulting in 0.25 mm 2 pixels. We maintained a fixed camera/light setup, moving samples on a translation stage with an electric rail under the hyperspectral camera following established HSI lab practices (Che et al., 2023; Cimoli et al., 2020). Two 50 W tungsten‐halogen light sources (Spectral Evolution, MA, USA) were positioned opposite to each other at angles facing the target to ensure uniform illumination and minimize any shadowing caused by the microtopography of the samples.…”

“…The pigment extraction absorbance spectra provided a valuable reference to evaluate its relationship with spectral reflectance and build tailored indices, or "proxies." Further studies on coralline algae pigmentation using HSI could focus on PC (detected in low quantities in this study), APC, chlorophylls, and carotenoids (Burdett et al, 2014;Che et al, 2023;Voerman, Marsh, et al, 2022).…”

Simultaneous Quantification and Visualization of Photosynthetic Pigments in Lycopersicon esculentum Mill. under Different Levels of Nitrogen Application with Visible-Near Infrared Hyperspectral Imaging Technology

“…This demonstrates that VNIR–HSI can be applied to the rapid and accurate prediction of the pigment concentration. Che et al [ 25 ] analyzed the phenotypes of photosynthetic pigments in Neopyropia yezoensis for phycoerythrin (PE), phycocyanin (PC), allophycocyanin (APC) and Chla using VNIR–HSI. Two machine learning techniques, partial least squares regression (PLSR) and support vector machine regression (SVR), were employed in the prediction model after several pre-processing approaches.…”