Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery

Effects of okadaic acid on Pyropia yezoensis: Evidence from growth, photosynthesis, oxidative stress and transcriptome analysis

The frequent occurrences of harmful algal blooms potentially threaten marine organisms. The phycotoxin okadaic acid (OA) has been globally detected in seawater, however, the knowledge of effects of OA on macroalgae is limited. This study investigated the effects of OA (0.01, 0.1 μM) on the growth, physiological and biochemical properties, and transcriptional expression of Pyropia yezoensis. Exposure to 0.1 μM OA for 48 h led to decreased growth, oxidative stress, and lipid peroxidation in P. yezoensis. Levels of reactive oxygen species, glutathione and malondialdehyde, and activity of catalase enzyme were increased, but activity of superoxide dismutase was decreased in P. yezoensis exposed to OA. Even at the low concentration of 0.01 μM, OA influenced the photosynthetic efficiency and stimulated the pigment levels, including phycoerythrin, phycocyanin, allophycocyanin and chlorophyll a. Analytical results of amino acids indicated that OA reduced the nutritional quality of P. yezoensis. The expression of genes involved in nitrogen metabolism was up-regulated, but the genes associated with ABC transporters and photosynthesis was down-regulated by the OA exposure, suggesting that OA may affect photosynthesis and enhance nitrogen uptake and assimilation processes. This study provides a new perspective on the chemical ecology risk of phycotoxins to marine macroalgae.

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

Crustose coralline algae (CCA) are a highly diverse group of habitat-forming, calcifying red macroalgae (Rhodophyta) with unique adaptations to diverse irradiance regimes. A distinctive CCA phenotype adaptation, which allows them to maximize photosynthetic performance in low light, is their content of a specific group of light-harvesting pigments called phycobilins. In this study, we assessed the potential of noninvasive hyperspectral imaging (HSI) in the visible spectrum (400-800 nm) to describe the phenotypic variability in phycobilin content of an Antarctic coralline, Tethysphytum antarcticum (Hapalidiales), from two distinct locations. We validated our measurements with pigment extractions and spectrophotometry analysis, in addition to DNA barcoding using the psbA marker. Targeted spectral indices were developed and correlated with phycobilin content using linear mixed models (R2 = 0.64-0.7). Once applied to the HSI, the models revealed the distinct phycoerythrin spatial distribution in the two site-specific CCA phenotypes, with thin and thick crusts, respectively. This study advances the capabilities of hyperspectral imaging as a tool to quantitatively study CCA pigmentation in relation to their phenotypic plasticity, which can be applied in laboratory studies and potentially in situ surveys using underwater hyperspectral imaging systems.

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

Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to rapidly and accurately obtain the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophylls (Chls) and total carotenoids (Cars) as indicators to study the variations in the leaf positions of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR-HSI). In this study, a "coarse-fine" screening strategy was proposed using competitive adaptive reweighted sampling (CARS) and the iteratively retained informative variable (IRIV) algorithm to extract the characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS-IRIV-PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R2), root mean square error (RMSE) and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43 and 2.38 for Chla; 0.8391, 0.53 and 2.49 for Chlb; 0.7899, 2.24 and 2.18 for Chls; and 0.7577, 0.27 and 2.03 for Cars, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of the pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis and fertilization decisions in plant growth management.