Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery  

作　　者：Shuai Che Guoying Du Xuefeng Zhong Zhaolan Mo Zhendong Wang Yunxiang Mao 

机构地区：[1]Key Laboratory of Marine Genetics and Breeding(Ministry of Education),College of Marine Life Sciences,Ocean University of China,Qingdao,266003,China [2]Key Laboratory of Utilization and Conservation of Tropical Marine Bioresource(Ministry of Education),College of Fisheries and Life Science,Hainan Tropical Ocean University,Sanya,572002,China [3]Yazhou Bay Innovation Institute,Hainan Tropical Ocean University,Sanya,572025,China [4]Laboratory for Marine Biology and Biotechnology,Pilot National Laboratory for Marine Science and Technology(Qingdao),Qingdao,266073,China

出　　处：《Plant Phenomics》2023年第1期60-72,共13页植物表型组学（英文）

基　　金：supported by the National Natural Science Foundation of China(grant no.32060829);the National Key R&D Program of China(2020YFD0901101);the 2020 Research Program of Sanya Yazhou Bay Science and Tech nology City(no.SKJC202002009);the Innovation Platform for Academicians of Hainan Province and Special Project of Central Government Guiding Local Science and Technology Development(grant no.ZY2020HN02);the Major Science and Technology Program of Yazhou Bay Innovation Institute of Hainan Tropical Ocean University(2022CXYZD001).

摘　　要：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 re

关 键 词：PREDICTION BREEDING VISIBLE 

分 类 号：Q945.11[生物学—植物学]