AUTHOR=Chen Xiaochen , Yang Biyun , Du Xiping , Li Qingyan , Li Zhipeng , Yang Yuanfan , Jiang Zedong , Zhu Yanbing , Ni Hui , Miao Xiongping 

TITLE=Rapid prediction of Porphyra photosynthetic pigments based on colorimetric parameters

JOURNAL=Frontiers in Sustainable Food Systems

VOLUME=Volume 9 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1553250

DOI=10.3389/fsufs.2025.1553250

ISSN=2571-581X

ABSTRACT=Photosynthetic pigments such as phycobiliproteins and chlorophyll a are important quality indicators of seaweeds. In this study, multivariate nonlinear regression (MNLR) models were developed and validated for the rapid determination of photosynthetic pigments in Porphyra haitanensis based on colorimetric parameters (L*, a*, b*). The contents of phycoerythrin, phycocyanin, allophycocyanin and chlorophyll a in P. haitanensis were within 1.499–8.882 mg/g, 1.402–7.634 mg/g, 0.315–1.623 mg/g, and 0.340–2.160 mg/g, respectively. The L*, a*, and b* values were within 13.47–32.97, −1.88 to 2.74, and 0.23–4.61, respectively. This study indicated that the pigment contents of P. haitanensis, especially phycoerythrin and phycocyanin, could be effectively predicted based on color parameters with R2 of 0.901 and 0.701, respectively. The MNLR model also showed that the relative errors of phycoerythrin and phycocyanin content prediction were less than 10 and 20%, respectively. However, the prediction of allophycocyanin and chlorophyll a proved to be more challenging and the model showed limited predictive power. This discovery may make it easier to employ non-destructive techniques to evaluate the phycoerythrin and phycocyanin content of P. haitanensis and other seaweeds, which is important for the expanding Porphyra industry as it may enable a rapid assessment of Porphyra quality. This finding demonstrates the potential of visual analysis for quality assessment of Porphyra, as well as the convenience and non-destructive nature of the method. Future research should focus on improving the model and developing accurate and rapid quality control methods for the industrialization and scientific application of Porphyra.