Rapid identification of phycobiliproteins in Porphyra yezoensis using near-infrared combined with convolutional neural network

Phycobiliproteins (PBPs) are the main pigment proteins and important indicators for evaluating the quality of Porphyra yezoensis (nori). This study aimed to develop a non-destructive and rapid method for determining PBP content (including total PBPs, phycoerythrin, phycocyanin, and allophycocyanin) in P. yezoensis, using near-infrared spectrum technology combined with a convolutional neural network (CNN), and to explore the influence of spectral preprocessing methods and machine learning algorithms on the predictive ability of the model. First, the spectral data was standardized using a combination of standard normal variable transformation and the first derivative to improve the accuracy and predictive ability of the model. We compared various models and determined that the CNN model performed better than conventional methods. After optimizing the number of convolutional layers, dropout rate, and learning rate, the performance of the CNN model was further improved. This study demonstrates the capability of the CNN model to leverage spectral data and solve regression problems to accurately measure PBPs. Moreover, for the first time, we established a functional equation between PBPs and P. yezoensis grades. This study provides a feasible and rapid method for the quantitative detection of PBPs in P. yezoensis.