Nondestructive estimation of specific macroelement contents in thalli of the red macroalga Pyropia yezoensis using hyperspectral imaging

In the economically important red macroalga Pyropia, carbohydrates and proteins contents are usually used to evaluate the nutritional status and commercial value. As main component elements of carbohydrates and proteins, C, N and S contents can be the suitable indicators. However, efficient and nondestructive estimation of these elements have not been well established in macroalgae, which is definitely needed in high-throughput phenotyping and selective breeding. In the current study, hyperspectral imaging was used to estimate the C, N and S contents in thalli of Pyropia yesoensis. Based on spectral information acquired by two hyperspectral cameras with range of 400 nm to 1700 nm, two machine learning methods, partial least squares regression (PLSR) and support vector machine regression (SVR), were performed to establish prediction models following different preprocessing methods. The result showed that SVR model following Savitzky-Golay (S-G) smoothing preprocessing performed the best for N content with excellent accuracy (RTest2 = 0.94, RMSE = 0.331, MAPE = 5.47