Nondestructive detection of potato starch content based on near-infrared hyperspectral imaging technology

Abstract

The traditional method of determining potato starch content is not only time-consuming and labor-intensive, but also very aggressive and destructive, which also causes serious pollution to the environment. Therefore, it is necessary to study the fast, efficient, and environment-friendly detection technology. Although near-infrared technology can solve these problems well, it cannot detect potato starch because of its dot shape, invisibility, and other shortcomings. Hyperspectral imaging technology has a new technology of near-infrared, which can simultaneously detect surface defects and internal physical and chemical components. In this article, the method of nondestructive testing of potato starch using near-infrared hyperspectral technology was studied. In thisarticle, successive projection algorithm, random frog, and genetic algorithm were used to predict the content of potato starch. The experimental results in this article showed that in random frog, the root mean square error (RMSEC) of correction set and the root mean square error of prediction (RMSEP) model RC2{R}_{\text{C}}^{2} and RP2{R}_{\text{P}}^{2} have become 0.87 and 0.84, respectively, and RMSEC and RMSEP have become 0.33 and 0.30%, respectively. Therefore, the best method to select the characteristic wavelength of potato starch is the random frog algorithm.

Keywords

• nondestructive detection of potato star content
• near-infrared hyperspectral imaging technology
• successful projection algorithm
• random leapfrog
• genetic algorithm