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As an important characteristic crop in China, sweetpotato plays an important role in the intake and supplement of nutrients. The saccharification characteristics of sweetpotato determine the edible quality and processing type. Exploring the saccharification characteristics of sweetpotato is of great significance to the selection of processing materials and the formation mechanism of service quality, but there are few relevant studies. A comparison study of two high saccharification varieties (Y25 and Z13) and one low saccharification variety (X27) was conducted to analyze their storage roots physical and chemical properties. The results show that the dry matter content, starch, and amylose content of Y25 and Z13 were significantly different from those of X27. Furthermore, the total amylase activity was significantly higher than that of X27. On the other hand, the starch gelatinization temperature was significantly lower than that of X27. The starch reduction in Y25 and Z13 is four times more than that in X27, and the maltose content of Y25 and Z13 is more than two times that of X27. Finally, the scores of sensory evaluation and physiological sweetness were significantly higher than those of X27. The results provide a theoretical basis for understanding the saccharification characteristics of sweetpotato varieties and are of guiding significance for the selection of sweetpotato parents.

Sparse representations are widely used tools in image super-resolution (SR) tasks. In the sparsity-based SR methods, linear sparse representations are often used for image description. However, the non-linear data distributions in images might not be well represented by linear sparse models. Moreover, many sparsity-based SR methods require the image patch self-similarity assumption; however, the assumption may not always hold. In this paper, we propose a novel method for single image super-resolution (SISR). Unlike most prior sparsity-based SR methods, the proposed method uses non-linear sparse representation to enhance the description of the non-linear information in images, and the proposed framework does not need to assume the self-similarity of image patches. Based on the minimum reconstruction errors, support vector regression (SVR) is applied for predicting the SR image. The proposed method was evaluated on various benchmark images, and promising results were obtained.