To investigate the feasibility of using near-infrared (NIR) spectral technology to detect the soluble solids content (SSC) of Malus micromalus Makino, rapid and non-destructive prediction models of SSC were studied using least-square support vector regression (LS-SVR), partial least squares regression (PLSR), and the error back propagation artificial neural network (BP-ANN). First, 110 samples of NIR diffuse reflectance spectra in the wavelength range of 400.41-1083.89 nm were obtained, and then were divided into the calibration set and prediction set by sample set partitioning based on the joint x-y distance (SPXY) algorithm. Second, we compared the prediction performance of the PLSR model after preprocessing by nine spectral preprocessing methods, and applied data dimension reduction methods (random frog, the successive projections algorithm (SPA), and principal component analysis) for variable selection. Finally, the effect of applying full spectrum and characteristic spectrum modeling on SSC prediction accuracy was compared and analyzed. The comparison studies confirmed that the optimal fusion model of SPA-LS-SVR had the best performance (R C = 0.9629, R P = 0.9029, RMSEC = 0.199, RMSEP = 0.271). The experimental results could provide a reference for future development of the internal component analysis system for Malus micromalus Makino based on NIR spectroscopy and its classification system using SSC as the classification standard. INDEX TERMS Least-square support vector regression, Malus micromalus Makino, near-infrared spectroscopy, soluble solids content, successive projection algorithm. The associate editor coordinating the review of this manuscript and approving it for publication was Geng-Ming Jiang. more popular with the local people. It is a good raw material for fruit processing and is rich in soluble solids content (SSC), acids, and calcium. It has been processed into brandy, beverages, pastries, and other products. In recent years, due to its high nutritional and economic value, it has been widely promoted by the local government as a special agricultural product of Fugu Valley to be shipped to all parts of the country and across the world.
Through the application of the super-efficiency DEA-Malmquist method, the article measures the efficiency of enterprise technological innovation, analyzes the characteristics of time and space evolution and efficiency change of technological innovation during 2006-2016, and quantitatively adjusts the input and output factors of improving enterprise technological innovation. The research shows that: first, the technical efficiency of technological innovation of enterprises: in terms of time series changes, the potential of input of existing production factors cannot be tapped, and the input of technical elements should be increased. Moreover, in the provinces with inefficient technical efficiency, there are widespread factors such as redundant input of factors and insufficient output of benefits. Each province and region can make quantitative adjustments to the input and output factors in the development of high-tech industries according to their own conditions. Second, enterprise technology innovation Malmquist efficiency index: average technical efficiency change, average technical level change and average total factor productivity change were 1.024, 0.976 and 0.996, respectively, both close to 1. There is a significant difference in technical efficiency between regions, and it is gradually developing towards economically developed areas along the southeast coast, and there is a significant spillover effect.
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