In order to evaluate the feasibility of miniature near infrared spectroscopy (NIRS) in detecting sugar content of fruits in situ, non-destructive, high-precision and fast detection method of apple sugar content was established by combining particle swarm optimization with back propagation (BP) neural network. The spectral data obtained by NIRscan(micro-NIRS) using single wavelength measurement mode and Hadamard transform measurement mode were studied. A variety of different data preprocessing methods and multiple linear regression, partial least squares, particle swarm optimization (PSO), BP neural network and other algorithms were used to establish the analysis model. The results show that the spectral data obtained by the working mode of Hadamard transform are better. First derivative combined with Savizky-Golay smoothing algorithm is used for data preprocessing. The prediction model of apple sugar content based on PSO and BP neural network has higher prediction accuracy. Predictive correlation coefficient and root mean square error are 0.9911 and 0.1502, respectively. NIRscan (micro-NIRS) is feasible for rapid and high-precision non-destructive testing of apple sugar content in the field.