In order to meet the development requirement of portable and low cost equipment in the field of non-destructive detection of fresh meat quality parameters, a new method based on multi spectral diffuse reflectance technology for fresh meat quality detection is proposed. Based on the diffusion approximation theory and combined with the sample scattering coefficient, absorption coefficient and refractive index of beef and other parameters, based on Monte Carlo simulation of thin vertical beam on the radio, on a certain divergence angle of LED light source are initialized with correction respectively from the light source position probability distribution and different angles of the irradiation probability distribution, angle, direction angle the probability distribution and different incident light angle sample reflection caused by the energy loss and influence on the photon weight, the LED divergence angle under different source detector diffuse reflectance and depth of detection distance, the optimum distance between the light source and the detector is 15 mm, then according to the distance, to build a multi spectral diffuse reflection detection platform, multi spectral detection platform by 8 groups of 470, 535, 575, 610, 650, 720, 780, 960 nm LED. The source composition corresponds to the quality parameters of fresh beef to be detected. At the same time, according to the 8 LED light source, the light source design layout structure of probe point symmetry, 8 light sources inside the probe to the detector as the center, symmetric distribution, while using LED light source divergence angle, determine the installation position of the light source to the sample surface and the vertical distance of each light source, to ensure the light source to the sample area is uniform. In addition, the probe embedded within the design of signal acquisition, amplification and transmission components, signal acquisition part uses the spectral response range of 400～1 100 nm light intensity detector, sample diffuse intensity after processing to the host computer through the signal acquisition and amplification circuit, and the software finished modeling and analysis. Finally in order to verify the performance of the detection system, with fresh beef freshness in the color parameter (L*, a*, b*) and the pH value as the index was tested using 60 samples, 8 light source under the original light intensity value and corrected reflectance values respectively, and then the beef samples according to 3∶1 the proportion is divided into set and prediction set correction, for the original value of light intensity and reflectivity values, respectively, using multiple linear regression (MLR), Multiple Linear Regression Partial Least Squares Regression partial least squares regression (PLSR) and partial least squares support vector machine regression Partial Least-Squares Support Vector Machine (LS-SVM) three methods, model parameters in the original light intensity and reflectivity data of the two cases, and get the best results. The results show that the results of modeling using reflectivity data are better than those of light intensity data. The MLR modeling results of parameters L*, a* and b* are better than those of PLSR and LS-SVR, and their correlation coefficients of prediction set are 0.983 2, 0.907 2 and 0.935 9, respectively, and the prediction set errors are 1.00, 2.14 and 0.67, respectively. The LS-SVR modeling results of parameter pH value are better than that of PLSR and MLR, and the correlation coefficient of the prediction set is 0.942 0 and the error is 0.19. Finally, using 20 pieces of beef samples which did not participate in the test to validate the model, the color of L*, a*, b* and pH parameters of the prediction value of the correlation coefficient and the measured value is greater than 0.85, the results proved that using multispectral diffuse reflection technology and building the multispectral reflectance detecting system are feasible for the detection of fresh beef the quality parameters, this method can provide reference and basis for the nondestructive testing instrument design of portable or micro fresh beef quality.