Meat and meat products are important components of the human food chain. Their quality is an important issue to consumers, government agencies and retailers. In China, the research and application of rapid and reliable methods for on-line detection of meat quality is still in badly need. Near infrared spectroscopy (NIRS) is an attractive technique for such applications, since it is fast, non-destructive method which requires small samples with a high-penetration radiation beam and free from further preparation of the samples is needed. Therefore, in this study, the overall objective was to investigate the use of a NIR hyperspectral imaging technique for accurate, fast and objective detection of fat content in various meat, and to compare with traditional standard chemical results. With near infrared scanning technology to pork, beef, mutton, and the national standard method (soxhlet extraction method) to determination of chemical values of fresh meat fat, with PLS (partial least squares) as a modeling method, and through the different spectral preprocessing methods respectively established the pigs, beef and mutton samples of near infrared spectrum parameters and the corresponding relationship between the fat content of model. Results show that for pork, select band 4 260～6 014 cm-1+a derivative+Norris derivative model built by the best effect, the correction coefficient of correlation and prediction correlation coefficient of 0.955 6 and 0.961 6 respectively; For beef, choose 5 226～7 343 cm-1 band+a derivative+S-G model built by the best effect, the correction coefficient of correlation and prediction correlation coefficient of 0.923 5 and 0.942 7 respectively; For mutton, select band 5 207～7 362 cm-1+a derivative + Norris derivative model built by the best effect, the correction coefficient of correlation and prediction correlation coefficient of 0.915 7 and 0.939 6 respectively; For fresh meat, choose select band of 5 156～6 065 cm-1+second derivative+S-G model built by the best effect, the correction coefficient of correlation and prediction correlation coefficient were 0.916 3 and 0.919 4, above all model correction of the correlation coefficient is greater than 0.91. Thus, the model has higher precision, meeting the needs of different meat products in the actual production.It is a nondestructive method with advantages such as fast analysis speed, low cost and high resolution.