It was well known that aquatic products collected from different locations might considerably differ in their types and quantities of nutrient components, therefore, resulting in different quality. It is generally believed that these differences are essentially caused by various environmental conditions and habitats where the aquatic products are grown and harvested. SoTherefore, the quality control of aquatic products was a major concern for both the health authorities and the public. Hence, a validated method was essential for a quality assessment point of view. Because of its advantages and popularity, fingerprint analysis was widely accepted and used in quality control systems of herbal medicines, and some methods have been proposed for the fingerprint analysis, including infrared spectrum (IR), gas chromatography (GC), high-pressure liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS); but fingerprint technology was rarely used in the quality control of aquatic products at present. For the first time, a validated infrared spectrum method coupled with cluster cluster analysis, principal component analysis and load factor analysis methods had been developed for the study of the infrared fingerprint of mussel, and Fourier transform infrared (FTIR) spectrometer was performed on the 24 batches samples collected from Dalian, Yantai, Qingdao, Zhoushan, Xiamen and Haikou. Similarity analysis results indicated that similarity of 21 batches samples was higher than 0.9, which accords with the fingerprint technique criterion. The samples from the same region had the similar characteristic peaks of infrared spectra and could be clustered together. The normalized spectra was selected to construct principal component analysis model in the range of fingerprint region 1 800-800 cm^-1, and according to the model, the first two principal components (PC1 and PC2) accounted for 96% of the variance information in the fingerprint region, and each sample was able to form distinct ccluster in the principal component space, then the identification of mussel from the six regions was basically achieved. Besides, to some extent, the sparse density of the samples distribution reflected the genetic relationship. The loading factors of the model were analyzed, and the results indicated that the differences between mussel samples mostly depended on the contents of unsaturated fatty acids and phosphatides. The contents of unsaturated fatty acids in mussel from Haikou and Xiamen were highest in all samples, and the contents of phosphatides in mussel from Dalian and Yantai were highest in all samples. On the whole, FTIR combined with cluster analysis, principal component analysis and load factor analysis provided an effective way to identify the regions of mussel rapidly and nondestructively, and could detect the unsaturated fatty acids and phosphatide contents in mussel from different areas.