本试验旨在研究利用傅利叶近红外光谱对鱼粉中掺杂的三聚氰胺进行定性鉴定及定量预测的可行性及方法。本研究收集饲用鱼粉106个，通过添加三聚氰胺标准品到饲用鱼粉中，以制备三聚氰胺含量不同的掺假鱼粉236个，三聚氰胺含量为0.1%～15.0%；然后用傅利叶近红外光谱仪采集饲用鱼粉、掺杂三聚氰胺鱼粉和三聚氰胺标准品的近红外光谱曲线，采用因子算法建立定性模型，利用偏最小二乘分析（PLS）建立定量分析模型。结果显示，根据三聚氰胺标准品和鱼粉的近红外光谱，可选取6 873.4～6 514.7 cm-1作为定性分析的特征光谱，而据此所建定性分析模型对检验集样品识别率为96.5%；用于定量分析的近红外特征光谱为7 560.0～7 058.5、6 915.8～6 098.1及4 601.6～4 246.7 cm-1,用此特征光谱所建0.1%～15.0%定量分析模型和0.1%～5.5%定量分析模型的交叉验证标准差平方根（RMSECV）分别为0.634%和0.210%，决定系数R2为94.77%和98.59%。用此二定量模型对检验集样品预测，其预测标准差平方根（RMSEP）分别为0.779%和0.188%,能较准确地预测鱼粉中掺杂三聚氰胺的含量。因此，傅利叶近红外光谱能够较准确、快速地鉴定鱼粉中是否掺杂三聚氰胺及定量预测其含量。

The study was aimed to explore the feasibility and methods of detecting and quantifying melamine adulterated in fish meal by Fourier transform near infrared spectroscopy (FT-NIR). In this study, a total of 106 commercial fed fish meal samples were collected, and 236 adulterated fish meal were prepared by adding different concentration gradient of melamine standard to the fed fish meal samples, melamine content ranging from 0.1% to 15.0%. The spectral data was collected by FT-NIR; a qualitative model was established by factorization algorithm; quantitive model was established by partial least squares (PLS) regression algorithm. The results showed as follows: 6 873.4～6 514.7 cm-1 was selected to establish qualitative analysis model according to the spectra of melamine standard and fed fish meal, and the correct classification rate of the qualitative analysis model to discriminating the validation set samples was 96.5%. And the characteristic spectra of quantitive analysis were 7 560.0～7 058.5, 6 915.8～6 098.1 and 4 601.6～4 246.7 cm-1. The root mean square error of cross validation (RMSECV) of the 0.1～15.0% model and the 0.1～5.5% model, which were established with these characteristic spectra, were 0.634% and 0.210%, and the coefficient of determination (R2) was 94.77% and 98.59%, and the root mean square error of prediction (RMSEP) was 0.779% and 0.188% respectively. In conclusion, FT-NIR could quickly and exactly detect whether fish meal was adulterated by melamine and predict melamine content.［Chinese Journal of Animal Nutrition, 2010，22（3）:741-749］