Donkey milk has aroused more attention in recent years since its nutrition composition shows a higher similarity to human milk than others. Due to the composition difference between cow milk and donkey milk, the present models available for cow milk analysis could not be applied to donkey milk without modifications. A rapid and reliable analysis method is required to measure the nutrition composition of donkey milk. Near infrared spectroscopy is a newly developed method in food industry, but no literature report was found regarding to its application in the analysis of donkey milk. Protein, fat, ash contents and energy value are the major nutrition factors of milk. In the present paper, these factors of donkey milk were investigated by Fourier transform near-infrared (FT-NIR) spectroscopy. The ranges of protein, fat and ash contents, and energy value in donkey milk samples were 1.15%-2. 54%, 0.34%-2. 67%, 0.28%-0. 57% and 355. 87-565. 17 cal center dot kg(-1), respectively. The IR spectra ranged f from 3 899. 6 to 12 493. 4 cm(-1), with a 1 cm(-1) scanning interval. When the principal least square (PLS) regression algorithm is used for spectral regions information extraction, the additional constraint makes the principal components of matrix X to be related with the components of Y which is to be analyzed. Various spectral regions and data pretreatment methods were selected for principal least square (PLS) regression model development. A comparison of the whole and optimized spectral region NIR indicated that the models of selecting optimum spectral region were better than those of the whole spectral region. It was shown that the protein, fat and ash contents, and energy value in donkey milk obtained by chemical methods were well correlated to the respective values predicted by the NIR spectroscopy quantitative analysis model (alpha= 0.05). The RMSEP values were 0.18, 0.117, 0.040 6 and 23. 5 respectively, indicating that these predicted values were reliable. These results suggested that FT-NIR spectroscopy could be used for the rapid detection of the composition of donkey milk by establishing NIR spectroscopy quantitative analysis models. Selecting an optimum spectral region and establishing a special NIR analysis model accordingly are key steps during the data pretreatment. The models of the optimum spectral region were better than the models of the whole spectral region. When irrelevant information was included in the models, it would interfere with the analysis and give less reliable results. Therefore, the selection of a right spectra region plays an important role in the set-up of quantitative analysis models. The accuracy and reliability of the standard data used in model settings are also critical to the final results. In order to improve the reliability and accuracy of the NIR methods, a wide range of component contents and more accurate standard data are definitely required.
