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中文摘要：
脂肪是奶粉中重要的组成部分,实现对奶粉中脂肪含量的快速、无损检测十分重要,为此研究了400-6666 cm^-1范围的红外光谱技术对不同品种奶粉的脂肪含量的无损检测.采用最小二乘支持向量机（LS-SVM）对光谱透射率值和脂肪含量值进行建模.模型在全红外波段范围对样本脂肪含量预测得到了较好的结果,绝对系数（R2p）达到0.9796,预测误差均方根（RMSEP）为0.8367.预测结果要优于BP人工神经网络（Back Propagation NeuralNetworks,BP-NN）.说明红外光谱技术能够实现奶粉脂肪含量的无损检测,检测过程比化学检测方法简单快速,操作性强.文章同时还研究了分别基于中红外光谱范围和近红外光谱范围的建模.模型预测结果显示分别基于中红外光谱和近红外光谱区域的模型预测效果都比全波段建模略差.本研究为今后奶粉脂肪含量快速无损检测仪器的开发奠定了理论基础.
英文摘要：
Fat is an important component in milk powder. It is very important to detect the fat content in milk powder fast and non-destructively. To achieve this purpose, near and mid-infrared （400 - 6666cm^-1）spectroscopy technique was used and least-squares support vector machine was applied to build a fat prediction model based on infrared spectra transmission value. The prediction result obtained from our model is better than that obtained from the back propagation neural networks （BP-NN） while the determination coefficient for prediction （ R^2p ） is 0. 9796 and root mean square error for prediction （ RMSEP） is 0. 8367. It is concluded that infrared spectroscopy technique can detect the fat content in milk powder fast and non-destructively, and the process is simple and easy to operate. Moreover, the prediction results based on the whole infra- red spectra were compared with those based only on near infrared spectra or mid-infrared spectra data. The results show that the performances of the model based only on mid-infrared spectra or near infrared spectra data are a little worse than those based on the whole infrared spectra data.
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