中文摘要：

为了对果醋糖度值进行快速准确检测,应用近红外光谱技术并结合最小二乘支持向量机分析方法建立了果醋糖度检测模型。应用近红外透射光谱获取五种类型共计300份果醋样本的光谱透射曲线,利用主成分分析方法对原始光谱数据进行降维处理,根据主成分的累计贡献率选取6个主成分。选取的主成分即作为光谱优化特征子集以替代原来复杂的光谱数据。随后将300份果醋样本数据随机分为定标集和预测集,利用最小二乘支持向量机在225个定标集样本数据基础上建立起果醋糖度预测模型,应用此模型对75个预测集样本进行糖度预测。根据预测均方根误差（RMSEP）和预测结果的相关系数（r）对预测模型进行评价,利用此模型得到的样本糖度预测值r=0.9939,RMSEP=0.363,均达到了较好的预测效果。

英文摘要：

For the fast and exact detection of sugar content of fruit vinegar, near infrared （NIR） spectroscopy technique combined with least squares support vector machines （LS-SVM） algorithm was used to build the prediction model of sugar content in the present research. NIR spectroscopy is a nondestructive, fast and accurate technique for the measurement of chemical components based on overtone and combination bands of specific functional groups. The pivotal step for spectroscopy technique is how to extract quantitative data from mass spectral data and eliminate spectral interferences. Principal component analysis （PCA） is a method which has been widely used in the spectroscopic analysis, and LS-SVM is a new data mining algorithm developed from the machine learning community. In the present study, they were used for the spectroseopie analysis. First, the near infrared transmittance spectra of three hundred samples were obtained, then PCA was applied for reducing the dimensionality of the original spectra, and six principal components （PCs） were selected according the accumulative reliabilities （AR）. The six PCs could be used to replace the complex spectral data. The three hundred samples were randomly separated into calibration set and validation set. Least squares support vector machines （LS-SVM） algorithm was used to build prediction model of sugar content based on the calibration set, then this model was employed for the prediction of the validation set. Correlation coefficient （r） of prediction and root mean square error prediction （RMSEP） were used as the evaluation standards, and the results indicated that the r and RMSEP for the prediction of sugar content were 0. 993 9 and 0. 363, respectively. Hence, PCA and LS-SVM model with high prediction precision could be applied to the determination of sugar content in fruit vinegar.