以三种不同陈酿方式的96个干红葡萄酒样品为试验对象,用傅里叶变换中红外光谱仪外加衰减全反射(attenuated total reflectance,ATR)附件扫描其光谱,然后分别用定性偏最小二乘法和支持向量机法建立三种不同陈酿方式葡萄酒的判别模型,10次随机划分建模集与预测集后建模,不同模式识别方法所建模型的建模集、预测集的判别准确率均高于90%。结果表明,采用中红外ATR光谱技术结合模式识别方法对不同陈酿方式红葡萄酒进行快速识别是可行的。
Based on the effect of sample size on the near- infrared （NIR） spectrum, the absorbance （log（R）） in any wavelength is divided into two parts, and one of them is defined as non-particle-size-related spectrometry （nPRS） because it is not influenced by particle size. To study the relationship between the absorbance and l^article size, the experiment material including nine samples with different particle size was used. According to the regression analysis, the relationship was studied as the reciprocal regression model, y = a ~ bx ＋ c/x. Meanwhile, the model divides absorbance into two parts, one of them forms nPRS. According to the nPRS, a new correction method, particle size regression correction （PRC） was introduced. In discriminate analysis, the spectra from three different samples （rice, glutinous rice and sago）, pretreated by PRC, could be directly and accurately distinguished by principal component analysis （PCA）, while by the traditional correction method, such as multiplicative signal correction （MSC） and standard normal variate （SNV）, could not do that.
