摘要
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.
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.
基金
The work was made possible with support from two research projects by the National Natural Science Foundation of China （Grant Nos. 61144012 and 31101289）.
