A study of the Near Infrared Spectroscopy of rice wine is studied, with the purpose to discuss the impact of spectral pretreatment methods on wine alcohol rapid quantitative model, and to compare and analyze the effects of Partial Least Squares quantitative model of spectroscopy after the five traditional methods which include smoothing, first derivative, second derivative, multiplicative scatter correction and standard normal transform and the three new methods which contain wavelet transform, Fourier transform and orthogonal signal correction. The results indicates that the cross-validated R=0.998 and RMSECV=O. 135 7 after the orthogonal signal correction is superior to the other pretreatment methods. Therefore, pretreatment with the noise reduction performance is superior to pretreatment with the sharpening performance which illustrate that the random noise of the spectrum is a major influence factor on model accuracy. The results provide an effective pretreatment method for establishing a stable near infrared spectroscopy fast quantitative model for alcohol content of rice wine, and it also offers a reference for rice wine quality online fast detection.%本文以黄酒近红外透射光谱为研究对象,探讨光谱预处理方法对黄酒酒精度快速定量检测模型的影响,对比分析了采用平滑、一阶导数、二阶导数、多元散射校正、标准正态变换结合去势等五种传统方法和小波变换、傅里叶变换、正交信号校正等三种新方法预处理后光谱的偏最小二乘建模效果.结果表明,正交信号校正处理后,模型交叉验证R和RMSECV分别为0.998和0.135 7,优于其他预处理方法.具有降噪性能的算法预处理性能要优于具有锐化性能的算法,说明了光谱中的随机噪声是影响建模精度的主要因素.研究结果为建立稳健的黄酒酒精度近红外快速检测模型提供了有效的预处理方法,对黄酒品质在线快速检测具有参考价值.
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