摘要
以黄酒酿造用大米(粳米、糯米、籼米)为对象,运用漫反射傅里叶变换红外光谱(DR-FTIR)与软独立模式分类(SIMCA)相结合的方法,对粳米、糯米、籼米进行模式识别研究,并建立相应的识别模型。结果显示,以1000～1750cm-1为特征波长,经Savitzky-Golay平滑、自动基线校正及标准矢量归一化(SNV)预处理后,采用交互留一验证法建立的3种大米的SIMCA识别模型,在α=0.05显著水平下,对预测集样本的识别率和拒绝率均可达100%。表明DR-FTIR与SIMCA相结合的方法可以成为黄酒酿造用大米品种模式识别的有效方法。
Diffuse reflectance Fourier transform infrared spectroscopy（DR-FTIR） combined with soft independent modeling of class analogy（SIMCA） was used to study pattern recognition of sticky rice,long-shaped rice and polished round-grained rice,and the corresponding models were developed by leave-one-out cross-validation based on such pre-treatments as ninepoint Savitzky-Golay smoothing,baseline correction and standard normal variate（SNV） normalization in the wavelength range of 1000-1750 cm-1.The results showed that all the SIMCA models were valid,and the identification rates and rejection rates of the prediction set samples were both 100% under the significance level of α = 0.05 indicating DR-FTIR combined with SIMCA to be an effective strategy for pattern recognition of rice varieties for Chinese yellow wine making.
出处
《食品科学》
CAS
CSCD
北大核心
2013年第16期284-287,共4页
Food Science
基金
绍兴市院校科技合作项目(2010708)
关键词
黄酒
大米
漫反射傅里叶变换红外光谱
软独立模式分类
主成分分析
Chinese yellow wine
rice
diffuse reflectance Fourier transform infrared spectroscopy（DR-FTIR）
soft independent modeling of class analogy（SIMCA）
principal component analysis（PCA）
