食品科学

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黄酒酿造用大米品种的模式识别研究

黄桂东,毛 健,姬中伟,傅健伟,邹慧君   

  1. 1.江南大学食品学院,江苏 无锡 214122;2.国家黄酒工程技术研究中心,浙江 绍兴 312000
  • 出版日期:2013-08-25 发布日期:2013-09-03

Pattern Recognition of Rice Varieties for Chinese Yellow Wine Making

HUANG Gui-dong,MAO Jian,JI Zhong-wei,FU Jian-wei,ZOU Hui-jun   

  1. 1. School of Food Science and Technology, Jiangnan University, Wuxi 214122, China;
    2. National Engineering Research Center of Chinese Rice Wine, Shaoxing 312000, China
  • Online:2013-08-25 Published:2013-09-03

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。

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

Abstract:

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.

Key words: Chinese yellow wine, rice, diffuse reflectance Fourier transform infrared spectroscopy (DR-FTIR), soft independent modeling of class analogy (SIMCA), principal component analysis (PCA)