基于规则的深度分类器结合近红外光谱技术判别烟用香精香料
Analytical Instrumentation（2019）
Abstract
采用近红外光谱技术对57种烟用香精香料进行分类研究,用SIMCA算法及基于规则深度分类器两种模式识别方法对715个样品光谱数据进行分类判别.异常光谱采用杠杆值法进行剔除,用判别准确率来评价分类模型效果.结果 表明:基于规则深度分类器的识别准确率优于SIMCA算法,同时比较预处理方法对识别准确率的影响,得出对于液体样品,采用漫透反射附件的近红外光谱技术对识别准确度有较大影响的结论.主要原因在于产生的光谱图存在基线漂移现象,一阶导数可提升模型识别准确率,其校正集准确率与预测集准确率分别为98.74％与98.07％,可以满足香精香料现场分析的需要.
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