Gasoline classification by source and type based on near infrared (NIR) spectroscopy data
作者： Roman M. Balabin , Ravilya Z. Safieva
DOI: 10.1016/J.FUEL.2007.07.018
关键词:
摘要: Abstract In this paper, we have tried to classify 382 samples of gasoline and fractions by source (refinery or process) type. Three sets near infrared (NIR) spectra (450, 415, 345 spectra) were used for classification gasolines into 3 6 classes. We compared the abilities three different methods: linear discriminant analysis (LDA), soft independent modeling class analogy (SIMCA), multilayer perceptron (MLP) – build effective robust model. all cases NIR spectroscopy was found be purposes. MLP technique most method model building.
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