title: Quantitative determination of Freon gas using an electronic nose.
abstract: This paper reports a quantitative electronic nose (enose) for the quantitative determination of Freon gas within the concentration range 0-1000 ppm in the presence of interfering gases such as water, lubricant and petrol vapours. This quantitative enose is a new type of Freon detection system, composed of an array of four sensors. The artificial neural network (ANN) and fuzzy logic type of ANN (FNN), in combination with the relative error concept in analytical chemistry, are integrated for both quantification and discrimination. The predicted results are satisfied with a pass rate of > 80% within the permitted relative errors. The results show that the Freon enose developed in this study is reliable for both the qualitative and quantitative determination of Freon gas and exhibits the merits of high sensitivity, anti-interference and accuracy.
authors.affiliation: Department of Chemistry, Fudan University, Shanghai, China.
authors.forename: Lijing
authors.initials: L
authors.lastname: Wen
authors.forename: Liangyi
authors.initials: L
authors.lastname: Zhang
authors.forename: Feng
authors.initials: F
authors.lastname: Zhou
authors.forename: Yu
authors.initials: Y
authors.lastname: Lu
authors.forename: Pengyuan
authors.initials: P
authors.lastname: Yang
mesh_terms: D000389:Air Conditioning; D000393:Air Pollutants / Q000032:analysis*; D001322:Autoanalysis; D005617:Chlorofluorocarbons, Methane / Q000032:analysis*; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer
chemical_list: D000393:Air Pollutants; D005617:Chlorofluorocarbons, Methane