Application of similarity coefficients to SIFT-MS data for livestock emission characterization

• Published in: Biosystems engineering Vol. 114; no. 1; pp. 44 - 54
• Main Authors: Heynderickx, Philippe M., Van Huffel, Katrijn, Dewulf, Jo, Van Langenhove, Herman
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.01.2013; Elsevier
• Subjects: Agricultural machinery and engineering; Agronomy. Soil science and plant productions; Biological and medical sciences; broiler chickens; data collection; emissions; engineering; farming systems; Fundamental and applied biological sciences. Psychology; Generalities. Biometrics, experimentation. Remote sensing; ions; mass spectrometry; oxygen; swine; Characterization; Bioengineering; Engineering; Coefficient; Similarity; Emission; Agriculture; Livestock; Data; Application
• ISSN: 1537-5110; 1537-5129
• DOI: 10.1016/j.biosystemseng.2012.10.004

Animal farming emissions from pig and broiler chicken livestock activities were analyzed by means of selected ion flow tube mass spectrometry (SIFT-MS) using full mass scans in the m/z range 15–250 with H3O+, NO+ and O2+ as precursor ions. The generated set of data was used to investigate the similarity between samples using raw experimental SIFT-MS data by means of similarity coefficients, which was the main goal of this paper. The so-called ‘Yule coefficient’ is chosen as the best tool to identify the similarity between samples. The proposed methodology is successfully applied on an artificially constructed data set as well as on data from different experimental sampling campaigns. From an engineering point of view, it is considered to be a promising technique to evaluate whether a sample corresponds to one of the catalogued library data sets. ► Selected ion flow tube mass spectrometry (SIFT-MS) is used as fast and convenient measuring technique. ► The use of an algebraic coefficient as singular descriptor for similarity between experimental SIFT-MS data. ► The so-called ‘Yule coefficient’ is chosen as the best tool to identify similarity between experimental SIFT-MS data.