Chemometric analysis and NIR spectroscopy to evaluate odorous impact during the composting of different raw materials

被引:82
作者
Toledo, M. [1 ]
Gutierrez, M. C. [1 ]
Siles, J. A. [1 ]
Garcia-Olmo, J. [2 ]
Martin, M. A. [1 ]
机构
[1] Univ Cordoba, Dept Inorgan Chem & Chem Engn, Campus Univ Rabanales,Carretera N-IV,Km 396, E-14071 Cordoba, Spain
[2] Univ Cordoba, NIR MIR Spect Unit, Cent Serv Res Support, E-14071 Cordoba, Spain
关键词
Dynamic olfactometry; Odor emission; NIR spectroscopy; Principal component analysis; Multivariate regression; DYNAMIC OLFACTOMETRY; ANALYSIS PCA; WASTE; SLUDGE; BIODEGRADABILITY; TECHNOLOGIES; EFFICIENCY;
D O I
10.1016/j.jclepro.2017.08.163
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study evaluated odor generated during the first stage of the composting process in a dynamic respirometer using different raw materials such as the organic fraction of municipal solid waste (OFMSW), a mixture of this organic fraction with orange peel waste (OFMSW-OPW), sewage sludge with bulking agent (SL) and a mixture of strawberry extrudate, fish waste, sewage sludge and bulking agent (SFWSL). The combination of near infrared reflectance (NIR) spectroscopy and chemometric analysis is proposed to correlate the chemical composition and the operational variables of each raw material to odor generated during the composting process. The operational variables temperature, dynamic respirometric index (DRI), airflow (Q), odor concentration (OC) and odor emission rate (OER) were monitored. Adequate linear correlations were obtained between temperature and DRI for each compostable substrate within a confidence interval of 10% and 30%. Operational variables were statistically analyzed by principal component analysis with 87% of total variance explained and from which the substrates were clearly grouped. Near infrared reflectance spectroscopy provided the chemical composition of each raw material, and was found to be an advantageous technique to predict the relationship between odor emissions and absorption bands. Odor emissions were also predicted from the operational variables by multivariate regression, with temperature and DRI being the most influential variables. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:154 / 162
页数:9
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