Characterization of biofuel mixtures by online near infrared spectroscopy

被引:0
作者
Geladi, P [1 ]
Lillhonga, T [1 ]
Reuter, L [1 ]
机构
[1] Swedish Univ Agr Sci, Unit Biomass Technol & Chem, S-75007 Uppsala, Sweden
来源
Progress in Chemometrics Research | 2005年
关键词
process chemometrics; principal component analysis; partial least squares regression; process analytical chemistry; near infrared spectroscopy; mixture triangle; validation;
D O I
暂无
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Off-line measurements of NIR spectra of fuel mixtures (coal, peat, biofuel) were carried out. Eleven mixtures were made according to a mixture triangle. For each mixture ten replicate NIR measurements were made with an InGaAs spectrometer measuring between 911 and 1709 nm in 128 bands. The reference measurements were moisture, energy and ash, all carried out according to laboratory standards. All these reference measurements were done in triplicate. The data analysis consists of a PCA analysis to explain the structure of the spectra. Different PLS models for calibration were tested. Some important conclusions are reported. The replicates allow the determination of bias and standard deviation for the obtained predictions. Energy and moisture content can be measured to a reasonable precision. Ash content can also be measured, but with larger errors.
引用
收藏
页码:193 / 208
页数:16
相关论文
共 13 条
[1]  
Box GEP, 1997, STAT CONTROL MONITOR
[2]   Chemometrics in spectroscopy. Part 1. Classical chemometrics [J].
Geladi, P .
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY, 2003, 58 (05) :767-782
[3]   Some recent trends in the calibration literature [J].
Geladi, P .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2002, 60 (1-2) :211-224
[4]  
GELADI P, UNPUB SPECTROCHIMI B
[5]  
Jackson JE, 1991, A user's guide to principal components
[6]   PROCESS ANALYSIS, MONITORING AND DIAGNOSIS, USING MULTIVARIATE PROJECTION METHODS [J].
KOURTI, T ;
MACGREGOR, JF .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1995, 28 (01) :3-21
[7]   Multivariate SPC methods for process and product monitoring [J].
Kourti, T ;
MacGregor, JF .
JOURNAL OF QUALITY TECHNOLOGY, 1996, 28 (04) :409-428
[8]  
LEE R, 2001, MULTIVARIATE STAT PR
[9]  
McLennan F., 1995, PROCESS ANAL CHEM
[10]  
Naes T., 2002, USER FRIENDLY GUIDE