Spectroscopic imaging and chemometrics: a powerful combination for global and local sample analysis

被引:156
作者
de Juan, A [1 ]
Tauler, R
Dyson, R
Marcolli, C
Rault, M
Maeder, M
机构
[1] Univ Barcelona, Dept Analyt Chem, Chemometr Grp, E-08028 Barcelona, Spain
[2] Solvias AG, Phys Chem, CH-4002 Basel, Switzerland
[3] Univ Newcastle, Dept Chem, Callaghan, NSW 2308, Australia
关键词
D O I
10.1016/S0165-9936(04)00101-3
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Merging spectroscopic imaging and chemometrics enhances the outcomes of instrumental technology and data analysis. Multivariate exploratory and resolution methods can be adapted to image analysis and provide global and local information about pure compounds in an imaged sample. Knowing in detail how the chemical compounds are distributed over the scanned surface gives valuable information about essential issues in the manufacture and the characterization of products, such as evenness of composition and, therefore, homogeneity of the sample. The power to detect and to locate impurities is also greatly enhanced because these unwanted compounds could show locally large concentrations (and signals), even though their abundance on the surface is very low. The capabilities of this combination are shown in an example of pharmaceutical product control, where analysis of the end product requires chemical characterization and quantitative information at global and local levels. The approach used and the kind of information obtained is general and can be applied to the analysis of images in other fields. (C) 2003 Elsevier Ltd. All rights reserved.
引用
收藏
页码:70 / 79
页数:10
相关论文
共 34 条
[1]   Rapid analysis of Raman image data using two-way multivariate curve resolution [J].
Andrew, JJ ;
Hancewicz, TM .
APPLIED SPECTROSCOPY, 1998, 52 (06) :797-807
[2]   Chemometric labeling of cereal tissues in multichannel fluorescence microscopy images using discriminant analysis [J].
Baldwin, PM ;
Bertrand, D ;
Novales, B ;
Bouchet, B ;
Collobert, G ;
Gallant, DJ .
ANALYTICAL CHEMISTRY, 1997, 69 (21) :4339-4348
[3]   Principal component analysis of visible and near-infrared multispectral images of works of art [J].
Baronti, S ;
Casini, A ;
Lotti, F ;
Porcinai, S .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1997, 39 (01) :103-114
[4]   Self-modeling mixture analysis of Raman microspectrometric investigations of dust emitted by lead and zinc smelters [J].
Batonneau, Y ;
Laureyns, J ;
Merlin, JC ;
Brémard, C .
ANALYTICA CHIMICA ACTA, 2001, 446 (1-2) :23-37
[5]   MultiSpec - a tool for multispectral-hyperspectral image data analysis [J].
Biehl, L ;
Landgrebe, D .
COMPUTERS & GEOSCIENCES, 2002, 28 (10) :1153-1159
[6]  
Bro R, 1998, J CHEMOMETR, V12, P223, DOI 10.1002/(SICI)1099-128X(199807/08)12:4<223::AID-CEM511>3.3.CO
[7]  
2-U
[8]  
Bro R, 1997, J CHEMOMETR, V11, P393, DOI 10.1002/(SICI)1099-128X(199709/10)11:5<393::AID-CEM483>3.3.CO
[9]  
2-C
[10]   Chemical image fusion. The synergy of FT-NIR and Raman mapping microscopy to enable a move complete visualization of pharmaceutical formulations [J].
Clarke, FC ;
Jamieson, MJ ;
Clark, DA ;
Hammond, SV ;
Jee, RD ;
Moffat, AC .
ANALYTICAL CHEMISTRY, 2001, 73 (10) :2213-2220