PMID- 22710429
OWN - NLM
STAT- MEDLINE
DCOM- 20121015
LR  - 20191210
IS  - 1943-3530 (Electronic)
IS  - 0003-7028 (Linking)
VI  - 66
IP  - 7
DP  - 2012 Jul
TI  - Fluorescence spectral analysis for the discrimination of complex, similar 
      mixtures with the aid of chemometrics.
PG  - 810-9
LID - 10.1366/12-06595 [doi]
AB  - An analytical method for the classification of complex real-world samples was 
      researched and developed with the use of excitation-emission fluorescence matrix 
      (EEFM) spectroscopy, using the medicinal herbs, Rhizoma corydalis decumbentis 
      (RCD) and Rhizoma corydalis (RC) as example samples. The data set was obtained 
      from various authentic RCD-A and RC-A, adulterated AD, and commercial RCD-C and 
      RC-C samples. The spectra (range: lambda(ex) = 215 approximately 395 nm and lambda(em) = 290 approximately 560 nm), 
      arranged in two- and three-way data matrix formats, were processed using 
      principal component analysis (PCA) and parallel factor analysis (PARAFAC) to 
      produce two-dimensional component-by-component plots for qualitative data 
      classification. The RCD-A and RC-A object groups were clearly discriminated, but 
      the AD and the RCD-C as well as RC-C samples were less well separated. PARAFAC 
      analysis produced somewhat better discrimination, and loadings plots revealed the 
      presence of the marker compound Protopine-a strongly fluorescing substance-as 
      well as at least two other unidentified fluorescent components. Classification 
      performance of the common K-nearest neighbors (KNN) and linear discrimination 
      analysis (LDA) methods was relatively poor when compared with that of the back 
      propagation- and radial basis function-artificial neural networks (BP-ANN and 
      RBF-ANN) models on the basis of two- and three-way formatted data. The best 
      results were obtained with the three-way fingerprints and the RBF-ANN model. 
      Subsequently, the quality of the commercial samples (RCD-C and RC-C) was 
      classified on the best optimized RBF-ANN model. Thus, EEFM spectroscopy, which 
      provides three-way measured data, is potentially a powerful analytical technique 
      for the analysis of complex real-world substances provided the classification is 
      performed by the RBF-ANN or similar ANN methods.
FAU - Ni, Yongnian
AU  - Ni Y
AD  - State Key Laboratory of Food Science and Technology, Nanchang University, China. 
      ynni@ncu.edu.cn
FAU - Lai, Yanhua
AU  - Lai Y
FAU - Kokot, Serge
AU  - Kokot S
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20120615
PL  - United States
TA  - Appl Spectrosc
JT  - Applied spectroscopy
JID - 0372406
RN  - 0 (Complex Mixtures)
RN  - 0 (Organic Chemicals)
RN  - 0 (Plant Extracts)
SB  - IM
MH  - Complex Mixtures/analysis/*chemistry/classification
MH  - Corydalis/chemistry
MH  - *Models, Chemical
MH  - Neural Networks, Computer
MH  - Organic Chemicals/analysis/chemistry/classification
MH  - Plant Extracts/chemistry
MH  - Principal Component Analysis
MH  - Spectrometry, Fluorescence/*methods
EDAT- 2012/06/20 06:00
MHDA- 2012/10/16 06:00
CRDT- 2012/06/20 06:00
PHST- 2012/06/20 06:00 [entrez]
PHST- 2012/06/20 06:00 [pubmed]
PHST- 2012/10/16 06:00 [medline]
AID - 660716 [pii]
AID - 10.1366/12-06595 [doi]
PST - ppublish
SO  - Appl Spectrosc. 2012 Jul;66(7):810-9. doi: 10.1366/12-06595. Epub 2012 Jun 15.