PMID- 22177063
OWN - NLM
STAT- MEDLINE
DCOM- 20120409
LR  - 20151119
IS  - 1873-4324 (Electronic)
IS  - 0003-2670 (Linking)
VI  - 712
DP  - 2012 Jan 27
TI  - One- and two-dimensional gas chromatography-mass spectrometry and high 
      performance liquid chromatography-diode-array detector fingerprints of complex 
      substances: a comparison of classification performance of similar, complex 
      Rhizoma Curcumae samples with the aid of chemometrics.
PG  - 37-44
LID - 10.1016/j.aca.2011.11.010 [doi]
AB  - Many complex natural or synthetic products are analysed either by the GC-MS (gas 
      chromatography-mass spectrometry) or HPLC-DAD (high performance liquid 
      chromatography-diode-array detector) technique, each of which produces a 
      one-dimensional fingerprint for a given sample. This may be used for 
      classification of different batches of a product. GC-MS and HPLC-DAD analyses of 
      complex, similar substances represented by the three common types of the TCM 
      (traditional Chinese medicine), Rhizoma Curcumae were analysed in the form of 
      one- and two-dimensional matrices firstly with the use of PCA (Principal 
      component analysis), which showed a reasonable separation of the samples for each 
      technique. However, the separation patterns were rather different for each 
      analytical method, and PCA of the combined data matrix showed improved 
      discrimination of the three types of object; close associations between the GC-MS 
      and HPLC-DAD variables were observed. LDA (linear discriminant analysis), BP-ANN 
      (back propagation-artificial neural networks) and LS-SVM (least squares-support 
      vector machine) chemometrics methods were then applied to classify the training 
      and prediction sets. For one-dimensional matrices, all training models indicated 
      that several samples would be misclassified; the same was observed for each 
      prediction set. However, by comparison, in the analysis of the combined matrix, 
      all models gave 100% classification with the training set, and the LS-SVM 
      calibration also produced a 100% result for prediction, with the BP-ANN 
      calibration closely behind. This has important implications for comparing complex 
      substances such as the TCMs because clearly the one-dimensional data matrices 
      alone produce inferior results for training and prediction as compared to the 
      combined data matrix models. Thus, product samples may be misclassified with the 
      use of the one-dimensional data because of insufficient information.
CI  - Copyright (c) 2011 Elsevier B.V. All rights reserved.
FAU - Ni, Yongnian
AU  - Ni Y
AD  - State Key Laboratory of Food Science and Technology, Nanchang University, 
      Nanchang, China.
FAU - Mei, Minghua
AU  - Mei M
FAU - Kokot, Serge
AU  - Kokot S
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20111115
PL  - Netherlands
TA  - Anal Chim Acta
JT  - Analytica chimica acta
JID - 0370534
SB  - IM
MH  - *Chromatography, High Pressure Liquid
MH  - Curcuma/*chemistry
MH  - Discriminant Analysis
MH  - *Gas Chromatography-Mass Spectrometry
MH  - Least-Squares Analysis
MH  - Medicine, Chinese Traditional
MH  - Principal Component Analysis
MH  - Rhizome/chemistry
MH  - Support Vector Machine
EDAT- 2011/12/20 06:00
MHDA- 2012/04/10 06:00
CRDT- 2011/12/20 06:00
PHST- 2011/09/15 00:00 [received]
PHST- 2011/10/31 00:00 [revised]
PHST- 2011/11/02 00:00 [accepted]
PHST- 2011/12/20 06:00 [entrez]
PHST- 2011/12/20 06:00 [pubmed]
PHST- 2012/04/10 06:00 [medline]
AID - S0003-2670(11)01484-X [pii]
AID - 10.1016/j.aca.2011.11.010 [doi]
PST - ppublish
SO  - Anal Chim Acta. 2012 Jan 27;712:37-44. doi: 10.1016/j.aca.2011.11.010. Epub 2011 
      Nov 15.