PMID- 21397073
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20110701
LR  - 20110314
IS  - 1873-4324 (Electronic)
IS  - 0003-2670 (Linking)
VI  - 689
IP  - 2
DP  - 2011 Mar 18
TI  - Biodiesel classification by base stock type (vegetable oil) using near infrared 
      spectroscopy data.
PG  - 190-7
LID - 10.1016/j.aca.2011.01.041 [doi]
AB  - The use of biofuels, such as bioethanol or biodiesel, has rapidly increased in 
      the last few years. Near infrared (near-IR, NIR, or NIRS) spectroscopy 
      (>4000cm(-1)) has previously been reported as a cheap and fast alternative for 
      biodiesel quality control when compared with infrared, Raman, or nuclear magnetic 
      resonance (NMR) methods; in addition, NIR can easily be done in real time 
      (on-line). In this proof-of-principle paper, we attempt to find a correlation 
      between the near infrared spectrum of a biodiesel sample and its base stock. This 
      correlation is used to classify fuel samples into 10 groups according to their 
      origin (vegetable oil): sunflower, coconut, palm, soy/soya, cottonseed, castor, 
      Jatropha, etc. Principal component analysis (PCA) is used for outlier detection 
      and dimensionality reduction of the NIR spectral data. Four different 
      multivariate data analysis techniques are used to solve the classification 
      problem, including regularized discriminant analysis (RDA), partial least squares 
      method/projection on latent structures (PLS-DA), K-nearest neighbors (KNN) 
      technique, and support vector machines (SVMs). Classifying biodiesel by feedstock 
      (base stock) type can be successfully solved with modern machine learning 
      techniques and NIR spectroscopy data. KNN and SVM methods were found to be highly 
      effective for biodiesel classification by feedstock oil type. A classification 
      error (E) of less than 5% can be reached using an SVM-based approach. If 
      computational time is an important consideration, the KNN technique (E=6.2%) can 
      be recommended for practical (industrial) implementation. Comparison with 
      gasoline and motor oil data shows the relative simplicity of this methodology for 
      biodiesel classification.
CI  - Copyright (c) 2011 Elsevier B.V. All rights reserved.
FAU - Balabin, Roman M
AU  - Balabin RM
AD  - Department of Chemistry and Applied Biosciences, ETH Zurich, Switzerland. 
      balabin@org.chem.ethz.ch
FAU - Safieva, Ravilya Z
AU  - Safieva RZ
LA  - eng
PT  - Journal Article
DEP - 20110126
PL  - Netherlands
TA  - Anal Chim Acta
JT  - Analytica chimica acta
JID - 0370534
EDAT- 2011/03/15 06:00
MHDA- 2011/03/15 06:01
CRDT- 2011/03/15 06:00
PHST- 2010/10/07 00:00 [received]
PHST- 2011/01/17 00:00 [revised]
PHST- 2011/01/19 00:00 [accepted]
PHST- 2011/03/15 06:00 [entrez]
PHST- 2011/03/15 06:00 [pubmed]
PHST- 2011/03/15 06:01 [medline]
AID - S0003-2670(11)00129-2 [pii]
AID - 10.1016/j.aca.2011.01.041 [doi]
PST - ppublish
SO  - Anal Chim Acta. 2011 Mar 18;689(2):190-7. doi: 10.1016/j.aca.2011.01.041. Epub 
      2011 Jan 26.