PMID- 18970766
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20121002
LR  - 20081030
IS  - 1873-3573 (Electronic)
IS  - 0039-9140 (Linking)
VI  - 70
IP  - 2
DP  - 2006 Sep 15
TI  - Authentication of vegetable oils on the basis of their physico-chemical 
      properties with the aid of chemometrics.
PG  - 293-300
LID - 10.1016/j.talanta.2006.02.037 [doi]
AB  - In food production, reliable analytical methods for confirmation of purity or 
      degree of spoilage are required by growers, food quality assessors, processors, 
      and consumers. Seven parameters of physico-chemical properties, such as acid 
      number, colority, density, refractive index, moisture and volatility, 
      saponification value and peroxide value, were measured for quality and 
      adulterated soybean, as well as quality and rancid rapeseed oils. Chemometrics 
      methods were then applied for qualitative and quantitative discrimination and 
      prediction of the oils by methods such exploratory principal component analysis 
      (PCA), partial least squares (PLS), radial basis function-artificial neural 
      networks (RBF-ANN), and multi-criteria decision making methods (MCDM), PROMETHEE 
      and GAIA. In general, the soybean and rapeseed oils were discriminated by PCA, 
      and the two spoilt oils behaved differently with the rancid rapeseed samples 
      exhibiting more object scatter on the PC-scores plot, than the adulterated 
      soybean oil. For the PLS and RBF-ANN prediction methods, suitable training models 
      were devised, which were able to predict satisfactorily the category of the four 
      different oil samples in the verification set. Rank ordering with the use of MCDM 
      models indicated that the oil types can be discriminated on the PROMETHEE II 
      scale. For the first time, it was demonstrated how ranking of oil objects with 
      the use of PROMETHEE and GAIA could be utilized as a versatile indicator of 
      quality performance of products on the basis of a standard selected by the 
      stakeholder. In principle, this approach provides a very flexible method for 
      assessment of product quality directly from the measured data.
FAU - Zhang, Guowen
AU  - Zhang G
AD  - Department of Chemistry, Nanchang University, Nanchang, Jiangxi 330047, China; 
      The Key Laboratory of Food Science of Ministry of Education, Nanchang University, 
      Nanchang, Jiangxi 330047, China.
FAU - Ni, Yongnian
AU  - Ni Y
FAU - Churchill, Jane
AU  - Churchill J
FAU - Kokot, Serge
AU  - Kokot S
LA  - eng
PT  - Journal Article
DEP - 20060329
PL  - Netherlands
TA  - Talanta
JT  - Talanta
JID - 2984816R
EDAT- 2008/10/31 09:00
MHDA- 2008/10/31 09:01
CRDT- 2008/10/31 09:00
PHST- 2005/12/23 00:00 [received]
PHST- 2006/02/02 00:00 [revised]
PHST- 2006/02/14 00:00 [accepted]
PHST- 2008/10/31 09:00 [pubmed]
PHST- 2008/10/31 09:01 [medline]
PHST- 2008/10/31 09:00 [entrez]
AID - S0039-9140(06)00168-8 [pii]
AID - 10.1016/j.talanta.2006.02.037 [doi]
PST - ppublish
SO  - Talanta. 2006 Sep 15;70(2):293-300. doi: 10.1016/j.talanta.2006.02.037. Epub 2006 
      Mar 29.