TY - JOUR
T1 - Comprehensive analysis of chromatographic data by using PARAFAC2 and principal components analysis
AU - Amigo Rubio, Jose Manuel
AU - Popielarz, Marta Jolanta
AU - Callejón, Raquel M.
AU - Morales, Maria L.
AU - Troncoso, Ana M.
AU - Petersen, Mikael Agerlin
AU - Toldam-Andersen, Torben Bo
PY - 2010/6
Y1 - 2010/6
N2 - The most straightforward method to analyze an obtained GC-MS dataset is to integrate those peaks that can be identified by their MS profile and to perform a Principal Component Analysis (PCA). This procedure has some important drawbacks, like baseline drifts being scarcely considered or the fact that integration boundaries are not always well defined (long tails, co-eluted peaks, etc.). To improve the methodology, and therefore, the chromatographic data analysis, this work proposes the modeling of the raw dataset by using PARAFAC2 algorithm in selected areas of the GC profile and using the obtained well-resolved chromatographic profiles to develop a further PCA model. With this working method, not only the problems arising from instrumental artifacts are overcome, but also the detection of new analytes is achieved as well as better understanding of the studied dataset is obtained. As a positive consequence of using the proposed working method human time and work are saved. To exemplify this methodology the aroma profile of 36 apples being ripened were studied. The benefits of the proposed methodology (PARAFAC2. +. PCA) are shown in a practitioner perspective, being able to extrapolate the conclusions obtained here to other hyphenated chromatographic datasets.
AB - The most straightforward method to analyze an obtained GC-MS dataset is to integrate those peaks that can be identified by their MS profile and to perform a Principal Component Analysis (PCA). This procedure has some important drawbacks, like baseline drifts being scarcely considered or the fact that integration boundaries are not always well defined (long tails, co-eluted peaks, etc.). To improve the methodology, and therefore, the chromatographic data analysis, this work proposes the modeling of the raw dataset by using PARAFAC2 algorithm in selected areas of the GC profile and using the obtained well-resolved chromatographic profiles to develop a further PCA model. With this working method, not only the problems arising from instrumental artifacts are overcome, but also the detection of new analytes is achieved as well as better understanding of the studied dataset is obtained. As a positive consequence of using the proposed working method human time and work are saved. To exemplify this methodology the aroma profile of 36 apples being ripened were studied. The benefits of the proposed methodology (PARAFAC2. +. PCA) are shown in a practitioner perspective, being able to extrapolate the conclusions obtained here to other hyphenated chromatographic datasets.
U2 - 10.1016/j.chroma.2010.04.042
DO - 10.1016/j.chroma.2010.04.042
M3 - Journal article
C2 - 20462590
SN - 0301-4770
VL - 1217
SP - 4422
EP - 4429
JO - Journal of Chromatography
JF - Journal of Chromatography
IS - 26
ER -