文摘
Chemometric analysis of chromatograms plays a fundamental role in characterization of foods or indetection of adulteration. Data for multivariate analysis of chromatographic profiles are usually obtainedby visual matching (VM) of peaks, the identities of which, as for peptide profiles from cheese extracts,are often unknown. To avoid the main disadvantages of VM, which is subjective and time-consuming,a novel approach was developed. Fuzzy logic was employed to handle in a systematic way uncertaintyin the position of peptide peaks, and chromatograms were processed by a rule-based membershipfunction. Processed data consisted of classes of retention time wherein peak heights were accumulatedby using the distance from the center of the class as a weight. The novel approach (fuzzy approach,FA) was compared with VM by using a real data set and by performing multivariate descriptivestatistical techniques (principal component analysis, multidimensional scaling, and nonhierarchicalcluster analysis). FA provided a fast, reliable, and objective alternative to VM and could be successfullyapplied for chemometric analysis of chromatographic profiles whenever knowledge of the identity ofpeaks is lacking or unnecessary.Keywords: Cheese; proteolysis; peptide profiles; fuzzy sets; chemometric analysis; PCA; MDS