The studies of the applicability of headspace sampling coupled to mass spectrometry in discrimination of differently aged beers are reported. The entire mass spectra of headspace components provided “fingerprints” of the beer samples and were used for classification purposes. PCA analysis of the mass spectra revealed clustering of samples according to the aging procedure, allowing automated sample classification by appropriate chemometric methods.
In this paper, chemometric approaches based on cluster analysis, classical and robust principal component analysis were employed to identify water quality in Daya Bay (DYB), China. The results show that these approaches divided water quality in DYB into two groups: stations S3, S8, S10 and S11 belong to cluster A, which lie in Dapeng Cove, Aotou Harbor and the north-eastern part of DYB, where water quality is related mainly to anthropogenic activities. The other stations belong to cluster B, which lie in the southern, central and eastern parts of DYB, where the quality is related mainly to water exchange with the South China Sea. Cluster analysis yields good results as a first exploratory method for evaluating spatial difference, but it fails to demonstrate the relationship between variables and environmental quality on the one hand and the untreated data on the other. However, with the aid of suitable chemometric approaches, the relationship between samples or variables can be investigated. Classical and robust principal component analysis can provide a visual aid for identifying the water environment in DYB, and then extracting specific information about relationships between variables and spatial variation trends in water quality.
The process of plant selection by insects is mediated by repellents and attractants. Several compounds may be involved in this interaction. Thus intraspecific variation of the compounds concentration play an important role in the herbivory. The best tool for the characteristic of this variation is chemometrics. The strategy of the analysis with the use of literature data on terpenes and sesquiterpenes variations in Pinus caribaea needles in relation to Atta laevigata herbivory is exemplified herein. Simple cluster analysis and principal components analysis were used for the data study. Two factors were found to be sufficient to describe total variation in more than 90%. Factor 1 is responsible for repellent properties. From factor loading, the relevant chemical compounds were identified.