Chemical identification multivariate methods

To base classification and possible identification upon the whole chemical information contained in the pollen, we used a multivariate method. For an... 

In an attempt to provide this focus, forty-seven active receptor model users from government, university, consulting and industry met for 2 1/2 days in February 1980 it. They addressed the models and the information required to use them in six separate task forces 1) Chemical Element Balance Receptor Models, 2) Multivariate Receptor Models, 3) Microscopic Identification Receptor Models, 4) Field Study Design and Data Management, 5) Source Characterization, and 6) Analytical Methods. The objectives of these interrelated task forces were to ... 

For phenolics in fruit by-products such as apple seed, peel, cortex, and pomace, an HPLC method was also utilized. Apple waste is considered a potential source of specialty chemicals (58,62), and its quantitative polyphenol profile may be useful in apple cultivars for classification and identification. Chlorogenic acid and coumaroylquinic acids and phloridzin are known to be major phenolics in apple juice (53). However, in contrast to apple polyphenolics, HPLC with a 70% aqueous acetone extract of apple seeds showed that phloridzin alone accounts for ca. 75% of the total apple seed polyphenolics (62). Besides phloridzin, 13 other phenolics were identified by gradient HPLC/PDA on LiChrospher 100 RP-18 from apple seed (62). The HPLC technique was also able to provide polyphenol profiles in the peel and cortex of the apple to be used to characterize apple cultivars by multivariate statistical techniques (63). Phenolic compounds in the epidermis zone, parenchyma zone, core zone, and seeds of French cider apple varieties are also determined by HPLC (56). Three successive solvent extractions (hexane, methanol, aqueous acetone), binary HPLC gradient using (a) aqueous acetic acid, 2.5%, v/v, and (b) acetonitrile fol-... 

Physical and chemical effects can be combined for identification as sample matrix effects. Matrix effects alter the slope of calibration curves, while spectral interferences cause parallel shifts in the calibration curve. The water-methanol data set contains matrix effects stemming from chemical interferences. As already noted in Section 5.2, using the univariate calibration defined in Equation 5.4 requires an interference-free wavelength. Going to multivariate models can correct for spectral interferences and some matrix effects. The standard addition method described in Section 5.7 can be used in some cases to correct for matrix effects. Severe matrix effects can cause nonlinear responses requiring a nonlinear modeling method. 

MacGregor, J.F., T. Kourti J. V. Kresta (1991), Multivariate identification a study of several methods , IFAC Advanced Control of Chemical Processes, Toulouse, Prance, 101-107. 

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