Flavonoids, phenolic acids and abscisic acid in Australian and New Zealand Leptospermum honeys

Flavonoids, phenolic acids and abscisic acid of Australian and New Zealand Leptospermum honeys were analyzed by HPLC. Fifteen flavonoids were isolated in Australian jelly bush honey (Leptospermum polygalifolium), with an average content of 2.22 mg/100 g honey. Myricetin (3,5,7,3',4',5'-hexahydroxyflavone), luteolin (5,7,3',4'-tetrahydroxyflavone) and tricetin (5,7,3',4',5'-pentahydroxyflavone) were the main flavonoids identified. The mean content of total phenolic acids in jelly bush honey was 5.14 mg/100 g honey, with gallic and coumaric acids as the potential phenolic acids. Abscisic acid was quantified as twice the amount (11.6 mg/100 g honey) of the phenolic acids in this honey. The flavonoid profile mainly consisted of quercetin (3,5,7,3',4'-pentahydroxyflavone), isorhamnetin (3,5,7,4'-tetrahydroxyflavone 3'-methyl ethyl), chrysin (5,7-dihydroxyflavone), luteolin and an unknown flavanone in New Zealand manuka (Leptospermum scoparium) honey with an average content of total flavonoids of 3.06 mg/100 g honey. The content of total phenolic acids was up to 14.0 mg/100 g honey, with gallic acid as the main component. A substantial quantity (32.8 mg/100 g honey) of abscisic acid was present in manuka honey. These results showed that flavonoids and phenolic acids could be used for authenticating honey floral origins, and abscisic acid may aid in this authentication. (C) 2002 Published by Elsevier Science Ltd.

Methodologies for calibration and predictive analysis of a watershed model

The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.