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A near-infrared reflectance (NIR) spectroscopy technique for the prediction of procyanidins in cocoa beans (Theobroma cacao) has been developed. A select group of cocoa liquors from different origins used for the manufacture of chocolate were analyzed to determine quantitative levels of procyanidin oligomers (monomer to decamer) using normal-phase high-performance liquid chromatography. The calibration set included seasonal, environmental, and fermentation variations to cover the concentration range of procyanidins found at naturally occurring levels. The sample set was analyzed on FOSS NIR System II 6500 spectrophotometers and partial leastsquares algorithms developed. Very good calibration statistics were obtained for the prediction of the total procyanidin oligomers (r 2 = 0.983) with standard deviation/standard error of cross validation ratio (SD/SECV ) of 5.68. A global version of this calibration, involving 20 Foss instruments produced a calibration r 2 = 0.98 with a SD/ SECV = 6.20. Procyanidins have attracted increasing attention because of the rapidly growing body of evidence associating these compounds with a wide range of potential health benefits. A rapid method for the analysis of procyanidins in cocoa liquors would be beneficial in quality-control environments and would provide cost benefits to manufacturing operations.

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The river Meuse serves as a drinking-water source for more than 6 million people in France, Belgium, and The Netherlands. Pharmaceuticals and pesticides, both designed to be biologically active, are important classes of contaminants present in this river. The variation in the presence of pharmaceuticals in time and space in the Dutch part of the Meuse was studied using a multicomponent analytical method for pharmaceuticals combined with univariate and multivariate statistical analyses of the results. Trends and variation in time in the presence of pharmaceuticals were investigated in a dead-end side stream of the Meuse that serves as an intake point for the production of drinking water, and 93% of the selected compounds were detected. Highest concentrations were found for the antidiabetic metformin. Furthermore, a spatial snapshot of the presence of pharmaceuticals and pesticides was made along the river Meuse. Principal component analysis was successfully applied to reveal that wastewater-treatment plant effluent and water composition at the Belgian border were the main factors determining which compounds are found at different locations. The Dutch part of the river basin appeared responsible for approximately one-half of the loads of pharmaceuticals and pesticides discharged by the Meuse into the North Sea. The present study showed that multicomponent monitoring in combination with principal component analysis is a powerful tool to provide insight into contamination patterns in surface waters.