Search

Characterization of variation of experimental results is achieved by repeating experiments. Frequently, results are averaged before data are analysed but that may not be the best practice because valuable information is then lost. Three other ways to analyze repetitions are: (1) each experiment is analyzed on its own (no pooling of data), (2) all experiments are analyzed together in one go (complete pooling), (3) data are analyzed together while allowing for similarities as well as differences in the result (partial pooling). Multilevel modeling uses partial pooling by partitioning variance over more than one level. Level 1 consists of the measurements themselves; higher levels consist of groups or clusters of measurements (repetitions, experiments at various temperatures, at various pH values, etc.) and parameters are analyzed both at the population and at the group/cluster level. The approach is applied to a case study in which heat-induced isothermal degradation of ascorbic acid was studied with 15 repetitions in an aqueous solution, making it a two-level study. The data were analyzed using averaging and complete pooling, complete pooling without averaging, no-pooling at all, and partial pooling. The kinetic model was established by letting the data decide about the order of the reaction, while this was compared to a model where the order was fixed at 1 (first-order model). Results show that both averaging with complete pooling, as well as complete pooling without averaging, strongly underestimate variation. The no-pooling technique overestimates variation, while partial pooling partitions variation over the levels and thus gives a better impression of the variation involved. The kinetics of ascorbic acid appear to be subject to strong variation when each experiment is considered separately because it is a compound that is very sensitive to all kinds of experimental conditions. With multilevel modeling it appeared to be possible to characterize the uncertainties involved much better than with single level modeling. A Bayesian analysis was performed, in which parameters are allowed to be variable, which is useful because multilevel modeling leads to characterization of variation of parameters. The Bayesian method allows to visualize the posterior distribution of parameters, thereby giving more insight in their behaviour. Also, a Bayesian analysis focuses more strongly on predictive accuracy of models, including multilevel models. The predictive accuracy of 4 models describing the same ascorbic acid data was compared and the multilevel model with reaction order estimated from the data performed by far the best in this regard. The pros and cons of multilevel modeling are discussed and it is concluded that multilevel modeling is to be preferred whenever the data allow to perform such an analysis.

Calcium ion selective electrode (Ca-ISE) was found to underestimate the actual Ca

The synthesis of amylolytic enzymes by the maltose not-utilizing Trichoderma viride strain CBS 354.44 requires the presence of starch or dextrins. Several readily utilizable carbon sources such as glucose and glutamic acid were shown to exert a strong catabolite repression which completely inhibited enzyme induction by starch or dextrins. Enzyme synthesis occurs in the exponential and in the stationary growth phase. In the latter, the ratio between saccharifying and dextrinizing enzyme activity is invariably high. In the exponential growth phase this ratio depends on the nature of the inducing substrate. Growth on starch results in an initially high production of dextrinizing activity, the saccharifying one becoming predominant in the course of exponential growth. The latter activity in dextrin DE 30 cultures is predominant from the very beginning. Thus, the amylolytic enzyme system of T. viride consists of at least two different enzymes, the synthesis of each being controlled specifically. The careful regulation of the synthesis of the dextrinizing enzyme is discussed with special reference to the production of non-utilizable maltose by the latter.

Several pulsed nuclear magnetic resonance (NMR) methods were evaluated to estimate the solid fat content of fats and oil-in-water emulsions. The methods were checked with samples of paraffin oil or triolein containing known quantities of crystalline tristearate. A method based on the signal of solid fat (with use of a correction factor, the “f-factor”) was rejected in this work for general use. Correct results were obtained with methods that used only the signal of the liquid phase. With emulsions, disturbances could arise due to the surfactant present and to possible solubilization of water in the oil phase, presumably by monoglycerides. Without these disturbances, solid fat content in emulsions could be estimated as in bulk fats, after correction of the liquid phase signal for the contribution of protons from the aqueous phase. The signal from fat crystals inside emulsion droplets differed from that of crystals of the same fat in bulk, which may have been due to difference in crystal size but not to difference in crystal modification. Measurements on natural cream showed that disturbances were also possible in this type of emulsion.