Sufficient conditions for unique solutions within a certain class of curve resolution models

Curve resolution is a class of techniques concerned with estimating profiles underlying a set of measurements of time-evolving chemical systems. In general, the estimated profiles are not unique. Both intensity and rotational ambiguities exist in the solutions of these problems. Constraints can be imposed on the solution to decrease the ambiguity. Some chemical systems show closure. It is proven that imposing a closure constraint is sufficient to solve the intensity ambiguity but not the rotational ambiguity. Some curve resolution techniques are concerned with estimating reaction rate or equilibrium constants from measurements of evolving systems. Then a kinetic model is imposed on the measured data. It is proven that imposing a certain class of kinetic models is sufficient to solve both the rotational and the intensity ambiguity. Copyright (C) 2001 John Wiley gr Sons, Ltd

A Framework for Efficient Process Development Using Optimal Experimental Designs

Introduction The aim of this study was to develop and demonstrate a framework assuring efficient process development using fewer experiments than standard experimental designs. Methods A novel optimality criterion for experimental designs (Iw criterion) is defined that leads to more efficient process development because: (a) prior knowledge is used in the experimental design to focus on optimal processing conditions and (b) a lean design is used which can dramatically reduce the number of experiments compared to standard designs. In this way, the criterion serves as a framework to connect a series of screening and optimization designs. Results The philosophy behind the Iw criterion is explained including a detailed step-wise discussion how to apply it in practice. Moreover, its advantages were shown in an industrial process development case using a screening and an optimization design that were not explicitly connected. In this paper, a reduction of 21% of experiments could be obtained compared to the traditional approach using standard experimental designs and no framework. Conclusions The Iw criterion is a valuable tool to increase accuracy and to speed up research that contain sets of experiments and where prior knowledge is already available or will be derived using screening designs

The possibilities of the use of N-of-1 and do-it-yourself trials in nutritional research.

BACKGROUND:N-of-1 designs gain popularity in nutritional research because of the improving technological possibilities, practical applicability and promise of increased accuracy and sensitivity, especially in the field of personalized nutrition. This move asks for a search of applicable statistical methods. OBJECTIVE:To demonstrate the differences of three popular statistical methods in analyzing treatment effects of data obtained in N-of-1 designs. METHOD:We compare Individual-participant data meta-analysis, frequentist and Bayesian linear mixed effect models using a simulation experiment. Furthermore, we demonstrate the merits of the Bayesian model including prior information by analyzing data of an empirical study on weight loss. RESULTS:The linear mixed effect models are to be preferred over the meta-analysis method, since the individual effects are estimated more accurately as evidenced by the lower errors, especially with lower sample sizes. Differences between Bayesian and frequentist mixed models were found to be small, indicating that they will lead to the same results without including an informative prior. CONCLUSION:For empirical data, the Bayesian mixed model allows the inclusion of prior knowledge and gives potential for population based and personalized inference

Metabolic Profiling Reveals Differences in Plasma Concentrations of Arabinose and Xylose after Consumption of Fiber-Rich Pasta and Wheat Bread with Differential Rates of Systemic Appearance of Exogenous Glucose in Healthy Men

Background: The consumption of products rich in cereal fiber and with a low glycemic index is implicated in a lower risk of metabolic diseases. Previously, we showed that the consumption of fiber-rich pasta compared with bread resulted in a lower rate of appearance of exogenous glucose and a lower glucose clearance rate quantified with a dual-isotope technique, which was in accordance with a lower insulin and glucose-dependent insulinotropic polypeptide response. Objective: To gain more insight into the acute metabolic consequences of the consumption of products resulting in differential glucose kinetics, postprandial metabolic profiles were determined. Methods: In a crossover study, 9 healthymen [mean6SEMage: 216 0.5 y;mean6 SEMbodymass index (kg/m2): 2260.5] consumed wheat bread (132 g) and fresh pasta (119 g uncooked) enriched with wheat bran (10%) meals. A total of 134 different metabolites in postprandial plasma samples (at 25, 30, 60, 90, 120, and 180 min) were quantified by using a gas chromatography–mass spectrometry–basedmetabolomics approach (secondary outcomes). Two-factor ANOVA and advanced multivariate statistical analysis (partial least squares) were applied to detect differences between both food products. Results: Forty-two different postprandial metabolite profiles were identified, primarily representing pathways related to protein and energy metabolism, which were on average 8% and 7% lower after the men consumed pasta rather than bread, whereas concentrations of arabinose and xylose were 58% and 53% higher, respectively. Arabinose and xylose are derived from arabinoxylans, which are important components of wheat bran. The higher bioavailability of arabinose and xylose after pasta intake coincided with a lower rate of appearance of glucose and amino acids. We speculate that this higher bioavailability is due to higher degradation of arabinoxylans by small intestinal microbiota, facilitated by the higher viscosity of arabinoxylans after pasta intake than after bread intake. Conclusion: This study suggests that wheat bran, depending on the method of processing, can increase the viscosity of the meal bolus in the small intestine and interfere with macronutrient absorption in healthy men, thereby influencing postprandial glucose and insulin responses. This trial was registered at www.controlled-trials.com as ISRCTN42106325

Assessing the performance of statistical validation tools for megavariate metabolomics data

Statistical model validation tools such as cross-validation, jack-knifing model parameters and permutation tests are meant to obtain an objective assessment of the performance and stability of a statistical model. However, little is known about the performance of these tools for megavariate data sets, having, for instance, a number of variables larger than 10 times the number of subjects. The performance is assessed for megavariate metabolomics data, but the conclusions also carry over to proteomics, transcriptomics and many other research areas. Partial least squares discriminant analyses models were built for several LC-MS lipidomic training data sets of various numbers of lean and obese subjects. The training data sets were compared on their modelling performance and their predictability using a 10-fold cross-validation, a permutation test, and test data sets. A wide range of cross-validation error rates was found (from 7.5% to 16.3% for the largest trainings set and from 0% to 60% for the smallest training set) and the error rate increased when the number of subjects decreased. The test error rates varied from 5% to 50%. The smaller the number of subjects compared to the number of variables, the less the outcome of validation tools such as cross-validation, jack-knifing model parameters and permutation tests can be trusted. The result depends crucially on the specific sample of subjects that is used for modelling. The validation tools cannot be used as warning mechanism for problems due to sample size or to representativity of the samplin