The diffusion-sorption drying model has been developed as a physics-based way to model the decreasing drying rate at low moisture contents. This new model is founded on the existence of different classes of water: free and bound water. The transition between these classes and the corresponding thermodynamics form distinct components of the drying model. This paper shows that the characteristics of the different classes of water and of the transition between them can be deduced from the GAB sorption isotherm. The parameters in the GAB sorption isotherm support the theory of localised sorption, establishing the existence of different classes of water. Moreover, the sorption mechanism retrieved from the GAB parameters is in accordance with the sorption mechanism, which is obtained from the moisture dependence of the net isosteric heat of sorption. This holds for experimental sorption data of corn and starch as well as for literature data on five vegetables and four fortified cassava products. An extremum in the net isosteric heat of sorption coincides with the transition between bound and free water, and the partition moisture content corresponds with the monolayer value derived from the GAB equation. This confirms that the GAB monolayer value can be chosen as model boundary between bound and free water. Moreover, it reveals that this method can be developed into a technique to estimate the bound water content in foods.
Sorption isotherms of corn and starch cylinders with immobilised catalase are experimentally determined at different temperatures for use in drying models in optimal control studies. This application of the sorption isotherm requires an accurate prediction of the sorption data at different temperatures for the low water activity range. The GAB equation is used for the prediction of the sorption isotherms. Two major problems are encountered by employing standard procedures, ie prediction of sorption at a w < 0.11 and sensitivity of the GAB parameters to the applied data range. An improved methodology is developed, consisting of extending the standard experimental procedure with additional data points in the low water activity range and changing the criterion in the regression procedure in the sum of squares, which is weighed by the variance of the experimental data. The new methodology leads to accurate, consistent and physically relevant parameters of the GAB equation, which are independent of the applied data range in the regression analysis and which result in accurate predictions of the sorption behaviour at low water activity. The sorption data at different temperatures at low water activity can be predicted in the best way with parameters obtained after direct regression based on weighed SSQ.
BackgroundClone-based microarrays, on which each spot represents a random genomic fragment, are a good alternative to open reading frame-based microarrays, especially for microorganisms for which the complete genome sequence is not available. Since the generation of a genomic DNA library is a random process, it is beforehand uncertain which genes are represented. Nevertheless, the genome coverage of such an array, which depends on different variables like the insert size and the number of clones in the library, can be predicted by mathematical approaches. When applying the classical formulas that determine the probability that a certain sequence is represented in a DNA library at the nucleotide level, massive amounts of clones would be necessary to obtain a proper coverage of the genome.ResultsThis paper describes the development of two complementary equations for determining the genome coverage at the gene level. The first equation predicts the fraction of genes that is represented on the array in a detectable way and cover at least a set part (the minimal insert coverage) of the genomic fragment by which these genes are represented. The higher this minimal insert coverage, the larger the chance that changes in expression of a specific gene can be detected and attributed to that gene. The second equation predicts the fraction of genes that is represented in spots on the array that only represent genes from a single transcription unit, which information can be interpreted in a quantitative way.ConclusionValidation of these equations shows that they form reliable tools supporting optimal design of prokaryotic clone-based microarrays.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.