![]() Wiley, New York (2005)Ĭarlin, B.P., Louis, T.A.: Bayesian Methods for Data Analysis. McGraw-Hill, New York (1992)īox, G.E., Hunter, J.S., Hunter, W.G.: Statistics for Experimenters: Design, Innovation, and Discovery, 2nd edn. 231, 2180–2198 (2012)īevington, P., Robinson, D.: Data Reduction and Error Analysis for the Physical Sciences, 2nd edn. Wiley, Chichester (2000)īerry, R., Najm, H., Debusschere, B., Adalsteinsson, H., Marzouk, Y.: Data-free inference of the joint distribution of uncertain model parameters. Wiley Series in Probability and Statistics. Keywordsīeaumont, M.A., Zhang, W., Balding, D.J.: Approximate Bayesian computation in population genomics. By essentially redoing the experimental fitting using proposed data sets, the method ensures that the inferred joint posterior density on model parameters is consistent with the given statistics and with the model. In this chapter, we present a general framework relying on the maximum entropy principle, and employing approximate Bayesian computation methods, to infer a joint posterior density on parameters of interest given summary statistics, as well as other known details about the experiment or observational system behind the published statistics. Rather, what is most commonly available in the literature are summary statistics on the data, on parameters of interest, or on functions thereof. Considering the lipid composition (EE), it is expected to optimize the T and VS values, in order to achieve the optimum extraction condition, aiming to make feasible the extraction of SSO rich in active compounds having ethanol as a safe solvent for health.Practical situations, where one is interested in employing Bayesian inference methods to infer parameters of interest, a significant challenge is that actual data is not available. The St was not significant in the process and therefore will be set at the lowest point in later experiments. The yield values increased with higher values of P and T, reaching 46% of the total mass. In PLE, at 90% significance, the variables T and SV were relevant in the process. The results were evaluated by statistical software. The values of the other process variables (number of cycles, n = 4) and pressure (P = 10,34 MPa) were fixed. The independent variables studied were the static time (St) (5, 7, and 9 min), temperature (T) (40, 50, and 60 ☌) and solvent volume (SV) (80, 100, and 120% of the fixed bed extractor volume). ![]() ![]() The experimental design applied was Box, Hunter & Hunter 23-1, with three central points, to identify relevant factors for the extraction of SSO with pressurized ethanol. The sunflower seeds (SS) were characterized (dry matter (DM), mineral matter (MM), crude protein (CP), crude fiber (CF), and ethereal extract (EE)), and for oil extraction, it was used an ASE-150 - Dionex. Significant variables in this process and their lower levels were identified for higher yields of sunflower seed oil (SSO) using pressurized ethanol. Pressurized liquid extraction (PLE) is an efficient technology that provides increased solubility of several compounds using different solvent.
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