Dimethyl Sulfide Stripping Behavior during Wort Boiling Using Response Surface Methodology

Desobgo, Z. S. C.; Stafford, Robert A.; Metcalfe, D. J. A.

doi:10.1094/asbcj-2015-0103-01

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…Other parameters of predictive models in biological systems that measure the relative deviation from the observed (experimental) and predicted (calculated) parameters were determined and results presented in Table 1. As observed, the closeness of the B f and A f to unity Equation (1)) and that of AAD to zero indicates reasonable agreements between the predicted and observed parameters (Desobgo, Stafford, & Metcalfe, 2015;Sobowale et al, in press).…”

Section: Statistical Models and Validationsupporting

confidence: 68%

Optimization of fermentation conditions fortingproduction using response surface methodology

Adebo

Njobeh

Mulaba‐Bafubiandi

et al. 2017

J Food Process Preserv

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This study investigated the effect of fermentation conditions (time and temperature) of sorghum on the composition of ting, using the Doehlert design of response surface methodology (RSM). Practical applicationsResponse surface methodology was used to study the influence of fermentation conditions on the quality of ting and optimal fermentation conditions were obtained at 34 8C for 24 hr. The findings in this study will be useful for ting processors to obtain a product with maximal beneficial composition and traits.

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Section: Statistical Models and Validationsupporting

confidence: 68%

Optimization of fermentation conditions fortingproduction using response surface methodology

Adebo

Njobeh

Mulaba‐Bafubiandi

et al. 2017

J Food Process Preserv

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“…The evaluation of the real performance of the predictive models obtained for both walnut and almond oil extraction was done according to Desobgo et al, (2015). According to these authors, a perfect agreement between observed and predicted responses is related to Bf and Af values of 1 and AAD of 0.…”

Section: Resultsmentioning

confidence: 99%

“…USA). For model validation, the response variable (OY, Y 1 ) was measured, in triplicate, under optimal extraction conditions, and the absolute average deviation (AAD), the Bias factor (Bf), and the Accuracy factor (Af) were calculated according to Desobgo et al, (2015). Finally, the percentage error between the predicted and the experimental values were calculated according to Barrera et al, (2016).…”

Section: Experimental Design and Response Surface Analysismentioning

confidence: 99%

Walnut and almond oil screw-press extraction at industrial scale: Effects of process parameters on oil yield and quality

et al. 2018

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Walnut and almond kernels are highly nutritious mainly due to their high oil contents. In this study, 32 factorial experimental designs were used to optimize processes for oil extraction by screw-pressing at industrial scale. Experimental designs included seed moisture content (SMC), and restriction die (RD) as the main processing parameters. Theoretical models were scanned against experimental data in order to optimize oil extraction conditions. The response variables analyzed were oil yield (OY), fine solid content (FC) in oil, and oil quality parameters. Fitted models for OY indicated maximum predicted values similar to the highest experimental values. Walnut oil extractions showed a maximum OY (84.5 ± 2.3 %) at 7.21% SMC, and 10 mm RD. For almond kernels, maximum OY (71.9 ± 3.5%) was obtained at 9.42% SMC, and 12 mm RD. Chemical quality parameters from both oils were in the ranges stated in Codex (FAO/WHO) standards for virgin (non-refined) oils.

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“…The high values of R 2 (.9565 for yield and .9787 for energy consumption) and Adj‐ R 2 (0.9130 for yield and 0.9574 for energy consumption) are approximately 1, showing a high degree of correlation between the predicted and experimental values. The quality of the model fitting was also validated by the absolute average deviation (AAD), the bias factor ( B f ), and the accuracy factor ( A f ) according to Desobgo, Stafford, and Metcalfe (). The values of B f (1.0017 for yield and 0.989 for energy consumption) and A f (1.07 for yield and 1.3103 for energy consumption) are close to 1 and the values of AAD (0.8558 for yield and 0.048 for energy consumption) are close to 0 as shown in Table indicates perfect agreements between observed and predicted responses.…”

Section: Resultsmentioning

confidence: 99%

“…values. The quality of the model fitting was also validated by the absolute average deviation (AAD), the bias factor (B f ), and the accuracy factor (A f ) according to Desobgo, Stafford, and Metcalfe (2015).…”

Section: Model Fitting and Validationmentioning

confidence: 99%

Optimization of mechanical oil extraction process from Camellia oleifera seeds regarding oil yield and energy consumption

Huang

et al. 2019

J Food Process Engineering

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Mechanical pressing is widely used for producing high‐quality vegetable oil in industry. However, mechanical pressing process faces a challenge from meeting the industrial demand for high yield with low energy consumption. Hence, the objective of this study was to optimize mechanical oil extraction process from Camellia oleifera seeds by maximizing the yield and minimizing the energy consumption. A Box–Behnken design of response surface methodology is adopted for the experiments of mechanical oil extraction from C. oleifera seeds. The effects of experimental factors namely moisture content (0–9% w.b.), applied pressure (10–45 MPa), pressing temperature (40–100°C) and extraction time (10–40 min) on yield and energy consumption were investigated by modeling the oil extraction process. An analysis of variance (ANOVA) revealed that moisture content, applied pressure and extraction time significantly effect on yield while applied pressure, pressing temperature, and extraction time have an obvious influence on energy consumption. In addition, the oil extracted under the processing conditions investigated was validated to be of acceptable quality. The Pareto fronts based on multiobjective genetic algorithm provides a set of optimal process parameters to obtain sustainable end products. Practical Application Moisture content, applied pressure, pressing time, and pressing temperature are primary processing conditions affecting oil extraction from C. oleifera seeds during the mechanical process. The use of inappropriate processing parameters may lead to low yield and high‐energy consumption. Understanding the mechanisms of oil extraction process thoroughly while taking important parameters into consideration is vital for the process to be efficient. This can be obtained by modeling oil extraction process using both experimental and theoretical methods. In this article, how these factors affect the yield and energy consumption of the system were investigated by modeling the oil extraction process. Processing conditions were optimized by multiobjective optimization technique based on NSGA‐II. The oil extracted under the processing conditions investigated was of acceptable quality. The data collected during this study can be utilized to design the equipment and to obtain sustainable end products.

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Dimethyl Sulfide Stripping Behavior during Wort Boiling Using Response Surface Methodology

Cited by 10 publications

References 17 publications

Optimization of fermentation conditions fortingproduction using response surface methodology

Optimization of fermentation conditions fortingproduction using response surface methodology

Walnut and almond oil screw-press extraction at industrial scale: Effects of process parameters on oil yield and quality

Optimization of mechanical oil extraction process from Camellia oleifera seeds regarding oil yield and energy consumption

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