Natto has attracted worldwide attention because of its health benefits and long history in Japanese food. It has been found that a potent fibrinolytic enzyme named nattokinase, which is extracted from natto, is able to prevent atherosclerosis. The production of nattokinase may be influenced by various factors such as temperature, shaking speed, volume of medium, fermentation time and so forth. Three‐step response surface methodology was applied to obtain the optimal operation conditions of the fermentation process in order to maximize the nattokinase yield. The three major steps are described as follows. First, the important factors for fermentation were identified by L8 orthogonal array experiment. The chosen factors were temperature (37 or 45C), shaking speed (110 or 150 rpm), volume of medium (80 or 120 mL), Brix of wheat bran extract (1.5 or 3°), Brix of soy meal extract (1 or 2°), glucose concentration (0.6 or 1.2%) and fermentation time (24 or 36 h). Second, a regression equation was established between the response (i.e., the enzyme activity) and the two statistically significant factors (i.e., the volume of medium and fermentation time). Third, the optimal solutions for the volume of medium and fermentation time were obtained based on the response surface of the regression equation. According to the response surface analysis, the optimal operation conditions for the fermentation process should be 80 mL and 37.0817 h for the volume of medium and the fermentation time, respectively, which resulted in 459.11 FU/mL as the predicted enzyme activity.
The process capability index is a simple quantitative way to assess the capability of a process. To measure the overall yield of a multiple-stream process, Wang et al. (2009) proposed a yield index, denoted by S M pk , and then provided a point estimator of S M pk for practical industrial applications. In addition to point estimation, interval estimation plays an important role in statistical inference on the process index. In the present paper, we consider the problem of constructing the confidence interval for S M pk . First, the sampling distribution of S M pk is derived by applying Central Limit Theorem and the method of cumulative distribution functions. Then, the confidence intervals for S M pk are obtained using numerical integration by coding a MATLAB computer program. Several useful tables are provided for the purpose of constructing the confidence intervals of S M pk . A numerical example is presented to show how these tables may be applied for interval estimation of S M pk in a real production process.
Process capability index, Process yield index, Multiple streams process,
Natto has recently attracted attention throughout the world due to its healthy benefits. In 1987 a potent fibrinolytic enzyme in the extract of natto was discovered and named as ‘nattokinase’. Many biotechnology companies produce nattokinase via the fermentation of Bacillus subtilis natto. Various factors in the fermentation process may affect the production of nattokinase. This work reports the application of Taguchi parameter design to identifying the importance of those factors and then to obtain the optimal condition of fermentation to achieve the maximum yield of enzyme. Seven controllable factors and one noise factor were involved in the experiment. Based on the analysis of experimental data, both the volume of medium and the fermentation time were considered to have a significant effect on nattokinase activity value. Their nominal values should be set as 80mL and 36h, respectively, to maximise the yield of nattokinase.
Purpose -The present paper aims to present the results of a simulation study on the behavior of the four 95 percent bootstrap confidence intervals for estimating C pk when collected data are from a multiple streams process. Design/methodology/approach -A computer simulation study is developed to present the behavior of four 95 percent bootstrap confidence intervals, i.e. standard bootstrap (SB), percentile bootstrap (PB), biased-corrected percentile bootstrap (BCPB), and biased-corrected and accelerated (BCa) bootstrap for estimating the capability index C pk of a multiple streams process. An analysis of variance using two factorial and three-stage nested designs is applied for experimental planning and data analysis. Findings -For multiple process streams, the relationship between the true value of C pk and the required sample size for effective experiment is presented. Based on the simulation study, the two-stream process always gives a higher coverage percentage of bootstrap confidence interval than the four-stream process. Meanwhile, BCPB and BCa intervals lead to better coverage percentage than SB and PB intervals. Practical implications -Since a large number of process streams decreases the coverage percentage of the bootstrap confidence interval, it may be inappropriate to use the bootstrap method for constructing the confidence interval of a process capability index as the number of process streams is large. Originality/value -The present paper is the first work to explore the behavior of bootstrap confidence intervals for estimating the capability index C pk of a multiple streams process. It is concluded that the number of process streams definitively affects the performance of bootstrap methods.
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