Comparing experimental design strategies for quality improvement with minimal changes to factor levels

Abstract

The 'small factor change' problem, where an experimental design strategy is used to find a certain amount of improvement in a response while changing the factor levels as little as possible, is addressed. Using a recently developed test bed for response surfaces, we have simulated a broad range of response surface functions and collected empirical results on the performance of seven experimental design strategies when confronted with this problem.

Bacillus cereus CGMCC 1.60196: a promising bacterial inoculant isolated from biological soil crusts for maize growth enhancement

“…Additionally, the optimal C source, N source, and inorganic salt of B. cereus CGMCC 1.60196 were selected from multi-nutrient and multi-level, which were 10 g/L glucose, 25 g/L yeast extract, and 15 g/L MgSO 4 , respectively ( p < 0.05; Figure 3 ). Consistent with previous studies, the optimal levels of key factors can be successfully screened through single-factor experiments, and the yield of target products was increased by multiple times ( Mcdaniel and Ankenman, 2000 ; Ahmed et al, 2019 ; Saha and Mazumdar, 2019 ). However, these experiments do not take into account the possible interactions between the factors, thereby resulting in an inability to obtain an optimal strategy.…”

Bacillus cereus CGMCC 1.60196: a promising bacterial inoculant isolated from biological soil crusts for maize growth enhancement

“…Additionally, the optimal C source, N source, and inorganic salt of B. cereus CGMCC 1.60196 were selected from multi-nutrient and multi-level, which were 10 g/L glucose, 25 g/L yeast extract, and 15 g/L MgSO 4 , respectively ( p < 0.05; Figure 3 ). Consistent with previous studies, the optimal levels of key factors can be successfully screened through single-factor experiments, and the yield of target products was increased by multiple times ( Mcdaniel and Ankenman, 2000 ; Ahmed et al, 2019 ; Saha and Mazumdar, 2019 ). However, these experiments do not take into account the possible interactions between the factors, thereby resulting in an inability to obtain an optimal strategy.…”

Comparing experimental design strategies for quality improvement with minimal changes to factor levels

“…Also, the fact that all of the other strategies are indistinguishable in terms of performance for this metric. McDaniel [9] provides details that confirm these conclusions at a 95% confidence level using the method for comparisons with the best suggested by Hsu [10].…”

A New Strain of Bacillus tequilensis CGMCC 17603 Isolated from Biological Soil Crusts: A Promising Sand-Fixation Agent for Desertification Control

“…Additionally, the optimal C source, N source, and inorganic salt of CGMCC 17603 were 25 g/L glucose, 15 g/L yeast extract, and 5 g/L MgSO 4 •7H 2 O, respectively (Figure 4). The results revealed that the OFAT optimization could effectively screen out the optimal levels of key factors, and further confirmed the rationality of factor gradients and the validity of these results [31]. Importantly, industrial-grade glucose and yeast extract are inexpensive and meet our low-cost requirements for culture medium.…”

“…2.4.1. One-Factor-at-a-Time Approach (OFAT) Optimization OFAT, which can effectively screen for significant main factors and their levels [31], was used to screen for optimum culture conditions and medium compositions that affect the biomass yield of the candidate Bacillus strain. The culture conditions included temperature, pH, agitation speed, and inoculation quantity, and gradient ranges from 22 to 42 • C (in 3 • C increments), 6.0 to 9.5 (in 0.5 unit increments), 90 to 230 rpm (in 20 rpm increments), and 0.5 to 4.0% (in 0.5% increments), respectively.…”

Bacillus cereus CGMCC 1.60196: a promising bacterial inoculant isolated from biological soil crusts for maize growth enhancement

“…Additionally, the optimal C source, N source, and inorganic salt of B. cereus CGMCC 1.60196 were selected from multi-nutrient and multi-level, which were 10 g/L glucose, 25 g/L yeast extract, and 15 g/L MgSO 4 , respectively ( p < 0.05; Figure 3 ). Consistent with previous studies, the optimal levels of key factors can be successfully screened through single-factor experiments, and the yield of target products was increased by multiple times ( Mcdaniel and Ankenman, 2000 ; Ahmed et al, 2019 ; Saha and Mazumdar, 2019 ). However, these experiments do not take into account the possible interactions between the factors, thereby resulting in an inability to obtain an optimal strategy.…”

Comparing experimental design strategies for quality improvement with minimal changes to factor levels

“…Also, the fact that all of the other strategies are indistinguishable in terms of performance for this metric. McDaniel [9] provides details that confirm these conclusions at a 95% confidence level using the method for comparisons with the best suggested by Hsu [10].…”

A New Strain of Bacillus tequilensis CGMCC 17603 Isolated from Biological Soil Crusts: A Promising Sand-Fixation Agent for Desertification Control

“…Additionally, the optimal C source, N source, and inorganic salt of CGMCC 17603 were 25 g/L glucose, 15 g/L yeast extract, and 5 g/L MgSO 4 •7H 2 O, respectively (Figure 4). The results revealed that the OFAT optimization could effectively screen out the optimal levels of key factors, and further confirmed the rationality of factor gradients and the validity of these results [31]. Importantly, industrial-grade glucose and yeast extract are inexpensive and meet our low-cost requirements for culture medium.…”

“…2.4.1. One-Factor-at-a-Time Approach (OFAT) Optimization OFAT, which can effectively screen for significant main factors and their levels [31], was used to screen for optimum culture conditions and medium compositions that affect the biomass yield of the candidate Bacillus strain. The culture conditions included temperature, pH, agitation speed, and inoculation quantity, and gradient ranges from 22 to 42 • C (in 3 • C increments), 6.0 to 9.5 (in 0.5 unit increments), 90 to 230 rpm (in 20 rpm increments), and 0.5 to 4.0% (in 0.5% increments), respectively.…”

Bacillus cereus CGMCC 1.60196: a promising bacterial inoculant isolated from biological soil crusts for maize growth enhancement

“…Additionally, the optimal C source, N source, and inorganic salt of B. cereus CGMCC 1.60196 were selected from multi-nutrient and multi-level, which were 10 g/L glucose, 25 g/L yeast extract, and 15 g/L MgSO 4 , respectively ( p < 0.05; Figure 3 ). Consistent with previous studies, the optimal levels of key factors can be successfully screened through single-factor experiments, and the yield of target products was increased by multiple times ( Mcdaniel and Ankenman, 2000 ; Ahmed et al, 2019 ; Saha and Mazumdar, 2019 ). However, these experiments do not take into account the possible interactions between the factors, thereby resulting in an inability to obtain an optimal strategy.…”

Comparing experimental design strategies for quality improvement with minimal changes to factor levels

“…Also, the fact that all of the other strategies are indistinguishable in terms of performance for this metric. McDaniel [9] provides details that confirm these conclusions at a 95% confidence level using the method for comparisons with the best suggested by Hsu [10].…”

A New Strain of Bacillus tequilensis CGMCC 17603 Isolated from Biological Soil Crusts: A Promising Sand-Fixation Agent for Desertification Control

“…Additionally, the optimal C source, N source, and inorganic salt of CGMCC 17603 were 25 g/L glucose, 15 g/L yeast extract, and 5 g/L MgSO 4 •7H 2 O, respectively (Figure 4). The results revealed that the OFAT optimization could effectively screen out the optimal levels of key factors, and further confirmed the rationality of factor gradients and the validity of these results [31]. Importantly, industrial-grade glucose and yeast extract are inexpensive and meet our low-cost requirements for culture medium.…”

“…2.4.1. One-Factor-at-a-Time Approach (OFAT) Optimization OFAT, which can effectively screen for significant main factors and their levels [31], was used to screen for optimum culture conditions and medium compositions that affect the biomass yield of the candidate Bacillus strain. The culture conditions included temperature, pH, agitation speed, and inoculation quantity, and gradient ranges from 22 to 42 • C (in 3 • C increments), 6.0 to 9.5 (in 0.5 unit increments), 90 to 230 rpm (in 20 rpm increments), and 0.5 to 4.0% (in 0.5% increments), respectively.…”