Experimental design methods for bioengineering applications.

http://informahealthcare.com/bty ISSN: 0738-8551 (print), 1549-7801 (electronic) Crit Rev Biotechnol, Early Online: 1–21 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/07388551.2014.973014

REVIEW ARTICLE

Experimental design methods for bioengineering applications _ Tug˘ba Keskin Gu¨ndog˘du, Irem Deniz, Gu¨lizar Çalı¸skan, Erdem Sefa S¸ ahin, and Nuri Azbar

Critical Reviews in Biotechnology Downloaded from informahealthcare.com by Dicle Univ. on 11/13/14 For personal use only.

Department of Bioengineering, Ege University, Bornova-Izmir, Turkey

Abstract

Keywords

Experimental design is a form of process analysis in which certain factors are selected to obtain the desired responses of interest. It may also be used for the determination of the effects of various independent factors on a dependent factor. The bioengineering discipline includes many different areas of scientific interest, and each study area is affected and governed by many different factors. Briefly analyzing the important factors and selecting an experimental design for optimization are very effective tools for the design of any bioprocess under question. This review summarizes experimental design methods that can be used to investigate various factors relating to bioengineering processes. The experimental methods generally used in bioengineering are as follows: full factorial design, fractional factorial design, Plackett–Burman design, Taguchi design, Box–Behnken design and central composite design. These design methods are briefly introduced, and then the application of these design methods to study different bioengineering processes is analyzed.

Bioprocess, Box–Behnken design, central composite design, full factorial design, Placket–Burrman design, Taguchi design

Introduction In an experiment, one or more process variables (or factors) are deliberately changed to observe the effect on one or more response variables. Statistical design of experiments is an efficient procedure for planning experiments and analyzing the results such that objective conclusions can be drawn. Determining the objectives of an experiment is the first step of an experimental design. Selecting the process factors and responses to be analyzed is the second important step in optimizing the important variables in a process. In general, experimental design is the design of a system to obtain information using various experimental techniques. Experimental design can be used to evaluate physical objects, chemical formulations, structures, components and materials. Bioengineering studies various areas of production including enzyme production, biological water treatment, tissue culture, nanoparticles, biofuel production, the isolation of microorganisms and the industrial biological production of compounds such as proteins, lipids and aromatic compounds. Every process is affected by different factors. Optimizing these parameters is an important step in obtaining maximum production with minimum costs. Developing an experimental design is the most effective means of process optimization. This review summarizes the experimental design methods that are used to investigate the effects of various factors on various bioengineering processes including: Full factorial design, fractional factorial design, Plackett–Burman (PB) design,

Address for correspondence: Nuri Azbar, Department of Bioengineering, Ege University, 35100, Bornova-Izmir, Turkey. Tel: +90 232 3884955 (31). Fax: + 90 232 3884955. E-mail: [email protected]

History Received 24 September 2013 Revised 25 August 2014 Accepted 19 September 2014 Published online 3 November 2014

Taguchi design, Box–Behnken design and central composite design. Each design method is briefly introduced, the advantages and disadvantages of each are briefly discussed and various processes are analyzed. Experimental design is a process in which important factors are selected and controlled using different designs to obtain important responses and is generally followed by statistical analysis. The literature contains a vast number of papers on statistical design, this review however specifically focuses on the experimental design applications commonly used in bioengineering studies, discussing the latest published papers.

Experimental design applications in bioengineering Full factorial design In statistics, a design that consists of two or more factors in which every factor interacts with each other at different levels is termed full factorial design. Full factorial design studies the effect of factors on response variables as well as the interaction between them. Factorial design was first used in the nineteenth century by John Bennet Lawes and Joseph Henry Gilbert. Ronald Fisher argued in 1926 that complex designs were more efficient than those in which one factor was studied at a time. The term ‘‘factorial’’ was first used by Fisher in his book ‘‘The Design of Experiment’’ (Fisher, 1935). It is practical to study the effect of many factors simultaneously at different levels. The resulting design is a combination of different variables at various levels, enabling the design to depict the interactions of different factors; this characteristic makes the design more efficient while dealing


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T. K. Gu¨ndog˘du et al.

with a large number of variables. Factorial design can be generally divided into full factorial design and fractional factorial design. A uniform framework is provided by single coded factor levels in an attempt to determine the effect of a particular factor in an experimental context. Factorial design is usually used in coded factor levels. The actual factor level corresponding to the factor level of a factorial design can be assigned very clearly while using it. After completing all the experiments and obtaining results, the analysis can be performed using either coded factors or actual factors (Wang & Wan, 2009). Using coded factors in the analysis has some advantages. For example, in coded factor level analysis, the coefficients of the model are dimensionless and directly comparable. Thus, this method is very effective for determining the real effects. After performing an analysis using coded factors, the results can be summarized using actual factors (Montgomery, 2008). In a full factorial design, the major aim is to determine the effect of each term and the interaction between these terms using a model. If there are three factors to be analyzed (a b c), then the first factor is tested at a levels, the second factor is tested at b levels and the third factor is tested at c levels. The number of runs of n factors at a levels is an. Two-level designs are the most commonly used designs that can be denoted as 2n (Wang & Wan, 2009). Various bioengineering processes with different full factorial analyses are summarized briefly below. Mendes et al. (2012) investigated that the daily relative growth rates of the red macroalgae Gralicaria domingensis in synthetic seawater. The effect of the combined influence of five factors [light (L), temperature (T) and nitrate (N), phosphate (P) and molybdate (M) concentrations] was determined using a full factorial design. The ranges of the experimental cultivation conditions used were as follows: T, 18–26 C; L, 74–162 mmol photons m2 s1; N, 40–80 mmol/L; P, 8–16 mmol/L and M, 1–5 nmol/L. The results were analyzed using the analysis of variance (ANOVA) test method. According to the ANOVA test, the factors N (p ¼ 0.01327) and T (p ¼ 0.00025) are highly significant at the a ¼ 0.05 level (Mendes et al., 2012). The production of poly-3-hydroxybutyrate (PHB) by Methylocytis hirsute from natural gas was first studied in two different media by Rahnama et al. (2012). After selecting the medium, the effects of methane-to-air ratio (20/80–50/50– 80/20) and nitrogen content (0–50–100 mg/L) on PHB production were determined in a bubble column using a 32 full factorial design. Both of these factors were found to be significant, and a maximum accumulation of 42.5% w/w of cell dry weight was achieved (Rahnama et al., 2012). Nalakath et al. (2012) investigated the effect of four factors on the bioconversion of carbon monoxide to ethanol and acetic acid by Clostridium autsethogenaum using a 24 full factorial design. The four studied factors were initial pH (4.75–5.75), initial total pressure (0.8–1.6 bar), cysteineHClH2O concentration (0.5–1.2 g/L) and yeast extract (YE) concentration (0.6–1.6 g/L). The maximum ethanol production was enhanced by up to 200% at conditions pH 4.75 and a YE concentration of 1.6 g/L. All the main effects and the interaction effects were found to be statistically significant

Crit Rev Biotechnol, Early Online: 1–21

(p50.05). Main effect plots indicated that increasing the initial pH and using higher YE concentrations negatively affected ethanol production, whereas increasing the initial pressure and the cysteine-HClH2O concentration had a positive effect. An ethanol production of 0.065 g/L was achieved using the following values of the factors: pH (4.75), pressure (1.6 bar), cysteine-HClH2O (1.2 g/L) and YE concentration (1.6 g/L; Nalakath et al., 2012). The optimal conditions for microwave-assisted enzymatic biodiesel synthesis were investigated using a 22 full factorial design. The important factors affecting the production of biodiesel were the ethanol-to-beef tallow ratio (X1, 6–12) and the reaction temperature (X2, 40–50 C). The transesterification yield (Y) was selected as the response. The reactions were catalyzed by lipase from Burkholderia cepacia, which was immobilized on silica, and were performed using 8–15 W of microwave radiation. High conversions were achieved using lower molar ratios of ethanol-to-beef tallow, and the effect of temperature was observed to be not significant based on statistical analysis. Under optimized conditions (a 1:6 molar ratio of beef tallow to ethanol at 50 C), the fatty acids in the original beef tallow were almost completely converted into ethyl esters in 8 h of reaction time, and a productivity of 92 mg ethyl esters/g h was achieved (a 6-fold increase; Da Ro´s et al., 2012). Virological testing of bottled water is another bioengineering application. A study was conducted to choose the best tool for detecting viruses in bottled water. Different approaches were examined; for example, the recovery of viral RNA was measured following the in situ lysis of virus particles in the aqueous phase. A second method detected the recovery of viral RNA following the lysis of virus particles, and the third detection method generated the lowest genome recovery, regardless of water and virus type. Two methods were compared because they were considered viruses. Using a 344121 full factorial design, viral RNA recovery was determined. The independent variables used were three types of water with 3 different mineral compositions; four viruses (poliovirus, hepatitis A virus, Norovirus and MS2 phage); three incubation times (1, 10 and 20 days) and two methods (A and B). Every factor except incubation time was found to be significant. Method A provided the best results, suggesting that this method should be used to detect the hepatitis A virus (Huguet et al., 2012). A study of the metabolic responses of a new neuronal human cell line, AGE1HN, to various substrate values showed that reduced substrate and pyruvate load improves metabolic efficiency leading to improved growth and a1 antitrypsin (A1AT) production. A 32 full factorial design was used to analyze the adaptation of metabolism to various pyruvate and glutamate concentrations. The concentrations of pyruvate tested were 2, 5 and 9 mM and the concentrations of glutamate tested were 5, 7.5 and 10 mM. The most important result was that higher pyruvate concentrations in the medium decreased cell proliferation and reduced the efficiency of substrate use. However, the highest viable cell density and A1AT concentration (167% of the batch) could be achieved without adding pyruvate (Niklas et al., 2012). To study the individual and combined effects of osmolality on the phases of cell growth and virus production, a


DOI: 10.3109/07388551.2014.973014

52 full factorial design was studied. A statistical design was used to investigate the effect of osmotic stress on cell physiology. Two factors were chosen for study: the osmolality of the cell growth (250, 290, 330, 370 and 410 nm) and the osmolality of the virus produced (250, 290, 330, 370 and 410 nm). Statistical analysis indicated that the growth of the cells under hyperosmotic conditions induced favorable physiological states for viral production. An 11fold increase in virus productivity was achieved (Shen & Kamen, 2012). A 33 full factorial design was used to investigate the effects of pH, temperature and agitation rate on bacterial growth and bacteriosin production. Statistical analysis showed that pH was the most important physical factor for bacterial growth and bacteriosin production (Martıńez-Cardenãs et al., 2012). The effect of different doses of surfactant and buffer were tested using a 41 31 full factorial experimental design in a lipase production by solid-state fermentation experiments. Four buffer-to-wet solids substrate ratios and three surfactant percentages in aqueous extracts were tested to determine the best conditions for lypolytic activity. For solid samples, the lypolytic activity reached 120 000 UA/g of dry matter (SantisNavarro et al., 2011). Aspergillus japonicus URM5620 can produce b-glucosidase (BG), total cellulase (FPase) and endogluconase (CMCase). To determine the effects of substrate amount, initial moisture, pH and temperature on the production of these enzymes, a 24 full factorial design was used. All the factors were found to be significant, and the optimum conditions for all the studied enzymes were as follows: 5 g substrate, 15% initial moisture, 25 C temperature and pH 6. After a 120-h fermentation under these conditions, the maximal activities for BG, FPase, CMCase were found to be 88.3, 953.4 and 191.64 U/g of dry substrate, respectively (Herculano et al., 2011). Wang & Wan (2011) used a 42 full factorial design to determine the combined effects of temperature and pH on bio-hydrogen production. Temperature and pH were found to interactively affect both hydrogen yield and the average hydrogen production rate. The maximum yield and hydrogen production rates were predicted as 308 mL H2/g of substrate and 11.5 mL/h, respectively (Wang & Wan, 2011). Hendry et al. (2011) designed a novel continuous supercritical water gasification reactor to investigate glucose gasification in supercritical water at high temperatures and low residence times. A 23 full factorial experiment was performed to investigate the effects of feed concentration, temperature and residence time on glucose gasification. The responses studied were the overall gasification efficiency (GE), carbon efficiency (CE), H2 yield, CH4 yield and gasification rates. The most important outcomes of the design were as follows: first the interaction between feed concentration and temperature has an important effect on both GE and CE. Second, temperature and residence time affected CE (Hendry et al., 2011). Inga edulis is a flavonoid-rich plant from Amazonia. To optimize the drying of its leaves by a forced convection dryer, a 32 full factorial design was used to determine the effects of temperature and airflow velocity (factors) on the drying time (response). Both factors were found to be significant for

Experimental design methods for bioengineering applications

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achieving a moisture content lower than 5.86/100 g during 40 min of drying time (Silva et al., 2011). Bioethanol can be produced by Pichia stipitis NRRLY7124 using rice straw hemicellulosic hydrolysate as a fermentation medium. To detect the effects of agitation and aeration rates on the bioconversion of xylose into bioethanol, a 22 full factorial experimental design was used. The factorial design assay showed strong interactive effects of the factors on ethanol production yields. The best results (YP/S ¼ 0.37 g/g and 75% process efficiency) were found using a 2.5 Vflask/ Vmedium ratio and 200 rpm agitation (Silva et al., 2010). Freshly harvested, air-dried rice straw can be pre-treated by two methods – using either NaOH or Ca(OH)2. Full factorial experiments were designed to measure the effects of alkaline loading and pre-treatment time on the delignification and sugar yield after enzymatic hydrolysis. Alkaline loading and reaction time both had significant effects on delignification, but only alkaline loading had a significant effect on enzymatic hydrolysis (Cheng et al., 2010). Testing of different species showed that Candida bijinensis exhibited the highest extracellular proteolytic activity response. Enzyme production using this yeast was optimized using a 23 full factorial test to test the effects of temperature, pH and soybean flour concentration. The highest production of extracellular proteases (573 U/mL) was achieved at 35 C, pH 6.5 and 0.5% w/w soybean concentration (de Arau´jo Viana et al., 2010). The effects of strain, media and agitation rate on traditional yoghurt production were investigated on the lactic acid and b-galactosidase yields. Based on ANOVA tests that were performed after the experiments, all the linear and interaction terms were found to be significant for lactic acid formation (Tari et al., 2010). A 24 full factorial design was used to investigate the effect of nitrogen source (YE, urea, ammonium sulfate and peptone) on the maximum variation of surface tension (DST) and emulsification index (EI) to determine the efficiency of biosurfactant production by Yorrowina lipolytica IMUFRJ 50682. The optimal result was 73.1% EI and 20.9 m Nm1 of DST (Fontes et al., 2010). Aeration and the agitation are the two main factors affecting the oxygen mass transfer coefficient, Marques et al. (2009) designed a 22 full factorial experiment to study these factors. Both variables showed statistically significant effects on kLa, and the highest values obtained were 70.3 s1 (extractive fermentation) and 241 s1 (simple fermentation) using a 5 vvm aeration rate and 1200 rpm agitation (Marques et al., 2009). An immobilization method for the inclusion of viable cells of Candida guillermondii into a polyvinyl alcohol (PVA) hydrogel using a freeze-thaw method was applied by de Cunha et al. (2009). Entrapment experiments were performed based on a 23 full factorial experimental design that tested PVA concentration, freezing temperature and the number of freezing–thawing circles as factors and using the xylose to xylitol bioconversion rate as the response. At the end of the experiments, a maximum yield of 0.49 g xylose/xylitol was achieved (da Cunha et al., 2009). Several experimental sets of full factorial design studies used in bioengineering are listed in Table 1.

MINITAB Release 15 (Minitab Inc. State College, PA) Minitab 16 (Minitab Inc. State College, PA)

Statistica 7.0 (StatSoft Co.)

Minitab 14 (Minitab Inc. State College, PA)

NA

(DOE++ software, ReliaSoft Corporation, Tucson, AZ) STATGRAPHICS Plus version 5 (Statistical Graphic Corp., Warrenton, VA) Microsoft Office Excel Solver tool.

Statistica 8.0

32

24

22

34 41 21

32

52

33

41 31

24

Polyhydroxy butyrate production by Methylocytis hirsute Ethanol production by Clostridium autsethogenaum

Biodiesel production

Virus detection in bottled water

Analyze the adaptation of metabolism to different substrates

Virus productivity

Bacterial growth and bacteriosin production

Lipase production

b-Glucosidase, total cellulose (FPase) and endogluconase (CMCase)

Software STATISTICA 9.1 (StatSoft Inc., 2010)

25

Full factorial design model

Microalgae production (Gralicaria domingensis)

Process

Table 1. Full-factorial design applications in bioengineering.

Conditions the best activities for b-glucosidase, FPase, CMCase were found as; 88.3, 953.4, 191.64 U/g dry substrate, respectively

Highest bacteriosin activity was found with LBIT 269 with induced B. thuringiensis The lypolytic activity reached up to 120 000 UA/g or dry matter

Highest viable cell density and A1AT concentration (167% of batch) can be reached without pyruvate feeding 11-fold increase in virus productivity

Method A should be used to detect hepatitis A virus

92 mg ethyl esters/g h production

Polyhydroxy butyrate accumulation 42.5% w/w of cell dry weight 0.065 g/L ethanol production

Maximum growth rate of 6.4% day1

Response

Herculano et al. (2011)

Santis-Navarro et al. (2011)

Martıńez-Cardenãs et al. (2012)

Shen & Kamen (2012)

Niklas et al. (2012)

Huguet et al. (2012)

Da Ro´s et al. (2012)

Nalakath et al. (2012)

Rahnama et al. (2012)

Mendes et al. (2012)

References


Buffer to wet solid substrate ratio (15–90–200–500 mL/g), the surfactant percentages in aqueous extract (2–6–10% v/v) (both factors are significant) Substrate amount (5–15 g/L), initial moisture (15–35%), temperature (25–35 C), pH (4–6) (All factors are significant)

Osmolality of cell growth (250–290–330– 370–410), osmolality of virus produced (250–290–330–370–410) (both factors are significant) pH (6.0, 7.2, 8.0), temperature (26, 28, 30 C), agitation (150, 180, 210 rpm) (pH is the significant factor)

Temperature (18–26 C), Light (74–162 mmol photons m2 s1), Nitrogen (40–80 mmol/L), phosphorus (8–16 mmol/L), M (1–5 nmol/L) (T and N are significant). Methane to air ratio (20/80–50/50–80/20), nitrogen content (0–50–100 mg/L) (All factors are significant) Initial pH (4.75–5.75), initial total pressure (0.8–1.6 bar), cystein-HCl.H2O concentration (0.5–1.2 g/L), yeast extract concentration (0.6–1.6 g/L) (All factors are significant) Ethanol to beef tallow ratio (6–12), reaction temperature (40–50 C) (ethanol to beef tallow ratio is significant) Three waters with three different mineral composition; four viruses, poliovirus– hepatitis A virus–Norovirus–MS2 phage; 3 incubations times, 1–10–20 days and two methods, A–B (All of the factors are significant except incubation times) Pyruvate (2–5–9 mM), glutamate (5–7.5–10 mM) (Pyruvate is significant)

Factors studied


4 Crit Rev Biotechnol, Early Online: 1–21

STATISTICA Kernel Release 7.1 (StatSoft Inc., 2006, Tulsa, OK) for Windows XP Statistica 6.0 JMP IN version 7 (SAS Institute, Cary, NC)

Statistica 8.0 (StatSoft, Tulsa, OK). ‘‘Design Expert’’ software version 7.1.3 (Stat-Ease, Inc., Minneapolis, MN)

Statistica 7.0

Statistic 6.0

Statgraphics 6.0

32

32

23

26

24

22

23

Drying of Inga edulis leaves by forced convection dryer Bioethanol production by Pichia stipitis NRRLY7124 Bioethanol production from rice straw

Extracellular proteolytic activity by Candida Bijinensis species Lactic acid production

Biosurfactant production

kLa determination in a fermentation system

Polyvinyl alcohol (PVA)gel immobilization of Candida guillermondii

NA: Not available.

NA

23

Supercritical water gasification

22

NA

42

Biohydrogen production

Agitation (100–200 rpm), aeration (V flask/V medium ratio of 2.5–5.0) Alkaline loading and reaction time (Both alkaline loading and reaction times had significant effects on delignification but only alkaline loading had significant effect on enzymatic hydrolysis) Soybean flour concentration (0.1–0.5% w/w), pH (6.5–8.5), temperature (25–35 C) Temperature, pH, speed of agitation, enzyme concentration, buffer concentration, surfactant type, surfactant concentration (all the linear and the interaction terms were found to be significant) Peptone (0–12.8 mg/L), yeast extract (5–15 mg/L), ammonium sulfate (0–10 mg/L), urea (0–0.2 mg/L) (ammonium sulfate and yeast extract had significantly influenced both dependent variables, DST and EI. On the other hand, urea did not significantly influence DST nor EI in the range studied) Agitation (700–1200 rpm), aeration (2.0–5.0 vvm) (Both variables showed statistically significant effects on kLa) PVA concentration (80–120 g/L), the freezing temperature (10 to 20 C), the number of freezing–thawing cycles (1–5)

Feed concentration (10–15%), temperature (750–800 C), average residence time (4–6.5 s) (feed concentration and temperature interaction have an effect on both GE and CE; temperature and residence time had an effect on CE) Temperature (40–55–70 C), airflow velocities of (0.6–1.2–1.8 m/s)

pH (6–7–8–9), temperature (30–35–40–45 C) (both factors are significant)

Marques et al. (2009)

70.3 s1 by extractive fermentation and 241 s1 by simple fermentation

da Cunha et al. (2009)

Fontes et al. (2010)

20.9 m Nm1 for DST and 73.1% for EI by using 0.5 g/ L yeast extract

Maximum yield of 0.49 g xylose/xylitol

Tari et al. (2010)

de Arau´jo Viana et al. (2010)

Cheng et al. (2010)

Silva et al. (2010)

Silva et al. (2011)

Hendry et al. (2011)

Wang & Wan (2011)

Lb22b strain in skim milk media under static condition produced 2.30 times more lactic acid than in whey

Extracellular protease production of 573 U/mL

27% delignification for lime pre-treatment and 23.1 % delignification for NaOH pre-treatment

5.86/100 g moisture content lower than 40 min drying time Product yield of 0.37 g/g with a process efficiency of 75%

The maximum yield and hydrogen production rates were predicted as 308 mL H2/g substrate and 11.5 mL/h, respectively Increasing reaction temperature from 750 to 800 C showed large increases in efficiencies (+29% for GE), yields and gasification rate (+8 g/L s)


DOI: 10.3109/07388551.2014.973014

Experimental design methods for bioengineering applications 5

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Plackett–Burman design PB designs, also known as ‘‘Hadamard Matrix Designs’’, are two-level fractional factorial designs developed by Plackett and Burman; this design type has been widely used to determine important factors in bioengineering systems (Wang & Wan, 2009). The number of runs for a PB design is set to multiple of 4 (e.g., 12, 16, 20 or 24). If the number of factors (‘‘real’’ factors) are less than n ¼ N 1, a subset of PB design for N runs can be used (John, 1996). In some cases, repetitions can be used to determine the experimental errors (Wang & Wan, 2009). Plackett–Burman design is widely used in several scientific areas because it presents several advantages; for example, it holds ‘‘hidden projection properties’’, which means that taking the projections of a design can result in a desirable experimental result that is not immediately apparent. One such hidden projection property of these designs is that resolution V designs can be constructed from the projections of their factors (Briggs, 2011). For the experimental results, the effects of factors are defined using a first-order polynomial equation model [Equation (1)] for the PB design; y ¼ 0 þ

k X

t xt

ð1Þ

t¼1

where y is the response, 0 is a constant, t is a linear coefficient, and xi is the coded factor level. For the case of two levels (L ¼ 2), orthogonal matrices can be generated as 1 or 1 (Hadamard matrices). This method could be used to find such matrices of size N which is equal to a multiple of 4. It worked for all such N up to 100 except N ¼ 92. PB designs are mostly used when n is a multiple of 4 not a power of 2. If there are more than 2 cases, Plackett and Burman give specifics for designs having a number of experiments equal to the number of levels L to some integer power, for L ¼ 3, 4, 5 or 7 (Wang & Wan, 2009). Plackett–Burman statistical design is widely used to determine the effective parameters of processes, especially bioprocesses, because biological materials do not have stable structures and are easily affected by the environment. In addition, biochemical and physicochemical variations occur in bioprocesses; therefore, the effective parameters must be determined. Prakash & Srivastava (2005) used the PB method to determine the effective parameters in azadirachtin production from Azadirachta indica. The authors studied the factors of glucose, nitrate, phosphate, magnesium, calcium and inoculum concentration. The Ñ value helped the authors to decide which factors are the most effective. All nutrients except phosphate and MgSO47H2O concentration yielded positive Ñ values. Inoculum level exhibited the numerically highest value, indicating that this factor had the greatest positive influence on biomass production. The next highest values were for nitrate and glucose, which were the main components of the microbial growth (Prakash & Srivastava, 2005). Gao et al. (2009) used the PB method to optimize the medium for the production of avermectin B1a using Streptomyces avermitilis. Corn starch, a-amylase, soya

flour, YE, Na2MoO42H2O, MnSO4H2O, (NH4)2SO4, CoCl2 and CaCO3 were studied as factors. At the end of the study, researchers presented a Pareto chart. In the chart, corn starch, YE, CaCO3, CoCl2, MnSO4H2O, soya flour, (NH4)2SO4 and a-amylase, Na2MoO42H2O had the greatest effect on B1a production using Streptomyces avermitilis (Gao et al., 2009). Plackett–Burman design is widely used to determine effective factors in bioengineering applications. These factors may have chemical, physical and environmental effects on the catalytic activity of enzymes (Table 2). Singh & Bishnoi (2012) used the PB method to optimize the enzymatic hydrolysis of microwave/alkali-pretreated wheat straw and ethanol production by yeast. The authors investigated the effects of substrate concentration, Tween-80 concentration, BGL dose, cellulase concentration, hydrolysis time and agitation on enzymatic activity. At the end of the study, all the factors presented negative Ñ values except for BGL dose and hydrolysis time. Substrate concentration, hydrolysis time, BGL and cellulase dosage were significant at a confidence level of greater than 95%, and the remaining factors were nonsignificant (Singh & Bishnoi, 2012). Yatsyshyn et al. (2010) studied on the recombinant strain of the yeast Candida famata 13–76 that overexpresses FMN1 gene coding for riboflavin kinase, accumulates significant amounts of flavin mononucleotide (FMN) in the cultural liquid. After medium composition optimization in shake flask cultures, the two-level PB design was performed to screen medium components that significantly influence the FMN production. Fifteen variables tested and KH2PO4, CaCl2, (NH4)6Mo7O24, CuSO4 and YE, were identified as the most significant factors for FMN production (confidence levels above 95%; Yatsyshyn et al., 2010). Another study was on the production optimization of a-amylase from Aspergillus oryzae CBS 819.72 fungus, using a by-product of wheat grinding (gruel) as sole carbon source. The screening of 19 nutrients for their influence on a-amylase production was performed using a PB design and KH2PO4, urea, glycerol, (NH4)2SO4, CoCl2, casein hydrolysate, soybean meal hydrolysate, MgSO4 were selected based on their positive influence on enzyme formation (Kammoun et al., 2008). Poly-"-lysine ("-PL) is a non-toxic biopolymer with antimicrobial properties. The "-PL production by Treptomyces noursei NRRL 5126 in shake-flask cultures was optimized by identifying the most significant medium components (glycerol, proteose peptone and ammonium sulfate) by Placket–Burman design and glycerol and glucose was decided to be the most effective components of the medium (Bankar & Singhal, 2010). For the production of laccase by Pleurotus florida NCIM 1243, 11 components were screened for their significant effect PB factorial design. At the end of the statistical analysis glucose (carbon source), asparagine (nitrogen source), CuSO4 (inducer) and incubation period were found to have highest positive influence on the laccase production (Palvannan & Sathishkumar, 2010). Taguchi design The history of the Taguchi method is based in Japan. The engineer Dr. Genichi Taguchi conducted extensive research on the design of statistical experiment techniques at the

Glucose (15–60 g/L), nitrate (15–90 mM), phosphate (0.5–2.5 mM), MgSO47H2O (0.18–0.74 g/L), inoculum level (0.22–0.88 g/L) Substrate concentration (10–50 g/L), Tween 80 concentration (0.05–0.1%), b-glucosidase dose (50–200 IU/g), cellulase concentration (5–15 FPU/g), hydrolysis time (12–48 h), agitation (100–150 rpm) Sucrose (20–30 g), urea (1–3 g), yeast extract (0.5–2 g), KH2PO4 (0.5–2 g) MgSO47H2O (0.2–1.2 g), CaCl24H2O (0.2–1.2 g), (NH4)6Mo74H2O (0.0012–0.1 g), ZnSO47H2O (0.000308–0.1 g), CuSO45H2O (0.000039–0.001 g), CoCl26H2O (0–0.005 g), Fe(NH4)2(SO4)26H2O (0–0.001 g), C6H5O7Na3NH2O (0–1 g), BeSO45H2O (0–0.124 g), NaF (0–0.042 g) (KH2PO4, CaCl24H2O, (NH4)6Mo74H2O, BeSO45H2O yeast extract have strong effect on flavin mononucleotide production) Corn starch (112–168 g/L), a-amylase (0.08–0.12 g/L), soya flour (22.4– 33.6 g/L), yeast extract (8–12 g/L), Na2MoO42H2O (0.0176–0.0264 g/L), MnSO4H2O (0.00184–0.00276 g/L), (NH4)2SO4 (0.2–0.3 g/L), CoCl2 (0.016–0.024 g/L), CaCO3 (0.64–0.96 g/L) (Corn starch has positive and also yeast extract has negative effect on avermectin B1a production) K2HPO4 (0.1–0.2 g/g), KH2PO4 (0.1–0.2 g/g), NaCl (0.05–0.1 g/g), MgSO4 (0.05–0.1 g/g), yeast extract (0.1–0.2 g/g), corn step liquor (0.1– 0.2 g/g), soyabean meal hydrolysate (0.1–0.2 g/g), urea (0.1–0.2 g/g), casein acid hydrolysate (0.1–0.2 g/g), glycerol

Design-Expert Version 5.0.9 (StatEase Corporation, USA)

Media optimization for cell growth and azadirachtin production in Azadirachta indica (A. Juss) suspension cultures Enzymatic hydrolysis optimization of wheat straw and ethanol production

Statistica 6.0 (Stat Soft, Inc.)

Minitab 15.0

SPSS (Version 11.0.12001, LEAD Technologies, Inc., USA)

Production of flavin mononucleotide by the recombinant strain of the Candida famata

Production of avermectin B1a by Streptomyces avermitilis 14-12A

a-Amylase production by Aspergillus oryzae CBS 819.72

Design Expert (Statease 6.1 trial version)

Factors studied

Software

Process

Table 2. Plackett–Burrman design applications in bioengineering.

KH2PO4, urea, glycerol, (NH4)2SO4, CoCl2, casein hydrolysate, soybean meal hydrolysate, MgSO4 had positive influence on enzyme formation

Maximum avermectin B1a production is observed with 168 g/L corn starch and 9.2 g/L yeast extract

Maximum flavin mononucleotide production was identified as 133.7 mg/L

Maximum dry cell weight and azadirachtin production were 21.2 g/L and 2.65 mg/g, respectively Substrate concentration, hydrolysis time, b-glucosidase and cellulase dosage were significant at confidence level of more than 95% on enzymatic hydrolysis

Response


(continued )

Kammoun et al. (2008)

Gao et al. (2009)

Yatsyshyn et al. (2010)

Singh & Bishnoi (2012)

Prakash & Srivastava (2005)

References

DOI: 10.3109/07388551.2014.973014


Palvannan & Sathishkumar (2010)

Glycerol and glucose has more effect on poly-"-lysine production level which are 41.81 ± 0.66 and 37.43 ± 0.18 mg/L, respectively Glucose, sucrose, raffinose, asparagine, KNO3, CuSO4, incubation period have positive effect on laccase production NA Production of laccase from Pleurotus florida NCIM 1243

NA: Not available.

NA Optimization of poly-"-lysine production by Streptomyces noursei NRRL 5126

(0.1–0.2 g/g), (NH4)2SO4 (0.1–0.2 g/g), CoCl2 (0.1–0.2 g/g), NH3 (0.1–0.2 g/g), CaCl2 (0.05–0.1 g/g), ZnCl2 (0.05– 0.1 g/g), FeCl3 (0.05–0.1 g/g), MnSO4 (0.05–0.1 g/g), Tween 80 (0.05– 0.1 g/g) Glycerol (3–7%), proteose peptone (0.2–0.6%), (NH4)2SO4 (0.4–1.2%), FeSO4 (0.001–0.005%), ZnSO4 (0.002–0.006%), MgSO4 (0.003–0.07%) Glucose (5–15 g/L), sucrose (5–15 g/L), raffinose (5–15 g/L), starch (5–15 g/L), asparagine (2–10 g/L), urea (2–10 g/L), KNO3 (2–10 g/L), xylidine (0.2– 2 mM), CuSO4 (50–500 mM), alcohol (0.2–2 mM), incubation time (120–240 h)

Response Software

Factors studied


Process

Table 2. Continued


Bankar & Singhal (2010)


References

8

Electronic Control Laboratory at the end of the 1940s. His fundamental goal was to achieve user-friendly experimental design. Taguchi developed several new approaches for experimental design in several scientific areas that are termed ‘‘Taguchi Methods’’. Taguchi design is type of fractional factorial design that uses an orthogonal array. The method enables experiments to be studied using a comparatively small number of runs while the effects of many factors on a response are studied at two or more levels. A method was developed by Taguchi for experimental design to investigate the affect of mean and variance of different parameters on process. It involves the usage of orthogonal arrays (OAs) to organize the parameters. Instead of testing all combinations like factorial design Taguchi method uses pairs of combinations. The Taguchi method can be effectively used when the number of variables is between 3 and 50. Taguchi arrays can be drawn out manually or found online. The arrays can be selected by the number of parameters and number of levels. Optimization of the performance can be done by Analysis of Variance, Fisher exact test or Chi-squared test (Briggs, 2011; John, 1996; Rao et al., 2008; Wang & Wan, 2009). Taguchi methods have some advantages over other methods. Taguchi methods take advantage of two-, threeand multi-level factor analysis. Taguchi designs are similar to familiar fractional factorial designs. However, Taguchi designs are widely used in scientific studies and industrial engineering because they have been used to establish several well-known experimental methods. Especially, Taguchi methods can rapidly, accurately and precisely provide technical information regarding experiments that are used to design and produce low cost, highly reliable products and processes. Applications that are used to produce Taguchi designs allow engineers to create flexible technology for the design and production of high quality products and to dramatically reduce research, development and delivery time (Rao et al., 2008). The Taguchi method has been successfully applied to the optimization of microbial fermentation medium components in bioengineering experiments and has been used to determine the major component involved or the quantity of a component. Different OAs are used in different optimization experiments. Several studies that are arranged using different OAs are shown in Table 3. Several requirements are necessary for microbial activities; macro- and micro-nutrients are important for biomass production, which is directly related to the valued output of a process. If any one of these nutrients is below the normal limit, microbial growth and product quality is directly affected. Therefore, the optimization of medium components has found increasing importance in bioengineering applications. Teng & Xu (2008) used the Taguchi method and RSM for optimize lipase production. The authors used an L18 (21 37) orthogonal array and studied many factors simultaneously. At the end of study, the authors recorded whole-cell lipase activity that was two times higher than a non-optimized condition using their method. The Taguchi method has the important advantage of requiring fewer runs than onefactor-at-a-time (OFAT) experimental methods. The results show that the optimum conditions for lipase production are as follows: agitation 200 rpm, inoculum 3.3–108 spores/L,

Qualitek-4 (Nutek Inc., Bloomfield Hills, MI)

NA


Statistica 8.0 (StatSoft Software, Inc.)


L18 (21 37)

L8 (27)

L27 (310)

L18 (21 37)

L18 (21 37)

Whole-cell lipase production in submerged fermentation by Rhizopus chinensis using statistical method

Griseofulvin production in a batch bioreactor

Biodegradation of 4-chlorophenol by Candida tropicalis PHB5

Hydrogen production by anaerobic fermentation of cow manure

Laccase production by Pleurotus ostreatus 1804

Software SPSS Version 11.0.1 (2001, LEAD Technologies, Inc., USA)

7

L18 (2 3 )

1

Taguchi design model

a-Amylase production by Aspergillus oryzae CBS 819.72

Process

Table 3. Taguchi design applications in bioengineering.

MgSO4 (0–0.1 g/g), KH2PO4 (0–0.1–0.2 g/g), urea (0–0.25–0.5 g/g), soybean meal hydrolysate (0–0.25–0.25 g/g), caseinic acid hydrolysate (0–0.25–0.5 g/g), glycerol (0–0.25–0.5 g/g), (NH4)2SO4 (0–0.05– 0.1 g/g), CoCl2 (0–0.05–0.1 g/g) Agitation (150–200 rpm), inoculum (3.3 106–3.3 107–3.3 108 spores/ L), maltose (0.5–1.5–2.5% w/v), olive oil (0.5–1.5–2.5% w/v), peptone (4–6– 8%), pH (5–6–7), K2HPO4 (0.1–0.2– 0.3%), fermentation volume (20–30– 40 mL/250 mL) Sucrose (37.5–50 g/L), K2HPO4 (1.25–1.5 g/L), NaNO3 (2.25–3.75 g/L), FeSO45H2O (0.005– 0.0125 g/L), pH (5.5–7.5), agitation (150–250 rpm), aeration rate (4–6 vvm) MgSO47H2O (0.1–0.2–0.3 g/L), phosphate ion (0.4–0.5–0.6 g/L), NaCl (0.04–0.05–0.06 g/L), yeast extract (0.5–1–1.5 g/L), CaCl22H2O (0.01– 0.02–0.03 g/L), temperature (28–30– 32 C), pH (5–6–7), inoculum size (1–2–3 % v/v), incubation time (50–55–60 h), agitation (120–150– 180 rpm), trace element solution (1–2–3 % v/v) Agitation (150–250 rpm), pH (5–6–7), temperature (50–60–70 C), substrate concentration (22.15–30.86– 39.57 g/L), K2HPO4 (3–4–5 g/L), ultrasound (20–30–40 W) pH (5.0–5.5), glucose (1–1.5–2 g/L), wheat bran (1–2–3 g/L), urea (0.5– 0.75–1 g/L), inoculum (5–7–10 discs), yeast extract (1–1.5–2 % w/v), inducer (0.5–1–1.5 mM), K2HPO4 (0.15–0.2– 0.25 % w/v)

Factors studied

Maximum laccase activity was determined as 716.9 U/mL

Temperature and pH were identified as significant parameters for hydrogen production

Maximum biomass and residual 4-chlorophenol concentration were defined as 752.5 mg/L, 501.03 respectively

Inoculum level, fermentation volume, olive oil and peptone had positive effect on lipase production and increased lipase production upon to two times Maximum griseofulvin production was identified as 1.19 g/L

After optimization of studied factors, a -amylase production was increased from 40.1 to 151.1 U/mL

Response


(continued )

Prasad et al. (2005)

Wang et al. (2012)

Basak et al. (2013)

Venkata-Dasu et al. (2003)

Teng & Xu (2008)

(Kammoun et al., 2008)

References

DOI: 10.3109/07388551.2014.973014




Qualitek-4 Version 4.82.0 (Nutek Inc., Bloomfield Hills, MI) Design Expert Version 6.07 (State-Ease, Inc, statistic made Easy Minneapolis, MN) Qualitek-4 (Nutek Inc., Bloomfield Hills, MI)

NA

L18 (21 37)

L16 (45)

L27 (313)

L18 (21 35)

Hydrogen production and wastewater treatment processes

Polyhydroxy alkanoates (PHA) synthesis by mixed culture

Ferrous bio-oxidation rate in a packed bed bioreactor

Optimization of lipase-catalyzed synthesis of xylitol ester

Tannase production by Bacillus licheniformis KBR6 in submerged culture

Maximum tannase produced was 0.43 U/mL

Maximum xylitol yield was 97.4%

Maximum Fe2+ bio-oxidation rate was determined as 7.8 g/L/h.

Das Mohapatra et al. (2009)

Adnani et al. (2010)

Mousavi et al. (2007)

Mohan & VenkateswarReddy (2012)

Mohan et al. (2009)

Maximum hydrogen productions were 1.92 mmol H2/day, 0.52 mol H2/kg COD-day whereas substrate degradation rate was 4.56 kg COD/ m3-day

Feed composition, organic loading rate (0.863–1.986–2.560–3.843–5.892 kg COD/m3day), inlet pH (5.5–7), nature of anaerobic mixed inoculums (anaerobic mixed culture treating distillery wastewater, anaerobic mixed culture treating chemical wastewater), nature of pre-treatment (heat-shock, chemical, untreated, combined pre-treatment of heat-shock, pH and chemical) FeCl3 (yes–no, 50 g/L), glucose (0–6– 12 g/L), volatile fatty acid (3–6–9 g/L), a:p:b (70:10:20–25:50:25–20:10:70 %), nh4cl (100–200–300 mg/L), K2HPO4 (50–100–150 mg/L), pH (5–7–8), microenvironment (aerobic– microaerophilic–anaerobic) Temperature (50–55–60–65 C), initial pH (1–1.5–2–2.5), dilution rate (0.1–0.2–0.3–0.4 h1), initial Fe3+ concentration (1–3–5–7 g/L), aeration rate (100–150–200–250 mL/min) Reaction time (7–18–24 h), temperature (30–50–60 C), enzyme amount (0.05–0.12–0.3 g), amount of molecular sieve (1–2.5–4 g), substrate molar ratio (0.33–0.5–1), xylitol concentration (0.015–0.008–0.005 g/mL) Temperature (30–35–40 C), pH (4–5), tannic acid concentration (0.5–1–1.5 g/ 100 mL), phosphate concentration (0.15–0.30–0.45 g/100 mL), nitrogen concentration (0.35–0.7–1.05 g/ 100 mL), Mg2+ ion concentration (0.05–0.1–0.15 g/100 mL Microenvironment, pH and glucose concentration had strong effect on bioplastic production

References

Response

Factors studied


NA: Not available

Software

Taguchi design model

Process

Table 3. Continued




DOI: 10.3109/07388551.2014.973014

maltose 0.5% (w/v), olive oil 1.5% (w/v), peptone 4% (w/v), K2HPO4 0.3% (w/v), fermentation volume 20 mL/250 mL flask and pH 5.5 (Teng & Xu, 2008). Taguchi design commonly used for optimization of enzyme production such as lipase (Adnani et al., 2010), tannase (Das Mohapatra et al., 2009), a-amylase (Kammoun et al., 2008) and laccase (Prasad et al., 2005). Candida tropicalis PHB5 is able to grow on 4-chlorophenol and metabolize this substrate. The Taguchi orthogonal array design methodology was used to improve the 4-chlorophenol biodegradation ability of strain PHB5. Experiments were undertaken to study the effects of eleven decided that biomass yield on 4-chlorophenol could be increased from 409.7 mg/L to 1046.9 mg/L and the amount factors affecting the growth of C. tropicalis on 4-chlorophenol and its degradation and it has been of 4-chlorophenol degraded could be increased from 314.5 mg/L to 949.7 mg/L (Basak et al., 2013). Venkata-Dasu et al. (2003) optimized griseofulvin production in a batch reactor with 8 runs using L8 (27). The authors determined the values of significant parameters for production. Sucrose, K2HPO4, NaNO3, FeSO45H2O, pH, agitation and aeration rate were tested as factors (VenkataDasu et al., 2003). Wang et al. (2012) used Taguchi Design to determine the most effective parameter values for the production of biohydrogen from wastewater using an L18 (21 37) orthogonal array; the factors tested were agitation, pH, temperature, substrate concentration, K2HPO4, concentration and ultrasound. At the end of the study, the authors reported that the most important factors were temperature, pH, substrate concentration, ultrasound, K2HPO4 concentration and agitation (Wang et al., 2012). Combined process efficiency with respect to fermentative hydrogen (H2) production and wastewater treatment was also evaluated in a series of batch experiments to detect the role of selected factors such as origin of inoculum, pre-treatment, inlet pH and feed composition under anaerobic microenvironment using mixed culture (Mohan et al., 2009). Another study based on polyhydroxy alkanoates (PHA) synthesis by mixed culture using L18 (21 37) Taguchi design showed that microenvironment, pH and glucose concentration had strong effect on bio-plastic production (Mohan & Venkateswar-Reddy, 2012). Mousavi et al. (2007) studied on process parameter optimization of the ferrous bio-oxidation rate by immobilization of a native Sulfobacillus species on the surface of low density polyethylene particles in a packed-bed bioreactor using Taguchi method. Five control factors, including temperature, initial pH of feed solution, dilution rate, initial concentration of Fe and aeration rate in four levels are considered in Taguchi technique. L16 orthogonal array has been used to determine the signal to noise (S/N) ratio (Mousavi et al., 2007).

Response surface methodology Response surface methodology (RSM) is used for developing, improving and optimizing processes based on various statistical and mathematical techniques. The main objective is to identify the conditions yielding the highest production using a


11

small number of experiments that provide a large amount of information (Erkucuk et al., 2009). Several RSM designs exist. In this review, Box–Behnken design (BBD) and central composite design (CCD), and their application to bioengineering processes, are discussed in detail. Box–Behnken design A BBD is a three-level fractional factorial design developed by Box and Behnken that is applied to determine the nature of the response surface in an experimental region (Majumder et al., 2009; Wang & Wan, 2009). The design is a combination of a two-level factorial design with an incomplete block design and in each block, a certain number of factors are put through all combinations for the design, while other factors are kept at the central levels. This design has several advantages, such as having three levels that can be coded as 1 (low), 0 (middle) and +1 (high; Majumder et al., 2009), creating an independent quadratic design (Das et al., 2012) and providing an easier way to arrange and to interpret the results. The design includes an incomplete block design, and each block consists of the maximum and minimum values, factorial design values and the central values of the factors (Majumder et al., 2009). Box–Behnken design also provides a time-consuming (Yin et al., 2011) and an economical alternative to CCD, because it has fewer factor levels and does not contain extremely high or low levels. BBD is widely used in bioengineering processes as it decreases the number of experimental sets (Table 4). For example, Coban et al. (2012) studied the effects of three factors (inoculum ratio, reducing sugar concentration and fermentation time) on bioethanol fermentation yield response using a BBD and 15 runs whereas Gharibzahedi et al. (2012) studied the optimization of canthaxanthin production using CCD with 20 runs. The correlation between the variables and responses can be determined by fitting to a second-order polynomial equation [Equation (2); Majumder et al., 2009]. Y ¼ 0 þ

X

i Xi þ

X

ii Xi2 þ

X

ij Xi Xi

ð2Þ

where Y is the predicted response variable; Xi and Xj (i ¼ 1, 3; j ¼ 1, 3, i 6¼ j) represent the independent variables and o, i, ii and ij are constant regression coefficients of the model. The BBD also has the important advantage of requiring fewer runs than OFAT experimental methods. This design provides enough data to fit quadratic models for bioengineering applications. For example, Chavalparit & Ongwandee (2009) studied the optimization of an electrocoagulation process for the treatment of biodiesel wastewater using quadratic regression models. The authors used a BBD for three factors (initial pH, applied voltage and reaction time), involving three blocks and a set of 15 experimental runs to optimize the operating conditions. The results showed that chemical oxygen demand (COD), oil and grease and suspended solids (SS) were effectively reduced (Table 4) by electrocoagulation under the following optimum conditions: pH 6.06, applied voltage 18.2 V, and reaction time 23.5 min (Chavalparit & Ongwandee, 2009).

94–98

90

97

98.3

27

15

27

29

17

10

29

17

15

Bacterial cellulose production

Extracellular 5-aminolevulinic acid (ALA) yield

Activity of heterotrophic nitrification–aerobic de-nitrification

Polysaccharides production

Enzyme activity (Laccase, Lip, MnP, Xylanase, CMCase)

Xylanase activity

Removal of chromium (VI) %

Xylanase production

Synthesis dipeptide derivative, NAc-Phe-Gly-NH2, catalyzed by immobilized a-chymotrypsin in a stirred tank bioreactor

92.71

97

94.5–99.4

92

98

NA

15

Bioethanol fermentation yield

R2

Number of runs

Response

Table 4. Box–Behnken design applications in bioengineering.

5 g/L 15 g/L 8.46 8.28 27.9 C 150 rpm 61.8 C 4.14 3.2 h 2.1% (w/v) Optimum level for all enzymes was different

Glycine LA Initial pH C/N ratio Temperature Agitation Extraction temperature Extraction pH Extraction time Complex enzyme amount Moisture content

50–60 mg/L 2 1.05 g 25 C 1.27 5.0% (v/v) 7.4 92.3 min

36.2 C 8.7

Surfactant Urea Initial moisture Inoculum age Harvest time Initial chromium concentration pH Cyanobacterial dose Temperature Concentration of urea Inoculum size pH of the medium Reaction time

Reaction temperature pH

Not in the domain of the experiment

30 days 6% 135 rpm 10 g/L

Incubation period Size of inoculum Agitation Succinate

Malt extract NH4Cl Yeast extract

1.25% 20 g/L 2 days 30 (g/L)

Optimized conditions

Inoculum (S. cerevisiae) ratio Reducing sugar concentration Fermentation time Carbohydrate of maple syrup

Variables

a-chymotrypsin

Ju et al. (2012)

Biswas et al. (2010)

Mona et al. (2011)

Narang et al. (2001)

Sharma & Arora (2010)

Yin et al. (2011)

Chen & Ni (2012)

Choorit et al. (2011)

Zeng et al. (2011)

Coban et al. (2012)

References


Melanocarpus albomyces IITD3A

Nostoc linckia

Melanocarpus albomyces IIS68

Phlebia floridensis

Tricholoma matsutake

Agrobacterium sp. LAD9

Rhodopseudomonas palustris KG31

Acetobacter xylinum BPR 2001

S. cerevisiae

Biocatalyst/substrate



98.65

NA

96 NA

12

15

29

15

15

15

17

15

30

24

Hydrogen production yield

Microalgae cell dry weight

Ethanol productivity

Glucan production

Spirulina biomass productivity, Specific growth rate, Doubling time

HLC III synthesis

Methane yield

Removal efficiencies of Chemical oxygen demand (COD) and oil and grease (O&G), suspended solid (SS)

CMCase activity, b-glucosidase activity, FPase activity, Xylanase activity

Yield of starcholeate

93

96

96.09

99.7

99

98

93.2

13


97

15


175 W/m2 35 mM 4.5 mM 5.15 3.49 mol 1.9 g/L 60 C 6.5 10 g/L 1 g/L 20 g/L 5 g/L 5.5 30 C 3.3% 6.5% 5.95% 0.52% 2.9% 0.40 g/L 40% 20% 3.2 h 12.6 h 6.7 50% 3d 50% 6.06

18.2 V 23.5 min 86–88 h 6.1–6.2 1.1 10.5 g 44 C 386 IU 0.18

Light intensity Glucose Glutamate Initial pH Glucose Linoleic acid Temperature pH Substrate concentration Bacteriological peptone Glucose Glycerol pH Temperature Inoculum level TRS concentration Sucrose Peptone Yeast extract Blend concentration Renewal rate Zarrouk medium Induction time Inoculum age pH Ca(OH)2 concentration Pretreatment time Inoculum Initial pH

Applied voltage Reaction time Fermentation time pH of the medium Substrate ratio (WB:RS) Substrate amount Reaction temperature Amount of immobilized lipase Starch/oleic acid molar ratio

Staphylococcus aureus (SAL3)

Aspergillus fumigatus ABK9

With using electrocoagulation (EC) system for treatment

Rice straw

Escherichia coli

Spirulina platensis

Leuconostoc dextranicum NRRL B-1146

Saccharomyces cerevisiae

Chlorella saccharophila

Thermophilic mixed culture

Mixed anaerobic mesophilic culture

Rhodobacter capsulatus JP91


(continued )

Horchani et al. (2010)

Das et al. (2012)

Chavalparit & Ongwandee (2009)

(Song et al., 2012)

Zhang et al. (2010)

Radmann et al. (2007)

Majumder et al. (2009)

Singh & Bishnoi (2013)

Isleten-Hosoglu et al. (2012)

Roy et al. (2012)

Ray et al. (2010)

Ghosh et al. (2012)

DOI: 10.3109/07388551.2014.973014


Akgun et al. (2011)

Erkucuk et al. (2009)

15 Yield of total alkannin

NA: Not available.

15 Stevioside and rebaudioside A yields

96.65

80 C 175 bar 5 g/min

80 C 17.4%

29 Xylanase activity

91.5 89.9

Temperature Concentration of ethanol–water mixture Temperature Pressure CO2 flow

Alkanna tinctoria

Steviare baudiana

Haddar et al. (2012) Bacillus mojavensis A21

Biocatalyst/substrate Optimized conditions

18.66 g/L 1.04 g/L 176 rpm 34.08 h 211 bar

Variables

Barleybran NaCl Agitation Cultivation time Pressure

Number of runs

R2


Response

Table 4. Continued


References


98

14

BBD is also useful for optimizing ethanol fermentation (Coban et al., 2012; Singh & Bishnoi, 2013); biological compounds (Akgun et al., 2011; Choorit et al., 2011; Erkucuk et al., 2009; Horchani et al., 2010; Ju et al., 2012; Majumder et al., 2009; Yin et al., 2011; Zhang et al., 2010); enzymes (Biswas et al., 2010; Das et al., 2012; Haddar et al., 2012; Narang et al., 2001; Sharma & Arora, 2010); bacterial cellulose (BC) production (Zeng et al., 2011); heterotrophic nitrification and aerobic denitrification (Chen & Ni, 2012); chromium removal (Mona et al., 2011); COD, O&G and SS (Chavalparit & Ongwandee, 2009); hydrogen (Ghosh et al., 2012; Ray et al., 2010; Roy et al., 2012) and microalgae production (Isleten-Hosoglu et al., 2012; Radmann et al., 2007) and methane yield (Song et al., 2012). As an example, Ghosh et al. (2012) studied the effects of glucose, glutamate concentration and light intensity on hydrogen production from glucose via single-stage photofermentation using a Box– Behnken design. The authors concluded that under optimal conditions with a light intensity of 175 W/m2, 35 mM glucose and 4.5 mM glutamate, a maximum hydrogen yield of 5.5 (±0.15) mol H2/mol glucose was obtained (Ghosh et al., 2012). Another study used a Box–Benkhen design and several initial pH conditions in a mixed batch anaerobic mesophilic culture fed with glucose and linoleic acid (LA) to optimize hydrogen production yield. The optimal values of initial pH, glucose and LA concentration for maximum hydrogen production yield were obtained as 5.15, 3.49 mol and 1.9 g L1, respectively (Ray et al., 2010). Sharma & Arora (2010) applied BBD to investigate the effects of moisture content, inorganic nitrogen source (NH4Cl) and malt extract on lignocellulolytic enzymes during solid state fermentation. Optimum manganese peroxidase production was attained (50–55 mg/g of WS) with increased moisture content and laccase production. These supplements also significantly (p50.05) enhanced the production of CMCase and xylanase (Sharma & Arora, 2010). Bacterial cellulose (BC, a polymer of glucose units) is produced by the bacterium Acetobacter xylinum BPR 2001. Zeng et al. (2011) optimized the factors of maple syrup carbohydrate (30 g carbohydrate/l), incubation period (30 days), size of inoculum (6%) and rotation speed (135 rpm) using a three-level, four-factor (27 run) BBD to increase BC production (Zeng et al., 2011). A Box–Behnken design was used to optimize environmental factors such as methane yield, nitrification–denitrification and the removal of COD. Chen & Ni (2012) investigated the effects of initial pH, C/N ratio, temperature and shaking speed on the activity of heterotrophic nitrification–aerobic denitrification using the strain LAD9. It was concluded that the maximum removal of ammonium occurred under the following conditions: initial pH, 8.46; C/N ratio, 8.28; temperature, 27.9 C and shaking speed, 150 rpm; temperature and shaking speed were found to have the largest effect (Chen & Ni, 2012). Mona et al. (2011) also used BBD to analyze the effectiveness of cyanobacterial biomass (Nostoc linckia) for the biosorption of Cr(VI) from aqueous waste using a laboratory-scale batch fermentor. The process parameters used in this study were as follows: initial metal concentration (10–100 mg/L), pH (2–6), temperature (25–45 C) and


DOI: 10.3109/07388551.2014.973014

cyanobacterial dose (0.1–2.0 g). Maximum Cr(VI) biosorption was obtained at pH 2–4 with an initial metal concentration of 10–55 mg/L in the aqueous solution (Mona et al., 2011). Song et al. (2012) aimed to maximize methane yield using BBD. The effect and interactions of Ca(OH)2 concentration (LC), pre-treatment time (PT) and inoculum amount (IA) on biogas production rates were evaluated at mesophilic temperature. It was concluded using 17 runs that optimum methane yield was achieved under the following conditions: LC, 50%; PT, 3 days; and IA, 50% (Song et al., 2012). Ju et al. (2012) studied the effects of various parameters on the synthesis of a derivatized dipeptide, N-Ac-Phe-Gly-NH2, which was catalyzed by immobilized a-chymotrypsin in a stirred tank bioreactor. A 3-factor, 3-level BBD predicted using 15 runs that the optimum conditions were as follows: reaction time, 92.3 min; reaction temperature, 36.2 C; and pH, 8.7. R2 and synthesis yield were 98.3 and 82.26%, respectively. Reaction time and pH significantly affected the yield of N-Ac-Phe-Gly-NH2, and the a-chymotrypsin–Fe3O4– CS nanoparticles could be used to synthesize the dipeptide (Ju et al., 2012). Repeated batch cultivation of S. platensis was studied in open raceway ponds at 30 C by Radmann et al. (2007). Using a light intensity of 3000 lux and a 12-h photoperiod for a total cultivation time of 60 days, three different dilutions were optimized. It was found that the productivity of S. platensis was 0.028–0.046 g L1 day1, and the maximum specific growth rate (mmax) changed from 0.038 to 0.138 day1 (Radmann et al., 2007). A study conducted by Das et al., (2012) investigated the influence of four parameters [fermentation time (86–88 h), medium pH (6.1–6.2), substrate amount (10.0–10.5 g) and substrate ratio (wheat bran:rice straw) (1.1)] on enzyme production. Endoglucanase, BG, FPase (filter-paper degrading activity) and xylanase activities of 826.2, 255.16, 102.5 and 1130.4 U/g, respectively, were achieved. It was concluded that all factors were important, and optimal combinations were at the midpoints of the edges as well as at the center of the cubical design region (Das et al., 2012; Haddar et al., 2012). Stevia rebaudiana extracts are used as a low-calorie sweetener and as a medicinal plant, and also for the production of stevioside and rebaudioside A. To determine the effects of pressure (150–350 bar), temperature (40–80 C) and ethanol percentage (0, 10 and 20% in water) on the glycoside composition of Stevia rebaudiana, a 15-run BBD was applied. The optimum conditions were determined to be 211 bar, 80 C and 17.4% ethanol, and these conditions yielded 36.66 mg/g stevioside and 17.79 mg/g rebaudioside A (Erkucuk et al., 2009). Central composite design A CCD is defined as a five-level fractional factorial design that uses external points and one point at the center of the experimental region (Tarley et al., 2009). This central point is supposed to have several properties, such as rotatability or orthogonality, to fit quadratic polynomials (Boey et al., 2011; Ruiz et al., 2011).


15

The CCD is an important alternative to the full factorial, three-level design as it requires fewer experiments while giving comparable results. Therefore, CCD is the most accepted experimental design for second-order models. CCD is superior to the OFAT method because it is possible to reveal corresponding interactions among the factors with CCD using a smaller number of experiments. In addition, the use of statistical approaches may expose the importance of individual factors and the effect of each factor on the response. Zain et al. (2007) identified the optimum carbon and nitrogen concentrations in a feed medium for obtaining maximum CGTase production using CCD; in addition, the interaction between the C/N ratio and the biomass production was evaluated as a secondary response. The authors determined the optimum concentrations of carbon and nitrogen sources to be 3.30% (w/v) and 0.13% (w/v), respectively, and these values led to a maximum CGTase activity of 80.12 ± 1.43 U/mL (Zain et al., 2007). CCD is also useful for bioprocesses that optimize the culture conditions for bioethanol (Dagnino et al., 2013; Harun & Danquah, 2011), biodiesel (Lee et al., 2011; Vicente et al., 1998; Yu¨cel, 2012), enzyme (Chen et al., 2002; Donzelli et al., 2005; Fakhfakh-Zouari et al., 2010; Heck et al., 2005; Sathya et al., 1998; Sifour et al., 2010), biological compounds (Correâ et al., 2012; Hollebeeck et al., 2012) and protein (Larentis et al., 2011; Qu et al., 2010) production and for COD removal (Ahmadi et al., 2005; Muthukumar et al., 2004) via microbial activities and cell culture techniques for intelligent bioprocessing (Domingues et al., 2010; Lim et al., 2007; Table 5). Gharibzahedi et al. (2012) also used CCD to evaluate the effect of three nutrient components, namely, D-glucose content (15–25 g/L), mannose content (10– 25 g/L) and Fe3+ concentration (20–40 ppm), on the growth of the D. natronolimnaea HS-1 mutant; biomass dry weight, total carotenoids and canthaxanthin production were the studied responses. The optimum fermentation medium composition was 25 g/L D-glucose, 15.12 g/L mannose and 36.77 ppm of Fe3+; there was no significant difference between the observed and predicted values (p50.05; Gharibzahedi et al., 2012). The results indicate that CCD is a key tool for optimizing the proportion of nutrient medium components and leads to the desired response variable goals. Xu & Ting (2004) determined optimal bioleaching conditions to achieve the maximum metal leaching efficiency for selected metals with 31 runs; four variables were studied, namely, sucrose concentration, inoculum spore concentration, fly ash pulp density and the time of addition of fly ash to the fungus Aspergillus niger. It was concluded that sucrose concentration and pulp density were more important factors than spore concentration or the time of addition of the fly ash (Xu & Ting, 2004). Bajpai et al. (2012) also used the RSM approach for predictive model building and the optimization of chromium adsorption onto activated carbon obtained from tamarind wood. As limited studies have been performed on chromium adsorption using the RSM approach using individual and combined effects, four process parameters [contact time, initial pH, initial Cr(VI) concentration and resin dose] were optimized with 30 runs to maximize the removal of hexavalent chromium. It was concluded that individual process parameters were more significant than the combined

0.9 NA

0.8319 0.92 0.9112

14

10

18

11

10

20

29

36

30

30

12

32

Polymethyl galacturonase production Biodiesel yield

Ethanol yield

Cellulase-free xylanase activity Glucans and xylose for bioethanol production Elastase production

b-1,3-Glucanase

Peptide activity



Pneumococcal surface adhesin A yield

Keratinases production

0.856

5.8% w/w 24 h 0.1 mM 25 C 16 h 30 g/L 3.151 g/L 2 g/L 0.5 g/L

Soy petone NaCl KCl

25 mM 0.8% 0.2% 107 spores/run 1.5% 5% 9.0 50 C 60 min 38.67 3.44 h 3.70 wt.% 115.87 C 40 C 5.3:1

0.1%

Ammonium ion Chitin Scleroglucan Inoculum Substrate Alcalase pH Temperature Hydrolysis time Methanol/oil molar ratio Reaction time Catalyst amount Reaction temperature Reaction temperature Molar ratio of methanol to oil Biocatalyst content Reaction time Inducer concentration Temperature Time Feather meal

Casein Corn steep flour K2HPO4 MgSO4.7H2O pH Glucose

1.7 mM 2.3 23 C 4.9 wt.% 0.54:1 30 C 2% 30 FPU 60 C 7 0.3% (w/v) 33 min Not in the domain of the experiment

Ornithine pH Temperature Catalyst amount MeOH/oil mass ratio Temperature Substrate Enzyme loading Temperature pH H2SO4 Time Glucose

Optimized conditions 70 mM

Variables Glycerol

Bacillus pumilus A1

recEscherichia coli

T. lanuginosus

Jatropha curcas

Porphyra yezoensis

Trichoderma atroviride strain P1

Bacillus sp. EL31410

Rice hulls

Bacillus coagulans BL69

Saccharomyces cerevisiae CA11

Aspergillus niger NCIM 548 Waste cockle shell

Streptomyces clavuligerus ATCC 27064

Biocatalyst/substrate

Fakhfakh-Zouari et al. (2010)

Larentis et al. (2011)

Yu¨cel (2012)

Lee et al. (2011)

Qu et al. (2010)

Donzelli et al. (2005)

Chen et al., (2002)

Dagnino et al. (2013)

Heck et al. (2005)

Ruiz et al. (2011)

Boey et al. (2011)

Sathya et al. (1998)

Domingues et al. (2010)

References


NA

0.9367

0.9899

0.9933

0.930

0.924

0.9078

12

Clavulanic acid production

R2 value

Number of runs

Response

Table 5. Central composite design applications in bioengineering.



0.997

0.94

0.988

0.88

17

20

31

20

15

30

13

Cyclodextrin glucanotransferase production

Canthaxanthin production

Gluconic acid production via bioleaching of fly ash

COD removal of acid dye effluent

Fenton’s peroxidation on the removal of COD from olive oil mill wastewater (OMW)

Removal of chromium (VI) from aqueous solution using weakly anionic resin

Baicalin-loaded nanoparticles carrier system

NA: Not available.

0.996

26

Glycerol-inducible lipase production

0.9976

0.9534

0.98

0.98

11

Oleic acid epoxide production

0.93

25

Bioethanol production

Aspergillus niger

Acid Red 88 dye

0.76% 0.76% 0.38% 3.30% (w/v) 0.13% (w/v) 25 g/L 15.12 g/L 36.77 ppm 156 g/L 0.6 107 % 2.7 0 days 5 g/L Alkaline 195 s 8.33

4 70% 62.5 min

1.96 145.4 mg/L 8.51 g/L 0.69% (w/v) 26.64% (w/w)

Tween 80 Glucose K2HPO4 Carbon concentration Nitrogen concentration content

Mannose content Fe3+ concentration Sucrose concentration Spore concentration Pulp density Time of addition Salt concentration pH Time H2O2-to-Fe(II) ratio

pH OMW concentration Contact time

Initial pH Initial Cr (VI) concentration Resin dose Lipid Drug/lipid ratio

D-glucose

10% 0.2% 3h 2.24%

Enzyme load Hydrogen peroxide load Reaction time Glycerol

Baicalin-loaded nanoparticles

Amberlite IRA 96 resin

Olive oil mill wastewater

Dietzia natronolimnaea HS-1

Bacillus sp. TS1-1

Geobacillus stearothermophilus strain-5

PSCI Amano lipase

Saccharomyces cerevisiae

0.5 g/L 1% (v/v) 15 g/L 140 C 30 min 55 C

KH2PO4 Acid concentration Temperature Microalgae loading Pre-treatment time Temperature


Hao et al. (2012)

Bajpai et al. (2012)

Ahmadi et al. (2005)

Muthukumar et al. (2004)

Xu & Ting (2004)

Gharibzahedi et al. (2012)

Zain et al. (2007)

Sifour et al. (2010)

Correâ et al. (2012)

Harun & Danquah (2011)

DOI: 10.3109/07388551.2014.973014



18



model of these parameters. Initial solution pH was the most significant parameter for chromium removal, followed by the resin dose. The optimum process conditions for chromium removal at 30 C using IRA 96 resin were as follows: contact time, 62.56 min; pH, 1.96; initial Cr(VI) concentration, 145.4 mg/L and resin dose, 8.51 g/L; a maximal removal of 93.26% was obtained (Bajpai et al., 2012). However, compared to CCD, OFAT is a classical approach and is time consuming, requires many experiments and is not able to detect interactive effects between factors. Moreover, the optimized process conditions obtained using this method are not reliable. CCD supported by statistical software and is a wellestablished approach for pharmaceutical formulation development and the optimization of few well-designed experiments; it is also suitable for pharmaceutical blending problems because it enables investigation with the least number of experiments (Gonzalez-Mira et al., 2010; Hao et al., 2012; Jin et al., 2008). The comparison of all experimental design methods For optimization studies in bioengineering applications, the complex polynomial function is solved to determine the interactions amongst the variables investigated. In addition, a minimum number of experimental runs is required to evaluate the interaction in the model with minimum time consumption and operational cost. Choosing the best experimental design depends on the number of the factors in the process (Table 6). Factorial design is the most common design for N-way ANOVA applications. In a factorial design, observations are made for each combination of the levels of each factor. Using a two-way design interaction, the effect of independent variables on dependent variables can be estimated. Thereby, interactions on every different level are revealed which cannot be observed via one way analysis. Main advantage of the factorial design is that it can lead more powerful test by reducing the error variance which is the main problem in t-tests. The disadvantage of the full factorial design is when the number of factors is 5 or greater, a large number of runs is required and is not very efficient (Fisher, 1935). CCD contains an imbedded factorial design with center points and is augmented with a group of axial points. One of the advantages of CCD is to reduce the number of experiments in the studies with a large number of factors and levels, while giving comparable results and corresponding interactions among the factors. In addition, CCD can provide high

Table 6. Summary table for choosing an experimental design (NIST/SEMATECH, e-handbook). Factors

Comparison

Screening

Optimization

1

One factor at a time Randomized design

Full or fractional factorial design

Central composite or Box–Behnken design

2–4

5 or more Randomized design

Taguchi or Plackett– Burrman Design

quality predictions in studying linear, quadratic and interaction effect factors which influence a system. Also, the CCD is robust and results in a model with high predictability, even if the data points are outside the tested domain. Compared to BBD, the R2 value from CCD generally shows higher value in case where both designs fit well into the linear polynomial model. Furthermore, CCD usually yields lower residual standard error value than BBD. Thus, CCD predicts more accurate data comparing to the actual experiment and is considered as the most accepted experimental design for second-order models. BBD method presents an alternative way to CCDs. They are a class of rotatable or nearly rotatable second-order designs based on three-level incomplete factorial designs (Ferreira et al., 2007). BBD has two main advantages over CCDs: (i) the first one is that they only require factors to be varied over three levels. This may decrease the cost of experiment if actual prototypes are being set in experimentation. (ii) The second one is that they usually (except for the five-factor case) require less total runs than the CCD (Lawson, 2010). BBD is a combination of a two-level factorial design with an incomplete block design and in each block, a certain number of factors are put through all combinations for the design, while other factors are kept at the central levels. This design has several advantages, such as having three levels that can be coded as 1 (low), 0 (middle) and +1 (high; Majumder et al., 2009), creating an independent quadratic design (Das et al., 2012) and providing an easier way to arrange and to interpret the results. The design includes an incomplete block design, and each block consists of the maximum and minimum values, factorial design values and the central values of the factors (Majumder et al., 2009). It also provides a time-consuming (Yin et al., 2011) and an economical alternative to CCD, because it has fewer factor levels and does not contain extremely high or low levels. The BBD has the important advantage of requiring fewer runs than OFAT experimental methods. This design provides enough data to fit quadratic models for many applications. The disadvantage of the BBD is having limited capability for orthogonal blocking compared to the CCD. Placket–Burman design is required when a large number of factors need to be screened for their effects at two levels. Various methods have been proposed to limit the number of candidate terms for experimental design model under study (Lawson, 2010). The PB factorial design can identify main factors from a large number of suspected contributor parameters for the desired response variables. Thus, these designs are enormously useful in preliminary studies where the aim is to identify variables that can be fixed or eliminated in further investigation (Vatanara et al., 2007). On the other hand, this design is that it allows a quick monitoring of all the factors through limited number of experiments. The weakness of the design is that replicates of design points are not included to permit the calculation of error terms (Li & Rasmussen, 2003). Experimental design using the Taguchi method is a powerful and effective approach to assist engineers to study and understand how several process parameters affect the process output without too much budget and time consumption (Antony, 1997). OAs employed in the Taguchi method

DOI: 10.3109/07388551.2014.973014

provide mainly three advantages to the users by: (i) reducing the number of experiments significantly, (ii) fostering the robustness of the process and (iii) minimizing the time consumption and operational cost (Hong, 2012; Chowdhurya et al., 2014). Last but not least, using the S/N ratio, to analyze the results helps to decrease the sensitivity of the system to sources of variation which provides better performance (Zolgharneina et al., 2014).

Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.


References Adnani A, Basri M, Malek EA, et al. (2010). Optimization of lipasecatalyzed synthesis of xylitol ester by Taguchi robust design method. Ind Crops Prod, 31, 350–6. Ahmadi M, Vahabzadeh F, Bonakdarpour B, et al. (2005). Application of the central composite design and response surface methodology to the advanced treatment of olive oil processing wastewater using Fenton’s peroxidation. J Hazard Mater, 123, 187–95. Akgun I, Erkucuk A, Pilavtepe M, Yesil-Celiktas O. (2011). Optimization of total alkannin yields of Alkanna tinctoria by using sub-and supercritical carbon dioxide extraction. J Supercrit Fluids, 57, 31–7. Antony J. (1997). Experiments in quality. J Manuf Eng, 76, 272–5. Bajpai S, Gupta S, Dey A, et al. (2012). Application of Central Composite Design approach for removal of chromium (VI) from aqueous solution using weakly anionic resin: modeling, optimization, and study of interactive variables. J Hazard Mater, 227, 436–44. Bankar SB, Singhal RS. (2010). Optimization of poly-"-lysine production by Streptomyces noursei NRRL 5126. Bioresource Technol, 101, 8370–5. Basak B, Bhunia B, Dutta S, Dey A. (2013). Enhanced biodegradation of 4-chlorophenol by Candida tropicalis PHB5 via optimization of physicochemical parameters using Taguchi orthogonal array approach. Int Biodeter Biodegrad, 78, 17–23. Biswas R, Sahai V, Mishra S, Bisaria VS. (2010). Bioprocess strategies for enhanced production of xylanase by Melanocarpus albomyces IITD3A on agro-residual extract. J Biosci Bioeng, 110, 702–8. Boey PL, Maniam GP, Hamid SA, Ali DMH. (2011). Utilization of waste cockle shell (Anadara granosa) in biodiesel production from palm olein: optimization using response surface methodology. Fuel, 90, 2353–8. Briggs B. (2011). Three quarter Plackett-Burman designs for estimating all main effects and two-factor interactions. Richmond (VA): Virginia Commonwealth University. Chavalparit O, Ongwandee M. (2009). Optimizing electrocoagulation process for the treatment of biodiesel wastewater using response surface methodology. J Environ Sci, 21, 1491–6. Chen Q, He G, Ali MAM. (2002). Optimization of medium composition for the production of elastase by Bacillus sp. EL31410 with response surface methodology. Enzyme Microb Technol, 30, 667–72. Chen Q, Ni J. (2012). Ammonium removal by Agrobacterium sp. LAD9 capable of heterotrophic nitrification–aerobic denitrification. J Biosci Bioeng, 113, 619–23. Cheng YS, Zheng Y, Yu CW, et al. (2010). Evaluation of high solids alkaline pretreatment of rice straw. Appl Biochem Biotechnol, 162, 1768–84. Choorit W, Saikeur A, Chodok P, et al. (2011). Production of biomass and extracellular 5-aminolevulinic acid by Rhodopseudomonas palustris KG31 under light and dark conditions using volatile fatty acid. J Biosci Bioeng, 111, 658–64. Chowdhurya A, Chakrabortyb R, Mitraa D, Biswasa D. (2014). Optimization of the production parameters of octyl ester biolubricant using Taguchi’s design method and physico-chemical characterization of the product. Ind Crops Prod, 52, 783–9. Coban I, Sargin S, Celiktas MS, Yesil-Celiktas O. (2012). Bioethanol production from raffinate phase of supercritical CO2 extracted Stevia rebaudiana leaves. Bioresource Technol, 120, 52–9.


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Correâ FA, Sutili FK, Miranda LSM, et al. (2012). Epoxidation of oleic acid catalyzed by PSCI-Amano lipase optimized by experimental design. J Mol Catal B Enzymatic, 81, 7–11. da Cunha MA, Converti A, Santos JC, et al. (2009). PVA-hydrogel entrapped Candida guilliermondii for xylitol production from sugarcane hemicellulose hydrolysate. Appl Biochem Biotechnol, 157, 527–37. Da Ro´s PC, de Castro HF, Carvalho AK, et al. (2012). Microwaveassisted enzymatic synthesis of beef tallow biodiesel. J Ind Microbiol Biotechnol, 39, 529–36. Dagnino E, Chamorro E, Romano S, et al. (2013). Optimization of the acid pretreatment of rice hulls to obtain fermentable sugars for bioethanol production. Ind Crops Prod, 42, 363–8. Das A, Paul T, Halder SK, et al. (2012). Production of cellulolytic enzymes by Aspergillus fumigatus ABK9 in wheat bran-rice straw mixed substrate and use of cocktail enzymes for deinking of waste office paper pulp. Bioresource Technol, 128, 290–6. Das Mohapatra P, Maity C, Rao R, et al. (2009). Tannase production by Bacillus licheniformis KBR6: optimization of submerged culture conditions by Taguchi DOE methodology. Food Res Int, 424, 430–5. de Arau´jo Viana D, de Albuquerque Lima C, Neves RP, et al. (2010). Production and stability of protease from Candida buinensis. Appl Biochem Biotechnol, 162, 830–42. Domingues LCG, Teodoro JC, Hokka CO, et al. (2010). Optimisation of the glycerol-to-ornithine molar ratio in the feed medium for the continuous production of clavulanic acid by Streptomyces clavuligerus. Biochem Eng J, 53, 7–11. Donzelli BGG, Siebert KJ, Harman GE. (2005). Response surface modeling of factors influencing the production of chitinolytic and b-1, 3-glucanolytic enzymes in Trichoderma atroviride strain P1. Enzyme Microb Technol, 37, 82–92. Erkucuk A, Akgun I, Yesil-Celiktas O. (2009). Supercritical CO2 extraction of glycosides from Stevia rebaudiana leaves: identification and optimization. J Supercrit Fluids, 51, 29–35. Fakhfakh-Zouari N, Haddar A, Hmidet N, et al. (2010). Application of statistical experimental design for optimization of keratinases production by Bacillus pumilus A1 grown on chicken feather and some biochemical properties. Process Biochem, 45, 617–26. Ferreira HS, Matos GD, David JM, et al. (2007). Box-Behnken design: an alternative for the optimization of analytical methods, S.L.C. Ferreira, R.E. Bruns. Anal Chim Acta, 597, 179–86. Fisher RA. (1935). The design of experiments. Oxford, England: Oliver & Boyd. 251 p. Fontes GC, Amaral F, Filomena P, et al. (2010). Factorial design to optimize biosurfactant production by Yarrowia lipolytica. J Biomed Biotechnol, 2010, 1–8. Gao H, Liu M, Liu J, et al. (2009). Medium optimization for the production of avermectin B1a by Streptomyces avermitilis 14–12A using response surface methodology. Bioresource Technol, 100, 4012–16. Gharibzahedi SMT, Razavi SH, Mousavi SM, Moayedi V. (2012). High efficiency canthaxanthin production by a novel mutant isolated from Dietzia natronolimnaea HS-1 using central composite design analysis. Ind Crops Prod, 40, 345–54. Ghosh D, Sobro IF, Hallenbeck PC. (2012). Optimization of the hydrogen yield from single-stage photofermentation of glucose by Rhodobacter capsulatus JP91 using response surface methodology. Bioresource Technol, 123, 199–206. Gonzalez-Mira E, Egea M, Souto E, et al. (2010). Optimizing flurbiprofen-loaded NLC by central composite factorial design for ocular delivery. Nanotechnology, 22, 045101. Haddar A, Driss D, Frikha F, et al. (2012). Alkaline xylanases from Bacillus mojavensis A21: production and generation of xylooligosaccharides. Int J Biol Macromol, 51, 647–56. Hao J, Wang F, Wang X, et al. (2012). Development and optimization of baicalin-loaded solid lipid nanoparticles prepared by coacervation method using central composite design. Eur J Pharm Sci, 47, 497–505. Harun R, Danquah MK. (2011). Influence of acid pre-treatment on microalgal biomass for bioethanol production. Process Biochem, 46, 304–9. Heck JX, Floˆres SH, Hertz PF, Ayub MAZ. (2005). Optimization of cellulase-free xylanase activity produced by Bacillus coagulans BL69 in solid-state cultivation. Process Biochem, 40, 107–12. Hendry D, Venkitasamy C, Wilkinson N, Jacoby W. (2011). Exploration of the effect of process variables on the production of high-value fuel


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gas from glucose via supercritical water gasification. Bioresource Technol, 102, 3480–7. Herculano PN, Porto TS, Moreira KA, et al. (2011). Cellulase production by Aspergillus japonicus URM5620 using waste from castor bean (Ricinus communis L.) under solid-state fermentation. Appl Biochem Biotechnol, 165, 1057–67. Hollebeeck S, Borlon F, Schneider YJ, et al. (2012). Development of a standardized human in vitro digestion protocol based on macronutrient digestion using response surface methodology. Food Chem, 138, 1936–44. Hong CW. (2012). Using the Taguchi method for effective market segmentation. Expert Syst Appl, 39, 5451–9. Horchani H, Chaaˆbouni M, Gargouri Y, Sayari A. (2010). Solvent-free lipase-catalyzed synthesis of long-chain starch esters using microwave heating: optimization by response surface methodology. Carbohydr Polym, 79, 466–74. Huguet L, Carteret C, Gantzer C. (2012). A comparison of different concentration methods for the detection of viruses present in bottled waters and those adsorbed to water bottle surfaces. J Virol Methods, 181, 18–24. Isleten-Hosoglu M, Gultepe I, Elibol M. (2012). Optimization of carbon and nitrogen sources for biomass and lipid production by Chlorella saccharophila under heterotrophic conditions and development of Nile red fluorescence based method for quantification of its neutral lipid content. Biochem Eng J, 61, 11–19. Jin X, Zhang Y, Xiao L, Zhao Z. (2008). Optimization of extended zeroorder release gliclazide tablets using D-optimal mixture design. Yakugaku Zasshi, 128, 1475–83. John PW. (1996). Nesting Plackett-Burman designs. Stat Prob Lett, 27, 221–3. Ju H-Y, Kuo C-H, Too J-R, et al. (2012). A green peptide synthesis – using a magnetic biocatalyst in a stirred-tank bioreactor. Biocatal Agric Biotechnol, 1, 20–4. Kammoun R, Naili B, Bejar S. (2008). Application of a statistical design to the optimization of parameters and culture medium for a-amylase production by Aspergillus oryzae CBS 819.72 grown on gruel (wheat grinding by-product). Bioresource Technol, 99, 5602–9. Larentis AL, Argondizzo APC, Esteves GS, et al. (2011). Cloning and optimization of induction conditions for mature PsaA (pneumococcal surface adhesin A) expression in Escherichia coli and recombinant protein stability during long-term storage. Protein Expr Purif, 78, 38–47. Lawson J. (2010). Design and analysis of experiments with SAS. UK: Chapman & Hall/CRC Taylor & Francis Group. Lee H, Yunus R, Juan J, Taufiq-Yap Y. (2011). Process optimization design for jatropha-based biodiesel production using response surface methodology. Fuel Process Technol, 92, 2420–8. Li W, Rasmussen HT. (2003). Strategy for developing and optimizing liquid chromatography methods in pharmaceutical development using computer-assisted screening and Plackett–Burman experimental design. J Chromatogr A, 1016, 165–80. Lim M, Ye H, Panoskaltsis N, et al. (2007). Intelligent bioprocessing for haemotopoietic cell cultures using monitoring and design of experiments. Biotechnol Adv, 25, 353–68. Majumder A, Singh A, Goyal A. (2009). Application of response surface methodology for glucan production from Leuconostoc dextranicum and its structural characterization. Carbohydr Polym, 75, 150–6. Marques DV, Torres BR, Porto ALF, et al. (2009). Comparison of oxygen mass transfer coefficient in simple and extractive fermentation systems. Biochem Eng J, 47, 122–6. Martıńez-Cardenãs JA, Norma M, Salcedo-Hernańdez R, et al. (2012). Effects of physical culture parameters on bacteriocin production by Mexican strains of Bacillus thuringiensis after cellular induction. J Ind Microbiol Biotechnol, 39, 183–9. Mendes LF, Vale LA, Martins AP, et al. (2012). Influence of temperature, light and nutrients on the growth rates of the macroalga Gracilaria domingensis in synthetic seawater using experimental design. J Appl Phycol, 24, 1419–26. Mohan SV, Raghavulu SV, Mohanakrishna G, et al. (2009). Optimization and evaluation of fermentative hydrogen production and wastewater treatment processes using data enveloping analysis (DEA) and Taguchi design of experimental (DOE) methodology. Int J Hydrogen Energy, 34, 216–26. Mohan SV, Venkateswar-Reddy M. (2012). Optimization of critical factors to enhance polyhydroxyalkanoates (PHA) synthesis by mixed


culture using Taguchi design of experimental methodology. Bioresource Technol, 128, 409–16. Mona S, Kaushik A, Kaushik C. (2011). Biosorption of chromium (VI) by spent cyanobacterial biomass from a hydrogen fermentor using Box-Behnken model. Int Biodeter Biodegrad, 65, 656–63. Montgomery DC. (2008). Design and analysis of experiments, 7th ed. Hoboken, NJ: John Wiley & Sons, Incorporated. Mousavi S, Yaghmaei S, Jafari A, et al. (2007). Optimization of ferrous biooxidation rate in a packed bed bioreactor using Taguchi approach. Chem Eng Process Process Intens, 46, 935–40. Muthukumar M, Sargunamani D, Selvakumar N, Venkata Rao J. (2004). Optimisation of ozone treatment for colour and COD removal of acid dye effluent using central composite design experiment. Dyes Pigments, 63, 127–34. Nalakath AH, Veiga MC, Kennes C. (2012). Biological conversion of carbon monoxide to ethanol: Effect of pH, gas pressure, reducing agent and yeast extract. Bioresource Technol, 114, 518–22. Narang S, Sahai V, Bisaria VS. (2001). Optimization of xylanase production by Melanocarpus albomyces IIS68 in solid state fermentation using response surface methodology. J Biosci Bioeng, 91, 425–7. Niklas J, Priesnitz C, Rose T, et al. (2012). Primary metabolism in the new human cell line AGE1. HN at various substrate levels: increased metabolic efficiency and a1-antitrypsin production at reduced pyruvate load. Appl Microbiol Biotechnol, 93, 1637–50. Palvannan T, Sathishkumar P. (2010). Production of laccase from Pleurotus florida NCIM 1243 using Plackett–Burman design and response surface methodology. J Basic Microbiol, 50, 325–35. Prakash G, Srivastava AK. (2005). Statistical media optimization for cell growth and azadirachtin production in Azadirachta indica (A. Juss) suspension cultures. Process Biochem, 40, 3795–800. Prasad KK, Mohan SV, Rao RS, et al. (2005). Laccase production by Pleurotus ostreatus 1804: optimization of submerged culture conditions by Taguchi DOE methodology. Biochem Eng J, 24, 17–26. Qu W, Ma H, Pan Z, et al. (2010). Preparation and antihypertensive activity of peptides from Porphyra yezoensis. Food Chem, 123, 14–20. Radmann E, Reinehr C, Costa J. (2007). Optimization of the repeated batch cultivation of microalga Spirulina platensis in open raceway ponds. Aquaculture, 265, 118–26. Rahnama F, Vasheghani-Farahani E, Yazdian F, Shojaosadati SA. (2012). PHB production by Methylocystis hirsuta from natural gas in a bubble column and a vertical loop bioreactor. Biochem Eng J, 65, 51–6. Rao RS, Kumar CG, Prakasham RS, Hobbs PJ. (2008). The Taguchi methodology as a statistical tool for biotechnological applications: a critical appraisal. Biotechnol J, 3, 510–23. Ray S, Reaume SJ, Lalman JA. (2010). Developing a statistical model to predict hydrogen production by a mixed anaerobic mesophilic culture. Int J Hydrogen Energy, 35, 5332–42. Roy S, Ghosh S, Das D. (2012). Improvement of hydrogen production with thermophilic mixed culture from rice spent wash of distillery industry. Int J Hydrogen Energy, 37, 15867–74. Ruiz HA, Silva DP, Ruzene DS, et al. (2011). Bioethanol production from hydrothermal pretreated wheat straw by a flocculating Saccharomyces cerevisiae strain – effect of process conditions. Fuel, 95, 528–36. Santis-Navarro A, Gea T, Barrena R, Sańchez A. (2011). Production of lipases by solid state fermentation using vegetable oil-refining wastes. Bioresource Technol, 102, 21, 10080–4. Sathya G, Naidu N, Panda T. (1998). Application of response surface methodology to evaluate some aspects on stability of pectolytic enzymes from Aspergillus niger. Biochem Eng J, 2, 71–7. Sharma RK, Arora DS. (2010). Production of lignocellulolytic enzymes and enhancement of in vitro digestibility during solid state fermentation of wheat straw by Phlebia floridensis. Bioresource Technol, 101, 9248–53. Shen CF, Kamen A. (2012). Hyperosmotic pressure on HEK 293 cells during the growth phase, but not the production phase, improves adenovirus production. J Biotechnol, 157, 228–36. Sifour M, Zaghloul TI, Saeed HM, et al. (2010). Enhanced production of lipase by the thermophilic Geobacillus stearothermophilus strain-5 using statistical experimental designs. N Biotechnol, 27, 330–6. Silva EM, Da Silva JS, Pena RS, Rogez H. (2011). A combined approach to optimize the drying process of flavonoid-rich leaves (Inga edulis) using experimental design and mathematical modelling. Food Bioprod Process, 89, 39–46.


DOI: 10.3109/07388551.2014.973014

Silva JPA, Mussatto SI, Roberto IC. (2010). The influence of initial xylose concentration, agitation, and aeration on ethanol production by Pichia stipitis from rice straw hemicellulosic hydrolysate. Appl Biochem Biotechnol, 162, 1306–15. Singh A, Bishnoi NR. (2012). Enzymatic hydrolysis optimization of microwave alkali pretreated wheat straw and ethanol production by yeast. Bioresource Technol, 108, 94–101. Singh A, Bishnoi NR. (2013). Ethanol production from pretreated wheat straw hydrolyzate by Saccharomyces cerevisiae via sequential statistical optimization. Ind Crops Products, 41, 221–6. Song Z, Yang G, Han X, et al. (2012). Optimization of the alkaline pretreatment of rice straw for enhanced methane yield. BioMed Res Int, 2013, 968692. Tari C, Ustok FI, Harsa S. (2010). Production of food grade b-galactosidase from artisanal yogurt strains. Food Biotechnol, 24, 78–94. Tarley CRT, Silveira G, dos Santos WNL, et al. (2009). Chemometric tools in electroanalytical chemistry: methods for optimization based on factorial design and response surface methodology. Microchem J, 92, 58–67. Teng Y, Xu Y. (2008). Culture condition improvement for whole-cell lipase production in submerged fermentation by Rhizopus chinensis using statistical method. Bioresource Technol, 99, 3900–7. Vatanara A, Rouholamini Najafabadi A, Gilani K, et al. (2007). A Plackett–Burman design for screening of the operation variables in the formation of salbutamol sulphate particles by supercritical antisolvent. J Supercrit Fluids, 40, 111–16. Venkata-Dasu V, Panda T, Chidambaram M. (2003). Determination of significant parameters for improved griseofulvin production in a batch bioreactor by Taguchi’s method. Process Biochem, 38, 877–80. Vicente G, Coteron A, Martinez M, Aracil J. (1998). Application of the factorial design of experiments and response surface methodology to optimize biodiesel production. Ind Crops Prod, 8, 29–35. Wang J, Wan W. (2009). Experimental design methods for fermentative hydrogen production: a review. Int J Hydrogen Energy, 34, 235–44.


21

Wang J, Wan W. (2011). Combined effects of temperature and pH on biohydrogen production by anaerobic digested sludge. Biomass Bioenergy, 35, 3896–901. Wang KS, Chen JH, Huang YH, Huang SL. (2012). Integrated Taguchi method and response surface methodology to confirm hydrogen production by anaerobic fermentation of cow manure. Int J Hydrogen Energy, 38, 45–53. Xu TJ, Ting YP. (2004). Optimisation on bioleaching of incinerator fly ash by Aspergillus niger-use of central composite design. Enzyme Microb Technol, 35, 444–54. Yatsyshyn VY, Fedorovych DV, Sibirny AA. (2010). Medium optimization for production of flavin mononucleotide by the recombinant strain of the yeast Candida famata using statistical designs. Biochem Eng J, 49, 52–60. Yin X, You Q, Jiang Z. (2011). Optimization of enzyme assisted extraction of polysaccharides from Tricholoma matsutake by response surface methodology. Carbohydr Polym, 86, 1358–64. Yu¨cel Y. (2012). Optimization of biocatalytic biodiesel production from pomace oil using response surface methodology. Fuel Process Technol, 99, 97–102. Zain WS, Illias RM, Salleh MM, et al. (2007). Production of cyclodextrin glucanotransferase from alkalophilic Bacillus sp. TS1-1: optimization of carbon and nitrogen concentration in the feed medium using central composite design. Biochem Eng J, 33, 26–33. Zeng X, Small DP, Wan W. (2011). Statistical optimization of culture conditions for bacterial cellulose production by Acetobacter xylinum BPR 2001 from maple syrup. Carbohydr Polym, 85, 506–13. Zhang C, Fan D, Shang La, et al. (2010). Optimization of fermentation process for human-like collagen production of recombinant Escherichia coli using response surface methodology. Chin J Chem Eng, 18, 137–42. Zolgharneina J, Asanjrania N, Bagtasha M, Azimib G. (2014). Multiresponse optimization using Taguchi design and principle component analysis for removing binary mixture of alizarin red and alizarin yellow from aqueous solution by nano g-alumina. Spectrochim Acta A Mol Biomol Spectrosc, 126, 291–300.

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