Bbtechnol. m$.1994, 10, 353-359
953
Effect of Glucose Concentration on the Biomass and Phytase Productions and the Reduction of the Phytic Acid Content in Canola Meal by Aspergillus carbonarius during a Solid-state Fermentation Process Sameer Al-Asheh and Zdravko Duvnjak' Department of Chemical Engineering, University of Ottawa, Ottawa, Ontario, Canada K1N 6N5
It was found that an increase in the glucose amount up to and including 6 g per system in the initial solid-state culture medium resulted in an increase in the rates of the biomass growth, enzyme concentration, and phytic acid content reduction in canola meal. Inhibition of the rates of the above processes was noticed in the systems with initial glucose amounts of 12 and 24 g. The canola meal systems with 12 and 24 g of glucose had longer growth phases than those tested with lower glucose amounts, and this resulted in their higher maximum enzyme activities. Logistic law was used to model the biomass production. Models that relate the enzyme production and the phytic acid content reduction with the time of the solid-state fermentation process for each glucose concentration are given, and they fit the experimental data produced in this work reasonably well.
Introduction Canada is the second largest rape seed producer and the largest rape seed exporter in the world. It produced more than 4.2 X 10s tons of canola seeds (which are a variety of rape seeds) in the year 1991/1992 (Statistics Canada, 1993). Canola meal is a byproduct of canola oil production from canola seeds. It contains 37-40 % protein, and it is used as a feedstuff for livestock and poultry (Clandinin, 1986). Canola meal contains 4 4 % phytic acid (myo-inositol hexaphosphoricacid), which reduces the nutritional value of the meal. It has been shown that the phytic acid in canola meal binds to multivalent cations such as Zn2+, Ca2+, and Fe3+,and so reduces their bioavailability. In spite of the lower availability of minerals in canola meal compared with those in soybean meal, canola meal is a better source of available calcium, iron, manganese, phosphorus, and selenium than soybean meal, while soybean meal is a better source of copper, potassium, and zinc than canola meal (Clandinin, 1986). It has also been reported that phytic acid inhibits enzymes such as a-amylase (Sharma et al., 1978),trypsin, tyrosinase, and pepsin (Graf,1986). Hydrolysis of phytic acid prior to animal consumption would increase the availability of inorganic phosphorus and other minerals in the animal diet (Han, 1988). This can be achieved enzymatically in the plants whose seeds contain endoenous phytase, such as beans (Chang et al., 1977),cotton seeds, soybean (Han, 1988),wheat (Peers, 1953),and mung beans (Mandal et al., 1972). Courtois and Joseph (1947)and Courtois and Perez (1948)demonstrated that phytase is a phosphomonoesterase, which hydrolyzes a number of other phosphoesters in addition to phytic acid. There are also plants, such as canola, that do not contain this enzyme. In such cases, other possibilities for the reduction of the phytic acid content should be considered. Chemical methods have been used to reduce the phytic acid content in canola meal, but a partial loss of nutritional constituenta,such as proteins and minerals, has often been noticed when these techniques were applied (Gillberg and 87567938/94/3010-0353$04.50/0
1
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7.5
5.0
2.5
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48
72
06
120
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168
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Figure 1. Effect of glucose added to canola meal systems on the biomass growth. Initial glucose concentration (g/SSC system): 0.0(0),2.0 (A),4.0 (+), 6.0 (X), 12.0 (e),24.0 (V). Table 1. Maximum Biomass Concentrations and Specific Growth Rates for Various Initial Glucose Concentrations maximum biomass glucose maximum biomass concentration, concentration specific growth rate, X, (g/g of wet SSC) WSSC system) fim 0.0 2.0 4.0 6.0 12.0 24.0
0.078 0.084 0.086 0.089
0.086 0.098
0.0560 0.0575 0.0595 0.0610 0.0355 0.0242
Tornell, 1976;Ford et al., 1978;Alli and Houde, 1987). Other techniques were also suggested. It has been reported that many microorganismsproduce phytase. Among them, Aspergillus ficuum was found to be a good producer of this enzyme when grown on a b a d medium (Hanet al.,
@ 1994 American Chemical Society and American Institute of Chemical Engineers
Biotechnol. Prog., 1994, Vol. 10, No. 4
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Time (h) Time (h) Figure 2. Correlation between the exDerimental biomass data ( 0 )and that predicted using eq 3 (-) for the systems with various miunta of glucose. 1987). Microbialphytases externally added tocanola meal can be used for reduction of its phytic acid content. Solid-state fermentation (SSF) of canola meal can be carried out for the reduction of its phytic acid content (Nair and Duvnjak, 1991). It has been demonstrated that Rhizopus oligosporusNRRL 2990, Aspergillus niger NRC 5765, Aspergillus carbonarius NRC 401121, Aspergillus ficuum NRRL 3135, and a wild strain of Saccharomyces cereuisiae can be used for this purpose. Solid-state fermentation may have great potential because of its simplicity and the reduced drying cost after processing (Mitchellet al., 1991). But there are alsodifficulties related to these processes, such as the poorly understood microbial growth pattern on solid substrates (Moo-Young et al., 19791,heterogeneity and complexity of the substrates, and problems in measuring fermentation parameters, the most critical of which is the biomass (Mitchell et al., 1990). In this study, Aspergillus carbonarius NRC 401121 was used for the production of phytase and the reduction of the phytic acid content in canola meal in an SSF process. I t has been reported that this microorganism could be suitable for such a purpose (Nair and Duvnjak, 1991), but extensive study in this respect has not been carried out.
Since canola meal is very scarce in free carbohydrates, the purpose of this work was to study the effect of added glucose to this commodity on biomass growth, phytase production, and reduction in phytic acid content in the meal during an SSF process. Mathematical models are given that show the effect of glucose concentration on the above-mentioned processes. Mathematical Modeling In this work, models for biomass production, enzyme activity, and phytic acid hydrolysis are given. Biomass. Although there are a number of models for growth in SSF, the only real previous attempt to model fungal growth kinetics in SSF is the logistic model of Okazaki et al. (1980). This model covered mycelialgrowth in the logarithmic and stationary phases, and therefore, it was used in this work to test its applicability in representing the relationship between the biomass growth and the time of fermentation for each initial glucose concentration. Okazaki and collaborators introduced the following logistic equation:
Biotechnol. Rog.., 1994,Vol. I O , No. 4
955
for the increasing phase of enzyme activity. Taking into account the fact that the model cannot predict the decay phase, a separate model for this part of enzyme activity was developed. In addition, an empirical model was also given for the increasing part of the enzyme activity curve. Increasing Phase of Enzyme Production. Two models were given for the increasing part of the enzyme activity curve; one is based on the logistic law and the other is empirical. ( a ) Using the ~ o g i s t i Law. c It is possible to assume that the enzyme production during the enzyme increasing phase is proportional to the biomass concentration; this was also suggested by Okazaki et al. (1980). Taking that into consideration, we can write the following equation:
4.0
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dt
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96
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where u is the enzyme activity and k , is a rate constant. Substitution of eq 3 into eq 5 gives
Figure 3. Effect of glucoseadded to canola meal systemson the production of phytase. Initial glucose concentration (g/SSC system): 0.0 (0),2.0 (A), 4.0 (+), 6.0 (X), 12.0 (e),24.0 (V).
dX = pmX(l- X/Xm) dt
(1)
where X is the biomass concentration. X, is the maximum biomass concentration that can be'atGined, Clm is the maximum specific growth rate, and t is the time of fermentation. Given the initial condition
Given the initial condition u = u o at t = O
where uo is the enzyme activity after inoculation, eq 6 upon integration gives the following expression, which relates the enzyme concentration with the time of the fermentation process and the logistic law parameters:
(2)
X = X o at t = O
where XOis the initial experimental biomass concentration, eq 1 upon integration gives: (3)
(7)
u = uo + k J m In(
s)
Pm
( b )Empirical Model. In addition to the above model, the phytase activity for the increasing phase (Figure 3) is also represented by the following empirical expression: u = uo + at exp(bt)
where
p = x m / x o- 1
(4)
This expression enables us to calculate the biomass concentration (X) for a particular initial glucose concentration a t any time of fermentation. The glucose concentrations are taken into account through the maximum biomass concentration (Xm) and the maximum specific growth rate (Pm). The values of the parameters Xm and pm can be obtained by fitting the experimental data of biomass for each glucose concentration with the logistic law expression. Enzyme Activity. In this process, the enzyme activity was increasing during a certain period of time and then it started decreasing. Bearing in mind that a logistic law model was given for the biomass concentration in this work, we also used that expression for the derivation of a model
(9)
where uo is the enzyme activity after inoculation, and a and b are empirical constants. Decay Phase of Enzyme Production. After the enzyme attained the maximum value, a decrease in its activity was noticed in this process. Relatively few studies have been made on the decay phase of enzyme production, since industrial batch processes usually are terminated before this phase begins. The decay of the enzyme formation shown in this work can be represented by the following empirical exponential correlation:
u =,u
exp[-k&
- t,)l
(10)
where u is the enzyme activity at time t, ,u maximum enzyme activity attained at time ,t is an empirical constant.
is the and k d
Table 2. Rate Constant t (eq 81, Empirical Constants 8 and b (eq 9), and Constant ka (eq 10) for Enzyme Production and Empirical Constant kp(eq 14) for the Reduction in Phytic Acid Content in the Media with Various Initial Glucose Concantratione
glucose concentration rate constant, (g/SSC system) k, (units/g of wet SSC-h) 0.0 2.0 4.0 6.0 12.0 24.0
0.531 0.547 0.565 0.594 0.473 0.455
a
empirical constant, empirical constant, empirical constant, empirical constant, (units/g of wet SSC) b (h-9 ka (day9 k p (h-') 0.0177 0.0205 0.0217 0.0238 0.0112 0.0057
0.0041 0.0032 0.0034 0.0032 0.0065 0.0099
1.136 0.973 0.833 0.664 0.245 0.150
22.28 23.63 26.08 29.42 17.90 16.25
Bbtechnol. frog., 1994, Vol. 10, No. 4
358
0
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Figure 4. Correlation between the experimentaldata for the production of phytase (0) and that predicted using eq 8 (-, for increasing phase), eq 9 (- - -,for increasing phase), and eq 10 (-, for the decreasing phase) for systems with various amounts of glucose. Phytic Acid. The reduction of phytic acid content depends on the enzyme concentration, and the latter is related to the biomass concentration. Therefore, either eq 8 or eq 3 could be used for the development of a model for the reduction of phytic acid content in canola meal during solid-state fermentation. Because eq 8, which was developed from eq 3, shows slight deviations from the experimental results, eq 3 rather than eq 8 was used in this work for the development of the model for the reduction of phytic acid content in order to avoid further transfer of the above-mentioned errors contained in eq 8. The rate of phytic acid hydrolysis is assumed to be firstorder with phytic acid concentration, P, and second-order with biomass concentration, X, that is,
compounds in solid media are much lower than those in liquid ones, a quadratic dependency of phytic acid on the biomass concentration is assumed in this work. Substitution of X in eq 11with its value from the logistic law (eq 3), and after separation of the variables, it gives the expression: (12) Given the initial condition
P = P o at t = O = -kpPX2
(13)
(11)
where P is the phytic acid concentration and k, is a rate constant. Considering that the phytic acid in canola meal is distributed more evenlythan the biomassof A. carbonarius through the meal, and that diffusion rates of the produced
where POis the phytic acid concentration after inoculation, eq 12 upon integration gives the following relationship between the phytic acid concentration and the time of fermentation for a given initial glucose concentration, which is taken into consideration, as has been mentioned, by X, and pm values:
Bbtechnol. hog., 1994, Vol. 10, No. 4
357 100
75 h
Materials and Methods Microorganism, Media, and Conditionsof Growt h. Aspergillus carbonarius NRC 401121 was used to study the biomass and phytase productions and the reduction in phytic acid content in canola meal during an SSF process. Spores of this microorganism were produced in a Roux bottle on a solid medium composed of 4.5%malt agar, 0.5%glucose, 0.5%yeast extract, and distilled water (all percentages are w/v). The medium was sterilized at 115 "C for 15 min, inoculated, and incubated at 30 "C until sporulation. The microorganismappeared as a white mass over the whole surface after 36 h and completely converted to a black color after 60 h of incubation. The produced spores were suspended in sterile-distilled water and kept at 4 "C for a maximum of 2 months for further use; no change in the activities of the spores during this period of time was noticed. The inoculum for SSF was prepared in a medium composed of 0.8%nutrient broth, 0.5 % glucose, and 0.5 % yeast extract in distilled water. Erlenmeyer flasks (250 mL) with 100 mL of the medium in each were sterilized at 115 "C for 15 min, cooled, and inoculated with 1 mL of the spore suspension. Incubation was carried out in a rotary shaker at 30 "C for 60 h. Since the microorganism grew in the liquid medium in the form of pellets, it was homogenized in a blender for 45 s prior to its utilization for inoculation. The mycelialinoculum was preferred over spores in order to avoid the lag phase of biomass growth. Fifty grams of canola meal, with a moisture content of 8.5%and a particle size diameter in the range of 0.18-1.4 mm, and some distilled water were put in a 500-mL Erlenmeyer flask and sterilized at 121 "C for 45 min. Glucose, sterilized separately at 115 "C for 15 min, was added to the sterile meal. The total amount of water added to each system was 40 mL. The medium without added glucose served as the control. The flask was then allowed to cool, inoculated with 20 mL of homogenized inoculum, and incubated at 30 "C. The pH of the medium after inoculation was 5.5. The biomass and phytase productions and the reduction of the phytic acid content in canola meal were followed during a static SSF. At least two fermentations were carried out in parallel for each glucose concentration. Two samples for analysis were taken from each fermentation system. Additional analyses or fermentations and analyses were carried out if the results from two parallel systems differed by more than 5 % Analysis of Samples. Phytic Acid. The method of Haug and Lantzsch (1983) for the rapid determination of phytic acid by measuring the residual iron concentration was adopted in this work. Phytic acid was extracted from approximately 2 g of wet sample with 33 mL of 2.4%HC1 under continuous shaking (200 rpm) for 1 h. After extraction, the suspension was centrifuged (SOOOg, 15 min) and the supernatant collected. Five milliliters of supernatant was treated according to the Haug and Lantzsch method (19831, and the absorbance was read at 519 nm using distilled water as the reference. The concentration of phytic acid was calculated from a standard curve prepared similarly for this acid. Enzyme Activity. Enzyme activity was determined in the crude enzyme preparation extracted from the wet samples of the canola meal culture. Extraction was carried
.
E 9
8
y
50
8
a
25
0
0
24
40
Time (h)
Figure 5. Effect of glucose added to canola meal systems on the phytic acid content reduction. Initial glucose concentration (g/ ssc system): 0.0(O),2.0 (A), 4.0 (+), 6.0 (X), 12.0 (e),24.0 (V).
out using a 2% aqueous solution of CaCl~2H20and shaking the suspension for 1 h on a rotary shaker at 200 rpm. The meal to extractant ratio was 1 5 (w/v). The suspension was squeezed through a double layer of cheesecloth and centrifuged (20000g, 10 min). The clear supernatant was designated as crude enzyme. Phytase activity was assayed by measuring the inorganic phosphorus released, using sodium phytate as the substrate. Spectrophotometric determination of the released phosphorus was performed using the Taussky-Schoor reagent as described by Harland and Harland (1980). The reaction mixture consisted of 5 mL of 0.2 M acetate buffer (pH 4.7), 1 mL of 2.25 mM phytic acid, and 0.1 mL of crude enzyme. The reactions were carried out in duplicate at 53 "C for 10 min and then stopped by adding 5 mL of 10% trichloroacetic acid. One unit of enzyme activity is defined as the amount of enzyme required to release 1 mg of inorganic phosphorus per hour at the given temperature and pH. Biomass in Solid Culture. Biomass in a sample of about 0.5 g of wet solid culture was measured by determining its glucosaminecontent. The glucosaminecontent was converted into biomass on the basis of theglucosamine measured in the biomass grown in a liquid medium. The hydrolysis for liberation of glucosamine in the microorganism was carried out according to the Sakurai et al. (1976) method. Liberated glucosamine was measured by the Blix (1948) method by reading the absorbance at 530 nm. Results and Discussion Biomass. The effect of glucose added to canola meal systems on biomass growth is shown in Figure 1. Compared with the control, which was not supplemented with this compound, faster growth and larger amounts of biomass were observed in the first 120 h of the process in the systems containing added glucose, when ita amount did not exceed 6 g per system. The systems containing 12 and 24 g of added glucose showed slower biomass growth than the control. To model these processes, the values of the parameters X, and ~.l, were calculated (Table 1) using eq 3 and applying an algorithm for least-squares estimation of nonlinear parameters (Marquardt, 1963) for the set of experimental data for each initial glucose concentration.
Biotechnol. Prog., 1994, Vol. 10, No. 4
358 100
n
8
76
W
2 2
50
0
3
2 a
26
0 0
24
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72
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Time (h)
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2
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n 0
I
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72
Time (h) Time (h) Figure 6. Correlation between the experimental data for the phytic acid content reduction (0) and that predicted using eq 14 (-) for the systems with various glucose amounts. This technique uses iterative methods to regress the residuals of the partial derivatives of the model with respect to the parameters until the iterations converge. Figure 2 shows that the logistic law expression (eq 3) represented the experimental biomass data for the increasing part of the curve of its production very well. Phytase. The effect of added glucose on enzyme production (Figure 3) showed a trend similar to that noticed for biomass growth. An increase in the amount of supplemental glucose up to and including 6 g increased the rate of enzyme production, while the systems with 12 and 24 g of added glucose resulted in a lower rate and a longer phase of enzyme production than those in the control system and those tested with lower glucose amounts. As a result of the extended phase of enzyme production, the maximum levels of enzyme activity in the systems with 12 and 24 g of glucose were higher than that in other systems (Figure 3). To model the increasing part of the enzyme activity curve, the k, constant (Table 2) for each initial glucose concentration was calculated by fitting eq 8 to the enzyme experimental data. The X, and pm values in this equation
were those previously obtained from the logistic law. The empirical constants a and b from the empirical model (eq 9) for the increasing part of the phytase formation curve were also calculated (Table 2) for various initial glucose concentrations by fitting eq 9 to the experimental data. The constants k d (Table 21,for the decay of enzyme activity after tmaxwere calculated by fitting eq 10 to the experimental data for different initial glucose concentrations. Comparison between the experimental data for enzyme production and the predicted values using eqs 8 and 9 for the increasing phase of enzyme production and eq 10 for the decreasing phase of enzyme production, depicted in Figure 4 for various glucose amounts, shows that all of these suggested models fit the experimental data reasonably well. Figure 4 demonstrates that the empirical model, which is used to represent the increasing phase of enzyme production, is slightly better than the model derived from the logistic law. Phytic Acid. If we take into account the increase in the production rates of enzyme activities with the increase in the glucose amount up to and including 6 g per system, it is possible to expect the rate of phytic acid content
Biotechnol. Prog., 1994, Vol. 10, No. 4
reduction to also be affected by the added glucose. The system with 6 g of added glucose had the highest rate of phytic acid hydrolysis (Figure 5); this observation is consistent with the observed trend in enzyme activity. To use the model for phytic acid content reduction, the values of the rate constant k , were calculated (Table 2) from eq 14 for different initial glucose concentrations by using the values of Xmand pm obtained previously from the biomass data for each glucose concentration and by fitting this equation to the experimental results from Figure 5. A comparison between experimental data for the reduction of the phytic acid content during the SSF processes and the calculated phytic acid concentrations using eq 14 is shown in Figure 6 for various times and initial glucose concentrations. The results show that the model is very good for the systems that do not contain more than 6 g of glucose. For the systems with 12 and 24 g of glucose, the model gives values that are approximations of the experimental results.
Conclusions The effect of glucose concentrations on the production of biomass and phytase and the reduction of phytic acid content in canola meal by a solid-state fermentation process has been studied using Aspergillus carbonarius NRC 401121. The results showed that glucose in amounts not exceeding 6 g in the canola meal systems had a positive effect on the rate of biomass growth, enzyme production, and phytic acid content reduction. These rates in the systems with 12 and 24 g of glucose were below the one measured in the control, which was not supplemented with the carbohydrate. The systems with 12 g and 24 g of glucose had extended growth phases. This resulted in higher maximum enzyme activities compared to those attained in the systems with lower glucose concentrations. The models for the biomass and phytase productions and the phytic acid content reduction during a solid-state fermentation process are given in this work. They fit the experimental data produced in this work reasonably well.
Notation phytic acid concentration (?%) initial phytic acid content (% ) solid-state culture solid-state fermentation biomass (g/g of wet SSC) maximum biomass concentration achieved (g/g of wet SSC) initial biomass (g/g of wet SSC) empirical constant in eq 9 (units/g of wet SSC) empirical constant in eq 9 (h-1) empirical constant in eq 10 (day-1) empirical constant in eq 14 (h-1) empirical constant in eq 8 (units/(g of wet SSCeh)) time of fermentation (h) time for maximum enzyme activity (h) enzyme activity (units/g of wet SSC) maximum enzymeactivity achieved (units/g of wet SSC)
initial enzyme activity (units/g of wet SSC) maximum specific growth rate of biomass (h-l)
359
Blix, G. The determination of hexosamines according to Elson and Morgan. Acta Chem. Scand. 1948,2,467-473. Chang, R.; Schwimmer, S.; Burr, H. K. Phytate removal from whole dry beans by enzymatic hydrolysis and diffusion. J. Food Sci. 1977,4,109&1101. Clandinin, D. R. Canola meal for livestock and poultry; Clandinin,D. R., Ed.; Canola Council of Canada: Winnipeg, Manitoba, Canada, 1986;pp 1-19. Courtois, J.; Joseph, G. Recherches sur la phytase 6. Action de diverses preparations phosphatiques sur quelques esters phosphoriques de l'inositol. Bull. Soc. Chem. Biol. 1947,29, 951.
Courtois, J.; Perez, C. Recherches sur la phytase 8. Teneur en inositophosphateetactivite phytasique de diveres grains. Bull. SOC. Chem. Biol. 1948,30, 195-198. Ford, J. R.; Mustakas, G. C.; Schmutz,R. D. Phytic acid removal from soybeansby a lipid protein concentrateprocess. J. Am. 1978,55,371-374. Oil Chem. SOC. Gillberg, L.; Tornell,B. Preparation of rapeseed protein isolates. J. Food Sci. 1976,30,1063-1069. Graf,E. Phytic acid-Chemistry and application; The Pillsbury Co., Pilatus Press: Minneapolis, 1986;pp 42-44. Han, Y. W. Removal of phytic acid from soybean and cottonseed meals. J. Agric. Food Chem. 1988,36 (61,1181-1183. Han, Y. W.; Gallagher, D. J.; Wilfred, A. G. Phytase production by Aspergillus ficuum on semisolid substrate. J. Ind. Microbiol. 1987,2, 195-200. Harland, B. F.; Harlan, J. Fermentative reduction of phytate in rye, white and whole wheat bread. Cereal Chem. 1980,57(3), 226-229.
Haug, W.; Lantzsch, H. J. Sensitive method for the rapid determination of phytate in cereals products. J. Sci. Food Agric. 1983,34,1423-1426. Mandal, N. C.; Burman, S.; Biswas, B. B. Isolation, purification and characterization of phytase from germinatingmungbeans. Phytochemistry 1972,11, 495-502. Marquardt, D. W. An algorithm for least squares estimation of nonlinear parameters. J.SOC.Ind. Appl. Math. 1963,ll (2), 431-441.
Mitchell, D. A.; Greenfield, P. F.; Doelle, H. W. Mode of growth of Rhizopus oligosporus on a model substrate in solid-state fermentation. World J. Microbiol. Biotechnol. 1990,6,201208.
Mitchell, D. A.; Greenfield, P. F.; Doelle, H. W. An empirical model of growth of Rhizopus oligosporus in solidstate fermentation. J. Ferment. Bioeng. 1991, 72 (3),224226.
Moo-Young, M.; Dougulis, A. J.; Chahal, D. S.; MacDonald, D. G. The Waterloo Process for SCP Production from Waste biomass. Process Biochem. 1979,14 (lo),38-40. Nair, V. C.; Duvnjak, Z.Phytic acid content reduction in canola meal by various microorganisms in a solid-statefermentation process. Acta Biotechnol. 1991,3,211-218. Okazaki, N.;Sugama, S.; Tanaka, T. Mathematical model for surface culture of koji mold. J. Ferment. Technol. 1980,58 (5), 471-476.
Peers, F.G. The phytase of wheat. Biochem. J . 1963,53,102110.
Sakurai, Y.; Lee, T. H.; Shiota, H. On the conventional method for glucosamine estimation in koji. Agric. Biol. Chem. 1976, 41 (4),619-624.
Sharma, C. B.;Goel, M.; Irshad, M. Myo-inositol hexaphosphate as a potential inhibitor of a-amylase of different origins. Phytochemistry 1978,17,201-204. Statistics Canada. Cereals and Oilseeds Review (catalogue 22007) 1993,16, 1.
Literature Cited Alli, I.; Houde, R. Characterizationof phytate in canola meal. 8th Progress Report,CanolaCouncilof Canada;CanolaCouncil of Canada: Winnipeg, Manitoba, Canada, 1987;pp 159-165.
Accepted November 23, 1993.O Abstract published in Advance ACS Abstracts, February 15, 1994.
