152 Biotechnol. Prog. 2000, 16, 152−162

Solid Substrate Fermentation of purpureus: Growth, Carbon Balance, and Consistency Analysis

Ariel Rosenblitt, Eduardo Agosin,* Javier Delgado, and Ricardo Pe´rez-Correa

Department of Chemical and Bioprocess Engineering, Pontificia Universidad Cato´lica de Chile, Casilla 306 Correo 22, Santiago, Chile

Solid substrate fermentation (SSF) of Monascus purpureus on rice is a promising new technology for obtaining natural pigments. However, before attempts can be made at maximizing pigment yield, all significant macroscopic compounds should be assayed. Here, Monascus purpureus has been grown on rice in batch mode, and the evolution of the main components, biomass, residual rice, O2,CO2, ethanol, acetic acid, and pigments, have been followed. This set of data, never previously studied for Monascus SSF, allowed both the performance of a macroscopic elemental balance, which accounted for 83-94% of the initial substrate carbon, and a check of data consistency. Standard consistency analysis showed a significant underestimation of the nitrogen fraction of biomass, but it was unable to discriminate the errors in the carbon balance as a result of the simultaneous presence of two gross errors in the system. A simple stoichiometric model in tandem with consistency analysis explained unaccounted carbon as an underestimation of CO2 and ethanol. Using the simplified method to estimate ethanol, the macroscopic balance accounted for 87-99% of the initial carbon.

Introduction balances to evaluate monitoring and control strategies Red rice (Angkak) is the fermentation product for submerged cultures (17, 18). These studies followed of cooked or autoclaved rice with Monascus spp. Interest changes in glucose, ethanol, monosodium glutamate, in is increasing, because it not only is a biomass, malate, and pigments. source of natural pigments but also contains an anti- SSF cultures of Monascus spp. have received minimal cholesterolemic agent (1). Its origin dates back to the attention from researchers (19-22) despite the high Chinese Yuan Dynasty (1280-1368 A.C.). Red yeast rice pigment yields compared to submerged cultivation (23). has long been used as food or as a colorant by the food Factors influencing the lack of interest may include (i) industry in many East Asian countries. The main con- biomass quantification difficulties, as it is inseparable sumer of Monascus pigments worldwide is Japan, which from organic substrates, (ii) lack of adequate sensors for increased its consumption from 100 tons/year in 1981 to on-line process variable measurements, and (iii) inac- 600 tons/year in 1992 (2). Monascus spp. produces six curate off-line measurements, yielding noisy kinetics. primary pigments (two yellow, two orange, and two red) These all hinder the application of modeling tools. that constitute the characteristic color of red yeast rice. To date, published works have focused on the influence These pigments have been studied extensively (3-6), and of single cultivation conditions on Monascus SSF perfor- Shin et al. (7) recently suggested that pigment production mance, such as substrate type, autoclaving time (22), pH may be stimulated by the action of hydrolytic enzymes. (20), initial moisture content (20, 21), and CO2 and O2 These authors sustain that the overproduction found in partial pressures (19). Few variables are followed, and mixed cultures may be a defensive response to cell wall biomass is not usually measured, making an accurate disruption by these enzymes. distribution of the carbon difficult to compute. Red yeast rice is produced on a commercial scale by This work collects new information that can contribute solid substrate fermentation (SSF) in southern China, to process modeling for applying modern bioprocess the Philippines, and Indonesia (8), presumably in tray technology to natural pigment production by cultivating bioreactors. This method of production is labor intensive Monascus spp. under SSF. Specifically, all of the signifi- and very difficult to automate. Modern bioprocess tech- cant compounds involved in growth (biomass, ethanol, nology, which is heavily reliant on accurate process acetic acid, CO2, and O2) and pigment production were models, needs to be applied to allow the development of measured for a defined set of culture conditions. The work more efficient, large-scale SSF bioreactors and operating also establishes the distribution of carbon among the policies (9). Before a microbial process model is developed, relevant compounds. Finally, a standard consistency however, as many as possible of the relevant compounds analysis (24) and a simple stoichiometric model were involved need to be quantified. applied to detect gross measurement errors. Several reports detail growth and pigment production kinetics in the submerged cultivation of Monascus spp. Materials and Methods (10-16). However, full accountability of the relevant Strain. The strain Monascus purpureus ATCC 16392 compounds has not been widely reported, and thus, was replicated on Petri dishes using 325 medium (20 g/L reliable process modeling is difficult to develop. Two malt extract, 20 g/L glucose, 1 g/L flesh peptone, and 20 independent works have applied macroscopic elemental g/L agar).

10.1021/bp0000048 CCC: $19.00 © 2000 American Chemical Society and American Institute of Chemical Engineers Published on Web 03/16/2000 Biotechnol. Prog., 2000, Vol. 16, No. 2 153

Figure 1. Experimental layout: (1) air compressor, (2) glass wool filter, (3) air distribution system, (4) heater and controller of water bath, (5) Raimbault column without inoculum (control), (6) Raimbault column with inoculum, (7) Raimbault column for ethanol measurement, (8) silica gel columns, (9) multiplexer, (10) air mass flowmeter, (11) drierite column, (12) gas analyzer (CO2,O2), (13) computer, (14) sampling bottle for ethanol measurement, (15) ethanol exhaust.

Inoculum. The liquid medium used was a variation Substrate-Free Biomass for Glucosamine and El- of that described by Lin and Demain (14): 50.0 g/L emental Composition Determination. To obtain sub- glucose, 9.0 g/L monosodium glutamate (MSG), 2.4 g/L strate-free biomass, the was grown on Petri dishes KH2PO4, 2.4 g/L K2HPO4, 8.0 g/L MgSO4‚7H2O, 0.5 g/L in an oven at 30 °C. The substrate, autoclaved rice soaked ‚ ‚ KCl, 0.01 g/L FeSO4 7H2O, 0.01 g/L ZnSO4 7H2O, and with a 0.015 M solution of ZnSO4‚7H2O, was covered 0.003 g/L MnSO4‚H2O. A suspension of spores and aseptically with cellophane. Biomass was harvested in mycelium from 10-day-old 325 medium Petri dishes was triplicate at 60, 120, 180, and 240 h of incubation. prepared aseptically with distilled water. One milliliter Moisture Content. Dry mass was estimated by drying of this suspension was added to a 300 mL shake-flask 1 g of fermented substrate for 72 h to constant weight at with 100 mL of the liquid medium and was kept in a 60 °C in a preweighed aluminum dish. rotary shaker at 29 °C and 150 rpm for 8 days. Biomass. The traditional method of estimating bio- Solid Substrate Medium. Ten gram portions of long mass by determining the glucosamine content was inad- grain rice, purchased locally, were placed in 200 mL equate when applied to biomass grown on rice. Conse- flasks and then soaked with 10 mL of a 0.015 M ZnSO ‚ 4 quently, a method based on the traditional glucosamine 7H2O solution. The soaked rice was autoclaved for 20 min at 121 °C in the flasks. The autoclaving time should not method described by Vignon et al. (25) and an alkaline be exceeded to prevent bed packing during cultivation. pretreatment of the sample as described by Ekblad and Solid Substrate Cultivation. Flasks containing ap- Na¨sholm (26) was employed. The adapted method, de- proximately 20 g of humidified autoclaved rice were scribed in detail elsewhere (27), produced no interference inoculated with 0.5 mL of the eight-day old submerged when determining the glucosamine. Biomass was hence culture. Specially devised columns, known as Raimbault determined from two 25 mg samples taken from the dried columns, (2 cm diameter and 25 cm length) were used and ground fraction of each column. for cultivation. Each Raimbault column was filled with Respiratory Gases. The system used for quantifying the autoclaved and inoculated rice of one flask. The CO2 and O2 is able to measure exhaust gas compositions approximate moisture of the initial mixture was 55% on from four columns on-line simultaneously, three with a wet basis. inoculum and one control (Figure 1). The system com- Culture Conditions. Standard cultures were incu- prised a silica gel column for drying the humid exhaust bated in columns for 10 days (235 h) in a thermoregulated air, a gas mass flowmeter (Aalborg Instruments GFM17) water bath at 30 °C. Air was filtered on-line with glass to measure the gas flow rate, an electrochemical oxygen wool. Columns were aerated continuously with 20-30 sensor (Columbus Instruments 0135-0345) and an mL/min of saturated air. In addition, the influence of infrared carbon dioxide sensor (Columbus Instruments light on pigment degradation was evaluated in columns 0135-190E). A calcium sulfate (drierite) column was incubated for 7 days at 30 °C; one set of columns was set for additional drying of exhaust air after the gas covered with aluminum foil, and another set, used as mass flowmeter and before oxygen and carbon dioxide control, was left uncovered. analyzers. Data acquisition was done with a program- Samples. Three columns were taken for analysis every mable logic controller (PLC, Hitachi) connected to a 16 h. Half of the content of each column was dried at 30 personal computer. A multiplexer system consisting of °C for 48 h and then ground. The other half was kept four on/off solenoid valves commanded by the PLC wet and immediately frozen at -18 °C for future analysis. permitted the selection of outlet gas from any column for 154 Biotechnol. Prog., 2000, Vol. 16, No. 2 analysis. Air flow and gas concentrations were sampled every 30 min. Ethanol. Each exhaust gas from the three inoculated columns was passed through identical sampling bottles (Figure 1). At 12 h intervals, a gas sample was taken from each bottle with a 1-mL tight-sealed syringe and injected directly into a Hewlett-Packard 6890 gas chromatograph equipped with an HP5 column (30 m, 0.32 mm i.d., 0.25 µm), helium at 1.4 mL/min, injector at 80 °C, splitless for 0.5 min, programmed for 3.5 min at 50 °C followed by a 40 °C/min gradient to 200 °C, and held at 200 °C for 2 min. A FID detector was used at 250 °C. Every time a sample was taken, the exhaust air flowrate was measured with an Aalborg Instru- ments GFM17 sensor. The ethanol read-out is not rep- resentative of that produced at a given point in time, because of the ethanol retained in the biofilm. Hence, a separate experiment was performed to correct this effect. Figure 2. Dry mass, biomass, and moisture content of M. Here, a known quantity of ethanol was added to three purpureus grown on rice. Bars correspond to one standard deviation. IDS refers to initial dry substrate. Raimbault columns each containing 20 g of wet rice. Ethanol was periodically measured in the exhaust gas at 1 h intervals, until exhaustion of ethanol in the solid where pertinent, from equipment supplier manuals. All bed. The ethanol/time trend determined in this experi- such data is presented in Appendix B. ment was later used to correct cultivation-based mea- surements. Results Acetic Acid. One gram of wet sample was agitated Evolution of Dry Mass and Moisture Content. At vigorously for 5 min at room temperature on a vortex the end of the culture, the dry mass (which includes with glass beads with 5 mL of 0.1% CTAB (cetyltrimethyl- residual rice and biomass) was 36.4% of its initial weight ammonium bromide) aqueous solution. The resulting (Figure 2), almost entirely as a result of the transforma- solution was filtered with a Millipore filter (13 mm tion of substrate into water, CO2, ethanol, and acetic acid. diameter, 0.22 µm pore diameter) and measured directly As the biomass fraction in the culture increases and the by HPLC (Merck-Hitachi LaChrom) with a BioRad columns are fed with saturated air, the bed is continu- Aminex ion exclusion HPX87H column (300 mm × 7.8 ously moistened, and they attained a final water content mm) using a DAD (diode array detector Merck-Hitachi of 79.5%. In addition, at this stage, the biomass and the L-7450A) fixed at 210 nm. Elution was carried out with residual substrate form a compact mass, exacerbating 0.004 M H2SO4 solution at a flowrate of 0.6 mL/min. oxygen transfer limitations inside occluded zones. Here, the acetic acid lost through stripping was neglected, Biomass. The glucosamine content of the biomass rose because the pH medium at the end of the culture is from 5.42% to 7.12% at the end of the culture. To get a around 4.5, very close to its pK value. Hence, half of the better estimation of biomass evolution, a nonlinear curve acid is dissociated and nonvolatile. In addition, of the was fitted to the glucosamine vs time data (Table 1 and remaining nondissociated fraction, 50% is intracellular Figure 3). and therefore not exposed to the gas flow. Figure 2 clearly shows three growth phases: a lag Pigments. One 50 mg sample was taken from the phase; an exponential-growth phase, extending from 40 dried and ground fraction of each column. These samples to 100 h of culture that coincided with the maximum were extracted twice with 40 and 20 mL of 95% ethanol production rate of CO2 (Figure 4a) and the maximum for1honarotary shaker. Between extractions, the substrate consumption rate; and finally a stationary samples were filtered with a Wathman filter #2. One phase in which total biomass remained relatively con- milliliter of the total extract was dried with nitrogen in stant. a water bath at 38 °C. This was later resuspended with CO2 Production and O2 Consumption Rates. The 1 mL of a 60% acetonitrile aqueous solution and then highest respiratory activity was observed at around 80 filtered with a Millipore filter (13 mm diameter, 0.22 µm h of culture and, as expected, corresponded to the growth pore diameter). Measurement was done by HPLC (Merck- phase. The respiratory quotient (RQ) took on values Hitachi LaChrom) with a LiChrospher 100 RP-18 end- above 1 during the growth phase and values close to 1 capped column, (5 µm, 250 mm × 4 mm) using a DAD after 90 h of culture (Figure 4a). The observed CO2,O2, (diode array detector L-7450A) fixed at 415 nm. Elution and RQ curves are similar to those described by Han and was carried out using a gradient of an aqueous aceto- Mudgett (19). nitrile solution, beginning with an isocratic regime at Ethanol and Acetic Acid Production. Many au- 60% for 5 min that was then increased linearly to 90% thors have reported on ethanol production in Monascus over 20 min and finishing with an isocratic elution for 5 spp. in submerged (10, 11, 15, 32) and solid-substrate min. A flowrate of 0.7 mL/min was applied. cultivation (21). In the current work, the highest ethanol Elemental Analysis. Elemental analysis of both production rate also occurred during the growth phase, biomass and substrate were carried out with a Fisons between 60 and 120 h of culture (Figure 4b). Although EA 1108 elemental analyzer. acetic acid production has been found in the submerged Consistency Analysis. Analysis of consistency was cultivation of Monascus spp. (11, 12, 17), its occurrence based on theory described elsewhere (24, 28-31). Its in SSF has not been reported before. Acetic acid is fundamentals are summarized in Appendix A. Reaction produced in the stationary phase, and it reached a rates, when required, were obtained by numerical dif- maximum concentration of 0.4 Cmmol/g IDS (Cmmol ) ferentiation of this work’s experimental data. Coefficients one carbon millimole, IDS ) initial dry substrate) at of variation were obtained from laboratory data and, around 200 h of fermentation. Biotechnol. Prog., 2000, Vol. 16, No. 2 155

Table 1. Evolution of Glucosamine Content and Biomass Composition of M. Purpureus during Culture on Cellophane-Covered Rice on Petri Dishes and Their Respective Fourth-Order Polynomial Fits time (h) glucosamine contenta (% w/w) compositionb ash content (%)

60 5.42 ( 0.422 CH1.6271O0.6622N0.0846 120 5.77 ( 0.099 CH O N 1.5942 0.5796 0.0727 3.01 180 6.22 ( 0.218 CH1.6843O0.5014N0.0681 240 7.12 ( 0.991 CH1.5142O0.4885N0.0658

4 3 2 c Polynomial Coefficients (P(t) ) a4t + a3t + a2t + a1t + a0) elemental content (%) glucosamine content (%) C H O N 0 1 0 1 0 a0 5.0000 × 10 4.3182 × 10 5.3030 × 10 4.6742 × 10 4.7727 × 10 -3 -2 -2 -1 -3 a1 7.1030 × 10 8.9372 × 10 4.5902 × 10 -1.4365 × 10 8.3713 × 10 -6 -4 -4 -3 -4 a2 4.8967 × 10 -5.2732 × 10 -7.0522 × 10 1.4892 × 10 -2.5666 × 10 -7 -6 -6 -6 -6 a3 -1.7970 × 10 3.1199 × 10 4.7152 × 10 -9.4802 × 10 1.6451 × 10 -10 -9 -8 -8 -9 a4 7.9080 × 10 -7.0717 × 10 -1.0271 × 10 2.0538 × 10 -3.1945 × 10 a Data are given with one standard deviation. b Elemental subindexes of biomass were considered with four decimal places of precision because consistency analysis is very sensitive to this values. c A glucosamine content of 5% w/w and a biomass elemental composition of CH1.4737O0.8118N0.0947 were assumed at t ) 0 in order to compute the polynomial fits.

Figure 4. (a) CO2 production rate, O2 consumption rate, and respiratory quotient and (b) ethanol, acetic acid, and theoretical ethanol production of M. purpureus grown on rice. Bars corre- spond to one standard deviation. Figure 3. Polynomial fit for (a) glucosamine content (%) vs time and (b) elemental content (expressed as percentage) vs time. primary yellow pigments (ankaflavin and monascin) and Bars correspond to one standard deviation. the two primary orange pigments (rubropunctatin and monascorubrin) were identified. Although very low con- It is worth noting, for stoichiometric modeling pur- centrations of primary red pigments (rubropunctamine poses, that no other volatile alcohol nor volatile organic and monascorubramine) usually found in Monascus spp. acid were detected in significant quantities. cultures were detected, HPLC/DAD analysis confirmed Pigments. Determining exact pigment yields was the presence of significant quantities of other, unidenti- beyond the scope of this work as they were insignificant fied, secondary red pigments (data not shown). These in terms of the masses involved in the process. The two may be red pigments complexed with amino acids, as 156 Biotechnol. Prog., 2000, Vol. 16, No. 2

primary red pigments (monascorubramine and rubro- punctamine) are less photostable than yellow pigments (37). The high degradation of primary red pigments would partly explain why these were detected in such low concentrations. Elemental Composition. The composition of the bio- mass showed significant carbon enrichment and nitrogen reduction during the culture (Table 1). Given the varia- tion of elemental composition over time, the usual procedure of working with an average biomass composi- tion is not applicable here. Therefore, a fourth-order polynomial fit was carried out to provide a more reliable biomass composition for the macroscopic balance used in the consistency analysis. At close to 4% glucosamine content (w/w), the nitrogen component of biomass composition (Table 1) is similar to that reported by Smits et al. (38) for Trichoderma reesei grown on wheat bran. The nitrogen content of the biomass, however, is less than that of Monascus ruber from submerged cultivation, which is about 5% (w/w) (17, 18). The elemental composition of the substrate was deter- mined as CH1.4455O0.7973N0.0335, which for the purposes of Figure 5. Pigment production of M. purpureus grown on rice. simplicity was assumed constant throughout the experi- ment. An ash content of 0.57% was obtained for the Table 2. Kinetic Parameters of Rice-Grown M. substrate. purpureus Pigment Production Macroscopic Balance. The macroscopic balance de- Chen and Johns this scribing the system was defined as follows: a b pigments (1994) work + f aCH1.4455O0.7973N0.0335 bO2 Yp/x (mg/g) + + + + monascorubramine (red) 114.2 cCHxOyNz dCO2 eH2O f CH3O0.5 gCH2O (1) monascin (yellow) 4.2 12.38 rubropunctatin (orange) 0.35 30.19 where CH1.44O0.79N0.033,CHxOyNz,CH3O0.5, and CH2O ankaflavin (yellow) 6.1 4.98 correspond to rice, biomass, ethanol, and acetic acid, monascorubrin (orange) 34.82 respectively, expressed on a 1-carbon mole or millimole Yp/s (mg/g) basis (Cmol or Cmmol). Biomass elemental composition monascorubramine (red) 32.0 was obtained from the polynomial fit described above monascin (yellow) 1.2 2.93 (Table 1 and Figure 3) each time consistency analysis rubropunctatin (orange) 0.1 7.18 ankaflavin (yellow) 1.7 1.18 was carried out. monascorubrin (orange) 8.28 Pigments have been omitted from the macroscopic balance. This omission represents a negligible source of a Only intracellular pigments measured. Strain used: M. pur- b error because (i) they represent a very small fraction of pureus UQM 192F (FRR 2190). Total pigments measured. Strain ∼ used: M. purpureus ATCC 16392. the mass involved in the process ( 3%); (ii) a proportion of the pigments are intracellular and therefore are described elsewhere (33-36). While pigment production already accounted for both in the biomass measurement appears at the end of the growth phase (Figure 5), and in its composition; and (iii) the extracellular pigments suggesting that they are secondary metabolites, this are nonquantifiable, but their weight is accounted for in study does not venture to determine what triggers the the residual rice weight nevertheless. synthesis. Carbon Balance. Between 83% and 94% of the initial Pigment-biomass (Yp/x) and pigment-substrate yields carbon was accounted for at any given sampling time (Yp/s) obtained in the current work were much higher than (Figure 6). At the end of the culture time 91% of the those reported by Chen and Johns (10) under submerged initial carbon was accounted for: 23% in the biomass, cultivation, although they used a different strain and only 17% in residual rice, 35% in CO2, 15% in ethanol, and measured intracellular pigments (Table 2). Although the 1% in acetic acid. The best accountability of the initial total pigment yield is similar in both studies, they carbon was detected at 195 h of culture, at 94%, while obtained a high yield of monascorubramine (primary red the worst was observed at 100 h of culture at 83%. pigment) that is almost absent in our cultures. This is Consistency Analysis. Collected data allowed the probably due to the presence of peptone in the medium checking of consistency at the 16 time points at which used by Chen and Johns (10), which favors monascoru- columns were taken (see Figure 2), although as the first bramine pigment production over rubropunctatin and four time points corresponded to the lag phase, these monascorubrin (orange pigments). Keeping their culture were discarded. Data consistency analysis was performed in darkness would also minimize monascorubramine for the growth phase with reaction rates computed at five degradation. time points between 50.5 and 114.5 h (results not shown): Essays on the effect of light showed that all pigments (i) The presence of a gross error was detected by have a certain degree of photosensitivity. After 170 h, calculating test statistic h (see Appendix A), which pigment concentrations in cultures without light-shield- yielded values higher than those given by a 95% confi- ing were 80.6%, 43.3%, and 37.9% lower for primary red, dence level. orange, and yellow pigments, respectively. These results (ii) It was not possible to identify the measurement of concur with those published previously, which hold that a single compound as the source of error on calculating Biotechnol. Prog., 2000, Vol. 16, No. 2 157

Table 3. Test Statistic h and Residual Fit ψ for Stationary-Phase Cumulative Dataa

time (h) hT h*T h*T/CO2 ψ*T/CO2 130.5 254.83 17.48 4.9624 0.0259 146.5 374.64 14.08 3.1335 0.0767 162.5 511.77 13.54 2.2587 0.1329 179.5 519.74 16.01 0.7153 0.3977 194.5 528.81 17.95 0.0175 0.8946 213.0 667.41 16.05 0.4939 0.4822 233.5 725.56 11.45 0.5703 0.4501

a hT, test statistic obtained using complete macroscopic balance (C, H, O, N); h*T, statistic using macroscopic balance without

nitrogen; h*T/CO2, test statistic obtained using a macroscopic

balance without nitrogen and on eliminating CO2; ψ*T/CO2, residual fit comparing vectors of residuals drawn from a balance without nitrogen, *T,j, and the column of Rr that corresponds to CO2. rates of many compounds were near zero. This analysis was done at seven points in time between 130.5 and 233.5 Figure 6. Carbon balance of M. purpureus grown on rice. h. Two global models were used to check whether residual fit Ψ over the vectors of the residuals for each nitrogen was the main error source in this phase, as it was in the growth phase. The first model included the time point, j, and the columns of the matrix Rr (see Appendix A). This means that (a) there were two or more main elemental balances (C, H, O, N), while the second compounds presenting gross measurement error; (b) excluded the nitrogen balance. Test statistics will be there are elemental composition errors; (c) there are termed hT and h*T for the first and second models, missing compounds; or (d) any combination of these respectively, where T denotes total (since we are using factors. cumulative data) and the asterisk signifies the model (iii) It was found that those vectors of residuals that without nitrogen. Similarly, vectors of the residuals of are close in time have similar directions, while those that cumulative data will be called T,j when the nitrogen are far apart have significantly different directions. This balance is included and *T,j when it is not, while j will was achieved by calculating residual fit Ψ over two stand for a certain time point. vectors of residuals of different time points instead of (i) It was confirmed that the nitrogen balance was also the main source of error in the stationary phase. Results calculating it from j and the columns of matrix Rr. This extension of the method is not described by Van der returned for test statistics h*T for the second model were Heijden et al. (29), but it proved useful for data analysis. significantly lower, from a statistical point of view, than (iv) A significant difference between the nitrogen those of the first model, hT, at every time point. This test consumed from rice and that incorporated into the statistic also corroborates that the biomass’ nitrogen synthesized biomass can be computed from the experi- content contains a gross measurement error. Finally, the mental data (Tables 1 and B.2). Hence, a macroscopic h*T test statistic values for the second model showed that balance without the nitrogen balance was established, some other error source must probabilistically be present. where test statistic h* (in which the asterisk denotes that The elimination method of Stephanopoulos et al. (24) the test statistic was obtained without the nitrogen (see Appendix A), performed on the model without the balance) yielded values within a 95% confidence interval nitrogen balance, suggests the CO2 measurement (h*T/CO2) for many time points, thereby indicating that a compound as the additional error source (See Table 3). containing nitrogen was responsible for the gross error (ii) The residual fit Ψ*T obtained by comparing the encountered. vectors of residuals *T,j and the column of matrix R*r The last two observations suggest that the gross error (note that this redundancy matrix does not include the is associated with a compound whose elemental composi- nitrogen balance) for the second model confirms CO2 tion changes over time (the error indicated in ii(b) above) measurement as the additional source of error (see Table and that it contains nitrogen. Therefore, an error in 3). biomass measurement was assumed, as biomass is the (iii) Further testing, calculating the residual fit Ψ*T only compound whose elemental composition changes. over residual vectors at different times, shows all residual An error in biomass measurement could be linked vectors to have similar directions (see Table 4 and Figure either to biomass concentration or to its elemental 7). Therefore the additional source of error is the same composition, although (ii) and (iii) above would indicate for all time points. the most probable explanation behind the error to be the latter possibility. This can occur because a gross error Discussion in biomass concentration would have induced consider- Dry Mass. Smits et al. (38) obtained a dry weight loss able similitude between the vectors of the residuals j and of 22% from a 125 h Trichoderma reesei QM9414 culture the biomass’ column of the reduced redundancy matrix, grown on wheat bran in Petri dishes. Their dry weight Rrx (note that X denotes biomass) (see Appendix A). loss is considerably lower than the 55% observed in this However, since this was not observed, it can be concluded work over the same culture time. The difference would that the cause of the error was indeed the elemental be due to wheat bran having a higher proportion of composition of the biomass. Applying the method of Van nondegradable material such as cellulose, lignin, etc. and der Heijden et al. (29) (using eq 9 in appendix A), on the the fact that Petri dish fermentation conditions may other hand, it was found that the nitrogen content was produce degradation kinetics different from those in underestimated by 31%, on average, during the growth aerated systems. However, the 63.6% loss of dry mass phase. observed here to the end of the culture is similar to that Cumulative data was used for the consistency analysis reported by other authors working with rice cultures of the stationary phase, because the measured reaction under similar conditions (19). 158 Biotechnol. Prog., 2000, Vol. 16, No. 2

Table 4. Residual Fit ψ Applied between E*T,j of Different Time Points

Ψ*T

*T,130.5 *T,146.5 *T,162.5 *T,179.5 *T,194.5 *T,213.0 *T,233.5

*T,130.5 1 0.6937 0.4568 0.142 0.0336 0.1085 0.1556 *T,146.5 0.7174 1 0.743 0.3066 0.0953 0.2479 0.3294 *T,162.5 0.4921 0.7419 1 0.4753 0.1633 0.3937 0.5061 *T,179.5 0.1463 0.2776 0.4559 1 0.4671 0.8831 0.9589 *T,194.5 0.0232 0.056 0.1165 0.428 1 0.5254 0.3967 *T,213.0 0.1104 0.2179 0.3718 0.8827 0.5598 1 0.8421 *T,233.5 0.2323 0.3789 0.5556 0.9652 0.512 0.8668 1 the weight loss of 22% in the work of Smits was much lower than the 63.6% in the current work. Possible biomass and residual rice measurement errors are certainly not responsible for the lack of carbon we have observed, because the dry mass assay, which comprised both biomass and residual rice is accurate. Any systematic underestimation of either one of these vari- ables would induce overestimation in the other. For example, a biomass underestimation of 0.2 g would imply an overestimation of residual rice by 0.2 g. Because biomass and residual rice respectively have a carbon content of 54% and 44%, a measurement error would produce a carbon account error of only 0.02 g. Therefore, a significant error in the carbon account can only be due to a systematic error in the measurement of volatile gaseous compounds, mainly CO2 and ethanol. While neglecting acetic acid in the outlet gas stream introduces Figure 7. Graphical comparison between the vectors of residu- an error in the carbon balance, it is found in insufficient als * (s), where j stands for each time point, and column of T,j concentrations to account for the 9% carbon deficiency reduced redundancy matrix R*ri (- - -) of each compound i. observed. The latter will be discussed next. Glucosamine Content and Biomass. The glucos- Ethanol Production. Many produce amine content of the biomass increased with culture time. ethanol when cultured under anaerobic conditions. How- This phenomenon has also been observed in agar plate ever, some microorganisms such as (42) can pro- cultures of Beauveria bassiana (39). Also, the average duce ethanol under aerobic conditions in the presence of high glucose concentrations. This results from high glu- glucosamine content of the biomass in the solid substrate - - cultures of Monascus purpureus performed here was cose flux through the EMP pathway (Ebden Meyerhoff similar to that reported for submerged cultivation of the Parnas), which generates a flux bottleneck at pyruvate. fungus (19). This would divert flux to ethanol formation instead of to the TCA cycle. High data dispersion was observed in biomass mea- It is believed that the ethanol produced in our cultures surements from 50 to 80 h of culture. The inherent is due to the high glucose concentration during exponen- heterogeneity of solid substrate cultivation and the tial phase (21). This hypothesis is supported by the difficulties in measuring biomass are habitually cited as following: the main causes of this problem. The method for deter- • In the solid-substrate fermentation of Monascus mining biomass used here presented low variance (40), performed in plastic bags, no ethanol was found at low indicating that the cause of data dispersion is associated glucose concentration, while high levels of ethanol were with culture heterogeneity. Each column evolves differ- found at high glucose levels (21). ently, because initial inoculum densities are not exactly • In this work, ethanol was found in significant identical and the columns are subject to different aeration quantities, but only in the growth phase. conditions during culture. Thus, the columns are not in • Monascus spp. belongs to the ascomycete class, the the same state of growth. same as S. cerevisiae (43). Biomass Elemental Composition. Results published • Monascus grows on ethanol (13). elsewhere for M. ruber grown under submerged culti- Other factors indicating that ethanol formation is not vation present a higher proportion of nitrogen (17), due to oxygen limitation are that even though the elemental composition of the biomass • Monascus is not able to grow under anaerobic is similar to that obtained here in Petri dishes. Smits conditions (19). et al. (38), however, also reported an unusually low nitro- • Bed compactness, which may cause oxygen starvation gen biomass fraction for Trichoderma reesei grown in when acute, was similar throughout the culture and not Petri dishes. solely in the growth phase where ethanol formation was The increase observed over culture time in the carbon detected. fraction of the biomass may be due to an increase of Stoichiometric Model Approach. In the model intracellular pigments, which have a carbon fraction of pathway, which includes the EMP pathway and the close to 70%. It could also be due to a possible increase TCA cycle, the metabolism of every 0.5 mol of glucose in glycogen content that has been reported for Saccharo- consumes 3 mol of O2 (because each NADH oxidation myces cerevisiae at low growth rates (41). consumes 0.5 O2) and generates 3 mol of CO2. However, Carbon Balance. The carbon account of 83-94% if ethanol overflow occurs, the metabolism of S. cerevisiae achieved in this work is a considerable achievement when yields, assuming the same pathway is used as that for compared to the 94% obtained by Smits et al.(38) because anaerobic ethanol production, 1 additional mol of CO2 for Biotechnol. Prog., 2000, Vol. 16, No. 2 159

The CO2 error could arise from three causes: (i) an error in the air mass flowrate sensor, (ii) an error in the CO2 analyzer, and (iii) heterogeneity of the cultivation system used. The second cause can be discarded since the CO2 sensor was carefully calibrated with a standard gas before the experiments. The third cause is the most likely, as the columns used for CO2 measurement were the only ones to have a strictly regulated air supply. Hence, the columns used in the analysis of biomass, acetic acid, and pigments may have exhibited behavior different from that of those used in the gas analysis. It is also possible that the air mass flowrate was not properly calibrated, as the Stephanopoulos et al. (24) and Van der Heijden et al. (29) methods concur in identifying O2 measurements as the second cause of error (results not shown). The underestimation of ethanol is commonplace in bioprocess engineering and in this particular case may be due to (i) an error in air mass flowrate measurement (measured with a different sensor from that of CO2), (ii) Figure 8. Stoichiometric model used to calculate theoretical the experimental setup for ethanol trapping and mea- ethanol production of M. purpureus. surement, and (iii) heterogeneity of the cultivation system used. The last two causes are considered the most every 2 Cmol of ethanol produced (24, 41) (Figure 8). This probable here, because the procedure applied to correct CO2 is not coupled with O2 consumption, which we will the ethanol measurements was overly simplistic given call nonrespiratory CO2. the complexity of the ethanol accumulation process in the Ethanol is presumably consumed by the same pathway columns. Additionally, it is not easy to maintain the same by which it is synthesized, as the reaction of acetaldehyde degree of aeration in each of the columns, which must to ethanol is reversible. As can be seen in Figure 7, net also contribute to error in the ethanol account. ethanol (produced minus consumed) generates one non- respiratory CO2. Assuming that Monascus behaves like Conclusions , the observed difference be- tween the CO2 production and the O2 consumption rates A full set of data has been obtained containing all of may correspond to the nonrespiratory CO2. Hence, this the significant compounds, in terms of mass, in the SSF difference was used to estimate the production of ethanol. culture of M. purpureus and has yielded a level of carbon Ethanol had been underestimated, as shown in Figure accounting comparable to that of other SSF studies. Even 4b. The carbon balance account improves to 87-99% when excluding pigments from the carbon balance, the when the experimental measurements of ethanol were measurement of all of the system’s relevant compounds replaced with its theoretical estimation. allows data accuracy to be checked, which is impossible Data Consistency. Consistency analysis was useful if not all of the system variables are determined and for detecting or identifying the error sources associated measured. with experimental design. According to the consistency A simple stoichiometric model, based on a proposed analysis, the nitrogen content of the biomass exhibited mechanism for ethanol production, together with a a gross error in both growth and stationary phases. This consistency analysis, neither of which have been per- may, however, be caused by the experimental system formed on SSF before, enabled the identification of the used to grow biomass for elemental analysis. It seems system’s sources of error. The accounting of 87-99% of that Petri dishes do not reproduce the culture conditions the initial carbon content on replacing measured ethanol prevailing in Raimbault columns accurately. An error in with its stoichiometric model estimate lets us conclude the determination of biomass nitrogen can be discarded, that no significant compound was omitted from the because a malfunctioning elemental analyzer would have system. This set of data can therefore be used reliably affected the measurement of the substrate’s nitrogen for future modeling. content also, thereby canceling the nitrogen error. Better knowledge of the metabolism of Monascus spp. The consistency analysis helped identify CO2 measure- would appear crucial to carry out a detailed metabolic ments as one of the main sources of error in the carbon flux analysis of the system. Also, furthering Monascus account. Stephanopoulos’ method (24) clearly indicates SSF quantitative studies will require accurate measure- CO2 as a sole source of error. The results of the method ments of ethanol, biomass, and the residual substrate. of Van der Heijden et al. (29) are less categorical, This could be achieved by using a setup with an inert however, since the CO2 comparison vector, although support in a single bioreactor. similar, does not match the residual vectors exactly (Figure 7). This means that there should be some Acknowledgment combination of error sources that would match the residual vectors better. In support of this last idea, the This work would not have been possible without the stoichiometric approach identifies the underestimation technical proficiency of Christian Valenzuela, Ximena of ethanol as a significant reason for the lack of carbon. Pen˜ a, and Lenka Torres. Fruitful discussion and the In fact, the direction of the vectors of residuals for the contribution of many original ideas of professors Jules stationary phase (Figure 7) along with the carbon balance Thibault (Universite´ de Laval) and He´ctor Jorquera analysis discussed above would suggest the system’s (Pontificia Universidad Cato´lica de Chile), Mauricio error sources to be a combination of errors in CO2 and Gonzalez, Andrea Belancic, Mario Fernandez, Claudio ethanol measurements. Gelmi, Marcial Pen˜ a y Lillo, and Jose Antonio Reyes were 160 Biotechnol. Prog., 2000, Vol. 16, No. 2 of invaluable help. Standard pigments were kindly Table B.1. Reaction Rates of Growth Phase at Five provided by Dr. Ludmila Martinkova from Academy of Points in Timea Sciences of the Czech Republic. Our thanks to Alex ‚ time reaction rates (Cmmol/g IDS h) Crawford for English proficiency. (h) rS rO2 rX rCO2 rEt rAc Appendix A. Consistency Analysis 50.5 -0.3742 - 0.0480 0.0937 0.0940 0.046 028 0.001 128 66.5 -0.3211 -0.0656 0.0825 0.1171 0.086 356 0.001 667 Consistency analysis and error source detection fol- 82.5 -0.3660 -0.0757 0.1012 0.1176 0.102 647 0.000 456 lowed methods that are described extensively elsewhere 98.5 -0.2948 -0.0732 0.1007 0.0999 0.073 476 0.001 968 (24, 28-31). The essential equation in consistency analy- 114.5 -0.0976 -0.0596 0.0311 0.0758 0.021 587 0.004 119 sis is the macroscopic balance rewritten in the following a Subindexes: S, residual rice; X, biomass; Et, ethanol; Ac, acetic matrix form: acid. IDS, initial dry substrate

Er ) 0 (2) and Stephanopoulos (31) and Stephanopoulos et al. (24) established an elimination procedure, where the mea- where the atomic matrix E contains the number of atoms sured rate of the candidate error source is calculated from of all compounds in the macroscopic balance, and the the other measured rates and the test statistic h is sought vector r contains the rates of reaction of the compounds. again. If the new h value is lower than the one given by As not all reaction rates are measured, the matrix system the chosen confidence level (bearing in mind that the new should be partitioned between the measured rates, rm, value has one less degree of freedom), it can be concluded and those to be calculated, rc: that the chosen reaction rate contains a gross measure- + ) ment error. Emrm Ecrc 0 (3) Van der Heijden et al. (29) proposed a method of comparison for locating errors. These authors distinguish From this equation, rc can be obtained by simple matrix between two kinds of errors, where the expected values inversion. When Ec is nonsquare, as is usually the case of the vectors of the residuals are given by systematic in consistency analysis, a pseudoinversion can be applied measurement errors: (28). Once rc has been expressed in terms of rm, after several algebraic steps (see the references (24, 28, 29)) E() ) R π ) v (8) the following balance can be obtained: ri ) and system definition errors, due to the omission of a Rrrm 0 (4) relevant compound or the wrong elemental composition of a compound: The reduced redundancy matrix, Rr, has one row less than the atomic matrix, E, for each calculated compound. ) # - ) When the vector of reaction rates rm is actually mea- E() K(EcEc I)Ei+1 v (9) sured, the above balance does not yield exactly zero as a result of random and gross measurement errors and Rri denotes the column of matrix Rr corresponding to provides a vector of residuals, : compound i (29). The column Ei+1 includes the elemental ) composition of an omitted compound or the error in the Rrrm  (5) elemental composition of one of the originally considered # compounds, and Ec is the pseudoinverse of Ec. The The components of  are the weighted sums of reaction reducing matrix, K, is an elemental operation matrix (see rate measurement errors. Assuming that the latter show ref 29). a normal distribution, the components of the residuals The detection procedure requires a statistical compari- are assumed to also. Then, a test statistic distributing son between the measured vector of residuals,  from eq 2 ø , with as many degrees of freedom as rows in Rr, can 5, and the expected values (called compared vectors) from be performed: eq 8 or 9. This comparison is achieved by computing a - weighted distance: h ) P 1 (6) 2 ) T -1 - T -1 2 T -1 where P is the covariance matrix of  and is obtained ∆  P  ( P v) /(v P v) (10) from the covariance matrix of reaction rates, F,(24) which is drawn from the analysis of measurement errors and then the special, residual fit Ψ is obtained (see (40): ref 29):

) T Ψ ) p(∆2 e ø2) (11) P RrFRr (7) 2 - The value yielded by the test statistic is compared to the Residual fit Ψ distributes ø with rank(P) 1 degrees of 2 freedom. The closer Ψ is to 1, the greater the similarity value of ø with rank(Rr) degrees of freedom for a chosen confidence level. If h is higher than the value of ø2 for between  and v. When the dimension of  and v is three this confidence level, the system should contain a gross or less a graphical display may ease their comparison. measurement error; if not, it can be concluded that only random errors exist. The value of ø2, typically with three Appendix B degrees of freedom, to which the test statistic h is Tables B.1, B.2, B.3, and B.4 show this work’s reaction compared is 7.81, which corresponds to a confidence level rates for the growth phase and cumulative data for of 95%. the stationary phase, as well as their corresponding If the existence of a gross measurement error is coefficients of variation. detected, its source can also be located. Two procedures Differences exist in the coefficients of variation over for this have been described in pertinent literature. Wang the time of culture because the accuracy of some of the Biotechnol. Prog., 2000, Vol. 16, No. 2 161

Table B.2. Cumulative Data in the Stationary Phasea exhibition Natural Colors for the Food Industry; Ismailia, Egypt, February 16, 1993; pp 49-60. cumulative data (Cmmol/g IDS) time (9) Mitchell, D. A.; Stuart, D. M.; Tanner, R. D. Solid-state (h) Sc O2 XCO2 Et Ac fermentation, microbial growth kinetics. In Encyclopedia of 130.5 -28.435 -6.114 8.106 9.684 5.494 0.195 Bioprocess Technology: Fermentation, Biocatalysis and Bio- 146.5 -28.707 -6.845 8.120 10.563 5.503 0.265 separation; Flickinger, M. C., Drew, S. W., Eds.; John Wiley 162.5 -29.054 -7.549 8.006 11.316 5.510 0.277 and Sons: New York, 1999; pp 2407-2429. 179.5 -29.713 -8.162 8.686 11.911 5.518 0.325 (10) Chen, M.-H.; Johns, M. R. Effect of carbon source on 194.5 -29.473 -8.573 9.170 12.311 5.522 0.434 ethanol and pigment production by Monascus purpureus. 213.0 -30.423 -9.010 8.593 12.724 5.527 0.359 Enzyme Microb. Technol. 1994, 16, 584-590. 233.5 -30.735 -9.440 8.566 13.154 5.532 0.318 (11) Hamdi, M.; Blanc, P. J.; Goma, G. Effect of aeration a conditions on the production of red pigments by Monascus Abbreviations: Sc, consumed rice; X, biomass; Et, ethanol; Ac, acetic acid; IDS, initial dry substrate purpureus growth on prickly pear juice. Process Biochem. 1996, 31, 543-547. Table B.3. Coefficients of Variation of Reaction Rate (12) Hamdi, M.; Blanc, P. J.; Loret, M. O.; Goma, G. A new Measurements of Growth Phase at Five Points in Timea process for red pigment production by submerged culture of Monascus purpureus. Bioprocess Eng. 1997, 17,75-79. coefficients of variation (%) time (13) Juzlova´, P.; Martı´nkova´, L.; Lozinski, J.; Machek, F. (h) rS rO2 rX rCO2 rEt rAc Ethanol as substrate for pigment production by the fungus 50.5 10.01 2.69 5.50 2.69 4.18 5.49 Monascus purpureus. Enzyme Microb. Technol. 1994, 16, 66.5 10.59 2.70 7.56 2.71 4.21 5.36 996-1001. 82.5 7.76 2.73 9.70 2.77 5.05 21.49 (14) Lin, T. F.; Demain, A. L. 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