Assessment of respiratory activity during surface-mould cheese ripening

Available online at www.sciencedirect.com

Journal of Food Engineering 85 (2008) 632–638 www.elsevier.com/locate/jfoodeng

Assessment of respiratory activity during surface-mould cheese ripening Arnaud He´lias *, Ioan Cristian Trelea, Georges Corrieu UMR782, Ge´nie et Microbiologie des Proce´de´s Alimentaires, INRA, AgroParisTech, BP 01, 78850 Thiverval-Grignon, France Received 22 June 2007; received in revised form 24 August 2007; accepted 3 September 2007 Available online 8 September 2007

Abstract The present study deals with the respiratory activity of the microbial consortia established on surface-mould cheese. A software sensor was developed, from a model linking the operating conditions established in the ripening room (temperature, relative humidity, carbon dioxide and oxygen concentration) to the cheese mass dynamic. It estimated the respiratory activity of the microbial consortia based on mass measurement only, without atmospheric gas composition sensors or off-line measurements. After some simplifications of the model, this observer can be seen as an integral Luenberger observer, with analytically defined gain values. This approach was validated on two ripening trials. Relative errors between estimated and calculated (from carbon dioxide and oxygen concentration measurements) respiratory activity were equal to 3.02% and 8.83% for Run 1 and Run 2, respectively. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Luenberger observer; Mass loss; Respiratory activity; Cheese ripening

1. Introduction The ripening of surface-mould cheese results from the growth and the activity of a microbial consortium at the rind. The ripening is characterised by physico-chemical and biological changes; aerobic metabolic pathways prevail. The microbial growth corresponds to a complex biofilm establishment, which is a key factor for the visual aspect and the taste of cheese. The microbial populations of cheese can be determined by microbial count or molecular approaches (Giraffa & Neviani, 2007). However, these off-line measurements imply complex protocols, and results are commonly delayed (e.g., due to incubation time). For solid substrate fermentation (SSF), which includes cheese ripening, Raimbault (1998) reported that microbial growth can be estimated with respiratory metabolism (oxygen consumption and carbon dioxide release measurements). However, with few exceptions, gas concentration sensors as oxygen or carbon dioxide are not used, probably because *

Corresponding author. E-mail addresses: [email protected] (A. He´lias), trelea@ grignon.inra.fr (I.C. Trelea), [email protected] (G. Corrieu). 0260-8774/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2007.09.001

of their lack of robustness (reliability, calibration and drift over time), as well as interpretation difficulties. In industrial cheese production, few sensors are available to monitor ripening. On-line sensors are generally used to measure the temperature and relative humidity of the ripening chamber atmosphere, two key variables for ripening chamber control. Measuring cheese mass loss during cheese ripening has several advantages:  This measurement is reliable. It can be easily implemented for a cheese or a stack of cheeses.  As a general rule, cheese mass loss during ripening has an impact on process productivity. For cheese with a protected designation of origin (PDO), weight is a conformity criterion (e.g., Camembert-Normandie or Epoisse PDO requires a final weight of 0.25 kg).  The mass is an integrative variable that reflects product evolution from both physical and biological points of view (He´lias, Mirade, & Corrieu, 2007). Some attempts have been described in the literature using sensory descriptors (Perrot et al., 2004) to evaluate microbial development during ripening, but they do not

A. He´lias et al. / Journal of Food Engineering 85 (2008) 632–638

633

Nomenclature A aij aw B C c h hw k L li m Psv(Tw) r rh Ts T1 u wC x

dynamic state matrix of the process element of row i and column j of matrix A cheese surface water activity (dimensionless) input matrix of the process output matrix of the process specific heat of cheese (J kg1 K1) convective heat transfer coefficient (W m2 K1) global heat transfer coefficient (W m2 K1) water transfer coefficient (kg m2 Pa1 s1) gain vector of the observer gain of the observer for row i cheese mass for 1 m2 of exchange surface (kg m2) saturation vapour pressure at temperature Tw (Pa) respiratory activity (mol m2 d1) ripening room relative humidity (expressed between 0 and 1) surface temperature of cheese (K) ripening room temperature (K) input vector molar mass of carbon (kg mol1) state vector

replace a real on-line measurement. For some species, models have been established on ‘‘cheese-like” substrate (Aldarf, Fourcade, Amrane, & Prigent, 2004, 2006), whey (Barba, Beolchini, Del Re, Di Giacomo, & Veglio, 2001) or for a reduced ecosystem (Riahi et al., 2007). They allow microbial growth determination, but their application to real cheese is not obvious (e.g., parameter reidentifications, curd properties and strain changes). Measurement of biological changes during cheese ripening would allow a more accurate monitoring of cheese production by enabling detection as soon as an incident occurs. The aim of this paper is the design and validation of an observer dedicated to the on-line estimation of respiratory metabolism (i.e., microbial activity estimation) from available on-line measurements: temperature, relative humidity and cheese mass. The state estimation (i.e., observation) is a recurrent task in control theory, and several methods were developed for linear and, more recently, non-linear systems. More details on the state estimation for bioprocesses can be found in Bastin and Dochain (1990) or Bernard and Gouze´ (2006). In the present study, a linear time variant integral Luenberger observer (Luenberger, 1966) is built based on a previously published model (He´lias et al., 2007). 2. Experimental ripening description 2.1. Ripening rooms Two runs were carried out with two pilot ripening chambers of 0.91 m3 (1.9  0.8  0.6 m) for Run 1, and 0.63 m3

Greek letters a respiration heat ðJ mol1 carbon Þ b1 slope of approximated linear relationship between temperature and saturation vapour pressure (Pa K1) b2 constant of approximated linear relationship between temperature and saturation vapour pressure (Pa) c conversion constant from per second to per day (s d1) d parameter of r positiveness relationship (mol m2 d1)  cheese emissivity (dimensionless) k latent heat of water vaporisation (J kg1) m eigenvalue of a matrix /E evaporative flux (kg m2 d1) /CR convective and radiative fluxes (J m2 d1) r Stefan–Boltzmann constant (W m2 K4) Superscripts  average value ^ estimated value (1.2  0.6  0.87 m) for Run 2. A complete description of these runs can be found in He´lias et al. (2007). In both cases, Camembert-type cheeses were manufactured using a previously described protocol (Leclercq-Perlat et al., 2004). After drainage, the moulded cheeses were transferred into the ripening chambers, placed into a refrigerated room to allow the temperature control close to 14 °C ± 1 °C. For each run, a cheese was continuously weighed using an electronic balance (Precisa XB620C, precision: ±0.01 g, Precisa, Poissy, FR). A combined sensor (Vaissala, Dewpoint transmitter HMP 243, Etoile Internationale, Paris, FR) measured the atmospheric temperature and the relative humidity of the ripening chamber. Atmospheric composition changes were also monitored with CO2 (infrared analyzer Iridium 100, City Technology, UK) and O2 (electrochemical sensor CiTycel, City Technology) sensors. When the ripening chamber was used without an input of air, the variations of CO2 and O2 concentrations were depending only on the exchanges between the atmosphere and the cheeses. All on-line data measurements were performed with a 6-min acquisition period. Water activity and specific heat were assessed in cheeses from Run 1. We considered that the values were representative of cheeses in both chambers as water activity variability is very low in soft cheeses (He´lias et al., 2007) and specific heat depends mainly of water content and fat matter, two cheese characteristics which are not varying in large range for a given cheese-type. The water activity at the cheese surface was daily measured on each face of two cheeses, using a nondestructive method (FA-st lab, GBX, Romans sur Ise`re, FR). Measurements with a differential scanning calorimeter

634

A. He´lias et al. / Journal of Food Engineering 85 (2008) 632–638

(Pyris 1, Perkin Elmer LLC, Norwalk, CT, USA) equipped with a liquid nitrogen cooling accessory (CryoFill, Perkin Elmer) were realised to determine specific heat on three different cheeses on days 1, 7, and 14. 2.2. Ripening runs For Run 1, 45 cheeses were placed on two parallel trays, 22 cheeses for the Run 2. Cheese radius and thickness were 5.5 and 3.5 cm, respectively. For Run 1, the defrosting cycle of the refrigerated room had an 8 h period. It induced an increase of 1 °C during the first hour of the cycle and a relative humidity variation of 2%. The relative humidity was manually controlled for Run 1 (through two cold traps placed inside the chamber) and automatically controlled for Run 2 (air was treated with a cold trap placed out of the chamber with a fan to manage air flow rate in this recirculation loop). According to Camembert making technology, the first ripening day was performed with a low value of relative humidity, close to 85% in order to get a surface drying of the curd. The 13 following days of the Run 1 were carried out with controlled variations of the relative humidity between 88% and 94%. For Run 2, the setpoint for the 13 following days was 92 % of relative humidity. To study the effect of air velocity on heat and mass transfer, fan was on between day 1 and day 7 and it was off the rest of the time. To avoid too high CO2 concentration into the ripening chamber, a manual gas renewal was performed periodically.

where c = 2.194  103 J kg1 K1, k = 2.47  106 J kg1, 2 a ¼ 4:693  105 J mol1 kg mol1. carbon and wC = 1.2  10 Three main phenomena are taken into account: (1) Heat transfer at the cheese surface. According to Hardy and Scher (2000), the cheese heat conductivity varies between 0.3 to 0.4 W m1 K1. Then, the cheese Biot number is comprised between 0.24 and 0.32. Consequently, the heat conduction inside the product is high enough to neglect the thermal gradient inside the cheese, and cheese surface temperature was assumed as representative of cheese temperature. The difference of temperature between the cheese surface and the ripening room atmosphere explains the establishment of convective and radiative fluxes: /CR ðT s Þ ¼ chðT 1  T s Þ þ crðT 41  T 4s Þ 1

3. Mass loss model

/E ðT s Þ ¼ ckðaw P sv ðT s Þ  rhP sv ðT 1 ÞÞ

3.1. Overview of the model

where

An overview of the dynamic mass evolution model of Camembert-type cheese is presented in Fig. 1 (He´lias et al., 2007). The model describes cheese surface temperature and mass variation rates as follows: ( 1 T_ s ¼ mc ð/CR ðT s Þ  k/E ðT s Þ þ arÞ ð1Þ m_ ¼ /E ðT s Þ  wC r

Fig. 1. Schematic view of the model. A broken arrow represents a variable influence on a phenomenon; a solid arrow shows the consequences (positive or negative) of a phenomenon on a state variable. The variables are the cheese surface temperature (Ts), the cheese mass (m), the ripening room relative humidity (rh), temperature (T1), oxygen (O2) and carbon dioxide (CO2) concentrations. The considered phenomena are the evaporative flux (/E), the convective and radiative fluxes (/CR) and the respiratory activity (r).

ð2Þ

where c = 8.64  10 s d ,  = 0.91, r = 5.67  108 W m2 K4. The heat transfer coefficient h varies as a function of air flow conditions, and it must be determined for each ripening condition. Subsets of mass experimental measurements were used for fitting this parameter by nonlinear least-square regression: d0– d4 for Run 1 (h = 2.51 W m2 K1), d1–d3 for Run 2, fan on (h = 4.36 W m2 K1), and d7–d9 for Run 2, fan off (h = 2.73 W m2 K1). (2) Water evaporation, which leads to cheese mass decrease and energy consumption: 4

k ¼ 0:66  108 h1:09

ð3Þ

ð4Þ

according to Mirade et al. (2004). Psv was calculated with the Goff-Gratch equation (WMO, 2000). In accordance with experimental measurements (He´lias et al., 2007), cheese surface water activity was assumed constant. Consequently, the moisture content inside the cheese was not taken into account to determine the water activity but experimental measurement was used. (3) Respiratory activity (r) of the cheese surface microflora. This phenomenon is exothermic and induces carbon loss: 1 r ðC6 H12 O6 Þ þ O2 ! H2 O þ CO2 þ a ð5Þ 6 In He´lias et al. (2007), r was calculated as the average derivation of oxygen and carbon dioxide concentrations in the ripening chamber over time. Derivative values were computed using the spline function (MatlabÒ). Since the ripening runs were carried out in closed ripening chambers (except for some air injections that were no longer than 30 min and that were performed everyday to decrease carbon dioxide content), gas concentration evolution versus time, apart

A. He´lias et al. / Journal of Food Engineering 85 (2008) 632–638

from air injection periods, depended only on microbial metabolism. The residual errors (differences between measured and predicted values of the cheese mass loss) at the end of the runs were lower than 3% of the total mass loss. The relative errors on the cheese mass loss rate (defined as the residual standard deviation divided by the value range) were lower than 3.5%. Model (1) cannot be directly used as a Luenberger observer to estimate the respiratory activity because r is an input variable and not a state variable. Consequently, we add r as a new state variable to (1): r_ ¼ 0

ð6Þ

With this equation, r is a constant, but the feedback will be used inside the observer to upgrade this first approximation.

635

the input vector built from on-line acquisitions: u ¼ ðT 1 ; ðrhP sv ðT 1 Þ þ aw b2 ÞÞ and the matrixes: 0 H w b1 c h þkka ~ mc B A¼@ 0

a ~ mc

0

ckaw b1 wC 1 hH kk c mc c mc ~ ~ B C B¼@ 0 0 A 0

0

T

1 0 C 0A

ð13Þ

ð14Þ

0 ð15Þ

ck

C ¼ ð0; 0; 1Þ

ð16Þ

Since rank (C, CA, CA2)T = 3, System (11) is observable. 4. Observer design 4.1. Luenberger observer

3.2. Model simplification The radiative heat flux relationship causes a nonlinearity; it was approximated as follows without loss of accuracy: rðT 41  T 4s Þ ’ 4r Te 31 ðT 1  T s Þ

ð7Þ

It is then possible to define a global heat transfer coefficient: h ¼ h þ 4r Te 31 H

ð9Þ

The exponential relationship between temperature and saturation vapour pressure induces another nonlinearity. However, since the ripening temperature varies in a low range (from 12 and 14 °C), an approximation can be made for saturation vapour pressure using a linear regression on the Goff–Gratch equation. The following relationship was used: P sv ðT H Þ ¼ b1 T H  b2

4.2. Gain values

ð8Þ

and /CR ðT s Þ ¼ chH ðT 1  T s Þ

Assuming an on-line measurement of cheese mass, the following observer can be established with L = (l1, l2, l3)T, on the basis of System (11): ( ^x_ ¼ A^x þ Bu þ Lð^y  mÞ ð17Þ ^y ¼ C^x

ð10Þ

where b1 = 102 Pa K1 and b2 = 27643 Pa. The relative error between the simplified relation (10) and the GoffGratch equation is equal to 0.48%. This simplification does not have real consequences on the mass estimation. The cheese mass loss rate varies very slowly as compared to the temperature variation rate, and for a short time frame, the mass can be assumed to be constant for the temperature differential equation of System (1). Assuming that ~ is the average mass for a given time frame, System (1) m with Eqs. (6)–(10) then give us  x_ ¼ Ax þ Bu ð11Þ y ¼ Cx

Considering the third column of A, the system dynamic (11) does not depend on m. From a conceptual point of view, the observer can be represented like in Fig. 2 where System (11) is broken down into two sub-systems in series. Within this framework, an observer based on m appears as an integral observer (Beale & Shafai, 1989) of the first system. If a correction is made to the first system, a feedback ^_ is not necessary and l3 is thus assumed to be equal to on m zero. The gain values have to be a compromise between the rapidity of error convergence and the sensitivity to some perturbations such as noise measurement. To ensure the error convergence towards zero, the real parts of m1, m2 and m3 (the eigenvalues of (A + LC)) have to be strictly negative, and the error convergence is mainly determined by the highest eigenvalue. Let us consider, for Run 1 with ~ ¼ 11:5 kg m2, the real parts (Fig. 3a) and the isolines m of the maximal values of the real parts (Fig. 3b) of the eigenvalues of (A + LC) as a function of l1 and l2 (similar

with the state vector: x ¼ ðT s ; r; mÞ

T

ð12Þ

Fig. 2. Observer scheme. The variables are the cheese surface temperature (Ts), the respiratory activity (r) and the cheese mass (m).

636

A. He´lias et al. / Journal of Food Engineering 85 (2008) 632–638

Fig. 3. (a) Real part of the eigenvalues of (A + LC) and (b) isolines of the highest real part of the eigenvalues of (A + LC) as a function of l1 and l2.

~ values varying shapes are obtained for Trial 2 or with m from 9.6 to 13.5 kg m2, i.e., from 250 to 350 g for one cheese with a surface exchange of 2.6  102 m2). The highest eigenvalue decreases very slowly when l1 and l2 increase. The lowest values of the highest eigenvalue correspond to a situation where the eigenvalues are close. Taking a unique eigenvalue (m1 = m2 = m3 = m) is a good compromise (i) to ensure a fast error convergence, (ii) to avoid oscillation of the observer, and (iii) to easily compute the gain value. In this case the following relationships could be established: a11 ð18Þ m¼ 3   2 a11 2 a32 a12 l1 ¼  þ 2 3 ð19Þ l2 a31 3 a31 a11 a 3 1 11  ð20Þ l2 ¼  a11 a32  a12 a31 3 4.3. Observer structure At each time t, the gain values can be computed using ~ ¼ mðtÞ to obtain a time variant linear observer. Finally, m to guarantee that the estimated cheese respiratory activity r always remains positive despite measurement errors and model simplifications (since r represents O2 consumption and CO2 production, it cannot be negative), the following correction is introduced to compute ^r_ ^r ð21Þ ^r þ d where d is a small parameter compared to the usual values of ^r (e.g., 1  103 mol m2 d1), and ^r  d thus implies fð^rÞ ’ 1. Finally, from Eqs. (1)–(21) ,the observer is 8 _ /CR ðT^s Þk/E ðT^s Þþa^r > > ^  mÞ þ l1 ð m < T^s ¼ ^ mc ð22Þ ^r_ ¼ fð^rÞl2 ðm ^  mÞ > > :_ ^ ¼ ð/E þ wC^rÞ m fð^rÞ ¼

5. Validation Fig. 4 shows the temperature, the relative humidity and the cheese mass recorded during the two runs. Using these data as observer input, the respiratory activity is estimated ^ 0 Þ ¼ mðt0 Þ, and comwith T^s ðt0 Þ ¼ T 1 ðt0 Þ, ^rðt0 Þ ¼ 0, mðt pared to calculated values obtained with CO2 and O2 concentrations (Fig. 5). The global evolution of the dynamic changes in respiratory activity is correctly represented. After three days at a low value close to 0, the respiratory activity increases between day 3 and day 5–6 to reach roughly 1.5 mol m2 d1. After that, a slow decrease appears from day 6 to day 10 and the respiratory activity becomes constant until the end of the run (approximately 0.5 mol m2 d1 for Run 1 and 0.75 for Run 2). For Run 1, a fast increase of the respiratory activity estimated with the observer is observed during the five first hours. This period corresponded to a manual injection of dry air performed to manually decrease chamber relative humidity. Due to induced air flow changes, which were not included in the model, the estimation does not fit. When this air injection ended, the estimated respiratory activity decreased to reach 0 at day 1, which corresponds to the calculated value obtained with CO2 and O2 concentrations. This convergence rate is in accordance with the error convergence rate of the observer. This event obviously showed the major role of the heat transfer coefficient value in order to obtain an accurate estimation. The differences between calculated and estimated values are 0.5  102 mol m2 d1 for Run 1 and 7.6  102 mol m2 d1 for Run 2. The relative errors, defined as the residual standard deviation divided by the value range, are 3.02% for Run 1 and 8.83% for Run 2. In practice, the unique parameter to fit is the heat transfer coefficient. It has to be precisely determined for each ripening condition, taking the ripening chamber geometry and its air flow pattern into account. To solve this kind of problem in the case of unknown ripening rooms, it is first proposed to assume a constant air flow velocity during

A. He´lias et al. / Journal of Food Engineering 85 (2008) 632–638

a

b

c

d

e

f

637

Fig. 4. On-line measured data used as observer inputs for Run 1 (a, c, e) and Run 2 (b, d, f). a, b: ripening chamber relative humidity (rh), c, d: ripening chamber temperature (T1), e, f: mass for one square meter of cheese (m).

Fig. 5. Evolution of the respiratory activity versus ripening time for Runs 1 and 2. () calculated values from CO2 and O2 concentration measurements; only 30 min mean values are represented for clarity. (–) estimated values.

the cheese ripening process. A practical and useful procedure would be to fit the heat transfer coefficient during the one or two first days of the ripening when the cheese respiratory activity is quite negligible. The fitted value could then be used to estimate the respiratory activity during the remainder of the cheese ripening.

6. Conclusion This study presented a respiratory activity observer applied to the growth of the consortia of surface-mould cheese ripening. It is based on a model describing cheese mass loss according to physical and biological phenomena.

638

A. He´lias et al. / Journal of Food Engineering 85 (2008) 632–638

Some simplifications are performed on the model to reduce its complexity without loss of accuracy, such as the linearisation of the radiative flux and saturated vapour pressure relationship. An integral Luenberger observer is obtained from the simplified model. We have chosen to take a unique pole for the error dynamic system that make it possible to obtain analytic relationships for observer gain values. This observer was validated with data collected from two ripening trials carried out in two ripening chambers. It succeeded in estimating the respiratory activity calculated from measurements of oxygen and carbon dioxide concentrations of the ripening chamber. As a consequence, microbial growth during cheese ripening can be determined from on-line measurement (temperature, relative humidity and mass), which can be easily performed in an industrial context. Acknowledgements The authors would like to thank T. Cattenoz, H. Guillemin, M.-N. Leclercq-Perlat, F. Lercornue, B. Perret, D. Picque, M. Savy, and C. Vermenot who were in charge of the ripening rooms design and monitoring and of the cheese ripening experiments. References Aldarf, M., Fourcade, F., Amrane, A., & Prigent, Y. (2004). Diffusion of lactate and ammonium in relation to growth of Geotrichum candidum at the surface of solid media. Biotechnology and Bioengineering, 87(1), 69–80. Aldarf, M., Fourcade, F., Amrane, A., & Prigent, Y. (2006). Substrate and metabolite diffusion within model medium for soft cheese in relation to growth of Penicillium camembertii. Journal of Industrial Microbiology and Biotechnology, 33, 685–692. Barba, D., Beolchini, F., Del Re, G., Di Giacomo, G., & Veglio, F. (2001). Kinetic analysis of Kluyveromyces lactis fermentation on whey: batch and fed-batch operations. Process Biochemistry, 36, 531–536.

Bastin, G., & Dochain, D. (1990). On-line estimation and adaptive control of bioreactors. Amsterdam, Netherlands: Elsevier. Beale, S., & Shafai, B. (1989). Robust control system design with the proportional integral observer. International Journal of Control, 50, 97–111. Bernard, O., & Gouze´, J.-L. (2006). State estimation. In D. Dochain (Ed.), Automatic control of bioprocesses (pp. 87–120). Paris, FR: Hermes Science. Giraffa, G., & Neviani, E. (2007). DNA-based, culture-independent strategies for evaluating microbial communities in food-associated ecosystems. International Journal of Food Microbiology, 67, 19–34. Hardy, J., & Scher, J. (2000). Physical and sensory properties of cheese. In A. Eck & J. C. Gillis (Eds.), Cheesemaking, from Science to Quality Assurance (pp. 447–473). Paris, FR: Lavoisier Publishing. He´lias, A., Mirade, P.-S., & Corrieu, G. (2007). Modelling of Camemberttype cheese mass loss in a ripening chamber, main biological and physical phenomena. Journal of Dairy Science, doi:10.3168/jds.20070272. Leclercq-Perlat, M.-N., Buono, F., Lambert, D., Latrille, E., Spinnler, H.-E., & Corrieu, G. (2004). Controlled production of Camemberttype cheeses. Part 1: Microbiological and physicochemical evolutions. Journal of Dairy Research, 35, 346–354. Luenberger, D. G. (1966). Observers for multivariable systems. IEEE Transactions on Automatic Control, 11, 190–197. Mirade, P. S., Rougier, T., Kondjoyan, A., Daudin, J. D., Picque, D., & Corrieu, G. (2004). Caracte´risation expe´rimentale de l’ae´raulique d’un haˆloir de fromagerie et des echanges air-produit. Lait, 84, 483–500. Perrot, N., Agioux, L., Ioannou, I., Mauris, G., Corrieu, G., & Trystram, G. (2004). Decision support system design using the operator skill to control cheese ripening. Journal of Food Engineering, 64, 321–333. Raimbault, M. (1998). General and microbiological aspects of solid substrate fermentation. Electronic Journal of Biotechnology, 1, http:// www.ejbiotechnology.info/. Riahi, M. H., Trelea, I. C., Picque, D., Leclercq-Perlat, M.-N., He´lias, A., & Corrieu, G. (2007). A model describing Debaryomyces hansenii growth and substrates consumption during a smear soft cheese deacidification and ripening. Journal of Dairy Science, 90, 2525–2537. WMO. (2000). General meteorological standards and recommended practices, Appendix A, corrigendum, World Meteorological Organization Technical Regulation. Geneva, CH.