modelling metabolites production by propionibacterium

MODELLING METABOLITES PRODUCTION BY PROPIONIBACTERIUM
FREUDENREICHII IN SWISS -TYPE CHEESE
M.Normand1,2, R.Richoux2, J.R. Kerjean2
1Agrocampus,
Laboratoire de mathématiques appliqués, 65 rue de Saint Brieuc, CS 84 215, 35 042 RENNES Cedex
2 ITFF – ACTILAIT, 73 rue de Saint Brieuc, BP50915, 35 009 RENNES Cedex
AIMS
Propionibacterium freudenreichii contributes to opening and typical flavour in Swiss cheese. Therefore, the kinetic of lactate catabolism
by propionic acid bacteria, during warm room ripening, is an important technological criterion.
In this context, we propose the use of non linear dynamic modelling and curves parameters classification for screening P.freudenreichii
strains on their metabolic kinetic properties.
P.freudenreichii in Swiss cheese-making
MILK
Lactose
Cheese making
CHEESE BEFORE RIPENING
Lactic acid bacteria
Ripening
Propionic acid bacteria
Lactate
RIPENED CHEESE
Propionate, Acetate, CO2,
Ammonia, Succinate...
EXPERIMENTAL DESIGN
Cheese making characteristics
- Standardized small-scale Swiss-type cheese-making: 10 litres vats, method ITFF (1) .
(Microfiltered milk, constant seeding for Lactic acid bacteria, Propionibacterium seeding at the same cell
concentration and cell stage development).
- Ripening: 21 days at 11°C and 28 days at 24°C under vacuum in BK1L film (Cryovac, France)
P.freudenreichii lactate catabolism:
three “pathways” (3)
Classical pathway (Fitz pathway)
2 Propionate
Data collecting
Measurements of Lactate (enzymatic method, LARF (2)) and Volatile fatty acids (gas chromatography,
LARF (2)) once a week during the warm room ripening.
1 Acetate
1 CO2
NON LINEAR DYNAMIC MODELLING
Aspartate deamination pathway
6 Succinate
Dynamic system:
d[Lactate] / dt = f(t) with f(t) = β0 / (1+exp (tmid- t) / β1 ) )
3 Acetate
+6 Aspartate
3 lactate
d[Propionate] / dt = -Kpropionate d[Lactate] / dt
3 CO2
CO2 fixing pathway
d[Acetate] / dt = -Kacetate d[Lactate] / dt
6 Ammonia
Unknown parameters:
1 Succinate
tmid = time value at the inflexion point of the curve,
β0 = numeric parameter representing the asymptote,
1 Acetate
β1 = scale parameter,
Kpropionate and Kacetate = yield coefficient for metabolite production.
Lactate, Acetate and Propionate concentration
(mg/100g of cheese)
500
1000
1500
1 H2O
Lactate
Propionate
β 1: slope
β0 / 2
Acetate
0
1 Propionate
1 H2O
Curves classification from the parameters
Measured data
1.0
1.5
2.0
t2.5
mid
3.0
3.5
4.0
Time
(weeks)
S19
1500
RESULTS
Acceleration of the kinetics between the three classes
1000
S2
From the first to the last class:
S13
- The final concentrations levels are:
S18
⇒ lower for the consumed product (lactate),
0
Thèmes de travail (1)
- The lactate consumption and the metabolites production start earlier,
500
S8
• Veille technologique (depuis 1986) : Suivi des publications mondiales : La
Documentation fromagère résumée, la Veille Britta, 40 veilles spécialisées
pour entreprises ex : sel, amines
S6
1.0
1.5
2.0
2.5
3.0
3.5
4.0
1500
• Propionibactéries 1994-2002 : constitution d’une collection industrielle,
leader sur le marché mondial en 2004, programme aidé par la Région
S20
S17
1000
• Qualité du lait pour la fromagerie :
protéines, butyriques (renversement de tendance 1984-1994)
500
S3
S15
0
S4
2.0
2.5
3.0
3.5
4.0
500
S10
S7
0
S5
S1
Class 1: Slow strains
Class with a slow fermentation. The lactate curve inflection is about 3.5 weeks. The lactate
final concentration is higher than 500 mg/100g of cheese. For the metabolites production,
acetate is about 200 mg/100g and propionate concentration is less than 500 mg/100g.
Class 2: Medium strains
Class with medium kinetics. The lactate curve inflexion is about 3.3 days. Lactate
consumption is higher than 950 mg/100g with a propionate and an acetate production about
700 mg/100g and 350 mg/100g.
Class 3: Fast strains
The lactate is used fast with an inflexion point at 2.6 weeks. Between 1100 and 1300 mg/100g
of lactate are consumed. The propionate production is higher than 900 mg/100g.
1000
S11
1.5
1500
1.0
S16
⇒ and higher for the produced metabolites (acetate and propionate).
1.0
1.5
2.0
2.5
3.0
3.5
4.0
CONCLUSION
This mathematical method in two steps – dynamic modelling and
curves classification – has concrete application for strains screening
on metabolic kinetics. The use of standardized indicators allows
strains or studies comparisons.
REFERENCES
(1) Richoux R., Kerjean J.R. ,(1995), Caractérisation technologique de souches pures de bactéries propioniques: test en minifabrication de fromages à pâte cuite, Le lait (75) 45-59.
(2) LARF: Laboratoire d’Analyses et de Recherche Fromagère – Rue de la Laiterie, BP19, 25620 MAMIROLLE.
(3) Crow V.L., Turner K.W., (1986), The effect of succinate production on other fermentation products in swiss-type cheese. N.Z. J.Dairy Technology (21) 217-227.