Food Biochemistry and Food Processing (2 edition)

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730 Part 7: Food Processing

Table 38.3.Probability of Residual Fraction Survivors

after MultipleDValues

DVa l u e (No= 103 )

Probability of Residual

Fraction Survivors After

90% of Destruction

1 D 102

2 D 101

3 D 100

4 D 10 −^1

5 D 10 −^2

nD 10 n−^3

calculated from the time interval onx-axis between which the

straight-line portion of the curve on they-axis shows a reduction

by logarithmic unit. For instance, as indicated in Figure 38.1

the number of survivor microorganisms is reduced from 100

(two logarithmic unit) to 10 (one logarithmic unit) in approx-

imate time interval of 10 minutes (24–14 minutes), which is

theDvalue at a given constant temperature. However, in to-

day’s computer era, theDvalue can be obtained from a log

(N) versustcomputer graph on a spreadsheet as the negative

reciprocal slope. The logarithmic nature of the survivor curve

indicates that complete destruction of microbial population is

not theoretically possible; a decimal fraction of the popula-

tion should remain even after an infinite number ofDvalues

(Table 38.3).

Thermal death time (TDT) is another concept that is com-

monly used in thermal processing of food. The concept some-

what contradicts the logarithmic destruction approach. As com-

pared to decimal reduction concept, TDT is the minimum heat-

ing time required to cause complete destruction of a microbial

population. In sequential study with microbial destruction as

a function of time (at a given temperature), TDT then repre-

sents a time between the shortest destruction and the longest

survival times. The difference between the two is sequentially

reduced or geometrically averaged to get an estimate of TDT.

The “death” in this instance generally indicates the failure of a

given microbial population, after the heat treatment, to show a

positive growth in the subculture media. Comparing the TDT

approach with the decimal reduction approach, one can easily

recognize that TDT value depends on the initial microbial load

(whereasDvalue does not). Further, if TDT is always measured

with reference to a standard initial load or load reduction, it

would simply represent a certain multiple ofDvalue. For exam-

ple, ifTDTrepresented the time to reduce the population from

N 0 = 102 toN= 10 −^1 , then TDT is a measure of 3Dvalues

(Figure 38.2). On the other hand, if it is based on 10^4 –10−^6 ,it

would represent a 10Dvalue. Therefore, TDT is a multiple ofD

(TDT=nD, werenis the number of decimal reductions).

Temperature Dependency of Kinetic Parameters

TheDvalue (and TDT) is strongly dependent on the temperature

applied during heating of bacterial suspension. Higher temper-

0.1

1

10

100

0 2 4 6 8 10

Time (min)

z

2D

3D

D

1D

D

Survivors

Figure 38.2.MultipleDand TDT.

atures will give shorterDvalues and vice versa. Temperature

dependency of kinematic parameters is expressed in terms ofD-

zor TDT-zmodels. By plotting the variousD-values (or TDT)

against temperature, again on a logarithmic scale, thez-value can

be obtained as temperature range for theD-value (or TDT) curve

to pass through one log cycle (Fig. 38.3). In this concept, the

temperature sensitivity indicator is defined as thezvalue. Phys-

ically,zrepresents the temperature change need to increase or

decrease the decimal reduction time (or TDT) by a factor of 10.

0.1

1

10

100

100 110 120 130

Temperature (°C)

z

log D-value

log (100) – log (10) = 1

Figure 38.3.Dvalue versus temperature curve andzvalue.