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the handbook of food engineering practice crc press chapter 10 ...

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Regardless <strong>of</strong> <strong>the</strong> value <strong>of</strong> m, equation (13) can be ex<strong>press</strong>ed in <strong>the</strong> form:<br />

Q(A) = k t (14)<br />

where <strong>the</strong> ex<strong>press</strong>ion Q(A) is defined as <strong>the</strong> quality function <strong>of</strong> <strong>the</strong> <strong>food</strong>.<br />

The form <strong>of</strong> <strong>the</strong> quality function <strong>of</strong> <strong>the</strong> <strong>food</strong> for an apparent zero, 1st, 2nd and<br />

mth order reaction can be derived from <strong>the</strong> eq.(14) and is shown in <strong>the</strong> following Table 1.<br />

The half life time <strong>of</strong> <strong>the</strong> reaction i.e. <strong>the</strong> time for <strong>the</strong> concentration <strong>of</strong> <strong>the</strong> quality index A to<br />

reduce to half its inital value is also included.<br />

Table 1. Quality function form and half life times for different order reactions.<br />

Apparent Reaction Quality Function Half Life time<br />

Order Q(A)t t 1/2<br />

0 A o - A t A o /(2k o )<br />

1 ln ( A o /A t ) ln2/k 1<br />

2 1/A o -1/A t 1/(k 2 A o )<br />

m(m≠1)<br />

1<br />

m-1 (A1-m t -Ao 1-m ) 2 m-1 -1<br />

k m (m-1) A1-m o<br />

To determine <strong>the</strong> quality function one assumes different values <strong>of</strong> m (0, 1 or<br />

o<strong>the</strong>r) and tries out a graphical or a least square linear fit to <strong>the</strong> corresponding equations<br />

(Table 1) <strong>of</strong> <strong>the</strong> experimental data. If <strong>the</strong> experiment has been carried out to at least 50%<br />

conversion and preferably 75%, it is usually easy to determine which reaction order and<br />

equation gives <strong>the</strong> best fit, ei<strong>the</strong>r graphically or by using statistical goodness <strong>of</strong> fit criteria.<br />

The coefficient <strong>of</strong> determination (R 2 ) <strong>of</strong> <strong>the</strong> linear regression is in most cases a sufficient<br />

<strong>10</strong>

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