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

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esidual plots. Alternatively, instead <strong>of</strong> <strong>the</strong> logarithmic equation for <strong>the</strong> first order reaction<br />

(Table 1) <strong>the</strong> exponential form can be used, where:<br />

A = A o exp ( - k t ) (15)<br />

and a nonlinear least square fitting computed, for determination <strong>of</strong> <strong>the</strong> k parameter. The R 2<br />

for this fit is given by equation (14) and is directly comparable to <strong>the</strong> R 2 from <strong>the</strong> linear<br />

regression for <strong>the</strong> zero-order model.<br />

A final pitfall that should be avoided when determining <strong>the</strong> apparent order,<br />

concerns reactions that exhibit a lag period. During a typical lag period <strong>the</strong>re is a build-up<br />

<strong>of</strong> a critical intermediate concentration. The rate <strong>of</strong> <strong>the</strong> reaction during <strong>the</strong> build-up period<br />

is is normally slower. In some cases, <strong>the</strong> reaction is not detectable due to analytical<br />

limitation as in <strong>the</strong> case <strong>of</strong> <strong>the</strong> formation <strong>of</strong> brown pigments monitored at 420 nm during a<br />

nonenzumatic Maillard type reaction. The most common approach to deal with a lag<br />

period , is to draw each data point and to look for <strong>the</strong> time where a distinct change in <strong>the</strong><br />

reaction rate occured. Obviously, this approach calls for special attrention as a change in<br />

<strong>the</strong> reaction mechanism may also take place. Typical reactions where lag period is<br />

observed are nonenzymatic browning (Labuza, 1982; Saguy, et al., 1979) and microbial<br />

growth.<br />

Once <strong>the</strong> apparent order <strong>of</strong> <strong>the</strong> quality deterioration reaction has been decided,<br />

fur<strong>the</strong>r statistical analysis and statistical evaluation <strong>of</strong> <strong>the</strong> parameter k, <strong>the</strong> rate constant is<br />

required, to get an estimate <strong>of</strong> <strong>the</strong> error in <strong>the</strong> determination <strong>of</strong> k (Labuza and Kamman,<br />

1983). If a linear regression method is used to estimate <strong>the</strong> parameters, <strong>the</strong>ir 95%<br />

confidence limits can be calculated using <strong>the</strong> Student t distribution. In addition to <strong>the</strong><br />

confidence limits, a list <strong>of</strong> standarized residuals and a residual plot is a useful statistical tool<br />

that allows evaluation <strong>of</strong> how well <strong>the</strong> chosen equation can model <strong>the</strong> data and also permits<br />

<strong>the</strong> recognition <strong>of</strong> extreme or outlier values that may be <strong>the</strong> result <strong>of</strong> experimental errors or<br />

o<strong>the</strong>r extraneous effects and should be excluded from <strong>the</strong> calcualtions (Arabshasi and<br />

14

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