Thermal Food Processing

Thermal Processing of Canned Foods 353
TABLE 11.2
The Effect of Come-Up Time (CUT) on Ball jh and NumeriCAL
jh Values
CUT (min) Ball jh Ball fh NumeriCAL jh NumeriCAL fh
4.5 a 1.77 ± 0.05 44.85 ± 0.42 1.74 ± 0.03 46.77 ± 0.39
7.0 b 1.90 ± 0.05 41.99 ± 0.56 1.71 ± 0.07 46.01 ± 0.15
21.5 b 1.49 ± 0.05 45.30 ± 0.13 1.70 ± 0.02 47.09 ± 0.40
Note: The product is 300 × 405 can of condensed mushroom soup (2:1, v/v)
processed in a hydrostatic sterilizer simulator at 121.1°C.
a Feed leg time simulation at 87.8°C.
b Normal come-up time (close to linear).
with NumeriCAL jh of 2.83, fh of 41.42 min, jc of 1.42, and fc of 50.85 min.
This set of input heating and cooling factors was then used to predict the product
temperatures for the same product with two process temperature deviations. The
results were plotted in the right curve and demonstrated that the model-predicted
product temperatures were in good agreement with physically measured product
temperatures under process temperature deviation conditions. The results suggest
that the model could be used in the computer-based real-time control for correcting
the process temperature deviations.
11.4 INTELLIGENT THERMAL PROCESS CONTROL
To ensure that the optimized process is adequately delivered to the product, the
critical control points used in the process calculation need to be satisfied and
properly controlled. However, a sterilizer temperature deviation can often occur
during a process due to steam supply interruption or failure. Figure 11.14 shows a
typical industrial example of process temperature deviations (two times), which
were recorded in a hydrostatic sterilizer. When the process temperature deviation
occurs, the product might not be safe and could cause potential public health
problems. For the low-acid or acidified canned food products in the U.S., a processor
has to isolate all temperature deviation-affected products, which must be evaluated
by a competent process authority for safety, as required by the FDA and USDA.
Often, process downtime and product quality loss could cause a significant economic
loss to the food processors. Therefore, developing a real-time lethality-based
computer predictive model, which is able to predict and trace the lethality of the
critical product during process temperature deviation, is of high interest to the food
canning industry. The importance and benefits of using this technology have been
recognized in commercial production. 19