Thermal Food Processing

128 Thermal Food Processing: New Technologies and Quality Issues
networks are only suitable for problems with a large amount of experimental
data, or those that can generate data using a separate computer simulator. In
addition, like all other models, trained ANN models can only be used for
predictions within the ranges of the variable being investigated. Otherwise,
the precision of prediction results by ANN models might not be guaranteed.
NOMENCLATURE
Variables
CT Cooling time, min
D Decimal deduction time, min
Dq Decimal destruction time for quality, min
En Equivalent unit energy consumption, kJ/kg
Er Relative average error, %
E Time-specific particle concentration function or total
square error
F Accumulated lethality value, min, or cumulative
particle concentration function
Fs Surface cook value, min
F Heating or cooling rate index, min
j Heat or cooling lag factor
g Final temperature difference between can center and
retort, °C
h Transfer coefficient, W/(m2 °C)
H Height of the can, mm
PT Process time or heating time, min
Qv Average quality retention for whole can, %
R Correlation coefficient or ratio of diameter to height
of can
RT Retort temperature, °C
T Temperature, °C
T1–T4 Step temperature for MRV function, °C
U Overall heat transfer, W/(m2 °C)
V Volume, m3 w Weight (neural network)
y Output value
Subscripts
c Cooling
di Desired output values
dh Diameter to height
fp Fluid to particle