Thermal Food Processing

26 Thermal Food Processing: New Technologies and Quality Issues
TABLE 1.5
Predictive Equations for the Dielectric Constant and Loss Factor of Foods
Food Type
Fatty/low-moisture-content foods
ε′ = 2. 63− 0. 0015T + 0. 162xw
Fruits/vegetables
ε′ = 2. 14 − 0. 104T + 0. 808xw
ε′′ = 3. 09 − 0. 0638T + 0. 213xw
Meat products
Pork
ε′ =−70− 01 . T + 17 . x + ( 15 . + 002 . T) x
w as
f
(GHz)
T
(°C)
x w
(%)
x a
(%)
x f
(%) R 2
1–3 0–100 0–30 0–5 70–100 0.98
≈2.45 0–70 50–90 — — 0.98
≈2.45 0–70 50–90 — — 0.90
2–3 0–70 60–80 0–6 0–20 0.89
Poultry
ε′ =−87. 3− 0. 051T + 1. 91xw
2–3 0–70 60–80 0–5 ≈10 0.90
+ ( 2+ 0. 02T)
xas
Source: Adapted from Calay, R.J. et al., Int. J. Food Sci. Technol., 29, 699–713, 1995.
lack of composition data could have probably led to the significant variability
observed. For a restricted data set containing about 30 data points for raw beef,
beef juice, raw turkey, and turkey juice, the best correlation for the dielectric
constant contains two components — water and ash: 31
ε′ = x (. 1 0707 − 0. 0018485T) + x (. 4 7947) + 8. 5452.
w as
(1.64)
Equation 1.64 has a correlation coefficient of 0.94, and the maximum error
in the prediction is 5%. The dielectric loss for this restricted data set was modeled
as a function of the mass percentage of water and temperature. The addition of
a temperature-dependent ash term resulted in the final correlation, which has a
high correlation coefficient (R 2 = 0.989) and an error lower than 10%:
ε′′ = x (. 3 447 − 0. 0187T + 0. 000025T2) + x ( −57.
093+
0. 231T ) −3.
599.
w as
(1.65)
ε′ and ε′′ of ham as a function of temperature (0 to 70°C), moisture (38.2 to
68.9%), and ash contents (1.78 to 6.80%) at 2450 MHz can be predicted from 72
ε′ =− 25. 49 + 1. 063x − 1. 041x + 0. 03452T
w as
ε′′ =− 150. 2 + 5. 243x + 6. 220x −0. 2845T −0.
04322
− 0. 4732x2 + 0. 002245T2 + 0. 1090x
T
w as x 2
w
as as
(1.66)
(1.67)