Jg. Ibarra et al., Combined IR imaging-neural network method for the estimation of internal temperature in cooked chicken meat, OPT ENG, 39(11), 2000, pp. 3032-3038
A noninvasive method for the estimation of internal temperature in chicken
meat immediately following cooking is proposed. The external temperature fr
om IR images was correlated with measured-internal temperature through a mu
ltilayer neural network. To provide inputs for the network, time series exp
eriments were conducted to obtain simultaneous observations of internal and
external temperatures immediately after cooking during the cooling process
. An IR camera working at the spectral band of 3.4 to 5.0 mum registered ex
ternal temperature distributions without the interference of close-to-oven
environment, while conventional thermocouples registered internal temperatu
res. For an internal temperature at a given time, simultaneous and lagged e
xternal temperature observations were used as the input of the neural netwo
rk. Based on practical and statistical considerations, a criterion is estab
lished to reduce the nodes in the neural network input. The combined method
was able to estimate internal temperature for times between 0 and 540 s wi
thin a standard error of +/- 1.01 degreesC, and within an error of +/-1.07
degreesC for short times after cooking (3 min), with two thermograms at tim
es t and t+30 s. the method has great potential for monitoring of doneness
of chicken meat in conveyor belt type cooking and can be used as a platform
for similar studies in other food products. (C) 2000 Society of Photo-Opti
cal Instrumentation Engineers. [S0091-3286(00)00711-X].