Combined IR imaging-neural network method for the estimation of internal temperature in cooked chicken meat

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].