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

Abstract

A noninvasive method for the estimation of internal temperature in chicken meat immediately following cooking is proposed. The external temperature from iR images was correlated with measured internal temperature through a multilayer neural network. To provide inputs for the network, time series experiments 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 µm registered external temperature distributions without the interference of close-to-oven environment, while conventional thermocouples registered internal temperatures. For an internal temperature at a given time, simultaneous and lagged external temperature observations were used as the input of the neural network. Based on practical and statistical considerations, a criterion is established 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 within a standard error of ± 1.01°C, and within an error of ± 1.07°C for short times after cooking (3 min), with two thermograms at times 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.