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

机译：红外成像-神经网络相结合的鸡肉内部温度估算

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摘要

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.
机译：提出了一种非侵入性的方法，用于立即估计烹饪后鸡肉的内部温度。红外图像的外部温度通过多层神经网络与测得的内部温度相关。为了为网络提供输入，进行了时间序列实验，以在冷却过程中进行烹饪后立即获得内部和外部温度的同时观察。在3.4至5.0μm的光谱带上工作的IR摄像机记录了外部温度分布，而没有接近烤箱的环境的干扰，而传统的热电偶记录了内部温度。对于给定时间的内部温度，同时和滞后的外部温度观测值被用作神经网络的输入。基于实际和统计方面的考虑，建立了减少神经网络输入中的节点的标准。组合方法能够在0到540 s的时间范围内估算内部温度，标准误差为±1.01°C，烹饪后短时间内（3分钟）的误差在±1.07°C以内，有时需要两个热分析图t和t + 30 s。该方法具有监测传送带式烹饪中鸡肉熟度的巨大潜力，可以用作其他食品中类似研究的平台。

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