Near-infrared quality monitoring modeling with multi-scale CNN and temperature adaptive correction

Considering the impact of temperature on near -infrared spectroscopy (NIRS), a temperature adaptive correction neural network model called TI -CNN has been developed. This model takes into account the temperature influence. First, a multi -scale CNN model is established to extract the global features of NIRS. Then, by establishing the quantitative relationship between the sample and the modeling error of the neural network, the influence of temperature on the spectral features. Based on the degree of influence, the weight is adaptively adjusted to quantify the effect of temperature on near -infrared spectral features. Finally, to verify the model performance, EPO-PLSR, OSC-PLSR, CNN, and TA -CNN were used for comparison on the test set. The results showed that traditional temperature correction models performed poorly, with a coefficient of determination R2 < 0.80. However, the neural network models based on multi -scale CNN, R2 > 0.80. Of these, the TICNN based on multi -scale CNN and temperature adaptive correction, demonstrated the best performance with R2 > 0.95. Therefore, the method proposed in this paper demonstrates superior performance compared to existing methods. It utilizes a multi -scale CNN to extract near -infrared spectral features and also incorporates temperature to adaptively correct the spectral features, resulting in superior model performance across various temperature conditions.