Detecting the content of the bright blue pigment in cream based on deep learning and near-infrared spectroscopy

The excessive content of additives in food is a radical problem that affects human health. However, traditional chemical methods are limited by a long cycle, low accuracy, and strong destructiveness, so a fast and accurate alternative is urgently needed. This paper proposes a prediction model introducing near-infrared spectroscopy and deep learning to perform fast and accurate non-destructive detection of artificial bright blue pigment in cream. The model results show that R2 is 0.9638, RMSEP is 0.0157, and RPD is 4.4022. In the preprocessing part, this paper compares the traditional preprocessing methods (SNV, MSC, SG) horizontally and innovatively proposes the use of autoencoders to mitigate the dimensionality of data, which has immensely improved the follow-up prediction effect. In addition, it tries to perform regression prediction on spectral data and establish a fully connected convolutional neural network model through deep learning, whose result indicators prove better than those of traditional methods such as PLSR and MLR. When constructing the deep learning model, this paper applies knowledge evolution to compress the model to achieve a lower calculation cost and higher accuracy. Compared with the traditional methods, the model proposed in this paper has greater accuracy and higher speed with samples undamaged, which is worth popularizing.