Feature Extraction Algorithm of Food Spoilage Early Warning Data Based on Machine Learning and Internet of Things

In view of the problems of the current method, such as the large error, low detection efficiency, and poor analysis ability of food deterioration early warning, a spectral feature extraction algorithm of food spoilage early warning data based on machine learning and Internet of Things was proposed. Based on Internet of Things technology, collect food spoilage warning images. The k-means algorithm is used to identify the food variable mass spectrometry data, and the three-dimensional visual feature reconstruction method is devised to reconstruct the image characteristics of the food variable mass spectrum, and the spectral characteristics of the food deterioration warning data are obtained. Based on machine learning, the features of food variable mass spectrometry image are decomposed, and the gray pheromone of food variable mass spectrometry image is extracted. Under the convolutional neural network structure, the spectral features of food deterioration early warning data are vectorized. Based on the results of spectral vectorization, the spectral features of food deterioration early warning data are extracted. The experimental results show that the proposed method can effectively improve the efficiency of food deterioration detection and reduce the error of food deterioration detection for safe environment.