Fast detection of cumin and fennel using NIR spectroscopy combined with deep learning algorithms

Abstract

Cumin and fennel are two types of food raw materials that have similar appearances but great differences in efficacy. Therefore, the efficient identification of these two types of food is very important to the related food processing industry and the development of food detection technology. In this study, a deep learning algorithm was designed to process Fourier NIR spectral data for the classification of cumin and fennel. The method was based on the difference in the signal intensity in NIR spectra between cumin and fennel, and the identification was performed by combining the improved multiscale fusion convolutional neural network (MCNN) and the bidirectional long-short-term memory network (BILSTM) based on NIR spectra. The classification accuracies of the MCNN and BILSTM models were 100% and 98.57%, respectively. The models have high sensitivity and fast analysis speed, which directly promote the development of real-time identification technology of cumin and fennel and verify the feasibility of deep learning algorithms in the field of food detection.