AUTHOR=Dong Jian-E , Zhang Ji , Li Tao , Wang Yuan-Zhong 

TITLE=The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy

JOURNAL=Frontiers in Microbiology

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2021.771428

DOI=10.3389/fmicb.2021.771428

ISSN=1664-302X

ABSTRACT=Boletes are favored by consumers because of their delicious taste and high nutritional value. However, as the storage period increases, their fruiting bodies will grow microorganisms and produce substances harmful to the human body. Therefore, we need to identify the storage period of boletes to ensure their quality. In this paper, two-dimensional correlation spectroscopy (2DCOS) images are directly used for deep learning modeling, and the complex spectral data analysis process is transformed into a simple digital image processing problem. We collected 2018 samples of boletes. After laboratory cleaning, drying, grinding and tablet compression, their fourier transform mid-infrared (FT-MIR) spectroscopy data was obtained. Then, we acquired 18162 spectral images belonging to nine datasets which are synchronous 2DCOS, asynchronous 2DCOS and integrative 2DCOS (i2DCOS) spectra of 1750-400 cm-1, 1450-1000 cm-1 and 1150-1000 cm-1 bands. For these data sets, we established nine deep residual convolutional neural network (ResNet) models respectively to identify the storage period of boletes. The result shows that the accuracy with the train set, test set and external validation set of the synchronous 2DCOS model on 1750-400 cm-1 band is 100%, and the loss value is close to zero, so this model is the best. The synchronous 2DCOS model on 1150-1000 cm-1 band comes next, and these two models have high accuracy and generalization ability which can be used to identify the storage period of boletes. The results has certain practical application value and provides a scientific basis for the quality control and market management of bolete mushrooms. In conclusion, our method is novel and extends the application of deep learning in the food field. At the same time, it can be applied to other fields such as agriculture and herbal medicine.