ArticleFrontiers in microbiology2021
The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy.
Article in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Analysis of Chemical Changes during Maturation ofACS omega · 2024Article
- Practical Qualitative Evaluation and Screening of Potential Biomarkers for Different Parts ofFrontiers in microbiology · 2022Article
- Application of spectral image processing with different dimensions combined with large-screen visualization in the identification of boletes species.Frontiers in microbiology · 2022Article
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4 authors.
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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 article, 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 2,018 samples of boletes. After laboratory cleaning, drying, grinding, and tablet compression, their Fourier transform mid-infrared (FT-MIR) spectroscopy data were obtained. Then, we acquired 18,162 spectral images belonging to nine datasets which are synchronous 2DCOS, asynchronous 2DCOS, and integrative 2DCOS (i2DCOS) spectra of 1,750-400, 1,450-1,000, and 1,150-1,000 cm
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