A new effective method for identifying boletes species based on FT-MIR and three dimensional correlation spectroscopy projected image processing
文献类型： 外文期刊
作者： Dong, Jian-E 1 ; Li, Jieqing 1 ; Liu, Honggao 1 ; Wang, Yuan Zhong 1 ;
作者机构： 1.Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Peoples R China
2.Southwest Forestry Univ, Coll Big Data & Intelligence Engn, Kunming 650224, Peoples R China
3.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Peoples R China
关键词： Boletes; Species; Image processing; Deep learning; 3DCOS projection
期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.4； 五年影响因子：3.9 ）
ISSN： 1386-1425
年卷期： 2023 年 296 卷
页码：
收录情况： SCI
摘要： This study proposed the necessity of identifying the species for boletes in combination with the medicinal value, nutritional value and the problems existing in the industrial development of boletes. Based on the preprocessing of Fourier transform mid-infrared spectroscopy (FT-MIR) by 1st, 2nd, SNV, 2nd + MSC and 2nd + SG, Multilayer Perceptron (MLP) and CatBoost models were established. To avoid complex preprocessing and feature extraction, we try deep learning modeling methods based on image processing. In this paper, the concept of threedimensional correlation spectroscopy (3DCOS) projection image was proposed, and 9 datasets of synchronous, asynchronous and integrative images are generated by computer method. In addition, 18 deep learning models were established for 9 image datasets with different sizes. The results showed that the accuracy of the three types of synchronous spectral models reached 100%, while the accuracy of the asynchronous spectral and integrative spectral models of 3DCOS projection images were 96.97% and 97.98% in the case of big datasets, which overcame the defects of poor modeling effect of asynchronous spectral and integrative spectral in previous twodimensional correlation spectroscopy (2DCOS) studies. In conclusion, the modeling results of 3DCOS projection images are perfect, and we can apply this method to other identification fields in the future.
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