College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China; College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming 650224, China.
College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Aug 5;296:122653. doi: 10.1016/j.saa.2023.122653. Epub 2023 Mar 21.
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 three-dimensional 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 two-dimensional 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.
本研究提出了在鉴定牛肝菌物种的同时，结合药用价值、营养价值以及牛肝菌产业发展中存在的问题的必要性。基于傅里叶变换中红外光谱（FT-MIR）的一阶、二阶、SNV、二阶+MSC 和二阶+SG 预处理，建立了多层感知器（MLP）和 CatBoost 模型。为了避免复杂的预处理和特征提取，我们尝试了基于图像处理的深度学习建模方法。本文提出了三维相关光谱（3DCOS）投影图像的概念，并通过计算机方法生成了 9 组同步、异步和综合图像数据集。此外，我们还为 9 个不同大小的图像数据集建立了 18 个深度学习模型。结果表明，三种同步光谱模型的准确率均达到 100%，而 3DCOS 投影图像异步光谱和综合光谱模型在大数据集的情况下准确率分别为 96.97%和 97.98%，克服了先前二维相关光谱（2DCOS）研究中异步光谱和综合光谱建模效果不佳的缺陷。总之，3DCOS 投影图像的建模效果非常完美，我们将来可以将这种方法应用于其他鉴定领域。
