武汉工程大学学报2024，Vol.46Issue(4)：410-416,423,8.DOI:10.19843/j.cnki.CN42-1779/TQ.202310012
用于奶油色素定量分析的注意力残差网络设计与验证
Design and verification of attention residual network for quantitative analysis of cream pigments
摘要
Abstract
Temperature change of sample causes fluctuation to its spectrum.As near-infrared spectroscopy is very sensitive to changes in physical conditions such as temperature,we took the indigo pigment in cream as the spectral quantitative analysis data and proposed a variable temperature attention residual network.This network integrates temperature and spectral features,and its backbone structure adopts a concurrent spatial and channel squeeze and excitation attention mechanism to integrate and enhance the features processed by the residual block.Subsequently,we used maximum pooling and random dropout layers for feature dimensionality reduction and model regularization.By comparing the network without the attention module with six commonly used regression analysis networks in deep learning,we verified its high applicability in this field;by comparing the variable temperature attention residual network with three optimization forms of the best model among the six networks,we verified its high performance.After we tuned the model,the difference between the training and test losses was reduced to 0.000 5,and the coefficient of determination and the relative analysis error reached the best values of 0.929 3 and 3.703 1,indicating that the model can perform quantitative analysis of spectra under variable temperature conditions in practice.关键词
近红外光谱/温度/注意力机制/残差网络/奶油色素Key words
near-infrared spectroscopy/temperature/attention mechanism/residual network/cream pigment分类
信息技术与安全科学引用本文复制引用
张芸,宋刚,刘军,谭正林,黄晓彤..用于奶油色素定量分析的注意力残差网络设计与验证[J].武汉工程大学学报,2024,46(4):410-416,423,8.基金项目
湖北省自然科学基金(2022CFC001) （2022CFC001）
浙江省生物标志物与体外诊断转化重点实验室开放基金(KFJJ 2023006) （KFJJ 2023006）
武汉工程大学第十四届研究生教育创新基金(CX2022331、CX2022348、CX2022365) （CX2022331、CX2022348、CX2022365）
