TY - JOUR
T1 - An Extreme Learning Machine optimized by Differential Evolution and Artificial Bee Colony for Predicting the Concentration of Whole Blood with Fourier Transform Raman Spectroscopy
AU - Wang, Qiaoyun
AU - Song, Shuai
AU - Li, Lei
AU - Wen, Da
AU - Shan, Peng
AU - Li, Zhigang
AU - Fu, Yongqing
N1 - Funding information: This work was supported by the National Natural Science Foundation of China (NFSC 11404054, 61601104), the Natural Science Foundation of Hebei Province (F2019501025, F2020501040), the Fundamental Research Fund s for the Northeastern Universities (N2023006), and International Exchange Grant (IEC/NSFC/201078) through Royal Society and NFSC.
PY - 2023/5/5
Y1 - 2023/5/5
N2 - Raman spectroscopy, with its advantages of non-contact nature, rapid detection, and minimum water interference, is promising for non-invasive blood detection or diagnosis in clinic applications. However, there is a critical issue that how to accurately analyze blood composition by Raman spectroscopy. In this study, we apply extreme learning machine (ELM) algorithm and a multivariate calibration regression model to analyze the results from Raman spectroscopy and determine the component’s concentrations in blood samples, including glucose, cholesterol, and triglyceride. Self-adaption differential evolution artificial bee colony (SADEABC) algorithm was further applied to increase the data’s accuracy and robustness. The obtained data for coefficient of determination, root mean square error of calibration, root mean square error of prediction, and relative percent deviation, were 0.9822, 0.3993, 0.3827, and 6.6679 for glucose, 0.9786, 0.2104, 0.2088 and 5.9533 for cholesterol, and 0.9921, 0.2744, 0.3433 and 10.5075 for triglyceride, respectively. Results demonstrated that the model based on SADEABC-ELM show much better prediction data than those models based on the ELM and ABC-ELM.
AB - Raman spectroscopy, with its advantages of non-contact nature, rapid detection, and minimum water interference, is promising for non-invasive blood detection or diagnosis in clinic applications. However, there is a critical issue that how to accurately analyze blood composition by Raman spectroscopy. In this study, we apply extreme learning machine (ELM) algorithm and a multivariate calibration regression model to analyze the results from Raman spectroscopy and determine the component’s concentrations in blood samples, including glucose, cholesterol, and triglyceride. Self-adaption differential evolution artificial bee colony (SADEABC) algorithm was further applied to increase the data’s accuracy and robustness. The obtained data for coefficient of determination, root mean square error of calibration, root mean square error of prediction, and relative percent deviation, were 0.9822, 0.3993, 0.3827, and 6.6679 for glucose, 0.9786, 0.2104, 0.2088 and 5.9533 for cholesterol, and 0.9921, 0.2744, 0.3433 and 10.5075 for triglyceride, respectively. Results demonstrated that the model based on SADEABC-ELM show much better prediction data than those models based on the ELM and ABC-ELM.
KW - Extreme Learning Machine
KW - Raman spectroscopy
KW - Artificial Bee Colony algorithm
KW - Self-Adaption Differential Evolution
KW - blood detection
U2 - 10.1016/j.saa.2023.122423
DO - 10.1016/j.saa.2023.122423
M3 - Article
SN - 1386-1425
VL - 292
JO - Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
JF - Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
M1 - 122423
ER -