Novel near-infrared spectrum analysis tool: Synergy adaptive moving window model based on immune clone algorithm

• Published in: Analytica chimica acta Vol. 1000; pp. 109 - 122
• Main Authors: Wang, Shenghao, Zhang, Yuyan, Cao, Fuyi, Pei, Zhenying, Gao, Xuewei, Zhang, Xu, Zhao, Yong
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 13.02.2018; Elsevier BV
• Subjects: Affinity; Algorithms; analytical chemistry; Antibodies; antigens; Cloning; Computer simulation; data collection; Digits; Gasoline; Genetic algorithms; Human performance; humans; I.R. radiation; Immune clonal algorithm; Immune system; Infrared analysis; Infrared spectra; Infrared spectroscopy; labor; least squares; Mathematical models; Modelling; Multivariate calibration; Near infrared radiation; near-infrared spectroscopy; Optimization; Partial least squares regression; Pre-processing methods; Pretreatment; Selection of spectral variables; soil; Spectral analysis; Spectroscopic analysis; Spectrum analysis; Immune clonal algorithm; Pre-processing methods; Partial least squares regression; Selection of spectral variables; Spectral analysis; Multivariate calibration
• ISSN: 0003-2670; 1873-4324; 1873-4324
• DOI: 10.1016/j.aca.2017.11.028

This paper presents a novel spectrum analysis tool named synergy adaptive moving window modeling based on immune clone algorithm (SA-MWM-ICA) considering the tedious and inconvenient labor involved in the selection of pre-processing methods and spectral variables by prior experience. In this work, immune clone algorithm is first introduced into the spectrum analysis field as a new optimization strategy, covering the shortage of the relative traditional methods. Based on the working principle of the human immune system, the performance of the quantitative model is regarded as antigen, and a special vector corresponding to the above mentioned antigen is regarded as antibody. The antibody contains a pre-processing method optimization region which is created by 11 decimal digits, and a spectrum variable optimization region which is formed by some moving windows with changeable width and position. A set of original antibodies are created by modeling with this algorithm. After calculating the affinity of these antibodies, those with high affinity will be selected to clone. The regulation for cloning is that the higher the affinity, the more copies will be. In the next step, another import operation named hyper-mutation is applied to the antibodies after cloning. Moreover, the regulation for hyper-mutation is that the lower the affinity, the more possibility will be. Several antibodies with high affinity will be created on the basis of these steps. Groups of simulated dataset, gasoline near-infrared spectra dataset, and soil near-infrared spectra dataset are employed to verify and illustrate the performance of SA-MWM-ICA. Analysis results show that the performance of the quantitative models adopted by SA-MWM-ICA are better especially for structures with relatively complex spectra than traditional models such as partial least squares (PLS), moving window PLS (MWPLS), genetic algorithm PLS (GAPLS), and pretreatment method classification and adjustable parameter changeable size moving window PLS (CA-CSMWPLS). The selected pre-processing methods and spectrum variables are easily explained. The proposed method will converge in few generations and can be used not only for near-infrared spectroscopy analysis but also for other similar spectral analysis, such as infrared spectroscopy. [Display omitted] •SA-MWM-ICA is first introduced into spectrum analysis field as a new optimization strategy.•SA-MWM-ICA is obviously better than usually methods, especially for spectra with relatively complex structure.•SA-MWM-ICA can take full advantage of the useful information throughout the spectral range.•SA-MWM-ICA can converge in fewer generation.•The parameter setting requirements of SA-MWPLS-ICA are easier to operate.