•A new wavelength interval combination optimization algorithm was proposed based on model popular analysis strategy.•The combination of spectral intervals can be optimized in a soft shrinkage manner.•Its computational intensity is economic benefit from fewer tune parameters and faster convergence speed.•WBS was proved to be a more efficient sampling method than WBMS especially for implementing MPA strategy.In this study, a new wavelength interval selection algorithm named as interval combination optimization (ICO) was proposed under the framework of model population analysis (MPA). In this method, the full spectra are divided into a fixed number of equal-width intervals firstly. Then the optimal interval combination is searched iteratively under the guide of MPA in a soft shrinkage manner, among which weighted bootstrap sampling (WBS) is employed as random sampling method. Finally, local search is conducted to optimize the widths of selected intervals. Three NIR datasets were used to validate the performance of ICO algorithm. Results show that ICO can select fewer wavelengths with better prediction performance when compared with other four wavelength selection methods, including VISSA, VISSA-iPLS, iVISSA and GA-iPLS. In addition, the computational intensity of ICO is also economical, benefit from fewer tune parameters and faster convergence speed.

•Visually identify the forbidden additive in powdered milk by NIR microscopy.•Different chemometrics treatments for decomposing multivariate data cube.•Fast, simple pretreatment and environmentally friendly analytical process.•Low price for analysis compared to wet chemical approaches.•Potential for on-line analysis.This paper presents a rapid calculation method for the imaging process in the identification and quantification of prohibited additives in milk. Data abstraction methods such as principal component analysis (PCA), classical least squares regression (CLS), and alternative least squares regression (ALS) were used. Different multivariate calculations provided possibilities of quantifying near-infrared (NIR) spectral data cube obtained from the surface of the complex mixture. The results of principal component decomposition confirmed that sample mixture identification is feasible using the PCA–CCI methods. Subsequently, CLSI was used for the direct quantitative analysis of the specific component. Behaving more conveniently than PLS without modeling, CLSI can obtain quantitative information as that melamine generally distribute at the low concentration range of 0–0.5 w/w. Moreover, ALSI can quantify the target component with higher accuracy than CLSI. Standard error of residue to predicted value is 0.0838. Lack of fit is 0.0841. Explanation of variables in the mixture is 99.30%, illustrating that the selective lack of rank is insignificant. Obviously, the most intuitive distribution images are constructed by ALSI among four imaging methods.