Application of invasive weed optimization and least square support vector machine for prediction of beef adulteration with spoiled beef based on visible near-infrared (Vis-NIR) hyperspectral imaging

Abstract

Different multivariate data analysis methods were investigated and compared to optimize rapid and non-destructive quantitative detection of beef adulteration with spoiled beef based on visible near-infrared hyperspectral imaging. Four multivariate statistical analysis methods including partial least squares regression (PLSR), support vector machine (SVM), least squares support vector machine (LS-SVM) and extreme learning machine (ELM) were carried out in developing full wavelength models. Good prediction was obtained by applying LS-SVM in the spectral range of 496-1000 nm with coefficients of determination (R²) of 0.94 and 0.94 as well as root-mean-squared errors (RMSEs) of 5.39% and 6.29% for calibration and prediction, respectively. To reduce the high dimensionality of hyperspectral data and to establish simplified models, a novel method named invasive weed optimization (IWO) was developed to select key wavelengths and it was compared with competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA). Among the four multivariate analysis models based on important wavelengths determined by IWO, the LS-SVM simplified model performed best where R² of 0.97 and 0.95 as well as RMSEs of 4.74% and 5.67% were attained for calibration and prediction, respectively. The optimum simplified model was applied to hyperspectral images in pixel-wise to visualize the distribution of spoiled beef adulterant in fresh minced beef. The current study demonstrated that it was feasible to use Vis-NIR hyperspectral imaging to detect homologous adulterant in beef.

Keywords: Beef adulteration; Extreme learning machine; Invasive weed optimization; Least squares support vector machine; Variable selection.