Spectral Feature Band Screening Method for Milk Fat Using an Improved Coati Optimization Algorithm
With the increasing demand for food safety and quality control,Near-Infrared(NIR)spectroscopy has shown significant potential for rapid detection due to its non-invasive and efficient characteristics.However,traditional feature selection methods often face challenges such as high computational complexity,dependency,and limited generalization when dealing with high-dimensional spectral data.To address these challenges,this paper proposed an improved Coati Optimization Algorithm(GD-Golden-SCOA)that incorporated the GoodNode strategy,Dynamic Opposite-Based Learning(DOBL),and the Golden Sine Algorithm(Golden-SA).The enhanced algorithm aimed to improve global search capability and parameter adaptability while minimizing human intervention.The study compared five preprocessing methods and evaluated the performance of full-wavelength modeling,traditional Genetic Algo-rithm(GA),Uninformative Variable Elimination(UVE),original Coati Optimization Algorithm(COA),and the proposed GD-Golden-SCOA in selecting feature bands for predicting milk fat content.The results showed that the improved algorithm not only re-duced the number of selected feature bands but also significantly enhanced prediction accuracy,achieving R2 values of 0.994 for the training set and 0.989 for the test set.These findings suggested that the GD-Golden-SCOA algorithm held considerable promise for applications in rapid and non-destructive detection.
Near-Infrared spectroscopyFeature band selectionCoati optimization algorithmGoodNodeDynamic opposite-based learning
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