Abstract

A new transfer approach was proposed to share calibration models of the hexamethylenetetramine-acetic acid solution for studying hexamethylenetetramine concentration values across different near-infrared (NIR) spectrometers. This approach combines Savitzky-Golay first derivative (S_G_1) and orthogonal signal correction (OSC) preprocessing, along with feature variable optimization using an adaptive chaotic dung beetle optimization (ACDBO) algorithm. The ACDBO algorithm employs tent chaotic mapping and a nonlinear decreasing strategy, enhancing the balance between global and local search capabilities and increasing population diversity to address limitations observed in traditional dung beetle optimization (DBO). Validated using the CEC-2017 benchmark functions, the ACDBO algorithm demonstrated superior convergence speed, accuracy, and stability. In the context of a partial least squares (PLS) regression model for transferring hexamethylenetetramine-acetic acid solutions using NIR spectroscopy, the ACDBO algorithm excelled over alternative methods such as uninformative variable elimination, competitive adaptive reweighted sampling, cuckoo search, grey wolf optimizer, differential evolution, and DBO in efficiency, accuracy of feature variable selection, and enhancement of model predictive performance. The algorithm attained outstanding metrics, including a determination coefficient for the calibration set (R