提出了一种新的转移方法来共享六亚甲基四胺-乙酸溶液的校准模型,以研究不同近红外(NIR)光谱仪上的六亚甲基四胺浓度值。该方法结合了Savitzky-Golay一阶导数(S_G_1)和正交信号校正(OSC)预处理,以及使用自适应混沌粪甲虫优化(ACDBO)算法的特征变量优化。ACDBO算法采用帐篷混沌映射和非线性递减策略,增强全球和本地搜索能力之间的平衡,并增加种群多样性,以解决传统粪甲虫优化(DBO)中观察到的局限性。使用CEC-2017基准测试函数进行验证,ACDBO算法表现出优越的收敛速度,准确度,和稳定性。在使用近红外光谱转移六亚甲基四胺-乙酸溶液的偏最小二乘(PLS)回归模型的背景下,ACDBO算法优于无信息变量消除等替代方法,竞争性自适应重加权抽样,布谷鸟搜索,灰狼优化器,差分进化,效率和DBO,特征变量选择的准确性,并增强模型预测性能。该算法获得了出色的指标,包括校准集的决定系数(Rc2)为0.99999,校准集的均方根误差(RMSEC)为0.00195%,验证集(Rv2)的确定系数为0.99643,验证集(RMSEV)的均方根误差为0.03818%,残差预测偏差(RPD)为16.72574。与现有的OSC相比,斜率和偏差校正(S/B),直接标准化(DS),和分段直接标准化(PDS)模型传递方法,新策略提高了模型预测的准确性和鲁棒性。它消除了有关六亚甲基四胺浓度的无关背景信息,从而最大限度地减少不同仪器之间的光谱差异。因此,这种方法产生的预测集(Rp2)的确定系数为0.96228,预测集(RMSEP)的均方根误差为0.12462%,相对错误率(RER)分别为17.62331。这些数字紧随使用DS和PDS获得的数字,记录了Rp2,RMSEP,RER值为0.97505,0.10135%,21.67030和0.98311、0.08339%,分别为26.33552。与OSC等传统方法不同,S/B,DS,和PDS,这种新颖的方法不需要在不同的仪器上分析相同的样品。这一特性显著拓宽了其模型转移的适用性,这对于转移特定的测量样品是特别有益的。
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 (Rc2) of 0.99999, a root mean square error for the calibration set (RMSEC) of 0.00195%, a determination coefficient for the validation set (Rv2) of 0.99643, a root mean squared error for the validation set (RMSEV) of 0.03818%, residual predictive deviation (RPD) of 16.72574. Compared to existing OSC, slope and bias correction (S/B), direct standardization (DS), and piecewise direct standardization (PDS) model transfer methods, the novel strategy enhances the accuracy and robustness of model predictions. It eliminates irrelevant background information about the hexamethylenetetramine concentration, thereby minimizing the spectral discrepancies across different instruments. As a result, this approach yields a determination coefficient for the prediction set (Rp2) of 0.96228, a root mean squared error for the prediction set (RMSEP) of 0.12462%, and a relative error rate (RER) of 17.62331, respectively. These figures closely follow those obtained using DS and PDS, which recorded Rp2, RMSEP, and RER values of 0.97505, 0.10135%, 21.67030, and 0.98311, 0.08339%, 26.33552, respectively. Unlike conventional methods such as OSC, S/B, DS, and PDS, this novel approach does not require the analysis of identical samples across different instruments. This characteristic significantly broadens its applicability for model transfer, which is particularly beneficial for transferring specific measurement samples.