The division of calibration and validation is one of the essential procedures that affect the prediction result of the calibration model in quantitative analysis of near-infrared (NIR) spectroscopy. The conventional methods are Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). These algorithms use Euclidean distance to cover as many representative samples as possible. This paper proposes an Adaptive Hybrid Cuckoo-Tabu Search (AHCTS) algorithm for partitioning samples based on optimization. The algorithm combines the characteristics of cuckoo search (CS) and tabu search (TS) and fused with an adaptive function. For comparison, using fishmeal samples as spectral analysis data, KS, SPXY, and AHCTS algorithms were used to divide the modeling samples to establish partial least squares regression (PLSR) models. The experimental results showed that the model established by the proposed algorithm performs better than KS and SPXY. It reveals that the AHCTS method may be an advantageous alternative for quantitative analysis of NIR spectroscopy.

校准和验证的划分是影响近红外（NIR）光谱定量分析中校准模型的预测结果的基本程序之一。常规方法是Kennard-Stone（KS）和基于联合x的样本集划分-y距离（SPXY）。这些算法使用欧几里得距离来覆盖尽可能多的代表性样本。提出了一种基于优化的自适应混合杜鹃-塔布搜索算法。该算法结合了布谷鸟搜索（CS）和禁忌搜索（TS）的特征，并与自适应功能融合。为了进行比较，使用鱼粉样本作为光谱分析数据，使用KS，SPXY和AHCTS算法对建模样本进行划分，以建立偏最小二乘回归（PLSR）模型。实验结果表明，该算法建立的模型性能优于KS和SPXY。结果表明，AHCTS方法可能是NIR光谱定量分析的一种有利替代方法。