Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Beni-Suef University, Alshaheed Shehata Ahmad Hegazy St., 62514, Beni-Suef, Egypt.
J AOAC Int. 2020 Nov 1;103(6):1660-1666. doi: 10.1093/jaoacint/qsaa075.
Niacin (NIA) is a water-soluble vitamin and the primary treatment of pellagra. No analytical method was found to assess NIA in complex mixtures with its official impurities.
Two validated, accurate, and selective chemometric models were developed to assay NIA in the presence of its four official impurities, including pyridine, a nephrotoxic and hepatotoxic substance. Additionally, the two selective chemometric models were compared by processing UV spectra in the range 220-305 nm and applying partial least squares regression (PLSR) and support vector regression (SVR) models.
A five levels five factors experimental design was chosen to exhibit a training set of 25 mixtures that had numerous variable percentages of tested substances. A test set consisting of 10 mixtures was designed to confirm the predictive power of the suggested models.
The presented results substantiate the strength of the developed multivariate calibration models to assay NIA specifically with high selectivity and accuracy (100.02 ± 1.312 and 100.04 ± 1.272 for PLSR and SVR models, respectively). The root mean square error of prediction for the validation set mixtures was applied as a main comparison tool and it was found to be 0.2016 and 0.1890 for PLSR and SVR models, respectively.
The results of the developed models and the reported HPLC method were statistically compared, where F-values and Student's t-tests did not show significant difference in regards to accuracy and precision. The SVR model proved to be more accurate than the PLSR model, producing a high generalization capacity, while PLSR was easy to implement and fast.
烟酸（NIA）是一种水溶性维生素，也是治疗糙皮病的主要药物。目前尚未发现分析方法可用于评估其与四种官方杂质（包括具有肾毒性和肝毒性的吡啶）在复杂混合物中的含量。
建立了两种经过验证的准确且选择性的化学计量学模型，用于在四种官方杂质存在的情况下测定 NIA，包括具有肾毒性和肝毒性的吡啶。此外，通过处理 220-305nm 范围内的 UV 光谱并应用偏最小二乘回归（PLSR）和支持向量回归（SVR）模型，对这两种选择性化学计量学模型进行了比较。
选择五水平五因素实验设计，以展示包含多种测试物质不同变量百分比的 25 个混合物的训练集。设计了一个包含 10 个混合物的测试集，以验证所提出模型的预测能力。
所提出的结果证实了所开发的多元校准模型在特定情况下具有高选择性和准确性（PLSR 和 SVR 模型分别为 100.02±1.312 和 100.04±1.272）的优势。验证集混合物的预测均方根误差被用作主要比较工具，发现 PLSR 和 SVR 模型的预测均方根误差分别为 0.2016 和 0.1890。
对所开发模型的结果和报道的 HPLC 方法进行了统计比较，F 值和学生 t 检验表明在准确性和精密度方面没有显著差异。SVR 模型比 PLSR 模型更准确，具有更高的泛化能力，而 PLSR 模型易于实现且速度较快。
