Abstract This study was concerned with the assay of ascorbic acid (ASC), rutin, and hesperidin (HES) in their combined formulation using a multivariate approach. Three chemometric-assisted spectrophotometric models namely: partial least squares, multivariate curve resolution-alternating least squares, and artificial neural networks were developed and validated. The quantitative analyses of all the proposed models were assessed by percentage recoveries, root mean square error of prediction, and standard error of prediction. The proposed models were used in the range of 10.0–70.0, 2.0–10.0, and 2.0–10.0 µg mL−1 for ASC, rutin, and HES, respectively. In addition, correlation coefficients between the pure and estimated spectral profiles were used for the qualitative analysis of a multivariate curve resolution-alternating least squares model. Artificial neural networks showed higher speed and methodological simplicity over the other two models. These models presented powerful multivariate statistical tools that were applied to the analysis of the combined dosage form in the Australian market. They have the ability to overcome difficulties such as colinearity and spectral overlaps. Statistical comparison between the proposed and reported methods showed no significant difference. The proposed methods can be used for the routine analysis of the studied drugs in quality control laboratories.

中文翻译：

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人工神经网络与偏最小二乘法和多元分辨率交替最小二乘法测定抗坏血酸、芦丁和橙皮苷的抗氧化剂配方

摘要 本研究涉及使用多变量方法测定组合制剂中的抗坏血酸 (ASC)、芦丁和橙皮苷 (HES)。开发并验证了三种化学计量辅助分光光度计模型：偏最小二乘法、多元曲线分辨率-交替最小二乘法和人工神经网络。所有提出的模型的定量分析通过百分比回收率、预测的均方根误差和预测的标准误差进行评估。所提出的模型分别在 10.0–70.0、2.0–10.0 和 2.0–10.0 µg mL-1 的范围内用于 ASC、芦丁和 HES。此外，纯谱图和估计谱图之间的相关系数用于多元曲线分辨率交替最小二乘模型的定性分析。人工神经网络显示出比其他两个模型更高的速度和方法的简单性。这些模型提供了强大的多变量统计工具，可用于分析澳大利亚市场的组合剂型。它们有能力克服诸如共线性和光谱重叠等困难。建议和报告的方法之间的统计比较显示没有显着差异。所提出的方法可用于质量控制实验室中研究药物的常规分析。它们有能力克服诸如共线性和光谱重叠等困难。建议和报告的方法之间的统计比较显示没有显着差异。所提出的方法可用于质量控制实验室中研究药物的常规分析。它们有能力克服诸如共线性和光谱重叠等困难。建议和报告的方法之间的统计比较显示没有显着差异。所提出的方法可用于质量控制实验室中研究药物的常规分析。