Traditional Partial Least Squares (PLS) and Advanced Artificial Neural Network (ANN) models were applied for the quantitative determination of paracetamol (PAR) and chlorzoxazone (CZX) together with their process-related impurities namely; 4-aminophenol (AP), 4‑chloroacetanilide (AC), 4‑nitrophenol (NP), 4‑chlorophenol (CP) and 2‑amino-4-chlorophenol (ACP). Both models were applied first to full spectrum data then the results were compared to those obtained after wavelength selection using Genetic Algorithm (GA). A 5-level 7-factor experimental design was used giving rise to 25 mixtures containing different proportions of the seven compounds. The calibration set was composed of 15 mixtures while 9 mixtures were used in the validation set to test the predictive ability of the suggested models. The four models PLS, ANN, GA-PLS and GA-ANN were successfully applied for the determination of PAR and CZX in their pure and pharmaceutical dosage form. One way ANOVA was carried out between the developed methods and the official ones for PAR and CZX and no significant difference was found. The four models can be easily applied for the determination of the selected drugs in quality control laboratories lacking expensive HPLC instruments.

采用传统的偏最小二乘（PLS）模型和高级人工神经网络（ANN）模型对对乙酰氨基酚（PAR）和氯唑沙宗（CZX）以及与过程相关的杂质进行定量测定；4-氨基苯酚（AP），4-氯乙酰苯胺（AC），4-硝基苯酚（NP），4-氯苯酚（CP）和2-氨基-4-氯苯酚（ACP）。两种模型都首先应用于全光谱数据，然后将结果与使用遗传算法（GA）进行波长选择后获得的结果进行比较。使用了5级7因子实验设计，生成了25种混合物，其中包含不同比例的7种化合物。校准集由15种混合物组成，而在验证集中使用9种混合物来测试建议模型的预测能力。四种型号PLS，ANN，GA-PLS和GA-ANN已成功应用于纯和药物剂型中PAR和CZX的测定。在已开发的方法与PAR和CZX的官方方法之间进行ANOVA的一种方法，没有发现显着差异。在缺乏昂贵的HPLC仪器的质量控制实验室中，这四个模型可以轻松地用于确定所选药物。