Abstract：
Herein, two different sustainable and green signal processing spectrophotometric approaches, namely, derivative spectroscopy and wavelet transform, have been utilized for effective measurement of the antiretroviral therapy abacavir and lamivudine in their pharmaceutical formulations. These methods were used to enhance the spectral data and differentiate between the absorption bands of abacavir and lamivudine in order to accurately measure their concentrations. For determining abacavir and lamivudine, the first derivative spectrophotometric method has been applied to the zero-order and ratio spectra of both drugs. The same approach has been tested using the continuous wavelet transform method where a second order 2.4 of rbio and bior wavelet families were found to be optimum for measuring both drugs. Validation of the proposed methods affirmed their reliability in terms of linearity over the concentration range 1.5-30 µg/mL and 1.5-36 µg/mL for abacavir and lamivudine, respectively, precision (RSD < 2 %), and accuracy with mean recoveries ranging between 98 % and 102 %. Additionally, these spectrophotometric methodologies were applied to real pharmaceutical preparations and yielded results congruent with a prior chromatographic method. Most prominently, the proposed methods stood out for their greenness and sustainability with 97 points as evaluated by the analytical eco-scale method and a score value of 0.79 as analyzed by AGREE method, thereby making them suitable for resource-limited settings and highlighting the potential for broader application of green analytical methods in pharmaceutical analysis.
摘要：
在这里，两种不同的可持续和绿色信号处理分光光度方法，即,导数光谱和小波变换，已用于有效测量抗逆转录病毒疗法阿巴卡韦和拉米夫定的药物制剂。这些方法用于增强光谱数据并区分阿巴卡韦和拉米夫定的吸收带，以准确测量其浓度。为了确定阿巴卡韦和拉米夫定，一阶导数分光光度法已应用于两种药物的零阶和比率光谱。已经使用连续小波变换方法测试了相同的方法，其中发现二阶2.4的rbio和bior小波家族对于测量两种药物都是最佳的。所提出的方法的验证确认了其可靠性在线性方面的浓度范围1.5-30µg/mL和1.5-36µg/mL的阿巴卡韦和拉米夫定，分别,精度（RSD<2%），和准确性，平均回收率在98%和102%之间。此外,这些分光光度法适用于实际的药物制剂，并产生与先前的色谱方法一致的结果。最突出的是，所提出的方法因其绿色性和可持续性而脱颖而出，通过分析生态尺度方法评估了97分，通过AGREE方法分析得分为0.79分，从而使它们适用于资源有限的环境，并突出了绿色分析方法在药物分析中广泛应用的潜力。
