A Novel Method to Assay Aspirin in Pharmaceutical Formulations by Smartphone Camera-Based Image Scanning Densitometry

Rimsha Khan, Jamil Anwar
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Abstract

Aspirin, a widely-used anti-inflammatory drug, can lead to serious consequences when overdosed. Therefore, there's a need for simple, cost-effective methods to determine its concentration and mitigate potential risks. This study aimed to develop a method for assessing aspirin in pharmaceutical preparations without the need for expensive equipment and with minimal sensitivity to ambient light. In this work, aspirin was subjected to a reaction with Fe(III), leading to the formation of violet-colored spots on filter paper and a 96-microwell plate. These colored spots were then captured using a smartphone in normal lighting conditions and analyzed on a computer. The integrated density of each spot was measured using a novel grayscale technique, and a calibration curve was created to relate integrated density to aspirin concentration. Analytical parameters and reagent concentrations were optimized for accuracy. To validate the method, three commercial aspirin samples were assayed and compared to ultraviolet-visible spectrophotometry, a reference method. The developed technique demonstrated excellent precision (coefficient of variation <0.68%) and relative errors below 5.2%. When compared to traditional color models like red-green-blue (RGB) and hue-saturation-luminosity (HSL), the grayscale model showed superior correlation (R2> 0.996), while the RGB model yielded less precise results (R2= 0.792). This study showcased the effectiveness of a cost-effective methodology for accurate aspirin quantification using a smartphone camera, even in the presence of ambient light.
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利用基于智能手机摄像头的图像扫描密度计检测药物制剂中阿司匹林含量的新方法
阿司匹林是一种被广泛使用的消炎药,一旦服用过量就会导致严重后果。因此,需要一种简单、经济有效的方法来确定阿司匹林的浓度并降低潜在风险。本研究旨在开发一种评估药物制剂中阿司匹林含量的方法,该方法无需昂贵的设备,对环境光的敏感性极低。在这项工作中,阿司匹林与铁(III)发生反应,从而在滤纸和 96 微孔板上形成紫色斑点。然后使用智能手机在正常照明条件下捕捉这些色斑,并在计算机上进行分析。使用新颖的灰度技术测量每个斑点的综合密度,并绘制校准曲线,将综合密度与阿司匹林浓度联系起来。对分析参数和试剂浓度进行了优化,以确保准确性。为了验证该方法,对三种商用阿司匹林样品进行了检测,并与紫外-可见分光光度法(一种参考方法)进行了比较。所开发的技术显示出极佳的精确度(变异系数为 0.996),而 RGB 模型得出的结果精确度较低(R2= 0.792)。这项研究展示了使用智能手机摄像头精确定量阿司匹林的高性价比方法的有效性,即使在有环境光的情况下也是如此。
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