TLC-smartphone in antibiotics determination and low-quality pharmaceuticals detection

被引:20
|
作者
Gad, Asmaa G. [1 ]
Fayez, Yasmin Mohammed [2 ]
Kelani, Khadiga M. [1 ,2 ]
Mahmoud, Amr M. [2 ]
机构
[1] Modern Univ Technol & Informat, Fac Pharm, Analyt Chem Dept, Cairo, Egypt
[2] Cairo Univ, Fac Pharm, Analyt Chem Dept, El Kasr El Aini St, Cairo 11562, Egypt
关键词
ORNIDAZOLE; OFLOXACIN; DRUGS; HPLC;
D O I
10.1039/d1ra01346g
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Thin layer chromatography (TLC) is a powerful and simple technique for screening and quantifying low quality and counterfeit pharmaceutical products. The detection methods used to detect and quantify separate analytes in TLC ranges from the densitometric method to mass spectrometric or Raman spectroscopic methods. This work describes the development and optimization of a simple and sensitive TLC method utilizing a smartphone CCD camera for verification of both identity and quantity of antibiotics in dosage form, namely ofloxacin and ornidazole. Mixtures of ofloxacin and ornidazole were chromatographed on a silica gel 60 F-254 plate as a stationary phase. The optimized mobile phase is n-butanol : methanol : ammonia (8 : 1 : 1.5 by volume). Iodine vapor has been used as a "universal stain" to visualize the spots on the TLC plates in order to obtain a visual image using the smartphone camera and a desk lamp as an illumination source, thus eliminating the need for a UV illumination source. The recorded images were processed to calculate the R-f values (R-f values for ofloxacin and ornidazole were 0.12 and 0.76, respectively) which provide identity of the drugs while spot intensity was calculated using a commercially available smartphone app and employed for quantitative analysis of the antibiotics and "acetaminophen" as an example of a counterfeit substance. The smartphone TLC method yielded a linearity of ofloxacin and ornidazole in the range of 12.5-62.5 mu g/band and 500-1000 mu g/band, respectively. The limit of detection was found to be 1.6 mu g/spot for ofloxacin and 97.8 mu g/spot for ornidazole. The proposed method was compared with the bench top densitometric method for verification using a Camag TLC Scanner 3, the spot areas were scanned at 320 nm. The R-f value of ofloxacin and ornidazole was calculated to be 0.12 and 0.76, respectively. The densitometric method yielded a linearity of ofloxacin and ornidazole in the range of 5-40 mu g/band and 5-50 mu g/band, respectively. The limit of detection was found to be 0.8 mu g/spot for ofloxacin and 1.1 mu g/spot for ornidazole. The proposed method has been successfully applied for the determination of ofloxacin and ornidazole present in more than one pharmaceutical dosage form and was comparable to the densitometric method.
引用
收藏
页码:19196 / 19202
页数:7
相关论文
共 50 条
  • [21] An Unsupervised Approach for Low-Quality Answer Detection in Community Question-Answering
    Wu, Haocheng
    Tian, Zuohui
    Wu, Wei
    Chen, Enhong
    DATABASE SYSTEMS FOR ADVANCED APPLICATIONS (DASFAA 2017), PT II, 2017, 10178 : 85 - 101
  • [22] Online Detection of Low-Quality Synchrophasor Measurements: A Data-Driven Approach
    Wu, Meng
    Xie, Le
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2017, 32 (04) : 2817 - 2827
  • [23] Low-quality image object detection based on reinforcement learning adaptive enhancement
    Ye, Jiongkai
    Wu, Yong
    Peng, Dongliang
    PATTERN RECOGNITION LETTERS, 2024, 182 : 67 - 75
  • [24] An algorithm improving objects detection for low-quality video using stochastic resonance
    Chen, Mingsheng
    Qin, Mingxin
    Sun, Jixiang
    Yin, Zhongqiu
    Ning, Xu
    Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology, 2013, 35 (01): : 103 - 107
  • [25] Machine learning for Internet of things anomaly detection under low-quality data
    Han, Shangbin
    Wu, Qianhong
    Yang, Yang
    INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2022, 18 (10)
  • [26] Endocuff to Improve Adenoma Detection: Supported by Low-Quality Evidence or Not Supported by High-Quality Evidence?
    Zhang, Yao
    Zhao, Sheng-Bing
    Bai, Yu
    CLINICAL GASTROENTEROLOGY AND HEPATOLOGY, 2018, 16 (06) : 984 - 985
  • [27] An Evaluation of Low-Quality Content Detection Strategies: Which Attributes Are Still Relevant, Which Are Not?
    Resende, Julio
    Durelli, Vinicius H. S.
    Moraes, Igor
    Silva, Nicollas
    Dias, Diego R. C.
    Rocha, Leonardo
    COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT I, 2020, 12249 : 572 - 585
  • [28] FE-DeTr: Keypoint Detection and Tracking in Low-quality Image Frames with Events
    Wang, Xiangyuan
    Chen, Kuangyi
    Yang, Wen
    Yu, Lei
    Xing, Yannan
    Yu, Huai
    2024 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2024), 2024, : 14638 - 14644
  • [29] Composited FishNet: Fish Detection and Species Recognition From Low-Quality Underwater Videos
    Zhao, Zhenxi
    Liu, Yang
    Sun, Xudong
    Liu, Jintao
    Yang, Xinting
    Zhou, Chao
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 30 : 4719 - 4734
  • [30] Marine Oil Spill Detection from Low-Quality SAR Remote Sensing Images
    Dong, Xiaorui
    Li, Jiansheng
    Li, Bing
    Jin, Yueqin
    Miao, Shufeng
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2023, 11 (08)