Literature DB >> 25872422

An iPhone-based digital image colorimeter for detecting tetracycline in milk.

Prinya Masawat1, Antony Harfield2, Anan Namwong3.   

Abstract

An iPhone-based digital image colorimeter (DIC) was fabricated as a portable tool for monitoring tetracycline (TC) in bovine milk. An application named ColorConc was developed for the iPhone that utilizes an image matching algorithm to determine the TC concentration in a solution. The color values; red (R), green (G), blue (B), hue (H), saturation (S), brightness (V), and gray (Gr) were measured from each pictures of the TC standard solutions. TC solution extracted from milk samples using solid phase extraction (SPE) was captured and the concentration was predicted by comparing color values with those collected in a database. The amount of TC could be determined in the concentration range of 0.5-10 μg mL(-1). The proposed DIC-iPhone is able to provide a limit of detection (LOD) of 0.5 μg mL(-1) and limit of quantitation (LOQ) of 1.5 μg mL(-1). The enrichment factor was 70 and color of the extracted milk sample was a strong yellow solution after SPE. Therefore, the SPE-DIC-iPhone could be used for the assay of TC residues in milk at the concentration lower than LOD and LOQ of the proposed technique.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Digital image colorimeter; Milk; Portable; Tetracycline; iPhone

Mesh:

Substances:

Year:  2015        PMID: 25872422     DOI: 10.1016/j.foodchem.2015.03.089

Source DB:  PubMed          Journal:  Food Chem        ISSN: 0308-8146            Impact factor:   7.514


  6 in total

1.  Monitoring of Cobalt and Cadmium in Daily Cosmetics Using Powder and Paper Optical Chemosensors.

Authors:  Ahmed Radwan; Islam M El-Sewify; Hassan Mohamed El-Said Azzazy
Journal:  ACS Omega       Date:  2022-04-28

Review 2.  Smartphone-Based Food Diagnostic Technologies: A Review.

Authors:  Giovanni Rateni; Paolo Dario; Filippo Cavallo
Journal:  Sensors (Basel)       Date:  2017-06-20       Impact factor: 3.576

3.  Detection and quantification of adulterations in aged wine using RGB digital images combined with multivariate chemometric techniques.

Authors:  Carlos Herrero-Latorre; Julia Barciela-García; Sagrario García-Martín; Rosa M Peña-Crecente
Journal:  Food Chem X       Date:  2019-07-05

4.  The Efficiency of Color Space Channels to Quantify Color and Color Intensity Change in Liquids, pH Strips, and Lateral Flow Assays with Smartphones.

Authors:  Joost Laurus Dinant Nelis; Laszlo Bura; Yunfeng Zhao; Konstantin M Burkin; Karen Rafferty; Christopher T Elliott; Katrina Campbell
Journal:  Sensors (Basel)       Date:  2019-11-21       Impact factor: 3.576

Review 5.  Recent trends in smartphone-based detection for biomedical applications: a review.

Authors:  Soumyabrata Banik; Sindhoora Kaniyala Melanthota; Joel Markus Vaz; Vishak Madhwaraj Kadambalithaya; Iftak Hussain; Sibasish Dutta; Nirmal Mazumder
Journal:  Anal Bioanal Chem       Date:  2021-02-15       Impact factor: 4.142

6.  Machine-Learning-Assisted Analysis of Colorimetric Assays on Paper Analytical Devices.

Authors:  Bidur Khanal; Pravin Pokhrel; Bishesh Khanal; Basant Giri
Journal:  ACS Omega       Date:  2021-12-02
  6 in total

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