Protein, enzyme and carbohydrate quantification using smartphone through colorimetric digitization technique

Abstract

In this paper the utilization of smartphone as a detection platform for colorimetric quantification of biological macromolecules has been demonstrated. Using V-channel of HSV color space, the quantification of BSA protein, catalase enzyme and carbohydrate (using D-glucose) have been successfully investigated. A custom designed android application has been developed for estimating the total concentration of biological macromolecules. The results have been compared with that of a standard spectrophotometer which is generally used for colorimetric quantification in laboratory settings by measuring its absorbance at a specific wavelength. The results obtained with the designed sensor is found to be similar when compared with the spectrophotometer data. The designed sensor is low cost, robust and we envision that it could promote diverse fields of bio-analytical investigations. Schematic illustration of the smartphone sensing mechanism for colorimetric analysis of biomolecular samples.

Smartphone-Based Device for Colorimetric Detection of MicroRNA Biomarkers Using Nanoparticle-Based Assay

“…The Krometriks’ accessory is intentionally designed to be simple and easy to use. Some smartphone-based systems contain various optical and electrical parts as components of the accessory [ 58 , 59 ]. These complex components are challenging to maintain and repair, making them impractical candidates for point-of-care and field applications.…”

A sensor-centric survey on the development of smartphone measurement and sensing systems

“…proposed a smartphone optosensor to measure streptomycin (STR) concentration in different food products [42]: the liquid sample under test is mixed with a colorimetric indicator (aptamerconjugated Au nanoparticles) and placed in a cuvette; the light source is generated by three colored LEDs with peak wavelengths 473, 520 and 625 nm and transmitted through the sample; the transmitted light is acquired by the smartphone camera and STR concentration estimated from the measured absorbance ratio at 625 and 520 nm (R 2 = 0.996); the system was successfully tested with honey, milk and tap water. A low-cost colorimetric test suitable to detect the concentration of different types of biological macromolecules was presented by Dutta et al in 2017 [43]: the sample under test, placed in a cuvette with a suitable reagent to produce a change in color, is irradiated with a white LED and a picture is taken with the phone camera. Image pixels are converted to HSV color space and the average brightness (V) of the image was found the parameter best suited to estimate the analyte concentration.…”

“…Despite the increasing interest in smartphone based sensing systems, there are also some gaps that from [190]. colorimetric alcohol concentration in saliva 0 -0.3% [38] colorimetric pH, protein and glucose in urine 5 -9, 0 -100 mg/dL, 0 -300 mg/dL [39] colorimetric blood hematocrit level 10 -65% [41] colorimetric streptomycin concentration in food 50 -267 nM [42] colorimetric BSA, catalase enzyme and carbohydrate 0 -1 mg/mL, 0 -1 mg/mL, 0 -140 µg/mL [43] colorimetric cloud coverage 4 -98% [48] colorimetric surface corrosion of iron N/A [50] irradiance measurement UVA solar irradiance 0 -10 mW/m 2 [60] irradiance measurement UVA aerosol optical depth 0.05 -0.20 [61] irradiance measurement UVB solar irradiance 1 -9 mW/m 2 [63] irradiance measurement atmospheric total ozone column 260 -320 DU [65] irradiance measurement SO 2 volcanic emission 0 -3. computer vision bacterial colony counter N/A [80] computer vision bacterial colony counter 0 -250 CFU [81] computer vision bacterial colony counter N/A [82] computer vision bacterial colony counter 0 -500 CFU [83] computer vision surveillance of fruit flies N/A [84] computer vision food recognition tool N/A [85] computer vision food recognition and nutritional value N/A [86] computer vision heart rate measurement N/A [87] mobile microscopy cell imaging (brightfield and fluorescent) N/A [88] mobile microscopy image analysis of green algae in freshwater N/A [89] sound recording and analysis chronic lung diseases average error 5.1%, detection rate 80 -90% [90] sound recording and analysis chronic lung diseases average error 8.01% [91] sound recording and analysis number of coughs detection rate 92% [92] sound recording and analysis respiratory rate estimation error < 1% [93] sound recording and analysis nasal symptoms N/A [94] sound recording and analysis snoring quantification correlation > 0.9 [95] sound recording and analysis hearing threshold in noisy environment N/A …”

Design of a 3D printed smartphone microscopic system with enhanced imaging ability for biomedical applications

“…These facets have limited the accessibility of the tool in resource‐limited regions (Breslauer et al ., ; Zhu et al ., ). Several research groups around the globe have recently been involved towards the development of a new platform using smartphone which can be reliably used for sensing of different physical and chemical parameters (Gallegos et al ., ; Lopez‐Ruiz et al ., ; Dutta et al ., ; Dutta et al ., ; Hossain et al ., ; Hussain et al ., ; Dutta et al ., ; Hussain et al ., ,b; Mutlu et al ., ; Chen et al ., ; Natesan et al ., ) and imaging of various biological samples (Vashist et al ., ; Knowlton et al ., ; Contreras‐Naranjo et al ., ; Scherr et al ., ; Yoon et al ., ; Sun & Hu, ; Jawale et al ., ; Purwar et al ., ). Currently, there are approximately 8.1 billion mobile phone subscribers around the world, and ∼40% of the phone subscribers use smartphones.…”