A 3D printed smartphone optosensing platform for point-of-need food safety inspection

Smartphone-based optical assays in the food safety field

“… [ 21 ] Antibiotic streptomycin honey, milk and tap water AuNP aggregation (aptamer based stabilization) based on colour change 8.97 μg Kg −1 honey: restricted milk: 200 μg L −1 50–250 nM 0.7–8% depending matrix 5 interferent compounds n.a. [ 28 ] tetracycline milk and honey novel dye-doped porous metal–organic framework (UiO-66)-based 0.007 mg L −1 honey: restricted milk: 100 mg L −1 0.44–2.6 mg L −1 0.59–4.91% various possible interferents n.a. [ 29 ] Aquatic toxin okadaic acid, saxitoxin shellfish cell viability in 96 micro-well plates 34 μg L −1 okadaic acid: 160 μg Kg −1 saxitoxin: 800 μg Kg −1 10–800 μg L −1 n.a.…”

Plasmonic molecular assays: Recent advances and applications for mobile health

“…Smartphones have also been employed as optical readers for various types of plasmon-resonant sensors such as coupling sensors, SPR, and LSPR sensors for use in the POC settings. Using its sensitive bright-field imaging mode, smartphones can be turned into miniature photometers for quantification of light absorbance of coupling-based colorimetric assays [214][215][216]. Moreover, a smartphone-based LFA assay reader has recently been commercialize by Cellmix [217].…”

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

“…In 2017 Kim et al proposed a smartphone based sensor to measure the blood hematocrit from 10% to 65% with a level of detection (LOD) of 0.1% [41]: in this case a microfluidic device, placed inside an acrylic box and illuminated with white light, was used and the hematocrit concentration was estimated from the red, green and blue components of the microchannel image captured by camera. 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.…”

“…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 …”