Spectral fingerprinting of chemical indicators, using computer screens as light sources and web cameras as imaging detectors, is an emerging approach for chemical sensing with the potential to coexist in common consumer electronic devices.The migration of this technique to mobile phones is key to extend this sensing approach to the most ubiquitous and familiar type of instrumentation. Here, we investigate the feasibility and performance of spectral fingerprinting on reference samples using a standard mobile phone as a complete measuring platform, where the screen provides controlled illumination while the front camera is the imaging detector. Key elements for the execution of such experiments are the software design, the definition of the sample layout, the type of alignment between the phone and the sample, and the influence of ambient illumination. This paper demonstrates the feasibility of reflectance fingerprinting on standard mobile phones and identify the operating conditions of the key parameters that produce an adequate evaluation performance.

Analysis of water and sand samples was done by reflectance measurements using a mobile phone. The phone’s screen served as light source and front view camera as detector. Reflected intensities for white, red, green and blue colors were used to do principal component analysis for classification of several compounds and their concentrations in the water. Classification of iron (III), chromium (VI) and sodium salt of humic acid was obtained using reflected intensities from blue and green light for concentrations 2-10 mg/l. Analysis of As(III) from 25-400 μg/l based on reflection of red light was performed utilizing the bleaching reaction of tincture of iodine containing starch. Enhanced sensitivity to low concentrations of arsenic was obtained by adding reflected intensities from white light to the analysis. Model colored sand samples representing discoloration caused by the presence of arsenic in groundwater were also analyzed.

A mobile phone was used to perform optical analyses of foods and beverages. The phone’s screen served as illumination source and front view camera recorded images. Reflected intensities were used to discriminate among the different samples analysed by principal component analysis. Samples studied illustrated the technique’s potential analytical capabilities with respect to adulteration and authenticity. Three coloured additives (red, green and blue) in the concentration range 2-10 mg/l in a lemon lime beverage were discriminated. Adulteration by up to 25% water of milk with 3% fat content was detected with an estimated detection limit of about 3% water. Changes occurring on a green onion surface over a 48 h aging period at room temperature were monitored. Five different cuts from lamb carcasses weighing 9 and 14 kg were classified by the method. Considerable additional work with regard to sampling, data treatment and quantifying results is necessary before the goal of using the technique as a point of purchase analytical tool can be realised.

A mobile phone has been used both as illumination source and image detector for quantitative optical analysis of colored liquid samples (4 different colorants) and solid samples (printed color patterns, plastic beads and colored sand grains). Even though the measurement conditions were far from ideal, because the light source was strongly polychromatic and the illumination was not a collimated light beam with homogeneous light intensity, a logarithmic concentration dependence, in accordance with the Beer-Lambert law, described the data of the colored liquids quite well. By utilizing blue-blue (420-510 nm), green-green (480-590 nm) and red-red (575-695 nm) illumination/detection combinations, each sample could be assigned a unique color signature for classification that agreed with reference absorbance spectra measured with a spectrometer. Quantification of validation samples within a few percent of the actual values was achieved. Also the long-term repeatability of the measurements was investigated and was surprisingly good for such a simple system. Analysis of the colored solid samples was more complex with results being dependent on the morphology and colorimetric properties of the samples.

The use of a mobile phone with a front side camera for the classification of liquid samples is described. The classification is based on the observation that there are different regions of the image captured by the mobile camera, one containing specular reflected light and one due to diffuse reflected light with transmission through the liquid. The specular reflected light contains information about the refractive index of the liquid sample whereas the diffuse light contains information about the color and absorption properties of the liquid. The information in the specular light is first elucidated. It is found that the reflectance of the region with specular reflected light increases linearly with increasing refractive index, n, in the range 1.33 < _n < 1.38 as expected from the Fresnel equations. The information in the specular light is then used together with the previously described diffuse light from another region of the image to analyze several types of liquid samples. It is shown that a combination of the two areas of the image improves the classification abilities of the mobile phone.