Optical Sensing With an Ordinary Mobile Phone

Sammanfattning: A major portion of the world’s population (≈ 80% of the total) lives in developing countries where lab instruments such as spectrophotometers are not widely available as their purchasing as well as maintenance is normally unaffordable. On the other hand, there are now around five billion mobile phone subscriptions worldwide and the current generation of standard mobile phones has several capabilities to perform user-defined analysis. This thesis contains work with respect to asses potentials and weaknesses of a standard mobile phone for use as a simplified spectrophotometric unit (as both the light source and detector) to perform analysis in the visible region (400-700 nm). A part of the work has been the development of the necessary software to be able to use an ordinary mobile phone to study diffuse and specular reflectance properties of the targeted samples using phone’s screen as controllable illumination source and the front view camera for simultaneous collection of spectral information.Papers I-III contain exploratory work performed to assess the potential of using the mobile phone as an optical sensor system. Papers IV and V present studies of more basic character of the interactions between the light from the phone screen and the sample, in particular for liquid samples.In paper I, tests with a virtual array of chemical indicators having areas with different colours were performed. Optimization of the alignment of the sample and the distance between the camera and the sample were carried out and the influence of ambient light was investigated. The lateral resolution of the images enables optical readout of sensor arrays as well as arrays for diagnostics.In paper II, the potential of using the technique for direct measurement of properties related to the quality of drinking water, food and beverages was investigated. Liquid samples were prepared in deionized water. Coloured compounds such as iron(III)chloride and humic acid were analyzed in the concentration range 0-10 mg/liter and were classified by their reflectance profiles with respect to the contamination type and its concentration. Colourless arsenic(III) was analyzed by its bleaching reaction with iodine/starch. An alternative arsenic detection method based on measurement of discolouration of iron containing sand was demonstrated.In paper III, it has been demonstrated that mobile phones can be used for qualitative analysis of foods and beverages, such as cold drinks, meat, vegetables and milk in terms of general food quality, safety and authenticity.In paper IV, the ability of the mobile phone system to measure absorption properties of liquid solutions is investigated. Different concentrations of colored solutions (reactive blue 2, Congo red and Metanil yellow) give rise to measurement data that are well described by the Beer-Lambert law. This is surprising since the measurement conditions were far from ideal, with a light source that was strongly polychromatic and an illumination that was not a collimated light beam with homogeneous light intensity. By analyzing red, green and blue light that was transmitted through the liquid a unique signature for classification and quantification was obtained. Also the repeatability and accuracy of the measurements were investigated and were surprisingly good for such a simple system. Analyses of reflectance properties of colored solid samples are also included and were more complex with results being dependent on the morphology and colorimetric properties of the different types of these samples.In paper V, it is found that different parts of the image data contain different information about liquid samples. While one part of the image gives information about the absorption properties as investigated in detail in paper IV, another part gives information about the refractive index of the sample. Measurements of samples with varying refractive index show trends expected from the Fresnel equations at zero incidence angle. Combined information from the two areas of the image offers new possibilities to classify samples.