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Smartphone Application for Methanol Determination in Sugar Cane Spirits Employing Digital Image-Based Method

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Abstract

A simple, accurate, and low-cost analytical procedure for methanol determination in sugarcane spirits (cachaça) employing analysis of digital images is presented. A portable system with cheap materials and lighting system to obtain images from a smartphone was built and an RGB system was used for analytical purposes. The reaction for determination of methanol in beverages consists in methanol oxidation to methanal, and consequent formation of a violet chromophore in the presence of chromotropic acid on heating in an oven at 80 °C for 15 min. From the system optimization, analytical curves that showed good linearity for the green channel (from RGB) was built, with regression coefficient (R 2) of 0.998. Relevant matrix effect in the samples was not verified, since the recovery percentage ranged from 83 to 110%. The results of methanol concentration in six cachaça samples obtained by the developed method were compared with using the spectrophotometric method with a confidence level of 95% (n = 3). The developed method has some economic and environmental benefits, since it has low reagent consumption with 800 μL per measured spot, as well as, it presents cheap and handle-easy devices, which encourages the quality control of this toxic and undesirable contaminant for smallholders and industries on production of sugar cane spirits.

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Correspondence to Willian Toito Suarez.

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Mathews O. K. Franco declares that he has no conflict of interest. Willian T. Suarez declares that he has no conflict of interest. Vagner B. dos Santos declares that he has no conflict of interest. Matheus V. Maia declares that he has no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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de Oliveira Krambeck Franco, M., Suarez, W.T., Maia, M.V. et al. Smartphone Application for Methanol Determination in Sugar Cane Spirits Employing Digital Image-Based Method. Food Anal. Methods 10, 2102–2109 (2017). https://doi.org/10.1007/s12161-016-0777-y

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  • DOI: https://doi.org/10.1007/s12161-016-0777-y

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