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- W4320169076 abstract "This work presents a deep learning approach for computer vision-based recognition of natural honey adulteration. The results are compared with the machine learning models using chemical data obtained with an innovative bimetallic voltammetric electrode that acts as an e-tongue. Computer vision is based on the recognition of the color of the sample, which changes with the addition of undesirable synthetic substances. The pictures were taken with a smartphone under controlled conditions using seven different background illumination colors for samples that contained 0 – 50 % foreign additives. In the best case root mean square error (RMSE) in the regression approach was 0.46 % for the external test set at R2 = 0.9993, calculated as a compatibility evaluation between the true values and the prediction result. The minimum RMSE for the voltammetric measurements on the quadruple-disk iridium-platinum electrode and the regression algorithms PCR, PLSR, and SVR was 0.25 % with R2 = 0.9998. It has been shown that when food adulteration affects its color, it is possible to successfully replace the classical e-tongue with computer vision. The convenient, rapid, and nondestructive approach does not require the use of equipment, reagents, or solvents and laboratory work techniques typical for analytical chemistry." @default.
- W4320169076 created "2023-02-13" @default.
- W4320169076 creator A5012701787 @default.
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- W4320169076 date "2023-03-01" @default.
- W4320169076 modified "2023-10-14" @default.
- W4320169076 title "Computer vision analysis of sample colors versus quadruple-disk iridium-platinum voltammetric e-tongue for recognition of natural honey adulteration" @default.
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- W4320169076 doi "https://doi.org/10.1016/j.measurement.2023.112514" @default.
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