01834naa a2200493 a 450000100080000000500110000800800410001902200140006002400560007410000190013024501270014926000090027652003710028565000300065665000180068665000260070465000260073065000220075665000180077865000340079665000300083065000210086065000310088165000100091265000250092265000140094765300240096165300260098565300200101165300210103165300200105265300200107265300140109265300150110665300140112165300190113570000190115470000170117370000260119070000190121670000220123570000230125777300600128021728202025-02-18       2024    bl uuuu     u00u1 u    #d  a0963-99697 ahttps://doi.org/10.1016/j.foodchem.2024.1414852DOI1 aBAQUETA, M. R.  aAuthentication of indigenous Brazilian specialty Canephora coffees using smartphone image analysis.h[electronic resource]  c2024  aThe prevention of coffee fraud through the use of digital and intelligence-based technologies is an analytical challenge because depending on the adulterant, visual inspection is unreliable in roasted and ground coffee due to the similarity in color and texture of the materials used. In this work, a 3D-printed apparatus for smartphone image acquisiton is proposed.  aGeographical distribution  aGrain quality  aMultivariate analysis  aProduct certification  aAnálise de Dados  aCafé Robusta  aCaracterísticas Agronômicas  aCertificação de Produto  aCoffea Canephora  aDistribuição Geográfica  aGrão  aMétodo Estatístico  aQualidade  aAmazônia Ocidental  aAnálise multivariada  aColor histogram  aConilon Capixaba  aEspírito Santo  aRegião Sudeste  aRondônia  aSmartphone  aSoutheast  aWestern Amazon1 aPOSTIGO, M. P.1 aALVES, E. A.1 aMORAES NETO, V. F. de1 aVALDERRAMA, P.1 aPALLONE, J. A. L.1 aDINIZ, P. H. G. D.  tFood Research Internationalgv. 196, 115133, Nov. 2024.