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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Authentication of roasted and ground coffee samples containing multiple adulterants using NMR and a chemometric approach

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Autor(es):
Milani, Maria Izabel [1] ; Rossini, Eduardo Luiz [1] ; Catelani, Tiago Augusto [1] ; Pezza, Leonardo [1] ; Toci, Aline Theodoro [2] ; Pezza, Helena Redigolo [1]
Número total de Autores: 6
Afiliação do(s) autor(es):
[1] Univ Estadual Paulista, Inst Quim, UNESP, Rua Prof Francisco Degni 55, BR-14800900 Araraquara, SP - Brazil
[2] Univ Fed Interacao Latino Amer, UNILA, Ist Latino Amer Ciencias Vida & Nat, Av Tancredo News 6731, CP 2044, BR-85867970 Foz Do Iguacu, PR - Brazil
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: FOOD CONTROL; v. 112, JUN 2020.
Citações Web of Science: 2
Resumo

Brazil is still the world's largest producer and exporter of coffee. In order to maximize profits, some producers may add lower cost materials (such as corn, barley, or even coffee husks) to commercial coffee. In view of the growing market for coffee products and the importance of coffee for the Brazilian economy, it is necessary to have a rapid, simple, and reliable methodology to identify and quantify coffee adulterants. NMR has proved to be a versatile and robust tool for the identification of adulterants in foods and beverages. Here, we explore the versatility of H-1 NMR assisted with chemometric tools, avoiding laborious data analysis, for the quantification of coffee adulteration. Six different adulterants were considered: barley, corn, coffee husks, soybean, rice, and wheat. The NMR-based methodology described here provided satisfactory LOD values (0.31-0.86%) for adulterants in medium and dark roast coffees. The statistical techniques PCA and SIMCA were employed for pattern recognition and the identification of pure and adulterated samples. Use of the SIMCA model enabled 100% correct classification for both training and prediction sets, ensuring the accuracy, traceability, and reliability of the results. (AU)

Processo FAPESP: 16/14773-0 - Desenvolvimento de métodos para detecção de adulterações em cafés torradaos e óleo de café
Beneficiário:Maria Izabel Milani
Modalidade de apoio: Bolsas no Brasil - Doutorado