Background: Coffee samples adulterated with roasted corn and roasted soybean were analyzed using a voltammetric electronic tongue equipped with a polypyrrole sensor array.
Methods: Coffee samples were adulterated in concentrations of 2%, 5%, 10% and 20% of roasted corn and roasted soybean; 5 replicates of each were used. The discrimination capacity of a voltammetric electronic tongue elaborated with a polypyrrole sensor array, was evaluated by principal component analysis and cluster analysis, while the capacity to perform quantitative determinations was carried out by partial least squares.
Results: The results obtained by the application of principal component analysis showed an excellent ability to discriminate adulterated samples. Additionally, the classifications obtained by cluster analysis was concordant with those obtained by principal component analysis. On the other hand, the evaluation of the ability to quantitatively analyze the adulterated samples showed that the polypyrrole sensor array provides sufficient information to allow quantitative determinations by partial least squares regression.
Conclusions: It could be concluded that the voltammetric electronic tongue used in this work allows the suf- ficient analysis of coffee samples adulterated with roasted corn and roasted soybean.
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http://dx.doi.org/10.17306/J.AFS.0619 | DOI Listing |
Foods
November 2024
Department of Food and Nutrition, Changwon National University, Changwon 51140, Republic of Korea.
The optimum processing conditions for green laver chips were determined using response surface methodology (RSM) to improve taste and reduce off-flavors by applying reaction flavor and air-frying techniques. The optimum composition (/) for the chips included 20% green laver, 20% hairtail surimi, and 60% flour. Additional ingredients included distilled water (90 mL) with GDL (3 g), NaHCO₃ (2 g), salt (1 g), sugar (12 g), roasted soybean powder (1.
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November 2024
Universidade Federal do Oeste da Bahia, Programa de Pós-Graduação em Química Pura e Aplicada, 47810-047 Barreiras, Bahia, Brazil. Electronic address:
The 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. The digital images are used to authenticate the geographical origin of indigenous canephora coffees produced at Amazon region, Brazil, against canephora coffees from Espírito Santo, Brazil, and to capture the adulteration of indigenous samples.
View Article and Find Full Text PDFNutr J
October 2024
Research Center for Biochemistry and Nutrition in Metabolic Diseases, Basic Science Research Institute, Kashan University of Medical Sciences, Kashan, 87159-73474, Iran.
Background: Emotional eating (EE) is particularly prevalent in overweight or obese women, who may turn to food as a way to cope with stress, sadness, or anxiety. Limited research has been conducted on the association between EE and nutritional intake. Therefore, present study was designed to explore this association in adult women with overweight and obesity.
View Article and Find Full Text PDFFood Chem X
October 2024
State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 Changjiang West Road, Hefei 230036, China.
Lu'an Guapian (LAGP) tea is one of the most famous teas in China. However, research on its suitable processing varieties is still lacking. This study analyzed the quality of LAGP tea made from three different tea varieties, namely, '' (AH1), '' (QTZ), and '' (SCZ), using molecular sensory science and metabolomics techniques.
View Article and Find Full Text PDFFood Chem
July 2024
LAQV/REQUIMTE, Laboratory of Bromatology and Hydrology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal.
Roasted ground coffee has been intentionally adulterated for economic revenue. This work aims to use an untargeted strategy to process SPME-GC-MS data coupled with chemometrics to identify volatile compounds (VOCs) as possible markers to discriminate Arabica coffee and its main adulterants (corn, barley, soybean, rice, coffee husks, and Robusta coffee). Principal Component Analysis (PCA) showed the difference between roasted ground coffee and adulterants, while the Hierarchical Clustering of Principal Components (HCPC) and heat map showed a trend of adulterants separation.
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