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Molecules ; 26(15)2021 Jul 28.
Artigo em Inglês | MEDLINE | ID: mdl-34361713


The textural properties of butter are influenced by its fat content and implicitly by the fatty acids composition. The impact of butter's chemical composition variation was studied in accordance with texture and color properties. From 37 fatty acids examined, only 18 were quantified in the analyzed butter fat samples, and approximately 69.120% were saturated, 25.482% were monounsaturated, and 5.301% were polyunsaturated. The butter samples' viscosity ranged between 0.24 and 2.12 N, while the adhesiveness ranged between 0.286 to 18.19 N·mm. The principal component analysis (PCA) separated the butter samples based on texture parameters, fatty acids concentration, and fat content, which were in contrast with water content. Of the measured color parameters, the yellowness b* color parameter is a relevant indicator that differentiated the analyzed sample into seven statistical groups; the ANOVA statistics highlighted this difference at a level of p < 0.001.

Manteiga/análise , Ácidos Graxos Insaturados/química , Ácidos Graxos/química , Água/análise , Animais , Cor , Ácidos Graxos/classificação , Ácidos Graxos/isolamento & purificação , Ácidos Graxos Insaturados/classificação , Ácidos Graxos Insaturados/isolamento & purificação , Análise de Alimentos/métodos , Humanos , Análise de Componente Principal , Paladar/fisiologia , Viscosidade
J Food Sci Technol ; 55(12): 4711-4718, 2018 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-30482967


The aim of this study was to evaluate the influence of honey botanical origins on rheological parameters. In order to achieve the correlation, fifty-one honey samples, of different botanical origins (acacia, polyfloral, sunflower, honeydew, and tilia), were investigated. The honey samples were analysed from physicochemical (moisture content, fructose, glucose and sucrose content) and rheological point of view (dynamic viscosity-loss modulus G″, elastic modulus G', complex viscosity η*, shear storage compliance-J' and shear loss compliance J″). The rheological properties were predicted using the Artificial Neural Networks based on moisture content, glucose, fructose and sucrose. The models which predict better the rheological parameters in function of fructose, glucose, sucrose and moisture content are: MLP-1 hidden layer is predicting the G″, η* and J″, respectively, MLP-2 hidden layers the J', while MLP-3 hidden layers the G', respectively. The physicochemical and rheological parameters were submitted to statistical analysis as follows: Principal component analysis (PCA), Linear discriminant analysis (LDA) and Artificial neural network (ANN) in order to evaluate the usefulness of the parameters studied for honey authentication. The LDA was found the suitable method for honey botanical authentication, reaching a correct cross validation of 94.12% of the samples.

J Sci Food Agric ; 98(11): 4304-4311, 2018 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-29427329


BACKGROUND: The aim of this study was to evaluate the usefulness of a voltammetric e-tongue (three electrodes: reference electrode (Ag/AgCl), counter electrode (glassy carbon electrode rod) and working electrode (Au, Ag, Pt and glass electrode)) for honey adulteration detection. For this purpose, 55 samples of authentic honey (acacia, honeydew, sunflower, Tilia and polyfloral) and 150 adulterated ones were analyzed. The adulteration was made using fructose, glucose, inverted sugar, hydrolyzed inulin syrup and malt wort at different percentages: 5%, 10%, 20%, 30%, 40% and 50%, respectively. The e-tongue has been compared with the physicochemical parameters (pH, free acidity, electrical conductivity (EC) and CIEL*a*b* parameters (L*, a* and b*)) in order to achieve a suitable method for the classification of authentic and adulterated honeys. RESULTS: The e-tongue and physicochemical parameters reached a 97.50% correct classification of the authentic and adulterated honeys. In the case of the adulterated honey samples, the e-tongue achieved 83.33% correct classifications whereas the physicochemical parameters only achieved 73.33%. CONCLUSION: The e-tongue is a fast, easy and accurate method for honey adulteration detection which can be used in situ by beekeepers and provide useful information on EC and free acidity. © 2018 Society of Chemical Industry.

Nariz Eletrônico , Contaminação de Alimentos/análise , Mel/análise , Ácidos/análise , Fenômenos Químicos , Eletrodos , Frutose/análise , Glucose/análise
J Food Sci Technol ; 54(13): 4240-4250, 2017 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-29184230


The purpose of this study was to investigate the physico-chemical properties (free acidity, pH, aw, ash content, moisture content, color (L*, a*, b*, hue-angle, chroma and yellow index), fructose, glucose and sucrose content) and textural parameters (viscosity, hardness, adhesion, springiness, cohesiveness, chewiness and gumminess) of 50 samples of honey of different botanical origin (acacia, polyfloral, honeydew, sunflower and tilia). In order to achieve the authentication of the honey samples analyzed, their data have been subjected to linear discriminant analysis (LDA) and principal component analysis (PCA).The PCA and LDA have proved the possibility of honey authentication using the physico-chemical and textural properties. LDA classified correctly 92.0% of the honeys based on their botanical origin, using the cross validation. In the LDA projection, the textural parameters (chewiness, hardness, cohesiveness, springiness) dominated the two functions.

J Food Sci Technol ; 53(1): 431-40, 2016 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-26787962


The aim of this study is to evaluate the chemical composition and temperatures (20, 30, 40, 50 and 60 °C) influence on the honey texture parameters (hardness, viscosity, adhesion, cohesiveness, springiness, gumminess and chewiness). The honeys analyzed respect the European regulation in terms of moisture content and inverted sugar concentration. The texture parameters are influenced negatively by the moisture content, and positively by the °Brix concentration. The texture parameters modelling have been made using the artificial neural network and the polynomial model. The polynomial model predicted better the texture parameters than the artificial neural network.