Factors affecting texture of ice cream

Trgo, C.; McKenna, Brian

doi:10.1533/9781855737082.2.373

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…It has been shown that rheological properties of ice cream clearly influence the mouth-feel (Trgo 2003). Among these attributes, viscosity has a major influence on sensory quality in general, and texture assessment in particular (Akoh 1998).…”

Section: Subjective Sensory Evaluationmentioning

confidence: 99%

“…It is of great important to ice cream producers to understand the procedures during structuring as well as to know the decisive influencing factors for stabilizing texture (Trgo 2003). Samples with BSG acquired the best body and texture scores, although the difference between CMC and BSG was not significant.…”

Section: Subjective Sensory Evaluationmentioning

confidence: 99%

“…In this study, all ice creams obtained body and texture scores better than average. It is of great important to ice cream producers to understand the procedures during structuring as well as to know the decisive influencing factors for stabilizing texture (Trgo 2003).…”

Section: Subjective Sensory Evaluationmentioning

confidence: 99%

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Predicting Total Acceptance of Ice Cream Using Artificial Neural Network

Bahramparvar

Salehi

Razavi

2013

Journal of Food Processing and Preservation

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Artificial neural network (ANN) models were used to predict the total acceptance of ice cream. The experimental sensory attributes (appearance, flavor, body and texture, coldness, firmness, viscosity, smoothness and liquefying rate) were used as inputs and independent total acceptance was output of ANN. Thirty, ten and sixty percent of the sensory attributes data were used to train, validate and test the ANN model, respectively. It was found that ANN with one hidden layer comprising 10 neurons gives the best fitting with the experimental data, which made it possible to predict total acceptance with acceptable mean absolute errors (0.27) and correlation coefficients (0.96). Sensitivity analysis results showed that flavor and texture were the most sensitive sensory attribute for prediction of total acceptance of ice cream. These results indicate that ANN model could potentially be used to estimate total sensory acceptance of ice cream. Practical Applications ANN techniques appear to be very applicable tools to overcoming some of the difficulties in sensory evaluation. The overall agreement between ANN predictions and experimental data was very good for sensory acceptance of ice cream. Therefore, this method can be applied to relevant sensory projects with satisfactory results.

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Section: Subjective Sensory Evaluationmentioning

confidence: 99%

Section: Subjective Sensory Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Total Acceptance of Ice Cream Using Artificial Neural Network

Bahramparvar

Salehi

Razavi

2013

Journal of Food Processing and Preservation

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show abstract

“…1 Polpa de açaí médio (Icefruit -Icefruit Comércio de alimentos, Tatuí, Brasil); 2 Molico (Nestlé, Araçatuba, Brasil); 3 Açúcar refinado (União, Limeira, Brasil); 4 Gordura de palma 370SE (Agropalma, Belém, Pará); 5 (Nutre, São Paulo, Brasil) ; 6 Beneo ® P95 (Orafit, Oreye, Bélgica) ; 7 Emustab-Emulsificante e estabilizante neutro(Duas Rodas, Jaraguá do Sul, Brasil); 8 Selecta tropical (Duas Rodas, Jaraguá do Sul, Brasil); 9 Carrageenan(Danisco, Redhill, Reino Unido); 10 Grindsted ® Guar(Danisco, Redhill, Reino Unido); * A = formulação aleatória. (TRGO, 2003(TRGO, -2004. Para a realização da análise, foram utilizados os seguintes parâmetros: velocidade de pré-teste, teste e pós-teste 2,0, 3,0 e 10 mm/s, respectivamente, para determinar a força (g/F) necessária para romper a amostra do produto.…”

Section: Formulaçõesunclassified