Roll-in shortenings effects on Danish pastries sensory properties studied by principal component analysis

Baardseth, Pernille; N˦s, Tormod; Vogt, Gjermund

doi:10.1016/s0023-6438(95)80015-8

Cited by 15 publications

(15 citation statements)

References 3 publications

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“…Observations of ingredient composition (Table 1) indicate that croissant, brioche, rye bread, butter cake and short biscuit contained either a high level of fat and/or sugar. It is widely known that changing the food matrix composition and manufacturing conditions of complex food systems such as breads, cakes and biscuits will influence the perception of the sensory characteristics (Abdallah, Chabert, Le Roux, & Louis-Sylvestre, 1998;Baardseth, Naes, & Vogt, 1995;Brauss, Balders, Linforth, Avison, & Taylor, 1999;Cardello & Schutz, 2003;Hebeda & Zobel, 1996;Heinio, Urala, Vainionpaa, Poutanen, & Tuorila, 1997). Abdallah et al (1998) showed that the level of fat and sugar in biscuits and cakes enhanced flavour intensity, which in turn was very closely related to pleasantness perceptions.…”

Section: Consumer Freshness Evaluationsmentioning

confidence: 99%

Consumer freshness perceptions of breads, biscuits and cakes

Heenan

Hamid

Dufour

et al. 2009

Food Quality and Preference

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Section: Consumer Freshness Evaluationsmentioning

confidence: 99%

Consumer freshness perceptions of breads, biscuits and cakes

Heenan

Hamid

Dufour

et al. 2009

Food Quality and Preference

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“…Estudio Conclusión (Baardseth et al, 1995) Efecto sobre las propiedades sensoriales de productos de pastelería mediante la evaluación de 11 diferentes tipos de grasa de origen animal o vegetal a 3 diferentes concentraciones.…”

Section: Autorunclassified

Análisis estadistico multivariado como herramienta Estratégica para el Control de procesos de calidad en la industria agroalimentaria

Domínguez

2011

Publ. investig.

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El uso de técnicas de estadística multivariada para el control de procesos y calidad en la industria agroalimentaria ha venido en crecimiento significativo desde la mitad de los años setenta, como una consecuencia de la revolución informática que facilitó el análisis de matrices de datos de gran tamaño. A diferencia de los métodos univariados de exploración de datos, la estadística multivariada utiliza como gran pilar el análisis de información descrita por tres o más variables que pueden ser estudiadas simultá-neamente y comprendidas de una manera rápida, eficiente y sencilla. Debido al extraordinario avance en las máquinas informáticas, hoy es posible aplicar estas metodologías para resolver problemas extremadamente complejos. Este artículo presenta las técnicas de estadística multivariada más reconocidas, así como la compilación de algunos trabajos que sirven como demostración de su aplicabilidad en el campo de los alimentos. Palabras clave: clasificación, exploración, modelamiento, optimización, regresión AbstractThe use of multivariate statistical techniques for quality and process control in the food industry has been growing significantly since the mid-seventies, as a result of the informatics revolution which facilitated the analysis of large data sets. Unlike univariate methods of data exploration, multivariate statistics uses as a major pillar the analysis of information described by three or more variables that can be simultaneously studied and understood in a fast, efficient and easy way. Thanks to the extraordinary advance in computing machines, it is now possible to apply these methodologies to solve extremely complex problems. This article presents the most recognized multivariate statistical techniques, as well as the compilation of some papers that serve as a demonstration of its applicability in the field of foods.

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“…PC scores from this analysis were used as response in an ANOVA modelling the design factors and interactions. Baardseth et al 10 similarly combined PCA on multiple responses with classical univariate modelling of single responses in order to obtain complementary information from their experiment. Ellekjaer et al 11 used PCA to identify the responses that reflected most of the variation between samples, in order to run ordinary (split-plot) univariate ANOVAs for each of the selected responses.…”

Section: The Real World Is Multivariatementioning

confidence: 99%

Restricted randomization and multiple responses in industrial experiments

Bjerke¹,

Langsrud²,

Aastveit

2007

Quality & Reliability Eng

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Two issues regarding designed experiments are discussed; restrictions on randomization and multiple responses. The former is typically related to hard-to-vary factors and factors appearing in separate stages of a process experiment. Randomization restrictions should be taken into account in the construction of the design as well as in the statistical modelling. In the paper, a case study of sausage production is discussed, having a split-plot model with correlated multiple responses. Multiple responses are handled in two ways, by principal component analysis (PCA) followed by ANOVA of the principal components, and by a newly developed alternative, the '50-50 MANOVA'. Multiple tests of correlated response variables are also described. Practical aspects of the planning, performing, response measurements, and statistical analysis are emphasized throughout. Hence, the paper aims to extend the utility of statistical methods in industry by linking design of experiments to multivariate analysis of the responses.

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Roll-in shortenings effects on Danish pastries sensory properties studied by principal component analysis

Cited by 15 publications

References 3 publications

Consumer freshness perceptions of breads, biscuits and cakes

Consumer freshness perceptions of breads, biscuits and cakes

Análisis estadistico multivariado como herramienta Estratégica para el Control de procesos de calidad en la industria agroalimentaria

Restricted randomization and multiple responses in industrial experiments

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