The role of the canonical biplot method in the study of volatile compounds in cheeses of variable composition

Abstract

SUMMARY:The canonical biplot method (CB) is used to determine the discriminatory power of volatile chemical compounds in cheese. These volatile compounds were used as variables in order to differentiate among 6 groups or populations of cheeses (combinations of two seasons (winter and summer) with 3 types of cheese (cow, sheep and goat's milk). We analyzed a total of 17 volatile compounds by means of gas chromatography coupled with mass detection. The compounds included aldehydes and methyl-aldehydes, alcohols (primary, secondary and branched chain), ketones, methyl-ketones and esters in winter (WC) and summer (SC) cow's cheeses, winter (WSh) and summer (SSh) sheep's cheeses and in winter (WG) and summer (SG) goat's cheeses. The CB method allows differences to be found as a function of the elaboration of the cheeses, the seasonality of the milk, and the separation of the six groups of cheeses, characterizing the specific volatile chemical compounds responsible for such differences.KEYWORDS: Canonical biplot; Cheeses; Seasonality; Type of milk (cow, sheep, goat); Volatiles RESUMEN: Papel del método biplot canónico en el estudio de compuestos volátiles en quesos de composición variable. El método biplot canónico (CB) se utiliza para determinar el poder discriminatorio de compuestos químicos volátiles en queso. Los compuestos volátiles se utilizan como variables con el fin de diferenciar entre los 6 grupos o poblaciones de quesos (combinaciones de dos temporadas (invierno y verano) con 3 tipos de queso (vaca, oveja y cabra). Se analizan un total de 17 compuestos volátiles por medio de cromatografía de gases acoplada con detección de masas. Los compuestos incluyen aldehídos y metil-aldehídos, alcoholes (primarios de cadena, secundaria y ramificada), cetonas, metil-cetonas y ésteres. Los seis grupos de quesos son, quesos de vaca de invierno (WC) y verano (SC); quesos de oveja de invierno (WSh) y verano (SSh) y quesos de cabra de invierno (WG) y verano (SG). El método CB permite la separación de los seis grupos de quesos y encontrar las diferencias en función del tipo y estacionalidad de la leche, caracterizando los compuestos químicos volátiles específicos responsables de tales diferencias.

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“…Figure 2 shows the canonical biplot of the variables evaluated in E. citriodora Hook plants, considering the groups of treatments. The canonical biplot is projected by the discrimination of the groups of treatments, in order to study the main variables responsible for their differentiation (González-Martín et al, 2016). The CDA approach is quite straightforward, based on the simple visual analysis of the plane with the projection of the two canonical discriminant functions (Sorbolini et al, 2016).…”

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“…Affective quantitative test: The scores given by the judges to the different attributes evaluated in the sensory panel were subjected to analysis of variance (ANOVA) in a randomized-block design, and the Scott-Knott test was used for the comparison of means, adopting the 5% error-probability level (Morris,1999). A canonical correlation analysis was employed to check the inter-relationship between sensory traits and types of cheese, (Dijksterhuis, 2008;Hakan & Zubeyde, 2012;Adhikari et al, 2003;Zhang et al, 2011;González-Martín et al, 2016 andKing et al, 2013). At the end of the test, the tasters were asked to express how much they agree, on a three-point scale (5 = totally agree, 2 = neither agree nor disagree, and 1 = totally disagree) with statements S1, S2, S3, S4, and S5, present in the questionnaire.…”

“…At the end of the test, the tasters were asked to express how much they agree, on a three-point scale (5 = totally agree, 2 = neither agree nor disagree, and 1 = totally disagree) with statements S1, S2, S3, S4, and S5, present in the questionnaire. Principal component analysis (PCA) was performed to check the relationship between the tasters and the answers (Dijksterhuis, 2008;Hakan & Zubeyde, 2012;Adhikari et al, 2003;Zhang et al, 2011;González-Martín et al, 2016;King et al, 2013). The number of component was determined by the method of the "elbow" (fold) on the chart screen plot (Dijksterhuis, 2008).…”

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“…Affective quantitative test: The scores given by the judges to the different attributes evaluated in the sensory panel were subjected to analysis of variance (ANOVA) in a randomized-block design, and the Scott-Knott test was used for the comparison of means, adopting the 5% error-probability level (Morris,1999). A canonical correlation analysis was employed to check the inter-relationship between sensory traits and types of cheese, (Dijksterhuis, 2008;Hakan & Zubeyde, 2012;Adhikari et al, 2003;Zhang et al, 2011;González-Martín et al, 2016 andKing et al, 2013). At the end of the test, the tasters were asked to express how much they agree, on a three-point scale (5 = totally agree, 2 = neither agree nor disagree, and 1 = totally disagree)…”

“…with statements S1, S2, S3, S4, and S5, present in the questionnaire. Principal component analysis (PCA) was performed to check the relationship between the tasters and the answers (Dijksterhuis, 2008;Hakan & Zubeyde, 2012;Adhikari et al, 2003;Zhang et al, 2011;González-Martín et al, 2016;King et al, 2013). The number of component was determined by the method of the "elbow" (fold) on the chart screen plot (Dijksterhuis, 2008).…”