Principal component analysis of physicochemical and sensory characteristics of beef rounds extended with gum arabic from Acacia senegal var. kerensis

Abstract

Principal component analysis (PCA) was carried out to study the relationship between 24 meat quality measurements taken from beef round samples that were injected with curing brines containing gum arabic (1%, 1.5%, 2%, 2.5%, and 3%) and soy protein concentrate (SPC) (3.5%) at two injection levels (30% and 35%). The measurements used to describe beef round quality were expressible moisture, moisture content, cook yield, possible injection, achieved gum arabic level in beef round, and protein content, as well as descriptive sensory attributes for flavor, texture, basic tastes, feeling factors, color, and overall acceptability. Several significant correlations were found between beef round quality parameters. The highest significant negative and positive correlations were recorded between color intensity and gray color and between color intensity and brown color, respectively. The first seven principal components (PCs) were extracted explaining over 95% of the total variance. The first PC was characterized by texture attributes (hardness and denseness), feeling factors (chemical taste and chemical burn), and two physicochemical properties (expressible moisture and achieved gum arabic level). Taste attribute (saltiness), physicochemical attributes (cook yield and possible injection), and overall acceptability were useful in defining the second PC, while the third PC was characterized by metallic taste, gray color, brown color, and physicochemical attributes (moisture and protein content). The correlation loading plot showed that the distribution of the samples on the axes of the first two PCs allowed for differentiation of samples injected to 30% injection level which were placed on the upper side of the biplot from those injected to 35% which were placed on the lower side. Similarly, beef samples extended with gum arabic and those containing SPC were also visible when scores for the first and third PCs were plotted. Thus, PCA was efficient in analyzing the quality characteristics of beef rounds extended with gum arabic.

Evaluation of gum arabic from Acacia senegal var kerensis and Acacia senegal var senegal as a stabilizer in low-fat yoghurt

“…The moisture loss of the yoghurt stabilized with A. senegal var kerensis gum was significantly different (P < 0.05) compared to the yoghurt stabilized with A. senegal var senegal gum at all levels of gum concentration. Gum arabic from A. senegal var kerensis has been shown to retain higher moisture content in food products (Mwove, A. Gogo, N. Chikamai, Omwamba & M. Mahungu, 2016, 2018. This can be explained by the high protein content of A. senegal var kerensis gum which is much higher than that of A. senegal var senegal gum.…”

“…The syneresis index for the gum-stabilized yoghurt decreased as the concentration level of the gum increased. This low syneresis in the A. senegal var kerensis gum-stabilized yoghurt can be attributed to the improved water holding capacity by the A. senegal var kerensis gum (Mwove et al, 2016(Mwove et al, , 2018. Enrichment of dry matter and / or of protein content are common means of avoiding whey separation in yoghurt (Tamime & Robinson, 1999).…”

Effect of Compositing Precooked Cowpea with Improved Malted Finger Millet on Anti-Nutrients Content and Sensory Attributes of Complementary Porridge

“…Principal component analysis was used to extract important information from the heterogeneous data, and reduce set of correlated variables to uncorrelated measures (principal components) without loss of original information [33]. The first four (PC's) accounted for approximately over 80% of the total variation observed (PC1 = 54%, PC2 = 12%, PC3 = 10%, PC4 = 6%).…”

“…Correlations loading plot for the complementary sensory attributes for the first three PCs represented in Figure 1. Measurement close to each other are positively related, while those that are negatively related are separated by 180˚, and those that are separated by 90˚ are independent and therefore they would be loaded on different PCs [33]. This therefore means that, interpretation and measurement of PCs was according to the correlations between each parameter and each PCs.…”

“…Figure 2 shows the scores biplot for PC1 versus PC2 for KNE 741, Snapping, Unmalted, malted, 0%, 21.26% and 32.75%. According to Mwove et al [33] C. O. Syeunda et al score plot represents loadings of sensory attributes in the multivariate space of two PC score vectors. From the figures, two samples were clearly distinguished based on the sensory attributes.…”