Identifying cheese whey an adulterant in milk: Limited contribution of a sensometric approach

Aquino, Leticia F.M.C.; Silva, A.C.O.; Freitas, Mônica Q.; Felicio, T.L.; Cruz, Adriano G.; Conté-Júnior, Carlos Adam

doi:10.1016/j.foodres.2014.03.001

Cited by 32 publications

(18 citation statements)

References 35 publications

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“…To identify natural variation among consumers in the product acceptance test, the variables were not subject to any pre-processing of data. The PLS model quality was evaluated by 2 parameters: coefficient of determination (R 2 ), related to the variation in the dependent variable explained by the regression model, and the cumulative index (Q 2 ), which measures the global contribution of the first components as to predictive quality of the model (Donadini et al, 2012;Aquino et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Preference mapping of dulce de leche commercialized in Brazilian markets

Gaze

Oliveira

Ferrão

et al. 2015

Journal of Dairy Science

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105

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Dulce de leche samples available in the Brazilian market were submitted to sensory profiling by quantitative descriptive analysis and acceptance test, as well sensory evaluation using the just-about-right scale and purchase intent. External preference mapping and the ideal sensory characteristics of dulce de leche were determined. The results were also evaluated by principal component analysis, hierarchical cluster analysis, partial least squares regression, artificial neural networks, and logistic regression. Overall, significant product acceptance was related to intermediate scores of the sensory attributes in the descriptive test, and this trend was observed even after consumer segmentation. The results obtained by sensometric techniques showed that optimizing an ideal dulce de leche from the sensory standpoint is a multidimensional process, with necessary adjustments on the appearance, aroma, taste, and texture attributes of the product for better consumer acceptance and purchase. The optimum dulce de leche was characterized by high scores for the attributes sweet taste, caramel taste, brightness, color, and caramel aroma in accordance with the preference mapping findings. In industrial terms, this means changing the parameters used in the thermal treatment and quantitative changes in the ingredients used in formulations.

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Section: Discussionmentioning

confidence: 99%

Preference mapping of dulce de leche commercialized in Brazilian markets

Gaze

Oliveira

Ferrão

et al. 2015

Journal of Dairy Science

Self Cite

105

View full text Add to dashboard Cite

show abstract

“…The product factor indicated that the panel was highly discriminant ( p < .005) in the eight attributes, as evaluated with an effectiveness in discrimination of 100%. This result indicated that all attributes could remain in the analyses and had potential relevance for the discrimination of adulterated cheese with varying concentrations of vegetal fat (Aquino et al, ). The p values for panelists indicate consensus ( p > .05) in how panelists evaluated the attributes, except for WHC, POR, RAS, and RAA.…”

Section: Resultsmentioning

confidence: 99%

“…For panel performance evaluation, the data were collected in a matrix of dimensions ( J * M*I) K , where J = 5 cheeses, M = 3 repetitions , I = 8 panelists, and K = 8 attributes. Panel performance was assessed using the following mixed model of two‐way ANOVA with interactions:

Y_{iks =} normalμ + α_{i} + β_{k} + {αβ}_{ik} + e_{iks}

where Y iks represented the result from panelist i for the product k ; μ is the overall mean; α i is the panelist effect (random); β k is product effect (fixed); αβ ik product per panelist interaction; and e ijk is the error term of the model with e iks ≈ N (͂0, σ 2 ) (Aquino et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…The sensory profile was represented by PCA generated with the mean values of attributes; additionally, the HCA Ward method was also applied to the first two dimensions obtained in PCA (Aquino et al, ). The Discriminant Analysis (DA) method stepwise procedure was used to determine if the sensory panel could correctly differentiate and classify cheeses (Karoui et al, ; Osorio, Koidis, & Photis, ).…”

Section: Methodsmentioning

confidence: 99%

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Use of quantitative descriptive analysis (QDA) coupled with multivariate statistical methods to detection and discrimination of adulterated fresh cheeses

Juárez-Barrientos

Tejeda-Paz

Ramírez‐Rivera

et al. 2018

Journal of Sensory Studies

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The most recurrent adulteration of cheeses consists of adding vegetable fat of lower cost than milk fat. Several analytical techniques are used to detect adulteration of cheeses but do not offer sensory perception information. The objective of this study was to detect and discriminate fresh cheeses adulterated with known concentrations of vegetable fat using sensory analysis coupled with multivariate techniques. The panel generated eight attributes through Quantitative Descriptive Analysis. The results showed that the power of discrimination was high (p < .05) in each panelist and panel level in all attributes evaluated. Principal Components Analysis explained the use of average value of the attributes, about 85% of the variability of the cheeses in two main components. Using dendrogram and confidence ellipses, the adulterated cheeses with a concentration of 15 g/L or higher of vegetable fat were identified. The discrimination of adulterated cheeses by this method was 92.5%. Practical applications The findings demonstrated the high sensitivity of the sensory panel, which aligned well with analytical statistical techniques for the differentiation and detection of cheeses, which may be applied well to other adulterated food products. The proposed methodology could be implemented in companies, research centers, and associations of producers with economic restrictions that do not allow for sophisticated analytical equipment for the determination of adulterated food products.

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“…However, the dairy market faces problems related to quality and safety of raw milk (RM) because of adulteration by adding substances with lower economic and nutritional value often occurs. Recently published studies indicate that milk is one of the most common foods involved in fraud occurrences (Aquino et al 2014Carvalho et al 2007). Minas Frescal cheese waste whey (MFCW) is a waste from Minas Frescal cheese production whose disposal is an issue of increasing importance (Carvalho et al 2013).…”

Section: Introductionmentioning

confidence: 99%

FTMIR-PLS as a promising method for rapid detection of adulteration by waste whey in raw milk

Vieira

Souza

Franca

et al. 2015

Dairy Sci. & Technol.

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In this paper, we propose a feasible, sample preparation free and fast validated methodology for the detection and quantitative analysis of Minas Frescal cheese waste whey as an adulterant in raw milk using mid-infrared spectroscopy with Fourier transform (FTMIR) along with the chemometric technique of partial least squares (PLS). The PLS model was built in accordance with Brazilian and international guidelines and was analytically validated through the estimate of figures of merit parameters, in accordance with ASTM E1655-05 standard. This model showing an effective and feasible method for quality control of raw milk can be adopted for the quality control by regulatory agencies, as shown by the satisfactory results obtained for all estimated figures of merit with no systematic errors and low errors, with R=0.99.

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Identifying cheese whey an adulterant in milk: Limited contribution of a sensometric approach

Cited by 32 publications

References 35 publications

Preference mapping of dulce de leche commercialized in Brazilian markets

Preference mapping of dulce de leche commercialized in Brazilian markets

Use of quantitative descriptive analysis (QDA) coupled with multivariate statistical methods to detection and discrimination of adulterated fresh cheeses

FTMIR-PLS as a promising method for rapid detection of adulteration by waste whey in raw milk

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