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Effect of Dairy, Season, and Sampling Position on Physical Properties of Trentingrana Cheese: Application of an LMM-ASCA Model. Foods 2022; 11:foods11010127. [PMID: 35010253 PMCID: PMC8750008 DOI: 10.3390/foods11010127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/22/2021] [Accepted: 01/01/2022] [Indexed: 02/04/2023] Open
Abstract
Trentingrana hard cheese is a geographic specification of the PDO Grana Padano. It is produced according to an internal regulation by many cooperative dairy factories in the Trentino region (northern Italy), using a semi-artisanal process (the only allowed ingredients are milk, salt, and rennet). Within the PSR project TRENTINGRANA, colorimetric and textural measurements have been collected from 317 cheese wheels, which were sampled bi-monthly from all the consortium dairies (n = 15) within the timeframe of two years, to estimate the effect on physical properties related to the season of the year and the dairy factory implant. To estimate the effect of the dairy and the time of the year, considering the internal variability of each cheese wheel, a linear mixed-effect model combined with a simultaneous component analysis (LMM-ASCA) is proposed. Results show that all the factors have a significant effect on the colorimetric and textural properties of the cheese. There are five clusters of dairies producing cheese with similar properties, three different couples of months of the year when the cheese produced is significantly different from all the others, and the effect of the geometry of the cheese wheel is reported as well.
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Orzel J, Krakowska B, Stanimirova I, Daszykowski M. Detecting chemical markers to uncover counterfeit rebated excise duty diesel oil. Talanta 2019; 204:229-237. [DOI: 10.1016/j.talanta.2019.05.113] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 05/28/2019] [Accepted: 05/31/2019] [Indexed: 11/26/2022]
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Marini F, de Beer D, Joubert E, Walczak B. Analysis of variance of designed chromatographic data sets: The analysis of variance-target projection approach. J Chromatogr A 2015; 1405:94-102. [PMID: 26087963 DOI: 10.1016/j.chroma.2015.05.060] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Revised: 05/22/2015] [Accepted: 05/24/2015] [Indexed: 11/19/2022]
Abstract
Direct application of popular approaches, e.g., Principal Component Analysis (PCA) or Partial Least Squares (PLS) to chromatographic data originating from a well-designed experimental study including more than one factor is not recommended. In the case of a well-designed experiment involving two or more factors (crossed or nested), data are usually decomposed into the contributions associated with the studied factors (and with their interactions), and the individual effect matrices are then analyzed using, e.g., PCA, as in the case of ASCA (analysis of variance combined with simultaneous component analysis). As an alternative to the ASCA method, we propose the application of PLS followed by target projection (TP), which allows a one-factor representation of the model for each column in the design dummy matrix. PLS application follows after proper deflation of the experimental matrix, i.e., to what are called the residuals under the reduced ANOVA model. The proposed approach (ANOVA-TP) is well suited for the study of designed chromatographic data of complex samples. It allows testing of statistical significance of the studied effects, 'biomarker' identification, and enables straightforward visualization and accurate estimation of between- and within-class variance. The proposed approach has been successfully applied to a case study aimed at evaluating the effect of pasteurization on the concentrations of various phenolic constituents of rooibos tea of different quality grades and its outcomes have been compared to those of ASCA.
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Affiliation(s)
- Federico Marini
- Department of Chemistry, Univeristy of Rome "La Sapienza", P.le Aldo Moro 5, I-00185 Rome, Italy
| | - Dalene de Beer
- Post-Harvest and Wine Technology Division, Agricultural Research Council (ARC), Infruitec-Nietvoorbij, Private Bag X5026, Stellenbosch 7599, South Africa
| | - Elizabeth Joubert
- Post-Harvest and Wine Technology Division, Agricultural Research Council (ARC), Infruitec-Nietvoorbij, Private Bag X5026, Stellenbosch 7599, South Africa; Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch, South Africa
| | - Beata Walczak
- Institute of Chemistry, The University of Silesia, Szkolna Street 9, 40-006 Katowice, Poland.
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Orzel J, Daszykowski M, Kazura M, de Beer D, Joubert E, Schulze AE, Beelders T, de Villiers A, Malherbe CJ, Walczak B. Modeling of the total antioxidant capacity of rooibos (Aspalathus linearis) tea infusions from chromatographic fingerprints and identification of potential antioxidant markers. J Chromatogr A 2014; 1366:101-9. [PMID: 25283576 DOI: 10.1016/j.chroma.2014.09.030] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2014] [Revised: 09/12/2014] [Accepted: 09/12/2014] [Indexed: 02/06/2023]
Abstract
Models to predict the total antioxidant capacity (TAC) of rooibos tea infusions from their chromatographic fingerprints and peak table data (content of individual phenolic compounds), obtained using HPLC with diode array detection, were developed in order to identify potential antioxidant markers. Peak table data included the content of 12 compounds, namely phenylpyruvic acid-2-O-glucoside, aspalathin, nothofagin, isoorientin, orientin, ferulic acid, quercetin-3-O-robinobioside, vitexin, hyperoside, rutin, isovitexin and isoquercitrin. The TAC values, measured using the oxygen radical absorbance capacity (ORAC) and DPPH radical scavenging assays, could be predicted from the peak table data or the chromatographic fingerprints (prediction errors 9-12%) using partial least squares (PLS) regression. Prediction models created from samples of only two production years could additionally be used to predict the TAC of samples from another production year (prediction errors<13%) indicating the robustness of the models in a quality control environment. Furthermore, the uninformative variable elimination (UVE)-PLS method was used to identify potential antioxidant markers for rooibos infusions. All individual phenolic compounds that were quantified were selected as informative variables, except vitexin, while UVE-PLS models developed from chromatographic fingerprints indicated additional antioxidant markers, namely (S)-eriodictyol-6-C-glucoside, (R)-eriodictyol-6-C-glucoside, aspalalinin and two unidentified compounds. The potential antioxidant markers should be validated prior to use in quality control of rooibos tea.
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Affiliation(s)
- Joanna Orzel
- Institute of Chemistry, The University of Silesia, 9 Szkolna Street, 40-006 Katowice, Poland
| | - Michal Daszykowski
- Institute of Chemistry, The University of Silesia, 9 Szkolna Street, 40-006 Katowice, Poland.
| | - Malgorzata Kazura
- Institute of Chemistry, The University of Silesia, 9 Szkolna Street, 40-006 Katowice, Poland
| | - Dalene de Beer
- Post-Harvest and Wine Technology Division, Agricultural Research Council (ARC), Infruitec-Nietvoorbij, Private Bag X5026, Stellenbosch 7599, South Africa
| | - Elizabeth Joubert
- Post-Harvest and Wine Technology Division, Agricultural Research Council (ARC), Infruitec-Nietvoorbij, Private Bag X5026, Stellenbosch 7599, South Africa; Department of Food Science, Stellenbosch University, Private Bag X1, Matieland (Stellenbosch) 7602, South Africa
| | - Alexandra E Schulze
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland (Stellenbosch) 7602, South Africa
| | - Theresa Beelders
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland (Stellenbosch) 7602, South Africa
| | - André de Villiers
- Department of Chemistry and Polymer Science, Stellenbosch University, Private Bag X1, Matieland (Stellenbosch) 7602, South Africa
| | - Christiaan J Malherbe
- Post-Harvest and Wine Technology Division, Agricultural Research Council (ARC), Infruitec-Nietvoorbij, Private Bag X5026, Stellenbosch 7599, South Africa
| | - Beata Walczak
- Institute of Chemistry, The University of Silesia, 9 Szkolna Street, 40-006 Katowice, Poland
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