Centre of Biological Engineering - University of Minho

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Effect of malaxation temperature on olive oil chemical and sensory profiles and their evaluation using an electronic tongue

Publication . Marx, Ítala; Rodrigues, Nuno; Veloso, Ana C.A.; Casal, Susana; Pereira, J.A.; Peres, António M.

Olive oil is highly appreciated due to its nutritional and organoleptic characteristics. Olive oils rich in bioactive compounds can be obtained by optimizing the time and temperature of the malaxation process. In this sense, this study aimed investigating the effect of the malaxation temperature (22 to 34°C) on the olive oil's physicochemical and sensory quality and, in more detail, on the phenolic profile. So, virgin olive oils were produced (November 2018), using olives from cv. Cobrancosa. Furthermore, the possibility of using an electronic tongue, i.e., a multisensor potentiometric device, comprising non-specific lipid polymeric with cross-sensitivity sensor membranes, to monitor the malaxation temperature influence on the olive oil's quality and phenolic composition, was evaluated. For that, multivariate statistical tools were developed for discriminating the olive oils according to the malaxation conditions as well as to predict some key quality parameters, including the extinction coefficients (K232, K268 and |ΔK|), free acidity, oxidative stability, peroxide value, bitterness index, total phenols, phenolic composition and gustatory-retronasal positive attributes. The study aims to determine the best malaxation temperature as well as to assess the versatility of the electronic tongue as a single-run, fast and cost-effective analytical device for olive oils quality evaluation.

2019Conference object

Open access

Amino acids profile in Serra da Estrela cheese: a compreensive study

Publication . Lima, M.J. Reis; Fontes, Luísa; Santos, Andreia O.; Falcão, Soraia; Veloso, Ana C.A.; Lemos, Edite Teixeira de; Vilas-Boas, Miguel; Peres, António M.

Milk and dairy products are of major importance in the human diet, since they are an excellent source of well-ballanced nutrients which are consumed in large amounts and are easy to manufacture.

2019Conference object

Open access

Optimization and validation of two methods to determine the levels of AFM1 in milk and cheese samples using immunoaffinity columns for extraction and HPLC-FLD for quantification

Publication . Vaz, Andreia; Gomes, Francileni Pompeu; Alves, A.; Rodrigues, Paula; Venâncio, Armando

Consumption of dairy products has expanded rapidly over the past decade and constitutes an important source of dietary protein. 1 Aflatoxin M1 (AFM1) is a potent carcinogen metabolite that can be present in milk from dairy cows that consume feed contaminated with Aflatoxin B1. Even though it is less toxic than its parent compound, AFM1 is hepatotoxic and carcinogenic, and is stable during milk pasteurization, storage and preparation of various dairy products. 2,3 Due to the toxicity of this molecule, its detection and quantification is extremely important. The objective of this work was to optimize and validate two methods, according to Commission Regulation (EC) nº 401/2006 of 23 February, to determine the levels of AFM1 in milk and in cheese, using immunoaffinity columns (IAC) for extraction and HPLC with fluorescence detection for quantification.4 The method for milk samples was adapted from VICAM – the supplier of the IAC, and for cheese samples was from r-biopharm and VICAM.5,6 For both methodologies, three levels of spiking in triplicate on two different days were performed. The calibration curve was linear from 0.047 to 4.7 μg L⁻¹ and the detection and quantification limits for milk and cheese were 0.001 μg L⁻1 and 0.003 μg L⁻¹, and 0.006 and 0.02 μg kg⁻¹, respectively. For milk samples, average recoveries determined at spiking levels of 0.020, 0.050 and 0.10 μg L⁻¹ were in the range of 62 % – 87 %, with intra-day precision (RSDr) in the range of 3.4 % – 9.5 %, and inter-day precision (RSDr) in the range of 5.4 % – 6.2 %. For cheese samples, average recoveries determined at spiking levels of 0.050, 0.10 and 0.25 μg L⁻¹ were in the range of 47 % – 74 %, with intra-day precision (RSDr) in the range of 3.8 % – 7.0 %, and inter-day precision (RSDr) in the range of 3.8 % – 5.8 %. Results of the validation process indicate that, except for the recovery in cheese samples, both methods are agree with the provisions of Commission Regulation (EC) nº 401/2006. Despite the recovery for cheese, both methods are precise for the quantification of AFM1 in milk and cheese.

2019Conference object

Open access

Assessing Serra da Estrela PDO cheeses’ origin-production date using fatty acids profiles

Publication . Lima, M.J. Reis; Bahri, Hamdi; Morais, Jorge Sá; Veloso, Ana C.A.; Fontes, Luísa; Lemos, Edite Teixeira de; Peres, António M.

Serra da Estrela is a Portuguese traditional cheese produced with raw ewe’s milk from “Churra Mondegueira” and “Bordaleira” autochthonous breeds and the wild thistle flower (Cynara cardunculus L.), which benefits from the status of Protected Designation of Origin. Cheese chemical composition, namely the fatty acids profile, depends on milk composition and on manufacturing practices. Thus, the identification of possible chemical biomarkers capable of classifying Serra da Estrela cheeses according to the dairy manufacturing plant, geographical origin or production date would be of utmost relevance for producers and consumers. A typical fatty acids profile, including 23 saturated and unsaturated fatty acids, was identified for the studied cheeses, being butyric, caproic, caprilic, capric, lauric, miristic, palmitic, stearic, oleic, linoleic and its trans-isomer and α-linolenic acids the most abundant ones (relative mean abundances ranging from 1.4% ± 0.5% to 23.9% ± 1.9%). Linear discriminant models were established based on the most discriminative fatty acids (namely, caproic, caprilic, undecanoic, lauric, pentadecanoic, palmitic, palmitoleic, heptadecanoic, oleic, linoleic trans-isomer, heneicosanoic and arachidonic acids) that included less abundant fatty acids, which were selected using the simulated annealing algorithm. The established models enabled assessing cheeses’ origin (models based on 10–12 fatty acids) and/or production date (model based on 20 fatty acids) with predictive sensitivities of 71–88%. Therefore, fatty acids profiles coupled with chemometric techniques, could be foreseen as a fingerprint of cheese’s genuineness, enhancing the consumers’ confidence when purchasing this high-value cheese.

2019Journal article

Open access

Identifying the geographical origin of Serra da Estrela PDO cheeses using fatty acids profiles

Publication . Fontes, Luísa; Lima, M.J. Reis; Bahri, Hamdi; Morais, Jorge Sá; Veloso, Ana C.A.; Lemos, Edite Teixeira de; Peres, António M.

Serra da Estrela is a traditional Portuguese cheese with a Protected Designation of Origin (PDO) certification. This cheese is produced from raw ewe’s milk from “Churra Mondegueira” and “Bordaleira” Portuguese autochthonous breeds and coagulated using wild thistle flower (Cynara cardunculus L.), and its production is geographically limited. Serra da Estrela is the most known and popular Portuguese cheese and is appreciated worldwide, being preferentially consumed as a soft cheese, with an average maturation of 30-45 days, although some consumers prefer to consume it as a hard cheese after at least 6 months of storage [1]. Due to its social and agroeconomic relevance, Serra da Estrela cheese is prone to geographical origin adulterations. The present work aims to verify if the fatty acids (FA) profile could be used as a geographical origin biomarker. The results showed that, although a similar FA profile (23 individual fatty acids identified, being the most abundant ones: C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, C16:0, C18:0, C18:1n9c, C18:2n6t, C18:2n6c and C18:3n3) could be established for all cheeses, regardless the producer, geographical origin and production date, the overall profile could be used for discriminating the cheeses according to their geographical origin (5 municipalities within the PDO region). A linear discriminant analysis (LDA) with the simulated annealing (SA) algorithm enabled establishing a classification model that was able to correctly classify 96% of the original grouped samples (Fig.1) and had a predictive sensitivity of 88% (leave-one-out cross-validation). So, FA profile could be used as a geographical origin authentication tool, providing the consumer a guarantee regarding this high-value and appreciated food.

2019Conference object

Open access