Effect of two rearing systems on quality of Cinta Senese sausages

Submitted 2020-07-02 | Accepted 2020-09-04 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.124-131Meat and fat of 24 Cinta Senese pigs were used to produce frankfurter-type sausages. The animals were raised in two rearing systems: i) fenced area with concentrate as exclusive feed (C, n=12) and ii) wood/pasture fenced area and grazing on natural available resources (acorn and herbaceous pasture) (P, n=12). Physicochemical characteristics, fatty acid composition and sensory attributes of the frankfurter-type sausages were assessed. Both sausages from C and P groups showed high fat content (> 23%) likely due to the high level of intramuscular fat of Cinta Senese meat. Frankfurter-type sausages obtained from P group had higher percentage of monounsaturated fatty acids and lower percentage of saturated fatty acids than the C group, probably due to the availability of grazing resources during the fattening period. However, in both types of sausages, the polyunsaturated to saturated fatty acids ratio was higher than the recommended lower limit of 0.40. Regarding the physical traits, differences between groups were found for the colour traits: P frankfurter-type sausages had lower lightness and higher redness and yellowness than C frankfurter-type sausages, likely due to the physical exercise associated to grazing activity of P animals. Texture parameters did not differ between groups for hardness and cohesiveness, whereas chewiness and springiness were higher in C than P samples. Feeding systems changed the perception of some sensorial properties, in particular taste and odour. Overall, Cinta Senese frankfurter-type sausages could represent an innovative product for local farms, allowing, in addition, the use of second-choice meat portions, once acquitted some improvements in the recipes.Keywords: frankfurter-type sausage, extensive farming, pasture, meat quality, pigReferencesAlirezalu, K. et al. (2019). Combined effect of natural antioxidants and antimicrobial compounds during refrigerated storage of nitrite-free frankfurter-type sausage. Food Research International, 120, 839–850. https://doi.org/10.1016/j.foodres.2018.11.048Andrés, A. I. et al. (2001). Oxid stability and fatty acid composition of pig muscles as affected by rearing system, crossbreeding and metabolic type of muscle fibre. Meat Science, 59, 39–47. https://doi.org/10.1016/s0309-1740(01)00050-xAOAC. (2019). Official methods of analysis. 21th ed., Association of Official Analytical Chemists, Washington, DC, USA.Ayo, J. et al. (2007). Effect of total replacement of pork backfat with walnut on the nutritional profile of frankfurters. Meat Science, 77(2), 173–181. https://doi.org/10.1016/j.meatsci.2007.02.026Cavestany, M. et al. (1994). Incorporation of sardine surimi in bologna sausage containing different fat levels. Meat Science, 38, 27–37. https://doi.org/10.1016/0309-1740(94)90093-0Daza, A. et al. (2009). Physical activity-induced alterations on tissue lipid composition and lipid metabolism in fattening pigs. Meat Science, 81, 641–646. https://doi.org/10.1016/j.meatsci.2008.11.001Dominguez, R. et al. (2017). Effect of the partial replacement of pork backfat by microencapsulated fish oil or mixed fish and olive oil on the quality of frankfurter type sausage. Journal of Food Science and Technology, 54, 26–37. https://doi.org/10.1007/s13197-016-2405-7Estévez, M. et al. (2006). Extensively reared Iberian pigs versus intensively reared white pigs for the manufacture of frankfurters. Meat Science, 72, 356–364. https://doi.org/10.1016/j.meatsci.2005.08.003Folch, J. et al. (1957). A simple method for the isolation and purification of total lipides from animal tissues. The Journal of Biological Chemistry, 226, 497–509.Font-i-Furnols, M. and Guerrero, L. (2014). Consumer preference, behaviour and perception about meat and meat products: An overview. Meat Science, 98, 361–371. https://doi.org/10.1016/j.meatsci.2014.06.025Franci, O. et al. (2007). Performance of Cinta Senese pigs and their crosses with Large White 2. Physical, chemical and technological traits of Tuscan dry-cured ham. Meat Science, 76(4), 597–603. https://doi.org/10.1016/j.meatsci.2007.01.020Monteiro, G. M. et al. (2017). Partial substitution of pork fat with canola oil in Toscana sausage. Innovative Food Science and Emerging Technologies, 44, 2–8. https://doi.org/10.1016/j.ifset.2017.07.013Nilzén, V. et al. (2001). Free range rearing of pigs with access to pasture grazing – effect on fatty acid composition and lipid oxidation products. Meat Science, 58, 267–275. https://doi.org/10.1016/s0309-1740(00)00164-9Okuyama, H. and Ikemoto, A. (1999). Needs to modify the fatty acids composition of meat for human health. In: the Proccedings of the 45th ICoMST, Yokohama, Japan. Vol. II, 638–640.Parrini, S. et al. (2020). Effect of replacement of synthetic vs. Natural curing agents on quality characteristics of Cinta Senese frankfurter-type sausage. Animals, 10, 14. https://doi.org/10.3390/ani10010014Pugliese, C. et al. (2005). Performance of Cinta Senese pigs reared outdoors and indoors. 1. Meat and subcutaneous fat characteristics. Meat Science, 69, 459–464. https://doi.org/10.1016/j.meatsci.2004.09.001Pugliese, C. et al. (2009). Effect of pasture in oak and chestnut groves on chemical and sensorial traits of cured lard of Cinta Senese pigs. 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Application of Virtual Fencing for the management of Limousin cows at pasture

A potential use of pasture-based systems requires an efficient grazing management strategy. Thanks to the Virtual Fencing (VF) physical fences are replaced by virtual ones and, when the animals approach the boundaries, they receive a paired stimulus: an audio cue followed by a low electrical pulse if animals cross over the fences. This study aims to i) to evaluate the animal’s ability to learn, and then respond positively, to VF ii) VFs’ efficiency to manage the herd within grazing areas virtually delimitated; iii) to assess the chronic stress related to the VF, evaluating the hair cortisol concentration (HCC), during the experiment. Twenty Limousine cows were fitted with a commercial VF-GPS collars (Nofence AS, Batnfjordsør, Norway). The experiment was divided into four trials: Trial zero (T0) with inactive collars to let the animals get acquainted with them; Trial one (T1) where three of the four virtual boundaries coincided with the physical ones, while the virtual one was set across the pasture to restrict the grazing area; Trial two (T2) in which the grazing area was further extended moving forwards the virtual board; Trial three (T3) in which the virtual line was set longways to the pasture. Results show a significant decrease of stimuli delivered (i.e., sounds and electrical pulses) (p < 0.001), among trials. Moreover, a reduction (p < 0.0250) in the ratio between sounds and electrical pulses was observed between T1 and T3, with T2 being like both. Regarding the cows’ learning capacity, the events in which the sounds were followed by electrical pulses were significantly less in T3 (p < 0.001). Furthermore, in T3 the duration of the audio tones was lower than T1 and T2 (p < 0.0005). Animals were increasingly kept inside the inclusion zones during the trials, with the lowest number of escape events from the inclusion zone registered in T3 (p < 0.001). No differences were observed in the HCC before and after the VF treatment. The progressive reduction of the studied parameters between following sessions, indicates an increase in associative learning through time. VF virtual fencing has proven to be an effective tool in managing Limousin cows at pasture. However, future research is needed to evaluate the animals’ performances in terms of grazing activities and on the assessment of chronic stress conditions as well

Point-of-Care and Label-Free Detection of Porcine Reproductive and Respiratory Syndrome and Swine Influenza Viruses Using a Microfluidic Device with Photonic Integrated Circuits

[EN] Swine viral diseases challenge the sector's sustainability by affecting productivity and the health and welfare of the animals. The lack of antiviral drugs and/or effective vaccines renders early and reliable diagnosis the basis of viral disease management, underlining the importance of point-of-care (POC) diagnostics. A novel POC diagnostic device utilizing photonic integrated circuits (PICs), microfluidics, and information and communication technologies for the detection of porcine reproductive and respiratory syndrome virus (PRRSV) and swine influenza A (SIV) was validated using spiked and clinical oral fluid samples. Metrics including sensitivity, specificity, accuracy, precision, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated to assess the performance of the device. For PRRSV, the device achieved a sensitivity of 83.5%, specificity of 77.8%, and DOR values of 17.66, whereas the values for SIV were 81.8%, 82.2%, and 20.81, respectively. The POC device and PICs can be used for the detection of PRRSV and SIV in the field, paving the way for the introduction of novel technologies in the field of animal POC diagnostics to further optimize livestock biosecurity.This research was funded by the EU's H2020 SWINOSTICS project under the grant agreement ID 771649.Manessis, G.; Frant, M.; Wozniakowski, G.; Nannucci, L.; Bennedetti, M.; Denes, L.; Balka, G.... (2022). Point-of-Care and Label-Free Detection of Porcine Reproductive and Respiratory Syndrome and Swine Influenza Viruses Using a Microfluidic Device with Photonic Integrated Circuits. Viruses. 14(5):1-21. https://doi.org/10.3390/v1405098812114

Cardoon Meal as Alternative Protein Source to Soybean Meal for Limousine Bulls Fattening Period: Effects on Growth Performances and Meat Quality Traits

Soybean meal is the most important protein source in beef cattle feeding. The research of alternative protein sources to replace soy use, avoiding negative effects on in vivo performance and on the product’s quality, is an important issue. In this context, cardoon represents a non-OGM resilient crop that can be cultivated in marginal lands for extracting its seed oil (utilized for biodiesel and biodegradable bioplastic production) and whose and the residual meal from its seed oil (utilized for biodiesel and biodegradable bioplastic production) could be a suitable by-product for animal feeding, due to its fairly high protein content. The aim of this study was to evaluate the feasibility of using cardoon meal as an innovative protein source during the Limousine bulls’ fattening period. Thirty-two bulls were divided into two groups and fed with a diet containing soybean meal (SG) or partially replacing soybean meal with cardoon meal as a protein source (CG), respectively. The feeding trial lasted about 11 months. Growth performances and meat physical–chemical traits were evaluated. No statistical differences in feed efficiency, average daily gain, or in the main meat quality indicators, as well as in fatty acid profiles were found among the groups. Therefore, cardoon meal could be considered as an alternative to soybean meal in fattening Limousine bulls in order to enhance the sustainability of the farming system

NIRS to assess chemical composition of sheep and goat cheese

Submitted 2020-07-01 | Accepted 2020-09-02 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.97-104The present study aimed to evaluate the performances of Fourier transform near-infrared spectroscopy technique to determine the chemical and the fatty acid composition of different types of cheeses. A total of 95 cheeses from sheep and goat raw milk were produced in small local dairies of Siena province (Tuscany). For each cheese, spectrum was collected in intact slices of the sample and fatty acid profile was determined in ground samples. Outliers were identified and different mathematical pre-processing treatments (SNV, MSC, baseline correction and de-trending) were applied when necessary. Considering traditional chemical analysis and raw cheese spectral data, calibration and cross-validation models were carried out using partial least squares regression (PLS). The best results were evaluated in terms of coefficient of determination in calibration and cross-validation (R2cv), and root mean square error in calibration and cross-validation, and residual prediction deviation (RPD). Moisture, protein and ash showed the best R2cv (0.89, 0.74 and 0.72, respectively) and RPD values (3.0, 2.6 and 2.1, respectively). Saturated, monounsaturated and polyunsaturated fatty acids showed R2cv which ranged from 0.75 to 0.67, and RPD 0.70). Obtained results are promising and additional samples could strongly increase the predictive ability for small dairy farms.Keywords: FT-NIRS, cheese, fatty acid, qualityReferencesAOAC, 2019. Official Methods of Analysis. 21st ed., Association of Official Analytical Chemists, Washington, DC, USA.Birth, G. and Hecht, H. (1987). The Physics of Near-Infrared Reflectance. Near Infrared Technology in the Agricultural and Food Industries. American Association of Cereal Chemists, Inc. St. Paul, Minnesota, USA.Cuibus, L. et al. (2014). Preliminary discrimination of cheese adulteration by FT-IR spectroscopy. Bulletin UASVM Food Science and Technology, 71, 142-146. https://doi.org/10.15835/buasvmcn-fst:10795De Marchi, M. et al. (2018). Invited review: Use of infrared technologies for the assessment of dairy products - Applications and perspectives. Journal of Dairy Science, 101, 10589–10604. https://doi.org/10.3168/jds.2018-15202Esbensen, K. H. et al. (2014). The RPD myth... NIR news, 25(5), 24-28. https://doi.org/10.1255/nirn.1462Faber, N. M. and Rajko, R. (2007). How to avoid over-fitting in multivariate calibration—The conventional validation approach and an alternative.Analytica Chimica Acta, 595(1-2), 98-106. https://doi.org/10.1016/j.aca.2007.05.030Folch, J., Lees, M. and Sloane Stanley, G. H. (1957). A simple method for the isolation and purification of total lipides from animal tissues. Journal of Biological Chemistry, 226, 497-509.Fox, P. F. et al. (1993). Cheese: Chemistry, Physics and Microbiology. London: Chapman & Hall.González-Martín, M. I. et al. (2017). Discrimination between cheeses made from cow’s, ewe’s and goat’s milk from unsaturated fatty acids and use of the canonical biplot method. Journal of Food Composition and Analysis, 56, 34-40. https://doi.org/10.1016/j.jfca.2016.12.005González-Martín, M. I. et al. (2020). The determination of fatty acids in cheeses of variable composition (cow, ewe's, and goat) by means of near infrared spectroscopy. Microchemical Journal, 156, 104854. https://doi.org/10.1016/j.microc.2020.104854Holroyd, S. E. (2013). Review: The use of near infrared spectroscopy on milk and milk products. Journal of Near Infrared Spectroscopy, 21 (5), 311-322. https://doi.org/10.1255/jnirs.1055Karoui, R. and Dufour, E. (2003). Dynamic testing rheology and fluorescence spectroscopy investigations of surface to centre differences in ripened soft cheeses. International Dairy Journal, 13, 973-985. https://doi.org/10.1016/S0958-6946(03)00121-3Karoui, R. et al. (2006). Chemical characterisation of European Emmental cheeses by near infrared spectroscopy using chemometric tools. International Dairy Journal, 16, 1211–1217. https://doi.org/10.1016/j.idairyj.2005.10.002Kraggerud, H. et al. (2014). Prediction of sensory quality of cheese during ripening from chemical and spectroscopy measurements. International Dairy Journal, 34, 6–18. https://doi.org/10.1016/j.idairyj.2013.07.008Lucas, A. et al. (2008a). Prediction of dry matter, fat, pH, vitamins, minerals, carotenoids, total antioxidant capacity, and color in fresh and freeze-dried cheeses by visible-near-infrared reflectance spectroscopy. Journal of Agricultural and Food Chemistry, 56, 6801–6808. https://doi.org/10.1021/jf800615aLucas, A. et al. (2008b). Prediction of fatty acid composition of fresh and freeze-dried cheeses by visible–near-infrared reflectance spectroscopy. International Dairy Journal,18, 595–604. https://doi.org/10.1016/j.idairyj.2007.12.001Manuelian, C. L. et al. (2017). Prediction of minerals, fatty acid composition and cholesterol content of commercial cheeses by near infrared transmittance spectroscopy. International Dairy Journal, 71, 107-113. https://doi.org/10.1016/j.idairyj.2017.03.011Markiewicz-Kęszycka, M. et al. (2013). Fatty acid profile of milk - a review. Bulletin of the Veterinary Institute in Pulawy, 57, 135–139. https://doi.org/10.2478/bvip-2013-0026Mazerolles, G. et al. (2001). Infrared and fluorescence spectroscopy for monitoring protein structure and interaction changes during cheese ripening. Le Lait, 81, 509-527. https://doi.org/10.1051/lait:2001148Morrison, W. R. and Smith, L. M. (1964). Preparation of fatty acid methyl esters and dimethylac-etals from lipids with boron fluorid methanol. Journal of Lipid Research, 5, 600–608.Nudda, A. et al. (2005). Seasonal variation in conjugated linoleic acid and vaccenic acid in milk fat of sheep and its transfer to cheese and ricotta. Journal of Dairy Science, 88, 1311-1319. https://doi.org/10.3168/jds.S0022-0302(05)72797-1Ozen, B. F. and Mauer, L. J. (2002). Detection of hazelnut oil adulteration using FT-IR spectroscopy. Journal of Agricultural and Food Chemistry, 50, 3898–3901. https://doi.org/10.1021/jf0201834Pierce, M. M. and Wehling, R. L. (1994). Comparison of sample handling and data treatment methods for determining moisture and fat in Cheddar cheese by near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 42, 2830-2835. https://doi.org/10.1021/jf00048a033Pollard, A. et al. (2003). Textural changes of natural Cheddar cheese during the maturation process. Journal of Food Science, 68, 2011-2016. https://doi.org/10.1111/j.1365-2621.2003.tb07010.xRodriguez-Otero, J. L., Hermida, M. and Cepeda, A. (1995). Determination of fat, protein, and total solids in cheese by near-infrared reflectance spectroscopy. 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Point-of-service diagnostic technology for detection of swine viral diseases

A research project is underway aiming to develop a field diagnostic tool for six important viruses of the pig sector, namely: African swine fever virus (ASFV), porcine reproductive and respiratory syndrome virus (PRRSV), swine influenza virus (SIV), porcine parvovirus (PPV), porcine circovirus (PCV2), and classical swine fever virus (CSFV)