PhD in Veterinary Sciences for Animal Health and Food Safety

Contacts

Supervisor

Dominga Soglia

Curriculum vitae

Curriculum Vitae

Phd thesis

“Near-Infrared Spectroscopy and Genetics approaches to predict meat quality and improve production performance in local Piedmontese chicken breeds "

 

• Scientific background/state of the art

In the 1960s, the emergence of agricultural industrialization supplanted the breeding of local chicken breeds, in favor of commercial hybrid, making their presence marginal. Today, the reinterest in local breeds responds to the terms of environmental sustainability, biodiversity and quality product (Padhi, 2016). In Piemonte region, Bianca di Saluzzo and Bionda Piemontese are the two local chicken breeds reared for meat production. However, they are considered slow growing breeds (Soglia et al., 2020). The low production performance represents a limit for the breeders which have to face with the fast growth of the commercial line. Study of the genetic polymorphisms and their association with productive traits are necessary for improving breed performance and also for biodiversity conservation. Furthermore, the consideration of public regarding meat quality has gradually increased in the recent years. This requirement has increased interest in Near-Infrared (NIR) Spectroscopy due to its ability to provide information about the molecular bonds and chemical constituents useful for characterizing foods and quality measurements (Andrés et al., 2007). This technique has been used successfully in several fields of food and feed analysis (Oh, 1995), but no studies are available on chicken meat (Zheng et al., 2023). Near-Infrared Spectroscopy and genetics studies are useful for the valorization, in term of meat quality and production performance, of local chicken breeds.

 

• Aims

The aim of this project is to improve production performance of local Piedmontese chicken breeds in order to reach the commercial size in a shorter time and thus to reduce management costs for breeders. The project will focus on DNA polymorphisms and on RNA expression of candidate genes to identify productive genetic markers useful for the breeding programs to obtain high-performance local chicken breeds. Furthermore, another aim of the project is to increase the consumer’s interest for these local meats, in order to increase their consumption and favor a more sustainable farming system. Traditional laboratory analysis will be used to evaluate meat composition in term of protein, fat and vitamins. However, these methods are costly and time consuming. Near-Infrared Spectroscopy is a rapid, non-invasive technique recently used to predict meat quality composition (Prieto et al., 2017). In this study we will compare the spectra obtained from a laboratory device (NIRSystems 5000 spectrophotometer) and those obtained from a pocket device (SCIO-NIR) in order to collect data directly at slaughtering and make it feasible to inexperienced people. The strong motivation for near-infrared spectroscopy to be applied commercial is closely related to the miniaturization of NIR spectrometers and the progress of chemometrics (Zheng et al., 2023).

 

• Materials and methods
• Genetic analysis 

Breast muscles, livers, spleens, brains and feathers will be collected after slaughtering for RNA expression and DNA polymorphism analysis (by NGS sequencing, Miseq platform). Candidate genes linked to growth performance (MC4R, POU1F1 and PAX) will be analyzed to test their association with carcass parameters (Kubota et al., 2019; Xu et al., 2012; Relaix et al., 2021). A total RNA analysis will be performed in order to evaluate which genes are expressed and to sudy the different expression between the breeds. 

• Meat Quality 

Meat quality is typically evaluated according to many properties, such as its color, tenderness, water-holding capacity, pH and moisture, protein and fat content. Color will be defined by L* a* b* coordinates: L* for perceptual lightness and a* and b* for the four unique colors of human vision: red, green, blue and yellow. Tenderness will be evaluated as the difference in the share force between cooked and not-cooked meat, water-holding capacity will be measured with “weep or purge” method (Warner, 2014), pH, moisture, protein and fat content will be measured using traditional laboratory analysis (Dabbou et al., 2019).

• NIR analysis 

Each breast and thigh samples will be scanned with a NIRSystems 5000 spectrophotometer (Agilent) and SCIO device (by Consumer Physics) to predict quality parameters (Zheng et al., 2023). Each spectrum will be matched with the corresponding qualitative traits (genetics) and with the quantitative traits (meat composition) in order to test the ability of NIR analysis in chicken meat. 

Depending on the above-mentioned variables, dedicated statistical and bioinformatics tools will be applied to highlight possible significant differences.

References

• Andrés S., Murray I., Navajas E.A., Fisher A.V., Lambe N.R., Bünger L. “Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy” (2007). Meat Science 76: 509-516.
• Dabbou S., Gasco L., Lussiana C., Brugiapaglia A., Biasato I., Renna M., Cavallarin L., Gai F. and Schiavone A. “Yellow mealworm (Tenebrio molitor L.) larvae inclusion in diets for free-range chickens: effect on meat quality and fatty acid profile” (2019). Renewable Agriculture and food System 35(5).
• Kubota S., Vandee A., Keawnakient P., Molee W., Yongsawatdikul J. and Molee “Effects of the MC4R, CAPN1, and ADSL genes on body weight and purine content in slow-growing chickens” (2019). Poultry Science 98: 4327–4337.
• Oh E.K., Grossklaus D. “Measurement of the Components in Meat Patties by near-Infrared Reflectance Spectroscopy” (1995). Meat Sci. 41, 157–162.
• Padhi M.K. “Importance of Indigenous Breeds of Chicken for Rural Economy and Their Improvements for Higher Production Performance” (2016). Hindawi Publishing Corporation Scientifica.
• Prieto N., Pawluczyk O., Gugan M.E.R., Aalhus J.L. “A review of the principles and applications of near infrared spectroscopy to characterize meat, fat, and meat products” (2017). Applied Spectoscopy 71(7): 1403-1426
• Relaix F., Bencze M., Borok M. J., Der Vartanian A., Gattazzo F., Mademtzoglou D., Perez-Diaz S., Prola A., Reyes-Fernandez P. C., Rotini A. and Taglietti V. “Perspectives on skeletal muscle stem cells” (2021). Nature Communication 12:692.
• Soglia D., Sacchi P., Sartore S., Maione S., Schiavone A., De Marco M., Bottero M.T., Dalmasso A., Pattono D., Rasero R.“Distinguishing industrial meat from that of indigenous chickens with molecular markers” (2017) Poult. Sci: 96, 2552–2561
• Soglia D., Sartore S., Maione S., Schiavone A., Dabbou S., Nery J., Zaniboni L., Marelli S., Sacchi P., Rasero R. “Growth Performance Analysis of Two Italian Slow-Growing Chicken Breeds: Bianca di Saluzzo and Bionda Piemontese” (2020). Animals: 10(6), 969.
• Tjørve K.M.C., Tjørve E. “The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the unified-Richards family” (2017). PLoS ONE 12, e0178691.
• Warner R.D. “Measuremments of Water-holding capacity and color: objective and subjective” (2014). Encyclopedia of Meat Sciences 2e
• Zhang S., Han R.L., Gao Z.Y., Zhu S.K., Tian Y.D., Sun G.R., Kang X.T. “A novel 31-bp indel in the paired box 7 (PAX7) gene is associated with chicken performance traits” (2014). Brit. Poult. Sci. 55, 31–36.
• Zheng X., Chen L., Li X. and Zhang D. “Non-Destructive Detection of Meat Quality Based on Multiple Spectral Dimension Reduction Methods by Near-Infrared Spectroscopy” (2023). Foods 12, 300.
• Xu H.Y, Wang Y., Liu Y.P., Wang J. and Zhu Q. “Polymorphisms and expression of the chicken POU1F1 gene associated with carcass traits” (2012). Molecular Biology Report 39, 8363-8371.

Research activities

Co supervisor

Achille Schiavone, Federica Raspa

Abstract

• ASPA 25th Congress (13-16/06/2023)

Monopoli, Italy.

“Evaluation of the effect of the breeding system on the expression of liver genes in local slow-growing chicken breeds”. F. Raspa, N. Stoppani, D. Soglia, F. Perini, E. Fiorilla, M. Profiti, S. Maione, A. Schiavone, P. Sacchi, E. Lasagna and C. Mugnai.

 

“Effect of bakery by-products inclusion in the broiler’s diet on growth performance, carcass yeld and gene expression profiling". K. Srikanthithasan, M. Gariglio, E. Fiorilla, A. Giorgino, L. Dellepiane, E. Diaz Cuna, D. Sola, V. Bongiorno, S. Bergagna, A. Schiavone, F. Raspa, M. Profiti, N. Stoppani, D. Soglia and C. Forte.

 

“Multiplex Digital Expression Gene Analysis (MuDEGA) of 11 liver poultry genes whit NGS approach”. F. Raspa, N. Stoppani, F. Perini, E. Fiorilla, M. Profiti, S. Maione, A. Schiavone, P. Sacchi, E. Lasagna, C. Mugnai and D. Soglia.

 

• Game of Research (15/12/2022)

Veterinary Medicin, University of Turin.

2° best poster:

“Study of productive and reproductive genetic marker in local chicken breed: DNA polymorphisms and RNA expression molecular analysis”. N.Stoppani and D. Soglia.

 

Personal Training

Multivariate Statistical Analysis.

Study of the structure of variance, covariance and correlation matrices.

Eigenvalues and eigenvectors.

Principal Component Analysis (PCA), Cluster analysis, Partial Least Square Regression (PLSR) and Disciminant Analysis.