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Schreuder J, Niknafs S, Williams P, Roura E, Hoffman LC, Cozzolino D. Non-destructive prediction of fertility and sex in chicken eggs using the short wave near-infrared region. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 322:124716. [PMID: 38991617 DOI: 10.1016/j.saa.2024.124716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 06/05/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
The objective of this study was to evaluate the ability of a handheld near-infrared device (900-1600 nm) to predict fertility and sex (male and female) traits in-ovo. The NIR reflectance spectra of the egg samples were collected on days 0, 7, 14 and 18 of incubation and the data was analysed using principal component analysis (PCA), linear discriminant analysis (LDA) and support vector machines classification (SVM). The overall classification rates for the prediction of fertile and infertile egg samples ranged from 73 % to 84 % and between 93 % to 95 % using LDA and SVM classification, respectively. The highest classification rate was obtained on day 7 of incubation. The classification between male and female embryos achieved lower classification rates, between 62 % and 68 % using LDA and SVM classification, respectively. Although the classification rates for in-ovo sexing obtained in this study are higher than those obtained by chance (50 %), the classification results are currently not sufficient for industrial in-ovo sexing of chicken eggs. These results demonstrated that short wavelengths in the NIR range may be useful to distinguish between fertile and infertile egg samples at days 7 and 14 during incubation.
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Affiliation(s)
- J Schreuder
- Stellenbosch University, Food Science Department, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - S Niknafs
- The University of Queensland, Centre for Animal Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St. Lucia, Brisbane, QLD 4072, Australia
| | - P Williams
- Stellenbosch University, Food Science Department, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - E Roura
- The University of Queensland, Centre for Animal Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St. Lucia, Brisbane, QLD 4072, Australia; The University of Queensland, Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St. Lucia, Brisbane, QLD 4072, Australia
| | - L C Hoffman
- Stellenbosch University, Food Science Department, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; The University of Queensland, Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St. Lucia, Brisbane, QLD 4072, Australia
| | - D Cozzolino
- The University of Queensland, Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St. Lucia, Brisbane, QLD 4072, Australia.
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Cozzolino D, Sanal P, Schreuder J, Williams PJ, Assadi Soumeh E, Dekkers MH, Anderson M, Boisen S, Hoffman LC. Predicting Egg Storage Time with a Portable Near-Infrared Instrument: Effects of Temperature and Production System. Foods 2024; 13:212. [PMID: 38254513 PMCID: PMC10814904 DOI: 10.3390/foods13020212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 12/21/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Determining egg freshness is critical for ensuring food safety and security and as such, different methods have been evaluated and implemented to accurately measure and predict it. In this study, a portable near-infrared (NIR) instrument combined with chemometrics was used to monitor and predict the storage time of eggs under two storage conditions-room temperature (RT) and cold (CT) storage-from two production systems: cage and free-range. A total of 700 egg samples were analyzed, using principal component analysis (PCA) and partial least squares (PLS) regression to analyze the NIR spectra. The PCA score plot did not show any clear separation between egg samples from the two production systems; however, some egg samples were grouped according to storage conditions. The cross-validation statistics for predicting storage time were as follows: for cage and RT eggs, the coefficient of determination in cross validation (R2CV) was 0.67, with a standard error in cross-validation (SECV) of 7.64 days and residual predictive deviation (RPD) of 1.8; for CT cage eggs, R2CV of 0.84, SECV of 5.38 days and RPD of 3.2; for CT free-range eggs, R2CV of 0.83, SECV of 5.52 days and RPD of 3.2; and for RT free-range eggs, R2CV of 0.82, SECV of 5.61 days, and RPD of 3.0. This study demonstrated that NIR spectroscopy can predict storage time non-destructively in intact egg samples. Even though the results of the present study are promising, further research is still needed to further extend these results to other production systems, as well as to explore the potential of this technique to predict other egg quality parameters associated with freshness.
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Affiliation(s)
- Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia;
| | - Pooja Sanal
- School of Agriculture and Food Sustainability, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; (P.S.); (E.A.S.)
| | - Jana Schreuder
- Food Science Department, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; (J.S.); (P.J.W.)
| | - Paul James Williams
- Food Science Department, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; (J.S.); (P.J.W.)
| | - Elham Assadi Soumeh
- School of Agriculture and Food Sustainability, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; (P.S.); (E.A.S.)
| | - Milou Helene Dekkers
- Queensland Animal Science Precinct (QASP), The University of Queensland, Gatton Campus, St. Lucia, Brisbane, QLD 4072, Australia; (M.H.D.); (M.A.); (S.B.)
| | - Molly Anderson
- Queensland Animal Science Precinct (QASP), The University of Queensland, Gatton Campus, St. Lucia, Brisbane, QLD 4072, Australia; (M.H.D.); (M.A.); (S.B.)
| | - Sheree Boisen
- Queensland Animal Science Precinct (QASP), The University of Queensland, Gatton Campus, St. Lucia, Brisbane, QLD 4072, Australia; (M.H.D.); (M.A.); (S.B.)
| | - Louwrens Christiaan Hoffman
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia;
- Food Science Department, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa; (J.S.); (P.J.W.)
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