PREDICTING THE TENDERNESS OF BROILER BREAST FILLETS USING VISIBLE/NEAR-INFRARED SPECTROSCOPY AND MACHINE LEARNING ALGORITHMS
DOI:
https://doi.org/10.11113/jurnalteknologi.v87.22999Keywords:
Chicken tenderness, Near infrared spectroscopy, principal component regression, artificial neural network, principal components neural networkAbstract
Chicken, as the most consumed meat worldwide, plays a crucial role in daily protein nutrition. Typically, consumers choose meat based on its tenderness, one of the quality traits in meat selections. In general, assessing the tenderness of chicken meat is commonly labour-intensive and destructive. An alternative approach to traditional methods is NIR spectroscopy, which is non-invasive, rapid, and economical. Portable NIR devices enable real-time, non-destructive meat quality assessment. Hence, this study investigates the effectiveness of a portable NIR spectroscopy system in predicting chicken meat shear force by comparing several machine learning algorithms, which are linear model (PCR) and non-linear model (ANN and PCR-ANN). The result shows prediction accuracy, Rp of 0.64, 0.73, and 0.91 for PCR, ANN, and PCR-ANN models, respectively. The calibration sets, Rc, for all models are 0.65, 0.73, and 0.92 for PCR, ANN, and PCR-ANN, respectively. The PCR-ANN model successfully outperforms both PCR and ANN, indicating it is amenable to non-invasive shear force prediction applications. In conclusion, the PCR-ANN model sufficiently achieved the best performance in predicting chicken meat tenderness using near-infrared spectral data. This study is essential for a number of reasons, including sustaining consumer satisfaction in the food industry, increasing industry returns, and increasing eating quality.
References
P. P. Patil, V. N. Patil, and N. A. Doshi. 2024. NIR Spectroscopy for Rapid Freshness Assessment and Quality Classification of Chicken Legs. J. Theor. Appl. Inf. Technol. 102(8): 3645–3656.
J. Tholen, J. Gohe, H. Dörksen, T. Kiesel, and M. Upmann. 2024. Tenderness Prediction for Beef using Novel Data Analysis Methods based on System Dynamic and Acoustic Signals. Food Phys. 1(May).
M. J. Hernandez-Sintharakao, C. J. Sarchet, J. E. Prenni, D. R. Woerner, C. Zhai, and M. N. Nair. 2023. Prediction of Tenderness, Juiciness, and Flavor Profile of 2 Beef Muscles with Different Aging Times using Rapid Evaporative Ionization Mass Spectrometry. Meat Muscle Biol. 7(1): 1–14. Doi: 10.22175/mmb.16120.
J. Wang, Q. Lei, D. Cao, T. Huang, and M. Huang. 2023. Effects of Different Ages on the Slaughter Performance, Muscle Quality and Nutritional Characteristics of 817 Broilers. J. Nanjing Agric. Univ. 46(1):159–168.
R. Zhu, J. Li, J. Yang, R. Sun, and K. Yu. 2024. In Vivo Prediction of Breast Muscle Weight in Broiler Chickens Using X-ray Images Based on Deep Learning and Machine Learning. Animals. 14.
M. Wei, L. Zhang, Y. Zhang, S. Li, G. Zhao, and B. Sun. 2023. Development Status and Countermeasures of Beef Cattle Slaughtering and Processing Industry in China. J. Jilin Agric. Univ. 45(4): 429–436.
P. Donald et al. 2022. Emerging Nondestructive Techniques for the Quality and Safety Evaluation of Pork and Beef: Recent Advances, Challenges, and Future Perspectives. Appl. Food Res. 2(June).
M. Kamruzzaman. 2023. Optical Sensing as Analytical Tools for Meat Tenderness Measurements - A Review. Meat Sci. 195(January).
T. Leng, F. Li, L. Xiong, Q. Xiong, M. Zhu, and Y. Chen. 2020. Quantitative Detection of Binary and Ternary Adulteration of Minced Beef Meat with Pork and Duck Meat by NIR Combined with Chemometrics. Food Control. 113(July).
L. Cheng, G. Liu, J. He, G. Wan, and J. Ban. 2020. Non-destructive Assessment of the Myoglobin Content of Tan Sheep using Hyperspectral Imaging. Meat Sci. 167(September).
J. M. Cáceres-Nevado, A. Garrido-Varo, E. De Pedro-Sanz, D. Tejerina-Barrado, and D. C. Pérez-Marín. 2021. Non-destructive Near Infrared Spectroscopy for the Labelling of Frozen Iberian Pork Loins. Meat Sci. 175(May).
L. Fengou, A. E. Lytou, G. Tsekos, P. Tsakanikas, and G.-J. E. Nychas. 2024. Features in Visible and Fourier Transform Infrared Spectra Confronting Aspects of Meat Quality and Fraud. Food Chem. 440(May).
J. Wyrwisz et al. 2019. Evaluation of WBSF, Color, Cooking Loss of Longissimus Lumborum Muscle with Fiber Optic Near-Infrared Spectroscopy (FT-NIR), Depending on Aging Time. Molecules. 24(4).
I. Ismail and N. Huda. 2024. Current Techniques and Technologies of Meat Quality Evaluation. Hand Book of Processed Functional Meat Products, F. A. Rather, S.A. Masoodi, Ed. Springer, Cham. 437–512.
N. Teimouri, M. Omid, K. Mollazade, H. Mousazadeh, R. Alimardani, and H. Karstoft. 2018. On-line Separation and Sorting of Chicken Portions using a Robust Vision-based Intelligent Modelling Approach. Biosyst. Eng. 167(March): 8–20.
L. Esposito, M. Mascini, F. Silveri, A. Pepe, D. Mastrocola, and M. Martuscelli. 2024. Food Bioscience a Machine Learning Approach to Uncover Nicotinamide and Other Antioxidants as Novel Markers for Chicken Meat Quality Assessment. Food Biosci. 58. Doi: 10.1016/j.fbio.2024.103577.
S. Atanassova, T. Stoyanchev, D. Yorgov, and V. Nachev. 2018. Differentiation of Fresh and Frozen-thawed Poultry Breast Meat by Near Infrared Spectroscopy Differentiation of Fresh and Frozen-thawed Poultry Breast Meat by Near Infrared Spectroscopy. Bulg. J. Agric. Sci. 24: 162–168.
M. de N. Bonin et al. 2020. Predicting the Shear Value and Intramuscular Fat in Meat from Nellore Cattle using Vis-NIR Spectroscopy. Meat Sci. 163(May).
R. Cama-moncunill et al. 2020. Prediction of Warner-Bratzler Shear Force, Intramuscular Fat, Drip-Loss and Cook-loss in Beef Via Raman Spectroscopy and Chemometrics. Meat Sci. 167(September).
J. Cafferky et al. 2020. Investigating the Use of Visible and Near Infrared Spectroscopy to Predict Sensory and Texture Attributes of Beef M. Longissimus Thoracis et lumborum. Meat Sci. 159.
Y. Liu, B. G. Lyon, W. R. Windham, C. E. Lyon, and E. M. Savage. 2004. Prediction of Physical, Color, and Sensory Characteristics of Broiler Breasts by Visible/Near Infrared Reflectance Spectroscopy 1. Poult. Sci. 83(8): 1467–1474. Doi: 10.1093/ps/83.8.1467.
I. Lanza et al. 2021. Assessment of Chicken Breast Shelf Life Based on Bench-top and Portable Near-infrared Spectroscopy Tools Coupled with Chemometrics. Food Qual. Saf. 5:1–11. Doi: 10.1093/fqsafe/fyaa032.
J. M. Balage, C. A. Gomide, M. D. N. Bonin, and A. C. Figueira. 2015. Predicting Pork Quality using Vis/NIR Spectroscopy. Meat Sci. 108(October): 37–43.
I. Murray, E. A. Navajas, A. V Fisher, and N. R. Lambe. 2007. Prediction of Sensory Characteristics of Lamb Meat Samples by Near Infrared Reflectance Spectroscopy. Meat Sci. 76(3): 509–516.
M. De Marchi, M. Penasa, M. Battagin, E. Zanetti, C. Pulici, and M. Cassandro. 2001. Feasibility of the Direct Application of Near-infrared Reflectance Spectroscopy on Intact Chicken Breasts to Predict Meat Color and Physical Traits. Poult. Sci. 90(7): 1594–1599. Doi: 10.3382/ps.2010-01239.
Q. Chen, Z. Guo, J. Zhao, and Q. Ouyang. 2012. Comparisons of Different Regressions Tools in Measurement of Antioxidant Activity in Green Tea Using Near Infrared Spectroscopy. J. Pharm. Biomed. Anal. 60(February): 92–97.
S. Li, L. Li, R. Milliken, and K. Song. 2012. Hybridization of Partial Least Squares and Neural Network Models for Quantifying Lunar Surface Minerals. Icarus. 221(1): 208–225.
H. M. Monavar, N. K. Afseth, J. Lozano, R. Alimardani, M. Omid, and J. P. Wold. 2013. Determining Quality of Caviar from Caspian Sea based on Raman Spectroscopy and using Artificial Neural Networks. Talanta. 111(July): 98–104.
M. Zareef et al. 2020. An Overview on the Applications of Typical Non-linear Algorithms Coupled With NIR Spectroscopy in Food Analysis. Food Eng. Rev. 12: 173–190.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
