Data Mining as a Tool to Infer Chicken Carcass and Meat Cut Quality from Autochthonous Genotypes
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Systematic Review Approach Decision
2.2. Data Collection
2.3. Data Analysis
2.3.1. Multicollinearity Prevention: Preliminary Testing
2.3.2. Data-Mining Chi-Squared Automatic Interaction Detection (CHAID) Decision Tree: Splitting, Pruning and Building
2.3.3. CHAID Decision Tree Cross-Validation
3. Results
3.1. Multicollinearity Prevention: Preliminary Testing
3.2. Data-Mining Chi-Squared Automatic Interaction Detection (CHAID) Decision Tree: Splitting, Pruning, and Building
3.3. CHAID Decision Tree Cross-Validation
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Cluster | Trait | Unit | References |
---|---|---|---|
Weight-related traits | Carcass/piece weight | g | [29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110] |
Carcass yield | % | ||
Cold weight | g | ||
Water-holding capacity | Drip loss | % | [29,31,32,36,38,40,41,42,44,45,52,53,56,57,58,59,61,62,63,65,67,68,70,71,72,73,74,75,76,81,82,84,89,93,103,104,105,108,109,111,112,113,114,115,116,117,118,119,120,121] |
Water-holding capacity | % | ||
Cooking loss | % | ||
Colour-related traits | L* meat | [29,31,32,34,36,38,39,40,41,42,44,45,47,50,52,53,57,58,59,61,62,63,64,65,68,70,71,73,74,75,76,80,82,84,93,98,99,101,103,104,111,112,113,115,117,118,119,121,122] | |
a* meat | |||
b* meat | |||
L* meat 72 h post-mortem | |||
a* meat 72 h post-mortem | |||
b* meat 72 h post-mortem | |||
L* skin | |||
a* skin | |||
b* skin | |||
Histological properties | Muscle fiber density | fibers/mm2 | [35,40,56,65,73,104,105,123] |
Muscle fiber diameter | µm | ||
Texture-related traits | Firmness | kg s−1 | [29,32,34,36,38,39,40,41,42,44,45,50,52,53,56,57,58,59,62,63,65,67,70,75,76,81,82,84,93,103,104,105,109,111,112,113,114,115,117,118,119,120,121,122] |
Total work | kg mm | ||
Shear force | N | ||
Hardness | N | ||
Springiness | Mm | ||
Cohesiveness | N | ||
Gumminess | N | ||
Chewiness | kg mm | ||
pH | pH | [29,30,31,32,33,34,36,38,39,40,41,42,45,47,50,52,53,57,58,59,61,62,63,64,65,67,68,70,71,72,73,74,75,76,82,84,87,89,93,98,99,101,103,104,105,111,112,113,114,115,117,118,119,120,121,122,124,125,126] | |
pH 24 h post-mortem | |||
pH 72 h post-mortem | |||
Content of flavour-related nucleotides | IMP | mg/g | [41,48,56,77,109,113,118] |
AMP | mg/100 g | ||
Inosine | mg/100 g | ||
Gross nutrients | Moisture | % | [6,30,33,34,36,38,39,40,41,42,43,47,49,52,55,56,58,59,60,61,64,65,67,68,70,72,75,77,78,80,83,84,87,89,92,93,99,100,102,103,104,105,107,109,111,112,113,114,115,116,119,120,121,124,125,127] |
Protein | % | ||
Fat | % | ||
Ash | % | ||
Collagen | % | ||
Cholesterol | mg/100 g |
Statistics/Traits | VIF 1 | Tolerance (1 − R2), |
---|---|---|
Chewiness | 4.0515 | 0.2468 |
Gumminess | 3.1989 | 0.3126 |
Hardness | 2.3258 | 0.4300 |
Shear force | 2.0546 | 0.4867 |
a* meat | 1.8862 | 0.5302 |
b* skin | 1.7745 | 0.5635 |
a* skin | 1.7044 | 0.5867 |
Muscle fiber diameter | 1.6223 | 0.6164 |
Cooking loss | 1.6202 | 0.6172 |
L* skin | 1.6152 | 0.6191 |
L* meat | 1.5910 | 0.6285 |
Water-holding capacity | 1.5580 | 0.6418 |
pH | 1.4108 | 0.7088 |
Drip loss | 1.3886 | 0.7201 |
pH 24 h post-mortem | 1.3486 | 0.7415 |
Moisture | 1.3462 | 0.7428 |
b* meat | 1.3408 | 0.7458 |
Total work | 1.2699 | 0.7875 |
IMP | 1.2534 | 0.7978 |
Springiness | 1.2183 | 0.8208 |
Cholesterol | 1.2101 | 0.8264 |
Cohesiveness | 1.1135 | 0.8981 |
Collagen | 1.1130 | 0.8985 |
Inosine | 1.1058 | 0.9044 |
Carcass/piece weight | 1.0949 | 0.9133 |
Carcass yield | 1.0898 | 0.9176 |
Protein | 1.0761 | 0.9293 |
AMP | 1.0735 | 0.9315 |
Ash | 1.0463 | 0.9558 |
Muscle fiber density | 1.0317 | 0.9692 |
Cold carcass weight | 1.0275 | 0.9732 |
Average age | 1.0267 | 0.9740 |
Fat | 1.0213 | 0.9792 |
Risk (Cp) | Estimate | Std. Error |
---|---|---|
Resubstitution error rate | 0.604 | 0.013 |
Cross-validation error rate | 0.622 | 0.013 |
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González Ariza, A.; Navas González, F.J.; León Jurado, J.M.; Arando Arbulu, A.; Delgado Bermejo, J.V.; Camacho Vallejo, M.E. Data Mining as a Tool to Infer Chicken Carcass and Meat Cut Quality from Autochthonous Genotypes. Animals 2022, 12, 2702. https://doi.org/10.3390/ani12192702
González Ariza A, Navas González FJ, León Jurado JM, Arando Arbulu A, Delgado Bermejo JV, Camacho Vallejo ME. Data Mining as a Tool to Infer Chicken Carcass and Meat Cut Quality from Autochthonous Genotypes. Animals. 2022; 12(19):2702. https://doi.org/10.3390/ani12192702
Chicago/Turabian StyleGonzález Ariza, Antonio, Francisco Javier Navas González, José Manuel León Jurado, Ander Arando Arbulu, Juan Vicente Delgado Bermejo, and María Esperanza Camacho Vallejo. 2022. "Data Mining as a Tool to Infer Chicken Carcass and Meat Cut Quality from Autochthonous Genotypes" Animals 12, no. 19: 2702. https://doi.org/10.3390/ani12192702
APA StyleGonzález Ariza, A., Navas González, F. J., León Jurado, J. M., Arando Arbulu, A., Delgado Bermejo, J. V., & Camacho Vallejo, M. E. (2022). Data Mining as a Tool to Infer Chicken Carcass and Meat Cut Quality from Autochthonous Genotypes. Animals, 12(19), 2702. https://doi.org/10.3390/ani12192702