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USE OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES(MARS) FOR PREDICTING PARAMETERS OF BREAST MEAT IN QUAILS.

Byline: T. Sengul, S. Celik and O. Sengul

Keywords: Quail, breast meat, meat color, MARS model.

INTRODUCTION

Meat color in poultry is affected by age, sex, genotype, feed, intramuscular fat distribution, water content of meat, pre-slaughter conditions, and processing techniques(Froning, 1995). Meat color is dependent largely on myoglobin concentration and partially on the presence of pigments such as hemoglobin in the medium. The discoloration of meat can be attributed to the amount of such pigments contained in the meat. The chemical structure of pigments, and ultimately, the rate of reflection of light that falls on meat change(Northcutt, 2007). The color of poultry meat varies from bluish white to yellow, depending on race, exercise, age and diet.

It was reported that characteristics associated with the color of meat in quails are usually hereditary to a moderate to high extent, and it may be the case that there is an effect of genes linked to sex(Mir et. al., 2017).An argument has emerged that genetics has a predominant role in the control of meat quality characteristics such as color and pH. Selection studies carried out to improve live weight have influenced the quality of meat. As live weight increases, water and protein levels in the carcass decrease, and the carcass fat, number of muscle fibers, muscle fiber lengths(Skiba et al., 2012). Heritability values of certain meat quality characteristics in Japanese quails were estimated for brightness(L*)=0.23, red color(a*)=0.45, and yellow color(b*)=0.22(Oguz et al., 2004). In chickens, sex was reported to be an influential factor on many parameters of meat quality(Mehaffey et al., 2006; Jaturasitha et al., 2008).

The color of quail meat is darker than that of chicken meat, and lighter than that of goose meat. Quails' breast and thigh muscles have almost no fatty tissue(Riegel et al., 2003). Muscle fibers are separated by a thin connective tissue giving the characteristic taste of the meat(Hejnowska et al., 1999). Lopez et al.(2011) reported that there was a significant relationship between sex and the pH(P<0.05) of the breast meat in the 24 hours after chickens are slaughtered. They have indicated that female chickens have a lower pH than male chickens. Tougan et al.(2013) reported that the taste of meat diminishes as the slaughter age decreases in poultry, but juiciness and tenderness increase. Bilal and Bostan(1996) informed that in quails, age affects the composition of carcass, and sex affects carcass yield.

Narinc et al.(2013) reported that the final pH, L*, a* and b* color values of breast meat quality characteristics of 5-week-old broilers were found 5.94, 43.09, 19.24 and 7.74, respectively. The authors have reported that there was a high and negative correlation between pH and body weight, and a lower and positive correlation between the L* value and these characteristics(P)= IPSn i=1(yi-yip)2/(1-M(I>>)/n)2

Where:

n is the number of training cases, yi is the observed value of a response variable, yip is the predicted value of a response variable, M(I>>) is a penalty function for the complexity of the model with I>> terms.

To measure the predictive performance of MARS, the following goodness of fit criteria were calculated(Willmott and Matsuura, 2005; Takma et al., 2012; Ali et al., 2015);

1. Coefficient of Determination

R2= 1-IPSn i=1(Yi-Yi)2/IPSn i=1(Yi-Yi)2

2. Adjusted Coefficient of Determination

Adj.R2= 1-1/n-k-1 IPSn i=1(Yi-Yi)2/1/n-1 IPSn i=1(Yi-Yi)2

3. Root-mean-square error(RMSE) presented by the following formula;

RMSE= a1/n IPSn i=1(Yi-Yi)2

4. Standard deviation ratio(SD ratio);

SD ratio= a1/n-1 IPSn i=1(Iui-Iu)2/1/n-1 IPSn i=1(Yi-E2)2

SD ratio estimates should be less than 0.40 for a good fit explained by some authors(Grzesiak et al., 2003; Grzesiak and Zaborski, 2012).

5. Mean absolute deviation(MAD):

MAD=1/n IPSn i=1 |Yi-Yip|

where: n is the number of cases in a set, k is the number of model parameters, Y i is the actual(observed) value of an output variable, Y ip is the predicted value of an output variable, s m is the standard deviation of model errors, s d is the standard deviation of an output variable.

Statistical evaluations on MARS algorithm was specified using STATISTICA program(12.5 version). See the book of Eyduran et al.(2019) to obtain more detailed information about MARS algorithm.

RESULTS

L *(brightness) value: A MARS model was constructed by selecting variety, sex, a *, b * and pH as independent variables to predict the L * value of the breast meat. Model fit statistics for the L * are presented in Table 1.

Table 1. Model 1 goodness of fit criteria and GCV values according to order of interactions.

Order###Maximum###Number###Number###GCV###R2###Adj. R2###SDratio###RMSE###MAD

of int.###number of BF###of BF###of terms

1###80###16###11###1.778###0.961###0.925###0.445###0.738###0.485

1###90###16###11###1.778###0.961###0.925###0.445###0.738###0.485

1###100###16###11###1.778###0.961###0.925###0.445###0.738###0.485

2###80###38###20###0.160###0.999###0.997###0.135###0.068###0.044

2###90###38###20###0.160###0.999###0.997###0.135###0.068###0.044

2###100###38###20###0.160###0.999###0.997###0.135###0.068###0.044

According to the goodness of fit results shown in Table 1, the best model was found to be the MARS model with 20 terms and the second degree interactions. For this model, the parameters were predicted as follows:

GCV=0.160, R2=0.999, Adj. R2=0.997, SDratio=0.135, RMSE=0.068 and MAD=0.044. Basis functions and coefficients are given in Table 2 according to the aforementioned model.

Table 2. Results of the Model 1 MARS algorithm(for L*).

###Basic function###Coefficient

###Constant(intercept)###38.772

BF1###max(0; Female)###18.479

BF2###max(0; b-8,79)*max(0; Female)###3.368

BF3###max(0; a-19.02)*max(0; Female)###30.774

BF4###max(0; 19.02-a)*max(0; Female)###-7.266

BF5###max(0; Wild-type)###-5.468

BF6###max(0; Wild-type)*max(0; Female)###48.052

BF7###max(0; a-15.89)*max(0; b-8.79)*max(0; Female)###-2.810

BF8###max(0; a-15.89)###-3.764

BF9###max(0; a-15.89)*max(0; Dark-Brown)###-1.336

BF10###max(0; a-15.89)*max(0; b-8.79)###0.251

BF11###max(0; a-15.89)*max(0; Wild-type)###-4.478

BF12###max(0; a-15.89)*max(0; Wild-type)*max(0; Female)###-15.370

BF13###max(0; b-8.79)###2.291

BF14###max(0; Dark-Brown)###3.024

BF15###max(0; a-15.89)*max(0; b-8.79)*max(0; Wild-type)###1.680

BF16###max(0; 19.02-a)*max(0; b-12.46)*max(0; Female)###206.121

BF17###max(0; pH-6.38)*max(0; Female)###-10.658

BF18###max(0; 6.38-pH)*max(0; Female)###-19.545

BF19###max(0; b-8.79)*max(0; Golden)*max(0; Female)###-0.633

Explanations for other basis functions and coefficients can be said to be similar to the results given in Table 2. The MARS equation of Model 1 that was obtained according to these results was as follows.

L=38.772+18.479*max(0; Female)+3.368*max(0; b-8.79)*max(0; Female)+30.774*max(0; a-19.02)*max(0;Female)-7.266*max(0;19.02-a)*max(0; Female)-5.468*max(0; Wild-type)+48.052*max(0; Wild-type)*max(0; Female)-2.81*max(0; a-15.89)*max(0; b-8.79)*max(0; Female)-3.764*max(0; a-15.89)-1.336*max(0; a-15.89)*max(0; Dark-Brown)+0.251*max(0; a-15.89)*max(0; b-8.79)-4.478*max(0; a-15.89)*max(0; Wild-type)-15.370*max(0; a-15.89)*max(0; Wild-type)*max(0; Female)+2.291*max(0;b-8.79)+3.024*max(0; Dark-Brown)+1.680*max(0; a-15.89)*max(0; b-8.79)*max(0; Wild-type)+206.121*max(0; 19.02-a)*max(0; b-12.46)*max(0; Female)-10.658*max(0; pH-6.38)*max(0; Female)-19.545*max(0; 6.38-pH)*max(0; Female)-0.633*max(0;b-8.79)*max(0; Golden)*max(0; Female).

Table 3. Predicted L* values based on the values of independent variables.

###a*###b*###pH###Variety###Sex###L*

###20###12###6.50###Wild-type###Male###32.262

###20###12###6.50###Wild-type###Female###38.235

###20###12###6.50###Dark-Brown###Male###31.498

###20###12###6.50###Dark-Brown###Female###52.589

###20###12###6.50###Golden###Male###33.965

###20###12###6.50###Golden###Female###53.023

###24###15###6.35###Wild-type###Male###77.945

###24###15###6.35###Wild-type###Female###51.868

###24###15###6.35###Dark-Brown###Male###27.293

###24###15###6.35###Dark-Brown###Female###77.815

###24###15###6.35###Golden###Male###35.104

###24###15###6.35###Golden###Female###81.692

a*(red color) value: A MARS algorithm was built by selecting variety, sex, L*, b* and pH as independent variables to predict the a* value of the breast meat. The model fit statistics used to predict a* using the MARS algorithm are given in Table 4.

Table 4. Model 2 goodness of fit criteria and GCV values according to order of interactions(for a*).

Order###Maximum###Number of###Number of###GCV###R2###Adj. R2###SDratio###RMSE###MAD

of int.###number of BF###BF###terms

###2###80###10###7###2.689###0.803###0.718###0.443###1.186###1.025

###2###90###10###7###2.689###0.803###0.718###0443###1.186###1.025

###2###100###10###7###2.689###0.803###0.718###0.443###1.186###1.025

###3###80###12###8###2.619###0.738###0.702###0.540###1.447###1.102

###3###80###12###8###2.619###0.738###0.702###0.540###1.447###1.102

###3###80###12###8###2.619###0.738###0.702###0.540###1.447###1.102

###4###100###40###19###0.242###0.999###0.992###0.031###0.082###0.057

The results of the MARS model including the basis function and the coefficients are presented in Table 5. A MARS model with 23 basis functions and 4-way interactions was selected as the most suitable model. For this model, the parameters were calculated as follows: GCV=3.011, R2=0.999, Adj. R2=0.992, SDratio=0.031, RMSE=0.082 and MAD=0.057.

Table 5. Prediction results of the Model 2 MARS algorithm(for a*).

###Basic function###Coefficient

###Constant(Intercept)###13.621

BF1###max(0; b-12.75)###-3.690

BF2###max(0; 12.75-b)###2.701

BF3###max(0; 38.7-L)###35.313

BF4###max(0; Golden)###1.521

BF5###max(0; b-12.46)* max(0; Golden)###-3.397

BF6###max(0; 12.46-b)* max(0; Golden)###-1.401

BF7###max(0; 38.7-L)* max(0; Golden)###3.353

BF8###max(0; 39.64-L)* max(0; Golden)###-2.055

BF9###max(0; 12.46-b)* max(0; Golden)*###1.357

BF10###max(0; Male)###23.286

BF11###max(0; pH-6.33)###-821.001

BF12###max(0; 6.33-pH)###9.254

BF13###max(0; b-12.24)###-7.872

BF14###max(0; pH-6.38)* max(0; Golden)###-20.650

BF15###max(0; 6.38-pH)* max(0; Golden)###22.809

BF16###L*max(0; 6.33-pH)###-5.826

BF17###max(0; 38.7-L)*pH###-1.675

BF18###max(0; 38.7-L)* max(0; Male)###1.791

###max(0; Male)

Table 6. Model 3 goodness of fit criteria and GCV values according to order of interactions(for b*).

Order###Maximum###Number###Number of###GCV###R2###Adj.###SDratio###RMSE###MAD

of int.###number of BF###of BF###terms###R2

###2###80###6###5###0.946###0.919###0.897###0.533###0.621###0.480

###2###90###6###5###0.946###0.919###0.897###0.533###0.621###0.480

###2###100###6###5###0.946###0.919###0.897###0.533###0.621###0.480

###3###80###42###21###0.029###0.999###0.996###0.132###0.038###0.026

###3###90###42###21###0.029###0.999###0.996###0.132###0.038###0.026

###3###100###42###21###0.029###0.999###0.996###0.132###0.038###0.026

Table 7. Prediction results of the Model 3 MARS algorithm(for b*).

###Basic function###Coefficient

###Constant###-10.003

BF1###max(0; a-15.89)###0.763

BF2###max(0; L-31.69)###0.264

BF3###max(0; a-15.89)*max(0; Wild-type)###2.737

BF4###max(0; L-31.69)*max(0; Wild-type)###2.226

BF5###max(0; L-31.69)*max(0; pH-6.16)*max(0; Wild-type)###-2.727

BF6###max(0; a-15.89)*max(0; Dark-Brown)###0.521

BF7###max(0; Dark-Brown)###16.0004

BF8###max(0; L-31.69)*max(0; Dark-Brown)###-0.0009

BF9###max(0; a-15.89)*max(0; pH-6.16)*max(0; Wild-type)###2.653

BF10###max(0; L-31.69)*max(0; pH-6.16)###0.521

BF11###max(0; L-31.69)*max(0; pH-6.16)*max(0; Female)###-0.892

BF12###max(0; Golden)###17.459

BF13###max(0; a-15.89)*max(0; Wild-type)*max(0; Female)###-9.330

BF14###max(0; L-31.69)*max(0; Wild-type)*max(0; Female)###5.656

BF15###max(0; Female)###0.849

BF16###max(0; a-19.02)*max(0; Female)###-0.180

BF17###max(0; 19.02-a)*max(0; Female)###0.029

BF18###max(0; L-31.69)*max(0; Dark-Brown)*max(0; Female)###0.066

BF19###max(0; L-31.69)*max(0; a-15.89)*max(0; Wild-type)###-0.368

BF20###max(0; pH-6.38)*max(0; Female)###2.649

The MARS equation of Model 2 according to these results was as follows.

a= 13.6 + 1.52 * Golden + 1.79 * Male + 35.3 * max(0,38.7-L) + 9.25 * max(0, b-12.2)+ 2.7 * max(0, 12.8 - b)-3.69 * max(0, b - 12.8) - 821 * max(0, 6.33 - pH)+ 23.3 * max(0, pH - 6.33) + 22.8 * L * max(0, 6.33-pH) - 5.83 * max(0, 38.7 - L) * pH + 3.35 * max(0, 38.7 - L) * Golden - 2.06 * max(0, 39.6 - L) * Golden +0.318 * max(0, L - 39.6) * Golden - 1.67 * max(0, L - 38.7) * Male-1.4 * max(0, 12.5 - b) * Golden - 3.4 * max(0, b - 12.5)*Golden - 20.7 * max(0, 6.38 - pH) * Golden - 7.87 * max(0, pH - 6.38) * Golden + 1.36 * max(0, b - 12.5) * Golden* Male

b*(yellow color) value: A MARS algorithm was created by selecting variety, sex, L*, a* and pH as independent variables to predict the b* value of the breast meat in quails. The model fit statistics for the MARS algorithm are given in Table 6.

The results of the MARS model including the basis function and the coefficients are presented in Table 7. A MARS model with 21 basis functions and 3-way interactions was obtained as the best model. For this model, the parameters were found as follows: GCV=0.029, R2=0.999, Adj. R2=0.996, SDratio=0.132, RMSE=0.038 and MAD=0.026.

The MARS equation obtained for Model 3 according to these results was as follows.

b*=-10.0031+0,763*max(0; a-15.89)+0.264*max(0; L-31.69)+2.737*max(0; a-15.89)* max(0; Wild-type)+2.226*max(0; L-31.69)*max(0; Wild-type)-2.727*max(0; L-31.69) *max(0; pH-6.16)*max(0; Wild-type)+0.521*max(0; a-15.89)*max(0; Dark-Brown)+16.0004*max(0; Dark-Brown)-0.0009*max(0; L-31.69)*max(0; Dark-Brown)+ 2.653*max(0; a-15.89)*max(0; pH-6.16)*max(0; Wild-type)+0.521*max(0; L-31.69) *max(0; pH-6.16)-0.892*max(0; L-31.69)*max(0; pH-6.16)*max(0; Female)+ 17.459*max(0; Golden)-9.330*max(0; a-15.89)*max(0; Wild-type)*max(0; Female)+ 5.656*max(0; L-31.69)*max(0; Wild-type)*max(0; Female) +0.849*max(0; Female)-0.180*max(0; a-19.02)*max(0; Female)+0.029*max(0; 19.02-a)*max(0; Female)+0.066*max(0; L-31.69)*max(0; Dark-Brown)*max(0; Female)-0.368*max(0; L-31.69)*max(0; a-15.89) *max(0; Wild-type)+2.649*max(0; pH-6.38)*max(0; Female).

DISCUSSION

The use of the MARS model in the stock farming area is very limited. However, the results obtained in the present study contain much more descriptive findings than commonly used models. The model have been successfully used in the subjects of cattle raising(Aytekin et al., 2018; Erturk et al., 2018), sheep raising(Karadas et al., 2017; Eyduran et al., 2017) and beekeeping(Aksoy et al., 2018a, Aksoy et al., 2018b).

This study presented detailed information on the definition of the color of the breast meat in quails belonging to different varieties and sexes through the MARS model. a*>15.89, b*>8.79, pH<6.38 and female quails had a significant effect on the L* value of the breast meat in quails in general. The factor that affected the L* the most was the basis function with 3-way interactions where a*12.46 and the quails were female. The a* value of the breast meat was positively influenced with interaction L*>31.69 and b*>8.79. However, it was not influenced by sex and the Wild-type variety.

The variables that increased the b* value of breast meat the most were the Golden variety, the Dark-Brown variety, and the L*> 31.69, Wild-type variety and female quail interaction, respectively.

Conclusions: In conclusion, in quails, the L*(Model 1) and b*(Model 3) values were explained better by the MARS model with second degree interactions, while the a* value(Model 2) was defined better by the model with first degree interactions.

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Author:T. Sengul, S. Celik and O. Sengul
Publication:Journal of Animal and Plant Sciences
Geographic Code:9JAPA
Date:Jul 23, 2020
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