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Validating Egg Component Proportions in Quail Receiving Methionine Supplementation

Essential amino acid methionine is a methyl donor which involved in protein and fat metabolism; thus, it holds an important role in the synthesis of albumen and egg yolk precursors. This research aimed to observe the effect of methionine on the weight of egg and its components and to prove if egg weight (EW) could be used to predict the albumen weight (AW) and yolk weight (YW). In total, 225 quails were allotted into three treatments and five replicates each with fifteen individuals. The treatments were basal diet (T0), basal diet with methionine supplementationat 0.6 (T1) and 1.2 g/kg (T2). ANOVA was conducted to test the effect of treatments. Simple linear regressions were built to predict AW and YW based on EW. Cross validation procedure was applied to test the predictability of the models. Quails in T2 and T1 produced heavier eggs (9.29±0.52 and 9.04±0.76 g) compared to T0 (8.77±0.55 g). The AW follow similar trends which were 5.37±0.38, 5.53±0.54, 5.75±0.44 g for T2, T1 and T0, respectively. The YW slightly differ where T1 had higher weight (2.64±0.30 g) followed by T2 (2.62±0.27 g) and T0 (2.50±0.32 g). Pearson’s correlation between EW and AW were between 69-87%whereas for EW and YW were between 61-71%. Simple linear regression predicting AW and YW from EW had accuracies between 94.3-96.2% and92.7-94.3%, respectively. Thus, methionine supplementation had significant effect on egg and egg components weights. Linear regression can be utilized to predict AW and YW based on EW with high accuracy.


Introduction
Egg quality is determined by several egg traits such as the weight of egg, eggshell, albumen and yolk as well as shell thickness [1].In the egg processing industries, the shell weight (SW),albumen weight (AW), and yolk weight (YW) affect the amount of the product [2]. Furthermore, the interior quality of the egg is affected by nutrient content in the diet [3]. The yellow yolk is composed of lipoproteins while albumen is composed of proteins. The protein requirements for laying quail is about 20% [4]. However, feeding with high-protein diet,particularly in hot, tropical climate such as in Indonesia, stimulates metabolic heat production leading to heat stress. Thus, it is required to provide the precise protein content in the diet. Feeding low-protein diet with supplementation of feed additive or amino acids may become an alternative to avoid heat stress while maintaining the quails' performances [5,6]. Methionine is considered to be the first essential amino acid in poultry nutrition, which limits the biological value of protein [7]. It also acts as a methyl group donor which plays a role in energy and protein metabolism [8]. Supplementation of methionine to low-protein diet showed beneficial effect on the performance of laying hens. On the contrary, methionine supplementation to high-protein diet did not show beneficial effect [9]. Harms et al [10] demonstrated that egg weight (EW) of laying hens increased as the methionine content in the diet increased. Accordingly, previous observation revealed that methionine supplementation increased EWor egg mass in quails [11,12]. Furthermore, increasing methionine consumption enhanced the albumen total solid and protein as well as yolk protein contents [9].
Egg is a multi-purpose animal product, which is utilized in various ways; either as a whole or separated between its albumen and yolk. For the later purpose, the information about egg composition is essential. Except EW, other egg traits including AW and YW are determined only after breaking the egg [13]. This method is complicated, takes a lot of time and causes economic losses due to broken eggs [14]. Prediction of AW and YW with the regression equation using EW as predictor allows to measure these variables without breaking the eggs [15]. Several studies have been conducted to determine the correlation between external and internal quality traits in laying hens, but only few studies have been performed on this subject in quails. For example, Kul and Seker [13]observed a positive correlation between EW and the internal quality traits such as AW and YW in quails. However, there is paucity of information on the predictability with cross validation method, particularly in quails fed diet supplemented with methionine. Therefore, the objective of this study was to build models to predict the AW and YW using EW data of quails (Coturnix coturnix japonica) receiving methionine supplementation in the diet.

Methodology Experimental design and diet formulation
In total, 225 laying quails (Coturnix coturnix japonica) were allotted into three treatments and five replicates each with fifteen individuals. The basal diet was formulated according to the recommendation of Nutrition Research Council [4] for laying quails, except the crude protein which was formulated below the standard (16.5%), as presented in Table 1. The basal diet was fed without methionine supplementation (T0) or with methionine supplementation at the levels of 0.6 g/kg (T1) and 1.2 g/kg (T2) according to the procedure of Ratriyanto et al. [16]. The dietary treatments were given for two periods of 28 days (2×28 days). The egg quality traits data were collected at the last 3 days of each period, namely days 26, 27 and 28. The eggs were individually weighed and then cracked. The yolkswere separated from the albumen using yolk separator and weighed thereafter. The AW was obtained by reducing the EW with the YW and SW [17].

Management and analysis of the data
The data of EW, AW and YW were obtained from two measurement periods. They were separated between T0, T1 and T2. The data were the subjected to analysis of variance, correlation, regression and cross validation. All data analyses were performed using custom scripts of R program [18].Analysis of variance was performed to determine the effect of dietarymethionine on EW,  [19].
Correlation between EW and AW or YW, and the regression model was calculatedto determine the relation between them. The correlation of EW~AWand EW~YWwere calculated according to Ott et al. [20] as follows: Where:REW,AY = correlation of EW~AWorEW~YW;,Cov (EW,AY) = covariant between EW~AWorEW~YW,σEW = standard deviation of EW,σAY = standard deviation of AW and YW,n =number of data,x = data; µ = average. A linear regression model was built to predict AW and YWusing EW data according to Gujarati [21]: y = α + βx Where:Yi= response (AW and YW),α =intercept,β = slope of independent variable,and xi =EW. The model built was compared to the predictor's ability by using the accuracy of the prediction model. Predictabilitywas obtained by employing the crossvalidation method to measure the performance of a prediction model [15] using most of the data to create a prediction model and save the rest for validation process [22]. Cross validation applied a regression formula obtained in another data to measure the accuracy of the formula in predicting another set of data. The crossvalidation was determined by selecting a certain amount of data (test set) and the remaining data are training set (Figure 1). Figure 1 shows the data 2-10 were defined as the training set that were used to create a regression equation, then to predict the test set of the data 1. The actual value of the test set data and predicted value based on the regression was then compared to determine the predictability of the regression equation.

Result and Discussion Actual weight of the egg, yolk and albumen
The averageEWof all treatments ranged between 8.77 and 9.29 grams ( Table 2), which were categorized in the normal range of quails' EW [23]. The AW and YWobtained in this study are also in the normal range, namely5.37-5.75 gramsand2.50-2.60grams, respectively. The EW is in direct proportion to albumen, yolkand eggshell. The albumen and yolk quality are the parameterswhich determine the internal egg quality [24]. Dietary methionine administration yielded heavier eggs associated with enhanced in AW and YW. Quails in T2 and T1 produced heavier eggs (9.29±0.52 and 9.04±0.76 g) compared to T0 (8.77±0.55 g). The AW follow similar trends which were 5.37±0.38, 5.53±0.54, 5.75±0.44 g for T2, T1 and T0, respectively. The YW slightly differ where T1 had higher weight (2.64±0.30 g) followed by T2 (2.62±0.27 g) and T0 (2.50±0.32 g).Methionine is an essential amino acid and serves as a methyl group donor which involved in the protein and energy metabolism process leading to improvement in EW [11]. Albumen composed of protein while yolk composed mainly of lipoproteins, thus involvement of methionine in the metabolism increase the synthesis of the albumen and yolk precursors.In this study, each variable (EW, AW and YW) was affected by methionine supplementation, thus further analyses were conducted separately based on the treatment.

Correlation of egg weight~yolk weight and egg weight~albumen weight
The statistical analyses showed the positive correlations of EW~AW and EW~YW ( Table 3).
Correlations of EW~AW are classified as high correlation with the correlation value above 0.7 while correlations of EW~YW are classified as moderate(0.3-0.7) to high correlation (above 0.7) [25]. Supplementation of 0.6 g/kg methionine generated the highest correlation both for EW~AW and EW~YW. In agreement with this result, previous observation indicated positive correlation of EW~AW and EW~YW in quails and chickens [13,26].However, the correlation values obtained in this study are lower than those observed in previous study, where correlation betweenEW~AW was 0.94 and correlation between EW~YW was 0.77 [27]. Predicting the interior quality of the egg components using the EW data is considered easier and without breaking the egg. Moreover, EW has proven as a good predictor for some egg [13] since the correlations between external and internal quality traits were high [28]. Kul and Seker [13] showed that almost all internal egg quality traits changed depending on the change occurred in the EW as indicated by positive correlations. The simple linear regression model that wasbuiltin this studyusedthe dependent variable (y), that is AW orYW and independent variable (x), that is EW. In total, six regression models were built in this study since there were 3 treatments in each variable of AW or YW ( Table 4).The coefficient of determination (R 2 ) indicates the capability of the model in explaining variations in the dependent variable [21]. The R 2 values ranged between 0 and 1, with the higher value indicating the the better fit to explain the relationship between the EW and the AW or YW. In agreement with this study, Seker [27] showed that EW can be used as a predictor for AW and YW with R 2 values for linear regression were 0.88 and 0.59 for AW and YW, respectively. In this regard, linear regression was accurate in predicting the relation between EW and AW or YW [27].

Linear regression model and cross validation
All the six models were then subjected to K-fold cross validation resulting the predicted values of AW and YW. The prediction accuracy were obtained by comparing the actual values with the prediction values [15] as presented in Table 5. Regression models had predictability ranged between 94.3-96.2% for AW and 92.7-94.3% for YW. The predictability values of all treatments were above 90%, thus its classified as very high accuracy. Similar to this study, a study in Japanese quail revealed that AW and YW were predictable from EW with enough accuracy [1]. The finding of this study supports that forecasting the weight of the egg and egg components without recourse to sensitive electronic scale has been a serious problem in egg production enterprise [26,29].

Conclusion
Linear regression model with EW as a predictor was considered as good model for predictingAWand YW in quailsreceiving methionine supplementation in the diet. All models for predicting AWand YW generated very high predictability. Based on this result, it is possible to use the EWas predictor of the AW and YW of quail eggs with high accuracy.