Application of Mathematical Models to Estimate Metabolizable Energy Contents of Energetic Concentrate Feedstuffs for Poultry
محورهای موضوعی : CamelM. Sedghi 1 , K. Tayebipoor 2 , B. Poursina 3 , M. Eman Toosi 4 , P. Soleimani Roudi 5
1 - Kian Jooje Aria Company, Mashhad, Iran
2 - Kian Jooje Aria Company, Mashhad, Iran
3 - Kian Jooje Aria Company, Mashhad, Iran
4 - Kian Jooje Aria Company, Mashhad, Iran
5 - Kian Jooje Aria Company, Mashhad, Iran
کلید واژه: Artificial Neural Network, stepwise regression, chemical composition, metabolizable energy, multiple linear regression,
چکیده مقاله :
A study using 51 wheat, 56 barley and 34 oat grain samples was conducted to investigate the feasibility of predicting the apparent metabolizable energy (AME) value of these cereals for poultry. Stepwise regression analyses were performed to evaluate the relationship of AME with starch, ether extract (EE), crude fiber (CF), soluble sugar (SS), ash and crude protein (CP) (for wheat and barley grain samples) or dry matter (DM), CF, ash and CP (for oat grain samples) as independent variables. According to the stepwise regression analyses, SS, CF and ash for wheat, CF, EE and starch for barley and CF and CP for oat were found to be useful predictors for AME prediction. Also, multiple linear regression (MLR) and artificial neural network (ANN) methods were developed to find the best models which can estimate the AME content of these cereals. Mean square deviation, Mean square variation and their components were used to evaluate the performance of MLR and ANN models. The results showed that AME of wheat can be predicted by SS, CF and ash. The CF, EE and starch are good independent variables to estimate AME content of barley samples. Also, CF and CP are good predictor parameters for AME prediction in oat samples. In case of model performance, the accuracy of the ANN model was stronger than MLR. Based on these results, it was concluded that the use of chemical composition in combination with the ANN model is a promising method to predict AME of wheat, barley and oat grain samples in poultry nutrition.
این مطالعه با هدف بررسی امکان پیش‌بینی انرژی قابل متابولیسم ظاهری (AME) در طیور با استفاده از آنالیز تقریبی در نمونه‌های گندم (51 نمونه)، جو (56 نمونه) و یولاف (34 نمونه) انجام شد. از آنالیز رگرسیون گام به گام برای ارزیابی رابطه بین AME با نشاسته، عصاره اتری (EE)، فیبر خام (CF)، قندهای محلول (SS)، خاکستر (ash) و پروتئین خام (CP) در نمونه‌های گندم و جو، و ماده خشک (DM) ، CF، Ash و CP در نمونه‌های یولاف استفاده شد. براساس نتایج رگرسیون گام به گام، SS، CF و Ash، بهترین متغیرهای ورودی برای پیش‌بینی AME در نمونه‌های گندم بودند. بعلاوه، نشاسته،CF و EE در نمونه های جو و CF و CP در نمونه‌های یولاف بهترین پارامترهای ورودی جهت تخمین AME شناسایی شدند. همچنین برای بدست آوردن بهترین مدل تخمین زننده AME در این غلات با استفاده از ورودی‌های انتخاب شده از رگرسیون خطی چندگانه (MLR) و شبکه عصبی مصنوعی (ANN) استفاده شد. نتایج MLR و ANN نیز نشان داد که SS، CF و ash فاکتورهای مؤثری جهت تخمین AME درگندم می‌باشند. برای پیش‌بینی AME نمونه‌های جو CF، EE و نشاسته متغییرهای مستقل خوبی به شمار می‌آیند. همچنین CF و CP پارامترهای خوبی برای پیش‌بینی AME در نمونه‌های یولاف می‌باشند. در ارتباط با کارایی مدل‌ها، دقت مدل ANN بالاتر از مدل MLR بود. بر اساس این نتایج، می‌توان چنین نتیجه‌گیری کرد که استفاده از ترکیبات شیمیایی موجود در نمونه‌های گندم، جو و یولاف، همراه با مدل ANN، روش کاربردی جهت پیش‌بینی AME این غلات در تغذیه طیور می‌باشد.
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