2017
DOI: 10.1371/journal.pone.0187292
|View full text |Cite
|
Sign up to set email alerts
|

A mathematical function for the description of nutrient-response curve

Abstract: Several mathematical equations have been proposed to modeling nutrient-response curve for animal and human justified on the goodness of fit and/or on the biological mechanism. In this paper, a functional form of a generalized quantitative model based on Rayleigh distribution principle for description of nutrient-response phenomena is derived. The three parameters governing the curve a) has biological interpretation, b) may be used to calculate reliable estimates of nutrient response relationships, and c) provi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
5
0
1

Year Published

2019
2019
2024
2024

Publication Types

Select...
6
2
1

Relationship

1
8

Authors

Journals

citations
Cited by 11 publications
(6 citation statements)
references
References 19 publications
0
5
0
1
Order By: Relevance
“…The performance of MLP-GA models is determined by root mean square error (RMSE) and coefficient of determination (R 2 ) as reported by Ahmadi (2017), as well as mean absolute percentage error (MAPE) [Eq. 1].…”
Section: Genetic Algorithm (Ga)mentioning
confidence: 99%
“…The performance of MLP-GA models is determined by root mean square error (RMSE) and coefficient of determination (R 2 ) as reported by Ahmadi (2017), as well as mean absolute percentage error (MAPE) [Eq. 1].…”
Section: Genetic Algorithm (Ga)mentioning
confidence: 99%
“…Each model consisted of linear or non-linear parameters (compartments) that could be converged to make a mathematical equation relating the nutrient doses with animal responses. In general, the biological models follow the non-linear manner that has been illustrated in different platforms, including broken lines [ [9] , [10] , [11] , 28 , 29 ], response surface methodology [ [30] , [31] , [32] ], uniform design [ 33 ], artificial neural networks [ 34 , 35 ], and three or four-parameter models [ 36 ] by researchers. The other methods used to estimate the amino acid requirements in monogastric animals are the non-linear logistic and saturation kinetic models [ 37 , 38 ], Rayleigh [ 36 ], and exponential models [ 39 ].…”
Section: Discussionmentioning
confidence: 99%
“…In general, the biological models follow the non-linear manner that have been illustrated in different platforms including spline broken lines 6-9,24 , response surface methodology (RSM) [25][26][27] , uniform design 28 , arti cial neural networks 29,30 , and three or four parameter models 31 by researchers. The other used methods to estimate the amino acid requirements in monogastric animals are the non-linear logistic and saturation kinetic models (SKM) 32,33 , Rayleigh model 31 , and exponential model 34 .…”
Section: Discussionmentioning
confidence: 99%