Predictions of food intake in ruminants from analyses of food composition

1996 ◽  
Vol 47 (4) ◽  
pp. 489 ◽  
Author(s):  
DP Poppi

Equations used to predict intake by cattle from some chemical or physical characteristic of food were examined. The equations are empirical or mechanistic in nature. Mechanistic equations are not used widely, usually only in a research context. The input to mechanistic models requires too much time to quantify to be used routinely. Empirical relationships form the basis of most feeding standards and are based on a wide variety of prescribed characteristics (digestibility, chemical composition, etc.), but the underlying principle is a relationship between intake and digestibility. Equations are modified to take account of feed types, animal weight and physiological state, rumen modifiers, hormone implants, environmental conditions, and whether grazing or hand fed. Quite significant differences exist between the equations in the intakes they predict in response to variation in weight, breed type, and feed digestibility. Equations can be very precise in their prediction when used with feed types and breed types on which they are based. Near infrared reflectance (NIR) offers the most potential for long-term development of equations. At present, NIR is used largely to determine chemical composition because of speed of operation, but long-term storage of data is simple, allowing further associative relationships to be developed readily. More sophisticated statistical procedures being employed to improve the precision of the relationships between intake and prescribed characteristics of food and NIR will be vitally important as they enable extra parameters to be incorporated at no extra cost or time for analysis.

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Lu Xu ◽  
Xian-Shu Fu ◽  
Chen-Bo Cai ◽  
Yuan-Bin She

Long-term storage can largely degrade the taste and quality of dried shiitake mushroom (Lentinula edodes). This paper aimed at developing a rapid method for discrimination of the regular and aged shiitake by near infrared (NIR) spectroscopic analysis and chemometrics. Regular (n=197) and aged (n=133) samples of shiitake were collected from six main producing areas in two successive years (2013 and 2014). NIR reflectance spectra (4000–12000 cm−1) were measured with finely ground powders. Different data preprocessing method including smoothing, taking second-order derivatives (D2), and standard normal variate (SNV) were investigated to reduce the unwanted spectral variations. Partial least squares discriminant analysis (PLSDA) and least squares support vector machine (LS-SVM) were used to develop classification models. The results indicate that SNV and D2 can largely enhance the classification accuracy. The best sensitivity, specificity, and accuracy of classification were 0.967, 0.953, and 0.961 obtained by SNV-LS-SVM and 0.933, 0.930, and 0.932 obtained by SNV-PLSDA, respectively. Moreover, the low model complexity and the high accuracy in predicting objects produced in different years demonstrate that the classification models had a good generalization performance.


2016 ◽  
Vol 201 ◽  
pp. 168-176 ◽  
Author(s):  
S.A. Petropoulos ◽  
G. Ntatsi ◽  
Â. Fernandes ◽  
L. Barros ◽  
J.C.M. Barreira ◽  
...  

2020 ◽  
Vol 164 ◽  
pp. 111170 ◽  
Author(s):  
Bruna Klein ◽  
Renata Bolzan Falk ◽  
Fabio Rodrigo Thewes ◽  
Rogerio de Oliveira Anese ◽  
Ingrid Duarte dos Santos ◽  
...  

2001 ◽  
Vol 6 (2) ◽  
pp. 3-14 ◽  
Author(s):  
R. Baronas ◽  
F. Ivanauskas ◽  
I. Juodeikienė ◽  
A. Kajalavičius

A model of moisture movement in wood is presented in this paper in a two-dimensional-in-space formulation. The finite-difference technique has been used in order to obtain the solution of the problem. The model was applied to predict the moisture content in sawn boards from pine during long term storage under outdoor climatic conditions. The satisfactory agreement between the numerical solution and experimental data was obtained.


Diabetes ◽  
1997 ◽  
Vol 46 (3) ◽  
pp. 519-523 ◽  
Author(s):  
G. M. Beattie ◽  
J. H. Crowe ◽  
A. D. Lopez ◽  
V. Cirulli ◽  
C. Ricordi ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document