aNDF, NDFd, iNDF, ADL and kd: What have we learned?
aNDF, NDFd, iNDF, ADL and kd: What have we learned?
aNDF, NDFd, iNDF, ADL and kd: What have we learned?
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analysis sho<strong>we</strong>d the highest correlation with the true lag. The discrete arbitrary lag of<br />
three hours was instead used, as an arbitrary proxy of an average commercial<br />
laboratory lag. This was done to analyze the option of using only one time point to<br />
estimate the rate. The rates of digestion obtained with our approach <strong>we</strong>re then<br />
compared for prediction with the rates obtained from the non-linear estimation (<strong>kd</strong>). The<br />
single point rates of digestion <strong>we</strong>re obtained using the 12, 24, 30 <strong>and</strong> 36 hour time<br />
points. Kd’s estimated by semi-logarithmic transformation <strong>and</strong> using the same <strong>iNDF</strong> as<br />
mentioned above <strong>we</strong>re also included for comparison. The non linear estimation used<br />
data up to 216 hours, therefore, the log-linear transformation estimation was obtained<br />
using different ranges of time points. Prediction accuracy was tested <strong>and</strong> compared<br />
using correlations <strong>and</strong> the mean square prediction error (MSPE) analysis of Theil (1966)<br />
<strong>and</strong> Bibby <strong>and</strong> Toutenburg (1977). The non-linear estimation allo<strong>we</strong>d us to obtain an<br />
approximated 95% confidence interval for the rates that was used as further comparison<br />
tool.<br />
All calculation procedures resulted in equations that described the data very <strong>we</strong>ll.<br />
Predicted <strong>and</strong> observed values <strong>we</strong>re highly correlated (P