Table 5.
List of the biomarkers of liver weight and technological yield of foie gras.
| Biomarkers of liver weight | Biomarkers of technological yield | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| with bucket method | with metabolite method | with bucket method | with metabolite method | |||||||||
| Important peaksε | BH p-Valueζ | correlation with LWη | VIPδ | BH p-Valueζ | correlation with LWη | Important peaksε | BH p-Valueζ | correlation with TYη | VIPδ | BH p-Valueζ | correlation with TYη | |
| Biomarkers of LW and TY | ||||||||||||
| Alanine | −0.83 | 1.55 | −0.9 | 0.89 | 1.43 | <0.001 | 0.81 | |||||
| Allantoin | 1/1 | 0.020 | −0.8 | 1/1 | 0.004 | 0.75 | ||||||
| Glucose | 2.87 | <0.001 | −0.95 | 21/22 | <0.001 | 0.92 | 2.86 | <0.001 | 0.94 | |||
| Glyceric acid | 2/2 | <0.001 | −0.89 | 2/2 | <0.001 | 0.90 | ||||||
| Glycogen | 2/2 | 0.007 | −0.97 | 2/2 | 0.01 | 0.98 | ||||||
| Lactate | 2/2 | <0.001 | 0.98 | 4.11 | <0.001 | 0.94 | 2/2 | <0.001 | −0.98 | 4.06 | <0.001 | −0.97 |
| Maltose | 12/12 | <0.001 | −0.97 | 12/12 | <0.001 | 0.90 | ||||||
| Taurine | 2/2 | <0.001 | −0.78 | 1.29 | <0.001 | −0.84 | 0.75 | 1.51 | <0.001 | 0.82 | ||
| Threonine | 0.98 | 1.72 | <0.001 | 0.96 | −0.96 | 1.72 | <0.001 | −0.95 | ||||
| Biomarkers of LW | ||||||||||||
| Arginine | 2/3 | 0.004 | −0.92 | |||||||||
| Glucuronic acid | 6/8 | <0.001 | −0.93 | |||||||||
| Glycerophosphocholine | 3/4 | 0.070 | −0.84 | |||||||||
| Malic acid | 2/4 | <0.001 | −0.61 | |||||||||
| Trans-4-hydroxy-L-proline | 6/7 | 0.005 | −0.81 | |||||||||
| Biomarkers of TY | ||||||||||||
| Creatine | 2/2 | <0.001 | −0.57 | |||||||||
| Ethanolamine | 2/2 | 0.009 | 0.95 | |||||||||
| Glutamic acid | 8/8 | 0.01 | 0.93 | |||||||||
| Guanidinoacetic acid | 0.46 | 1.04 | <0.001 | 0.86 | ||||||||
For each biomarker, the number of important peaks compared with the total number of 1H-NMR peaks is indicated. The important peaks contained at least one bucket with a VIP > 1 to explain the first latent variable of the PLS model of liver weight or technological yield.
The models of the effects of the relative metabolite concentration on the liver weight and technological yield were computed. The p-values were corrected with the Benjamini–Hochberg procedure and indicated.
The Pearson correlation of the metabolite relative concentration obtained with bucket data or metabolite data and the liver weight or the technological yield was indicated.
The PLS model to describe the liver weight or the technological yield with metabolite data was plotted. The first latent variable enabled to separate the fatty livers in function of the liver weight or the technological yield. The metabolites with VIP superior to 1 were selected. The VIP of the metabolite was indicated.