Improving the use of bioethanol by-products in monogastric feeding
GADY C ¹*, COZANNET P ², ALLEMAN F ¹, GERAERT PA ³, DALIBARD P ³
Eighteen DDGS samples (8 wheat DDGS and 10 corn DDGS) were obtained from 6 different countries. Each sample was analysed for proximate and total amino acids (TAA) and evaluated for true ileal amino acid digestibility (AAD) using caecectomized cockerels. Wheat DDGS were classified as low and standard whereas corn DDGS showed an additional high protein type. Important variations were observed among the total lysine and threonine of the wheat DDGS and among all total amino acids of the corn DDGS with the exception of total cystine. Both wheat and corn DDGS showed low lysine digestibilities (38 and 62%) and low cystine digestibilities (56 and 64%) associated to high variations. Original cereals, processing conditions and disposition of solubles, could explain the variations. The potential of NIRS (Near Infrared Reflectance Spectroscopy) was also investigated. The feasibility study show that the NIRS calibrations explain more than 95 and 80% of the variation respectively for TAA and DAA (with the exception of tryptophan digestibility, R²=0.66). Accuracies associated to the models varied from 2 to 6% (excepted for DLys, 9%). These encouraging results indicate that this tool is promising for the prediction of the nutritional variability measured among DDGS.
Introduction
Distillers dried grains with solubles (DDGS) are by-products issued from the conversion of cereals such as corn, wheat, barley, rye, etc. into bioethanol. Milling, cooking and fermentation phases constitute the basic steps to produce ethanol separated by distillation. The residual liquid phase, including smallest particles and yeasts, is dehydrated and it is mixed to the dried solid fermentation residues. DDGS have been mainly used in ruminant feeding. Due to the growing interest in this renewable source of energy, DDGS is becoming more and more available as a feed ingredient. Up to now, limited information is available about nutrient contribution of DDGS in monogastric diets. Therefore, our studies were conducted firstly to determine the nutrient variability of corn and wheat DDGS in poultry and secondly to investigate how to predict this variability.
Materials and methods
Near Infrared Spectroscopy (NIRS) is based on a simple principle: the absorption of the infrared rays by organic matter. A sample can partially or selectively absorb this radiation. It thus provides information about the present organic molecules: its chemical composition. The NIRS spectrum consists of energy vibrations ranging from 800-2500 nanometers (wavelengths) whereas the visible region covers from 400 to 800 nm (VAN KEMPEN, 2001).
For the present experiment, 18 DDGS samples (8 wheat DDGS and 10 corn DDGS) obtained from different countries were analysed in both the visible and the NIRS regions. Each sample was also analysed for proximate and total amino acids (TAA) and evaluated for true ileal amino acid digestibility (AAD) using caecectomized cockerels. Digestibility coefficients have been corrected by endogenous amino acid values that were determined with a protein-free diet. The procedure used for these in vivo tests has been described by GREEN et al. (1987). The potential of NIRS was investigated in parallel to the qualification of the nutritional variability among DDGS. Correlations between spectral characterizations and reference data in total amino acids and amino acid digestibility were developed using partial least squares (PLS) regression technique. The spectral data were primarily subjected to mathematical pre-treatments.
Results and discussion
Table 1 shows the nutrient variability measured among the eight wheat DDGS. Tested samples were classified according to their protein content and they were then ranged from low protein to standard type (28.3 vs 36.1 % DM). The proximate composition of standard wheat DDGS indicated important variability in fat, crude fiber and ashes, which was reflected in their respective CV's (16.3; 16.1 and 11.0%). In terms of total amino acid composition, most important variations appeared on lysine, threonine and tryptophan. Nutritional variations among these 8 wheat DDGS seemed to be influenced by either processing particularities but not by geographical origins. Differences in amino acid composition may be related to the stillage recycling that are added at the end of the process.
In addition, our results did not show any trend correlating total nitrogen to total lysine whereas the correlation coefficient for methionine and crude protein has been found around 0.68. Table 1 also ilustrated that the lowest digestibility coefficients have been measured for lysine and cystine relative to the other ones. Respective digestibility coefficients have been found at 38.2 and 55.8% in association to a wide variability particularly lysine digestibility (CV=27.1%). An explanation to both the non correlation between protein and total lysine and the low lysine and cystine digestibilities should be the sensitivity to the high temperatures used during the drying process (CROMWELL et al., 1993). This could be emphasized by the fact that some of our studied wheat DDGS come from processing under evolution and optimisation (SINGH et al., 2007).
Table 1| Standard protein ¹ | Low protein ² | |||
|---|---|---|---|---|
| Mean | SD | CV% | ||
| Protein | 36.1 | 2.3 | 6.3 | 28.3 |
| Fat | 6.6 | 1.1 | 16.3 | 11.6 |
| Crude Fiber | 7.9 | 1.3 | 16.1 | 10.4 |
| Ashes | 5.4 | 0.6 | 11.0 | 4.8 |
| Total Amino Acids (% protein) | ||||
| Lysine | 1.93 | 0.36 | 18.4 | 1.70 |
| Methionine | 1.45 | 0.07 | 5.1 | 1.50 |
| Cystine | 1.84 | 0.11 | 5.4 | 1.13 |
| Threonine | 3.32 | 0.57 | 17.1 | 3.40 |
| Tryptophan | 1.07 | 0.12 | 11.5 | 0.73 |
| Amino acid digestibility for poultry (%) | ||||
| Protein | 81.3 | 3.3 | 4.1 | |
| Lysine | 38.2 | 10.4 | 27.1 | |
| Methionine | 77.7 | 5.1 | 6.6 | |
| Cystine | 55.8 | 5.3 | 9.6 | |
| Threonine | 67.9 | 7.0 | 10.4 | |
| Tryptophan | 71.6 | 7.5 | 10.5 | |
| Humidity (%) | 11.1 | 2.8 | 25.5 | 12.8 |
¹ Seven wheat DDGS from Sweden (1), Germany (3) and France (3).
² One wheat DDGS from Finland.
| High protein ¹ | Standard protein ² | Low protein ³ | |||
|---|---|---|---|---|---|
| Mean | Mean | SD | CV% | ||
| Protein | 45.5 | 31.8 | 2.8 | 8.9 | 26.3 |
| Fat | 11.0 | 12.0 | 3.3 | 27.7 | 9.8 |
| Crude Fiber | 15.1 | 10.7 | 3.3 | 31.2 | 8.4 |
| Ashes | 2.1 | 4.5 | 0.6 | 13.4 | 5.4 |
| Total Amino Acids (% protein) | |||||
| Lysine | 2.48 | 2.75 | 0.78 | 28.3 | 2.37 |
| Methionine | 2.21 | 1.92 | 0.29 | 15.2 | 1.57 |
| Cystine | 2.03 | 1.92 | 0.07 | 3.9 | 1.82 |
| Threonine | 3.73 | 3.65 | 0.30 | 8.2 | 3.64 |
| Tryptophan | 0.70 | 0.87 | 0.09 | 10.3 | 0.80 |
| Amino acid digestibility for poultry (%) | |||||
| Protein | 90.2 | 84.5 | 2.3 | 2.7 | 79.8 |
| Lysine | 78.4 | 62.5 | 7.7 | 12.3 | 44.6 |
| Methionine | 92.1 | 86.5 | 3.9 | 4.5 | 81.4 |
| Cystine | 79.3 | 64.1 | 6.7 | 10.4 | 45.9 |
| Threonine | 82.0 | 73.0 | 3.0 | 4.1 | 61.8 |
| Tryptophan | 78.6 | 73.0 | 3.9 | 5.4 | 64.7 |
| Humidity (%) | 8.5 | 9.9 | 1.0 | 10.3 | 10.0 |
¹ Two batches of corn DDGS from US and one from China.
² Five corn DDGS origin US (4), China (1) and Finland (1).
³ One corn DDGS from US.
Corn DDGS were classified as low, standard and high protein type (Table 2). Protein content varied from 26.3 to 45.5% DM. In the standard class, the total amino acids that showed the most important variations in concentrations are lysine, methionine, threonine and tryptophan (respectively 28.3, 15.2, 8.2 and 10.3%). Other recent studies (BATAL et al., 2006; PARSONS et al., 2006) confirmed the amino acid concentrations we measured. The different classes of corn DDGS, reflected the efforts to develop and improve co-products with added value concurrently to new process evolutions. New processing and fractionation steps are thus being developed to remove the germ and pericarp fibre prior to the grinding process (SNGH et al., 2007). Consequently, these corn DDGS were higher in protein and lower in total dietary fibre with comparison to the standard ones. Differences in nutritional quality were further emphasized with the amino acid digestibilities. Indeed, in terms of digestibility, lysine and cystine appeared more variable and also lower than other amino acids. Digestibility coefficients among the three categories of corn DDGS (high, standard and low protein content) were respectively 78.4, 62.5 and 44.6% for ysine and 79.3, 64.1 and 45.9% for cystine. However, both lysine and cystine digestibility coefficients are respectively improved by +15.9 and 15.2 points in the high protein corn DDGS compared to the standard samples. In addition, our lysine and cystine digestibility results were found -7.1, -9.5 and -9.8, -12.9 p. cent point lower than those reported by BATAL et al. (2006) and PARSONS et al. (2006) whereas the other ones appeared similar. These variations and lower digestibility probably mainly reflect heat damage during the process whereas the amino acid composition can be affected by adding recycled solubles in different proportions.
Our investigations regarding the NIRS approach have been done using a single database combining both wheat and corn DDGS in relation with the global distribution of the population (Figure 1). Calculated regressions showed rather high correlations. Models explained more than 95% of the variation measured among total amino acids. Standard error of calibrations associated to the models varied from 2.03 to 3.89% with the exception of total lysine (5.58%). Except for tryptophan digestibility (R²=0.66), calibrations explained more than 80% of the variability evaluated among amino acids digestibilities. They were associated to errors going up to 1.63 to 5.88% with the higher error for the prediction of the lysine digestibility. With the exception of TLys and DLys, the presence of high protein corn DDGS in the database did not affect the performance of the models. Spectral information measured in the visible part explained part of the variation measured among total amino acids and lysine and methionine digestibilities. These results confirmed and precised the observations presented by FASTINGER et al. (2006), FASTINGER et MAHAN (2006) correlating dark colored DDGS to 10 to 15% lower lysine digestibility in swine and those of BATAL et al. (2006) correlating color measurement to amino acid digestibility in poultry. Whereas several of our models of prediction did not show any added value coming from the visible compared to the single NIR information, remaining calibrations clearly gained by the information measured in the NIR region.
Our feasibility study demonstrated that NIRS is a promising tool for the prediciton of nutritionally relevant parameters, such as the total and amino acid digestibility in emerging by-products of the bioethanol industry such as wheat and corn DDGS. Because its accurate capability in characterizing the specific nutritional value batch per batch, and its reactivity, the NIRS is thus a method of choice for qualifying the current and the evolving DDGS variability.
Figure 1
NIRS calibration for protein (% as fed) in wheat and corn DDGS
References
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¹ Adisseo France SAS, 03600 Commentry
² INRA, UMR SENAH, 35590 St-Gilles
³ Adisseo France SAS, 92160 Antony
* Corresponding author: cecile.gady@adisseo.com