Modelling the metabolic characteristics of proteins in dairy cattle from co-products of bioethanol processing: comparison of the NRC 2001 model with the DVE/OEB system

Correlating molecular spectroscopy and molecular chemometrics to explore carbohydrate functional groups and utilization of coproducts from biofuel and biobrewing processing

Dried distillers grains with solubles (DDGS) was coproducts from bioethanol and biobrewing industry. It was an excellent resource of protein and energy feedstuff in China. Conventional studies often focus on traditional nutritional profiles. To data, there is little research on molecular structure-nutrition interaction of carbohydrate in coproducts. In this study, five kinds of corn-grain based DDGS and two kinds of barley-grain based DDGS were collected from different manufactures in the north of China. They were coded as "1, 2, 3, 4, 5, 6, and 7", respectively. The primary purposes of this project were to investigate the molecular structure-nutrition interaction of carbohydrate in coproducts, in terms of (1) carbohydrate-related chemical composition and nutrient profiles, (2) predicted values for energy in coproducts for animal, and (3) in situ digestion of dry matter. The result showed that acid detergent fiber content in corn DDGS and barley DDGS had negative correlation with structural carbohydrate peak area, cellulose compounds, and carbohydrate component peaks (first, second, and total peak area), which were measured with molecular spectroscopy. The correlation between carbohydrate peak area (second and total) and digestible fiber (tdNDF) were negative. There were no correlation between carbohydrate spectral intensities and energy values, carbohydrate subfractions partitioned by CNCPS system, and in situ rumen degradation. The results indicate that carbohydrate spectral profiles (functional groups) are associated with the carbohydrate nutritive values in coproducts from biofuel and biobrewing processing.

Interactive association between biopolymers and biofunctions in carinata seeds as energy feedstock and their coproducts (carinata meal) from biofuel and bio-oil processing before and after biodegradation: current advanced molecular spectroscopic investigations

Recent advances in biofuel and bio-oil processing technology require huge supplies of energy feedstocks for processing. Very recently, new carinata seeds have been developed as energy feedstocks for biofuel and bio-oil production. The processing results in a large amount of coproducts, which are carinata meal. To date, there is no systematic study on interactive association between biopolymers and biofunctions in carinata seed as energy feedstocks for biofuel and bioethanol processing and their processing coproducts (carinata meal). Molecular spectroscopy with synchrotron and globar sources is a rapid and noninvasive analytical technique and is able to investigate molecular structure conformation in relation to biopolymer functions and bioavailability. However, to date, these techniques are seldom used in biofuel and bioethanol processing in other research laboratories. This paper aims to provide research progress and updates with molecular spectroscopy on the energy feedstock (carinata seed) and coproducts (carinata meal) from biofuel and bioethanol processing and show how to use these molecular techniques to study the interactive association between biopolymers and biofunctions in the energy feedstocks and their coproducts (carinata meal) from biofuel and bio-oil processing before and after biodegradation.

Metabolic characteristics of the proteins in yellow-seeded and brown-seeded canola meal and presscake in dairy cattle: comparison of three systems (PDI, DVE, and NRC) in nutrient supply and feed milk value (FMV)

To the authors' knowledge, there is little research on metabolic characteristics of the protein in newly developed yellow and brown types of canola meal and canola presscake. The objectives of this study were to (1) identify differences in the metabolic characteristics of the protein among yellow-seeded ( Brassica juncea ) and brown-seeded ( Brassica napus ) canola meal and brown-seeded (B. napus) canola presscake modeled for dairy cattle, (2) determine the extent of ruminal and intestinal digestion and absorption of the protein, (3) determine feed milk value, and (4) compare three evaluation systems in modeling nutrient supply to dairy cattle, namely, the DVE/OEB system (DVE, truly absorbed protein in the small intestine; OEB, degraded protein balance), the National Research Council (NRC) 2001 model, and the PDI system (protein truly digestible in the small intestine). Comparison was made in terms of (1) ruminally synthesized microbial protein, (2) truly absorbed protein in the small intestine, (3) endogenous protein, (4) total metabolizable protein, and (5) degraded protein balance. The results showed that there were significant differences in the truly absorbed protein supply, protein degraded balance, and feed milk value (P < 0.05) among the different types of canola meal. Yellow-seeded canola meal had significantly higher (P < 0.05) intestinal digestibility of rumen undegraded crude protein (%dRUP) than brown-seeded canola meal and presscake (%dRUP, 90 vs 75 and 60%, respectively). Yellow-seeded canola meal also had higher (P < 0.05) total metabolizable protein predicted by all three models (DVE, 312 vs 192 and 128 g/kg DM; MP, 287 vs 193 and 168 g/kg DM; PDIA, 264 vs 168 and 137 g/kg DM, respectively), lower (P < 0.05) degraded protein balance (OEB, 84 vs 104 and 102 g/kg DM; DPB, 49 vs 60 and 57 g/kg DM, respectively), and higher (P < 0.05) feed milk value (6.3 vs 3.9 and 2.6 kg milk/kg feed, respectively) than the brown-seeded canola meal and presscake. In the model comparison, the supply of endogenous protein predicted by the DVE/OEB system was higher (P < 0.05) than that predicted by the NRC-2001 model. Moreover, a high proportion of the variability in truly absorbed rumen-undegraded feed protein in the small intestine and the total metabolizable protein predicted by the DVE/OEB system was found that can be accounted for by the equivalent parameters predicted by the NRC-2001 model. The truly absorbed rumen-synthesized microbial protein values predicted from the PDI system were 19% lower than those predicted from the DVE/OEB system. Between the two latest mentioned models, no differences were detected in truly absorbed rumen-undegraded feed protein, microbial protein supply based on available energy, and degraded protein balance. All of the parameters predicted by the PDI system can be accounted for by the equivalent parameters predicted by the DVE/OEB system. When the PDI system and NRC-2001 model were compared, the overall means for microbial protein supply based on energy and truly absorbed rumen-synthesized microbial protein were found to be lower than those predicted by the NRC-2001 model. Although the factors used in quantifying calculations as well as the evaluation system's concepts differ among each other, all three protein evaluation systems employed in this study efficiently predict the potential nutrient supply to the animal from feedstuffs as affected by processing. In conclusion, the yellow-seeded canola meal provided the highest total metabolizable protein and the lowest degraded protein balance.

Use of a dry fractionation process to manipulate the chemical profile and nutrient supply of a coproduct from bioethanol processing

With an available processing technology (fractionation), coproducts from bioethanol processing (wheat dried distillers grains with solubles, DDGS) could be fractionated to a desired/optimal chemical and nutrient profile. There is no study, to the author's knowledge, on manipulating nutrient profiles through fractionation processing in bioethanol coproducts in ruminants. The objectives of this study were to investigate the effect of fractionation processing of a coproduct from bioethanol processing (wheat DDGS) on the metabolic characteristics of the proteins and to study the effects of fractionation processing on the magnitude of changes in chemical and nutrient supply to ruminants by comparing chemical and nutrient characterization, in situ rumen degradation kinetics, truly absorbed protein supply, and protein degraded balance among different fractions of coproduct of wheat DDGS. In this study, wheat DDGS was dry fractionationed into A, B, C, and D fractions according to particle size, gravity, and protein and fiber contents. The results showed that the fractionation processing changed wheat DDGS chemical and nutrient profiles. NDF and ADF increased from fraction A to D (NDF, from 330 to 424; ADF, from 135 to 175 g/kg DM). Subsequently, CP decreased (CP, from 499 to 363 g/kg DM), whereas soluble CP, NPN, and carbohydrate increased (SCP, from 247 to 304 g/kg CP; NPN, from 476 to 943 g/kg SCP; CHO, from 409 to 538 g/kg DM) from fraction A to D. The CNCPS protein and carbohydrate subfractions were also changed by the fractionation processing. Effective degradability of DM and CP and total digestible protein decreased from fraction A to D (EDDM, from 734 to 649; EDCP, from 321 to 241; TDP, from 442 to 312 g/kg DM). Total truly absorbed protein in the small intestine decreased from fraction A to D (DVE value, from 186 to 124 g/kg DM; MP in NRC-2001, from 193 to 136 g/kg DM). Degraded protein balance decreased from wheat DDGS fractions A-D (DPB in the DVE/OEB system, from 245 to 161 g/kg DM; DPB in NRC-2001, from 242 to 158 g/kg DM). The fractionation processing had a great impact on the chemical and nutrition profiles. Total truly digested and absorbed protein supply and degraded protein balance were decreased. The processing relatively optimized the protein degraded balance of the coproducts to dairy cattle. Compared with the original wheat DDGS (without fractionation), fractionation processing decreased truly absorbed protein supply of DVE and MP values. In conclusion, fractionation processing can be used to manipulate the nutrient supply and N-to-energy degradation synchronization ratio of coproducts from bioethanol processing. Among the fractions, fraction A was the best in terms of its highest truly absorbed protein DVE and MP values. Fractionation processing has great potential to fractionate a coproduct into a desired and optimal chemical and nutrient profile. To the author's knowledge, this is the first paper to show that with fractionation processing, the coproducts from bioethanol processing (wheat DDGS) could be manipulated to provide a desired/optimized nutrient supply to ruminants.

Detecting molecular features of spectra mainly associated with structural and non-structural carbohydrates in co-products from bioEthanol production using DRIFT with uni- and multivariate molecular spectral analyses

The objective of this study was to use DRIFT spectroscopy with uni- and multivariate molecular spectral analyses as a novel approach to detect molecular features of spectra mainly associated with carbohydrate in the co-products (wheat DDGS, corn DDGS, blend DDGS) from bioethanol processing in comparison with original feedstock (wheat (Triticum), corn (Zea mays)). The carbohydrates related molecular spectral bands included: A_Cell (structural carbohydrates, peaks area region and baseline: ca. 1485–1188 cm⁻¹), A_1240 (structural carbohydrates, peak area centered at ca. 1240 cm⁻¹ with region and baseline: ca. 1292–1198 cm⁻¹), A_CHO (total carbohydrates, peaks region and baseline: ca. 1187–950 cm⁻¹), A_928 (non-structural carbohydrates, peak area centered at ca. 928 cm⁻¹ with region and baseline: ca. 952–910 cm⁻¹), A_860 (non-structural carbohydrates, peak area centered at ca. 860 cm⁻¹ with region and baseline: ca. 880–827 cm⁻¹), H_1415 (structural carbohydrate, peak height centered at ca. 1415 cm⁻¹ with baseline: ca. 1485–1188 cm⁻¹), H_1370 (structural carbohydrate, peak height at ca. 1370 cm⁻¹ with a baseline: ca. 1485–1188 cm⁻¹). The study shows that the grains had lower spectral intensity (KM Unit) of the cellulosic compounds of A_1240 (8.5 vs. 36.6, P < 0.05), higher (P < 0.05) intensities of the non-structural carbohydrate of A_928 (17.3 vs. 2.0) and A_860 (20.7 vs. 7.6) than their co-products from bioethanol processing. There were no differences (P > 0.05) in the peak area intensities of A_Cell (structural CHO) at 1292–1198 cm⁻¹ and A_CHO (total CHO) at 1187–950 cm⁻¹ with average molecular infrared intensity KM unit of 226.8 and 508.1, respectively. There were no differences (P > 0.05) in the peak height intensities of H_1415 and H_1370 (structural CHOs) with average intensities 1.35 and 1.15, respectively. The multivariate molecular spectral analyses were able to discriminate and classify between the corn and corn DDGS molecular spectra, but not wheat and wheat DDGS. This study indicated that the bioethanol processing changes carbohydrate molecular structural profiles, compared with the original grains. However, the sensitivities of different types of carbohydrates and different grains (corn and wheat) to the processing differ. In general, the bioethanol processing increases the molecular spectral intensities for the structural carbohydrates and decreases the intensities for the non-structural carbohydrates. Further study is needed to quantify carbohydrate related molecular spectral features of the bioethanol co-products in relation to nutrient supply and availability of carbohydrates.