volatile fatty acid
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Chemosphere ◽  
2022 ◽  
Vol 288 ◽  
pp. 132444
Ashutosh Kumar Pandey ◽  
S. Pilli ◽  
P. Bhunia ◽  
R.D. Tyagi ◽  
Rao Y. Surampalli ◽  

Biology ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 93
Fanlin Kong ◽  
Yanfang Liu ◽  
Shuo Wang ◽  
Yijia Zhang ◽  
Wei Wang ◽  

It is well known that the methionine dipeptide (MD) could enhance the dairy cows milking performance. However, there is still a knowledge gap of the effects of MD on the rumen fermentation characteristics, microbiota composition, and digestibility. This experiment was conducted to determine the effect of different nitrogen sources with a total mixed ration on in vitro nutrient digestibility, fermentation characteristics, and bacterial composition. The treatments included 5 mg urea (UR), 25.08 mg methionine (Met), 23.57 mg MD, and no additive (CON) in fermentation culture medium composed of buffer solution, filtrated Holstein dairy cow rumen fluid, and substrate (1 g total mixed ration). Nutrient digestibility was measured after 24 h and 48 h fermentation, and fermentation parameters and microbial composition were measured after 48 h fermentation. Digestibility of dry matter, crude protein, neutral detergent fiber (NDF), and acid detergent fiber (ADF) in the MD group at 48 h were significantly higher than in the CON and UR groups. The total volatile fatty acid concentration was higher in the MD group than in the other groups. In addition, 16S rRNA microbial sequencing results showed MD significantly improved the relative abundances of Succinivibrio, Anaerotruncus, and Treponema_2, whereas there was no significant difference between Met and UR groups. Spearman’s correlation analysis showed the relative abundance of Succinivibrio and Anaerotruncus were positively correlated with gas production, NDF digestibility, ADF digestibility, and acetate, propionate, butyrate, and total volatile fatty acid concentrations. Overall, our results suggested that the microbiota in the fermentation system could be affected by additional nitrogen supplementation and MD could effectively enhance the nutrient utilization in dairy cows.

2021 ◽  
pp. 126426
Tao Xing ◽  
Zhi Wang ◽  
Feng Zhen ◽  
Huiliang Liu ◽  
Defang Wo ◽  

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 295-295
Genevieve M D’Souza ◽  
Kelsey Harvey ◽  
Luiz Fernando Dias Batista ◽  
Reinaldo F Cooke ◽  
Luis O Tedeschi

Abstract The objective of this study was to describe two chromatography equipment and their methods (EM) and to evaluate their adequacy in estimating ruminal volatile fatty acid concentrations (VFA). Adequacy was assessed through precision and accuracy using three standard mixtures of known acetate, propionate, and butyrate concentrations. The standard mixtures were prepared for VFA analysis using high-performance liquid chromatography (HPLC) or gas chromatography (GC). Each mixture was injected ten times into each EM. The comparison was assessed with rumen fluid samples from four cannulated steers offered three diets at 2% BW. Diet A simulated a forage-based diet offered to cattle during the winter. Diet B simulated a grower-type diet offered to weaned calves. Diet C simulated a finisher-type diet offered to finishing cattle. Rumen fluid was collected three hours after the morning feeding for seven days for each diet and strained through 8 μm porosity fiberglass wool. Two 2-mL aliquots were stored at -20°C for HPLC analysis, while two 8-mL aliquots were diluted with 2 mL of 25% meta-phosphoric acid and stored at -20°C for GC analysis. Chromatograms without a flat baseline were removed from the analysis. For the adequacy evaluation, HPLC (R2 = 0.997; Cb = 0.874) was more precise and accurate at estimating total VFA than GC (R2 = 0.447; Cb = 0.763). When compared with the standards, HPLC estimated less (P < 0.001) total VFA (98.8 ± 10.3 mM) than GC (110.5 ± 17.4 mM). Concentrations for acetate, propionate, and butyrate in rumen fluid samples were estimated for each EM and analyzed using a random coefficients model. Similarly, estimates for acetate, propionate, and butyrate were less for HPLC than GC (P ≤ 0.002). VFA estimation differs depending on EM chosen. Further research should identify the source of difference in VFA estimation from each EM.

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