RESULTS: Samples collected from sampling point 1 (company A) and sampling point 9 (company B) yielded the highest total fungal load (>log 4 CFU g-1 ). The prevalent fungal genera isolated were Aspergillus, Fusarium, and Penicillium spp. Aflatoxin B1 was detected in 8.3% of corn samples, and 7.4% of corn-based poultry feed samples along the feed supply chain, whereas AFs B2 , G1 , and G2 were not detected.
CONCLUSION: The incidence of mycotoxigenic fungi along the integrated poultry feed supply chain warrant continuous monitoring of mycotoxin contamination to reduce the exposure risk of mycotoxin intake in poultry. © 2020 Society of Chemical Industry.
RESULTS: Both methods differed considerably in the mass recoveries of the individual cell wall components, which changed on how we assess their degradation characteristics. For example, Method B gave a higher degradation of lignin (61.9%), as compared to Method A (33.2%). Method A, however, showed a better correlation of IVGP with the ratio of lignin to total structural carbohydrates, as compared to Method B (Pearson's r of -0.84 versus -0.69). Nevertheless, Method B provides a more accurate quantification of lignin, reflecting its actual modification and degradation. With the information on the lignin structural features, Method B presents a substantial advantage in understanding the underlying mechanisms of lignin breakdown. Both methods, however, could not accurately quantify the cellulose contents - among others, due to interference of fungal biomass.
CONCLUSION: Method A only accounts for the recalcitrant residue and therefore is more suitable for evaluating ruminal digestibility. Method B allows a more accurate quantification of cell wall, required to understand and better explains the actual modification of the cell wall. The suitability of both methods, therefore, depends on their intended purposes. © 2019 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
RESULTS: Here, we describe an automated screen, to enable high-throughput optimisation of 12 nutrients for microalgae production. Its miniaturised 1,728 multiwell format allows multiple microalgae strains to be simultaneously screened using a two-step process. Step 1 optimises the primary elements nitrogen and phosphorous. Step 2 uses Box-Behnken analysis to define the highest growth rates within the large multidimensional space tested (Ca, Mg, Fe, Mn, Zn, Cu, B, Se, V, Si) at three levels (-1, 0, 1). The highest specific growth rates and maximum OD750 values provide a measure for continuous and batch culture.
CONCLUSION: The screen identified the main nutrient effects on growth, pairwise nutrient interactions (for example, Ca-Mg) and the best production conditions of the sampled statistical space providing the basis for a targeted full factorial screen to assist with optimisation of algae production.