Thermo Fisher Scientific Achieves Precise, Reliable and Fast Nitrogen/Protein Determination in Animal Feed

Protein is one of the most important nutrients in animal feed, providing animals with the necessary building blocks to produce their own proteins in order to grow, maintain or produce muscles, enzymes, hormones, milk, wool, etc. However, the quantity of protein required by animals is very precise as excess amounts can lead to amino acid deficiency and generate unnecessary amounts of energy. As a result, the precise determination of protein content in finished animal feed, through the determination of nitrogen, is fundamental to achieving high nutritional quality of these products.

The Dumas Combustion method, approved by the Association of Official Analytical Chemists (AOAC), monitors nitrogen concentration in animal feed in a much quicker, safer and more reliable way than the conventional Kjeldahl method. The FLASH 4000 which is based on the Dumas combustion method, provides significant advantages over the Kjeldahl method. It requires no sample digestion or toxic chemicals, offering a much safer, more economical and less environmentally damaging solution for protein determination.

The novel analyzer effectively tests for levels of nitrogen in a wide range from low to high content without matrix effect and with reduced sample preparation. The analyzer achieves a relative standard deviation of less than 2 % according to the official Dumas Combustion method, indicating that fine homogenization is not necessary. In addition, no memory effect is observed when changing the type of sample, ensuring complete detection of the nitrogen present in the sample.

The applicability of the FLASH 4000 extends even further, providing an automated, precise and reliable method to monitor levels of nitrogen/protein in any type of food and beverage using large sample amounts of more than 2g.
These capabilities make the analyzer the optimum choice for quality control and R&D environments in the food industry, drastically improving their autonomy and reducing downtime, maintenance and cost per analysis.