The dry matter basis calculation is a crucial concept in understanding the nutritional content of cat foods. It allows for a direct comparison of the nutrient levels in different pet food products, regardless of their moisture content.
Understanding Dry Matter
Dry matter refers to the solid components of a food, excluding the water or moisture content. This is an important distinction, as the moisture content of pet foods can vary significantly, from as low as 5-10% in dry kibble to as high as 75-85% in canned or wet foods.
Calculating Dry Matter
To calculate the dry matter basis, you need to know the moisture content of the pet food. The formula is:
Dry Matter % = 100% – Moisture %
For example, if a cat food has a moisture content of 10%, the dry matter percentage would be:
Dry Matter % = 100% – 10% = 90%
Comparing Nutrient Levels
Once you have the dry matter percentage, you can use it to calculate the actual nutrient levels in the food. This is important because pet food labels’ “as-fed” values can be misleading, as they are based on the food’s total weight, including moisture.
For instance, let’s compare the protein content of a dry food with 32% protein and 10% moisture and a canned food with 8% protein and 80% moisture:
Dry food:
Dry Matter Protein % = 32% / (100% – 10%) = 35.6%
Canned food:
Dry Matter Protein % = 8% / (100% – 80%) = 40%
In this example, canned food has a higher protein content on a dry matter basis, even though the “as-fed” protein percentage is lower than that of dry food.
Importance of Dry Matter Basis
Comparing nutrient levels on a dry matter basis is crucial for cat owners to make informed decisions about their cat’s diet. It allows them to evaluate the nutritional value of different cat food products, regardless of their moisture content.
Additionally, pet food manufacturers, veterinarians, and nutritionists use the dry matter basis to formulate and assess the quality of cat foods.
Citations:
[1] https://www.semanticscholar.org/paper/654c3e3d92dc56ac7bb74f45954d1e0c2e35844f
[2] https://www.semanticscholar.org/paper/6479745d2984369eecc11151fa5c581abdf96ffc
[3] https://www.semanticscholar.org/paper/ab29447695d9a7ac92ffcb1a880f6caf2b82c100
[4] https://www.semanticscholar.org/paper/47791f65c2dc893fe8a0e9dd24cfb9e2b63a2f96
[5] https://www.semanticscholar.org/paper/80bc329325a1f894ea4f6e27fdfa34390ed44395
