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Proximate Analysis


Proximate Analysis

   General. The proximate analysis is a scheme for routine description of animal feedstuffs devised in 1865 by Henneberg and Stohmann of the Weende Experiment Station in Germany (Lloyd et al., 1978). It is often referred to as the Weende System and was principally devised to separate carbohydrates into two broad classifications: crude fiber and nitrogen free extract (NFE). The system consists of determinations of water (moisture), ash, crude fat (ether extract), crude protein, and crude fiber. As indicated, NFE is a component of the system, but it is measured by difference rather than by analysis. Students should be aware that the proximate analysis system is both comparative and predictive in nature, as this will aid in understanding the broad purposes for development of the system.

   First, let us consider the comparative aspects of the system. Most people, even those unfamiliar with livestock feeds and feeding, could look at a sample of first-cut alfalfa hay and a sample of dormant winter range grass and readily surmise that the alfalfa was a higher quality feed than the range grass. Alfalfa simply looks better, or at least from the human vantage point, it has more eye appeal than dormant winter range grass. Fewer people, however, could tell how much better the alfalfa is in terms of any specific nutrient (e.g., protein, fiber) or the production either feed will support. Thus, one important reason for development of the proximate analysis scheme was to allow comparison of feeds on a specific basis. It is often stated that one can not compare apples with oranges, but one can compare the protein as a percentage of dry weight in apples and oranges, and in doing so, make some realistic judgements about the nutritional value of each fruit. By the same token, proximate analysis allows one to make legitimate comparisons of feeds on the basis of specific nutrients, allowing one to judge how much better one feed is than another in terms of specific nutrients.

   Now, what about the milk production or growth rate alfalfa hay will support compared with the dormant range grass? This question is addressed by the predictive nature of proximate analysis. If we know all the proximate components of various feeds, and the production the feeds will support, we might be able to develop regression equations to help us predict performance by livestock fed these feeds. In the more typical case, we try to use proximate components to predict factors related to performance, like digestibility and intake and then relate these predicted values to estimates of performance. As will be noted later, the proximate system has some failings that prevent it from being an extremely valuable predictive aid, and considerable research has been conducted in recent years to refine and add to the basic system to make it a better predictive tool.

   To summarize, the proximate analysis system is an old scheme of laboratory analyses that allows comparison of feeds on the basis of specific nutrients and, to some extent, prediction of components of animal performance. Next, we will take a look at the specific analyses involved in the proximate analysis scheme.

   Dry Matter. Dry matter or, more specifically, moisture determination is probably the most frequently performed analysis in the nutrition laboratory. It is an important analysis, in that the

 
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concentration of other nutrients is usually expressed on a dry matter basis (as a percentage of the dry matter). Because most students seem to have an inordinate amount of difficulty with conversion of nutrients to a dry matter basis, an example showing how this is done, as well as an example of dry matter determination calculations, is given in Table 3-2.

   Moisture or dry matter content is extremely important to the livestock industry, particularly those segments that deal with high-moisture feeds. Consider, for example, a feedlot with 20,000 tons of silage inventory. Obviously, as silage is removed from storage, its dry matter content can change, resulting in a change in the feedlot's inventory of silage dry matter. Without routine dry matter determinations, the feedlot could grossly over or underestimate its inventory and perhaps over or undercharge customers for feed. There are numerous examples of the importance of accurate measurements of moisture in the livestock industry that should be readily apparent to most students.

   From the author��s experience, students typically believe that dry matter analysis is the simplest analysis they perform in the lab. For the most part, this is true because it routinely consists of weighing the sample into a tared (previously weighed) pan, placing the sample in a 100 to 105��C oven for 12 to 24 h and reweighing. Moisture content is simply the loss in weight from evaporation of water. This procedure works well for most feeds, but with some feeds, especially high-moisture, fermented feeds, some problems can be encountered. High-moisture feeds usually contain volatile nutrients that can be lost with 100��C oven drying. Volatile nutrients of greatest importance are short chain fatty acids (acetic, propionic, butyric, etc.), but essential oils (menthol, camphor) also can be important with some feeds. Drying samples at 100��C can volatilize some of these materials, resulting in greater moisture (lower dry matter) values than expected. This concept is considered in some detail along with some other important points in an article by Goss (1980). The errors associated with moisture determination in corn have been the subject of research (Fox and Federson, 1978), and the saponification method of Hood et al. (1971) seems to be a valid method to use with fermented feeds.

   Common methods of dry matter determination and suggested occassions for their use are listed in Table 3-1. Students also should check the AOAC (1995) publication for additional methods. . The routine procedure for 100��C oven dry matter determinations is attached to this chapter.Table 3-1. Common methods of dry matter analysis and suggested occasions for their use

 
Method Occasions for use

100��C drying Most mixed feeds, hays, range grasses with 85 to 99% DM

Freeze dryinga High-moisture, fermented feeds

Saponification High-moisture, fermented feeds

Vacuum dryingbMeat or tissue samples

aByers (1980) indicated that freeze drying does not yield significantly different values than saponification. See J. Anim. Sci. 51:158.

bRecommended by AOAC for meat samples.

Table 3-2.   Dry matter calculations and conversion of nutrients to a dry matter basis (100oC drying oven example)

                  Dry Dry

Replicate      Pan wt.      Pan + sample   Sample wt. Pan + sample sample

  1. 1.0000       3.0000 2.0000 2.9000 1.9000
  2. 1.0000       3.1000 2.1000 2.9500 1.9500

% DM

Rep 1 = 1.9000 �� 2.0000 = .95 x 100 = 95.00% Rep 2 = 1.9500 �� 2.1000 = .9285 x 100 = 92.85%

x = 93.93%

s = 1.52

CV = 1.52 �� 93.93 = .0161 x 100 = 1.61%

Converting nutrients to a dry matter basis (dmb):

Suppose a feed contains 10% crude protein on a fresh (as-fed) basis (moisture included) and has 10% moisture (90% dry matter).

CP, dmb = 10/.90 = 11.11%, where .90 = dry matter factor or percentage dry matter expressed as a decimal.

To convert from a dry basis to a fresh basis, simply multiply the value on a dry matter basis by the dry matter factor. 


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