<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title><![CDATA[Feed Milling Articles and Resources]]></title><description><![CDATA[Articles]]></description><link>http://www.feedmachinery.com/articles/</link><copyright><![CDATA[Copyright Feed Milling Articles and Resources]]></copyright><generator>sNews CMS</generator><item><title><![CDATA[Evaluating Feed Components and Finished Feeds: Part 1]]></title><description><![CDATA[               Tim Herrman     Extension State Leader    Grain Science and Industry  Kansas State University         Ingredient quality is the foundation upon which an animal  ration is built. Therefore, establishing an ingredient quality evaluation  program is an essential component of a successful feed processing operation.  Routine evaluation of finished feed quality will help ensure that proper  ingredient storage, proportioning, grinding, and mixing were performed. This  bulletin contains information pertaining to ingredient specifications,  suggestions on which feed ingredient and finished feed properties should be  analyzed, types of assays to perform, and how to interpret lab results. The  first step in evaluating ingredients and finished feed quality involves  collecting a representative sample as described in the bulletin, Sampling:  Procedures for Feed (Herrman 2001).   
    Ingredient  Specifications.     Ingredient specifications are essential to a quality assurance  program. Specifications serve as the basis from which purchasing agreements are  written, feed rations are formulated, and ingredient inspections are performed.  Ingredient descriptions and general nutritional specifications may be found in  the following publications: AFIA Feed Ingredient Guide II (American Feed  Industry Association (AFIA), 1992), the Association of American Feed Control  Officials(AAFCO) Official Publication (2000), and the Feedstuffs Reference  Issue (2001). A partial list of nutritional properties of these ingredients are  in   Table 1  .   
          
      
        
  
                
  
    Sensory and Physical  Properties      Sensory property evaluation, including inspection of ingredient  color, odor, texture, moisture, temperature, and a visual inspection for  physical purity (absence of foreign material and insect infestation) enables  one to quickly assess whether the ingredient should be rejected. It also  enables the person responsible for receiving the ingredient to confirm product identity  (same as re- corded on the bill of lading). The inspection process should be  accompanied by a reference sample for comparison. Physical property evaluation  usually involves testing incoming grain and feed ingredients for bulk density,  purity, and texture. All of these properties will determine how the material  unloads, conveys into and out of bins, stores, and performs during processes.   
    Bulk density of a  material   represents the mass per unit volume. This characteristic is  commonly ex- 3) or kilograms pressed as pounds per cubic foot (lb/ft 3 ). The  bulk density of a material per cubic meter (kg/m is measured by weighing the  amount of material that fills a one-cubic-foot box. Bulk density can vary significantly  for the same ingredient due to differences in particle size, moisture content,  or compaction. Bulk density of a feed ingredient is important for inventory control  purposes and will determine how the ingredient will perform during batching and  blending. When a feed ration requires blending ingredients that differ widely  in bulk density, the feed processor should ensure that the particle size of the  feed ingredients is similar, use a binding agent (fat or molasses), and load the  mixer using an ingredient sequence that optimizes the blending action of the  mixer. For example, high- density ingredients should be added early to vertical  mixers and late in the batching sequence for horizontal mixers.   
    Test weight   is a  bulk density measurement applied to grain, and the value represents the weight  (expressed as pounds) in a Winchester  bushel (2150.42 cubic inches). Specifications for test weights of different grains  and grades are presented in the bulletin Grain Grading Standards in Feed  Manufacturing (Herrman and Kuhl, 1997). The procedure for measuring test weight  is included in separate bulletins listing the grains&#39; name (e.g., Corn: Grading  Procedures, 2000).  
      
        
  
                
  
  Ingredient   purity   refers to the absence of contaminants. The source of these contaminants may be physical  (e.g., glass), chemical (e.g., seed treatment), and microbial (e.g.,  mycotoxin). The use of hand sieves to inspect for physical contaminants enables  rapid evaluation of material. For example, the use of two sieves (12/64-inch  round-holed sieve placed on top of a 5/64-inch round hole sieve) separates  dockage (non- wheat material) from wheat. The material on the top sieve and the  pan underneath the bottom sieve contain dockage. Visual inspection is then  performed on the dockage-free wheat (wheat on top of the 5/64-inch sieve) to  identify non-wheat material which is referred to as foreign material. Chemical  and microbial contaminants can be performed by laboratories listed at the back  of this bulletin (  Table 2   and   Table 3  ).   
                 
  Further details on analyses for microbial  contaminants and their effect on animal performance are presented in the  following bulletins: Mycotoxins in Feed Grains and Ingredients (TrigoStockli  and Herrman, 2000) and Quality Management for Feed Related Disease Prevention (Herrman  and Stokka, 2001).   
    Texture   of an  ingredient is measured visually and with sieves. Soybean meal texture is  described visually as "homogeneous, free-flowing, without coarse particles or  excessive fines" (AFIA, 1992). Soybean meal texture measured by sieve analysis  is described as "95 to 100 percent through U.S. Standard Sieve No. 10; 40 to 60  percent through U.S. Standard Sieve No. 20; and a maximum 6 percent through  U.S. Standard Sieve No. 80" (AFIA, 1992). For further information about sieves and  particle-size analysis, refer to the bulletin titled Evaluating Particle Size  (Herrman, 2001).   
    Nutritional  Properties     Nutritional properties of feed ingredients require laboratory  analysis; this usually entails expensive analytical equipment operated by  professional chem- ists. Many feed companies use commercial labs for these  analyses (  Table 1  ). Most analysis techniques involve the use of procedures  tested and approved by scientific organizations such as the Association of Official  Analytical Chemists&#39; (AOAC, 2000) Official Methods of Analysis and the American  Association of Cereal Chemists&#39; (AACC, 1995) Approved Methods of Analysis.   

        
  
     

         Other Parts to this Article    
        Evaluating Feed Components and Finished Feeds:  Part 2     
        Evaluating Feed Components and Finished Feeds:  Part 3    
   
  ]]></description><pubDate>Thu, 04 Jan 2007 00:59:00 +0000</pubDate><link>http://www.feedmachinery.com/articles/feed_ingredients/feed_evalution/</link><guid>http://www.feedmachinery.com/articles/feed_ingredients/feed_evalution/</guid></item><item><title><![CDATA[Evaluating Feed Components and Finished Feeds: Part 2]]></title><description><![CDATA[      Tim Herrman     Extension State Leader    Grain Science and Industry  Kansas State University   
      Moisture     
Moisture content affects an ingredient&#39;s nutritional content  and its performance during handling, storage, and processing. Both direct and  indirect measures of ingredient and finished feed moisture are approved for feed  industry use. Direct methods include oven drying and distillation while  indirect methods include near infrared (NIR) spectral analysis, conductance,  and water activity. The oven drying method involves the removal of free water  from a sample through heating and measurement of weight loss. This procedure is  based on the principle that the boiling point of pure water is 212 degs F (100 degs C)  at sea level. The likelihood that a compound will decompose or volatilize (turn  from solid to vapor) determines the type of oven used (convection, forced draft,  or vacuum oven). A vacuum oven lowers the boiling point of water and allows the  oven drying procedure to be performed at a lower temperature, thus reducing  loss of dry matter through volatilization. Calculation of moisture content and  total solids is performed as follows:   
      

  
        
  
    

  In the case of semi-moist products (e.g., dog food) the Karl  Fischer method is preferred. Water is extracted with methanol from pet food  that contains other volatile components, and an aliquot is titrated with Karl  Fischer reagent. This test is good for products containing between 20 to 30  percent moisture (AOAC Official Method 991.02). Moisture content in  heat-sensitive feed ingredients is measured using the distillation method. In  this technique, the ingredient is boiled in a solvent and water is driven off  from the sample, condensed, and measured (AOAC Official Method 925.04). Indirect  moisture measurement for feed grains involves the use of an electrical moisture  meter (AACC Method 44-11). Another indirect moisture measurement can be  performed using a beam of light in the near infrared (NIR) frequency with a  spectrophotometer. This method works well for feed grains, feed ingredients,  and finished feeds.  
    Protein    Proteins are comprised of amino acids which are the building  blocks of protein. When formulating a complete feed, the nutritionist creates a  feed ration with a complete balance of amino acids. A shortage of one amino  acid in a complete ration can cause animals to experience depressed growth  rate, poor feed conversion, and reduced reproductive performance. Most protein  tests evaluate the nitrogen (N) content of the sample; nitrogen is present in  protein molecules at about 16 percent. The combustible nitrogen analyzer has  grown in popularity as the preferred method for measuring N. This technique is  reliable, quick, does not involve the use of highly corrosive acids and bases, and  its cost is fairly reasonable. Additionally, the use of optical measurement of  protein content using NIR technology works well for cereal grains, oilseeds,  and finished feed. Assaying feed for individual amino acids is expensive and is  seldom performed by a feed company. Thus, nutritionists use standard values for  amino acid content in feed ingredients based on the National Research Council  publications (NRC, 2001).  
    Fat    Crude fat content is measured by extracting fat with an  ethyl ether solvent and then weighing the extracted fat in a vessel after the  solvent has been evaporated. Crude fat is a term that refers to both fats and  oils or a mixture of the two and all other organic soluble compounds. The  melting point of most fats is such that they are solid at ordinary room  temperature, while oils have lower melting points and are liquids at room temperatures.  Fats are high-energy ingredients containing about 2.25 times the amount of  energy as other nutrients. Fat analyses should include moisture, impurities,  unsaponifiable materials (M.I.U.), and free fatty acids (FFA). FFA  content should not exceed 15 percent. Additionally, NIR technology works well  for measuring oil content in oilseed crops (e.g., soybeans), corn, and on  complete feeds.  
    Fiber    Crude fiber includes the materials that are indigestible to  humans and non-ruminant animals. It is defined as the material that is  insoluble in dilute acid and dilute alkali under specified conditions. Crude  fiber is used as an index of an ingredient&#39;s feeding value since materials high  in fiber are typically low in nutritional value.  
    Minerals    Mineral analysis procedures are described in the National  Feed Ingredient Association&#39;s (NFIA, 1991) Laboratory Methods Compendium,  Volume I. Calcium constitutes about 2 percent of the body weight and is  important for bones, teeth, and muscle contraction and relaxation, especially  the heartbeat; has a role in the transmission of nerve impulses; is necessary  for blood clotting; and activates a number of enzymes. Phosphorus is closely  associated with calcium, thus, a deficiency or overabundance of one will  interfere with the utilization of the other. Phosphorus is involved with bone  formation and maintenance, teeth development, milk secretion, and building  muscle tissue; it is an essential element in genetic material, metabolic functions,  and osmotic and acid-base balance. Magnesium interacts with calcium and  phosphorus. If extremely low, magnesium will cause calcium to be deposited in  soft tissues forming calcified lesions. An excess of magnesium upsets calcium  and phosphorus metabolism. Sodium helps control the osmotic pressure and acid- base  balance in body fluids (upon which depends the transfer of nutrients to the  cells and removal of waste material from cells). Sodium is associated with  muscle contraction and nerve function.  

  
        
  
    

    Pepsin Digest    Pepsin digest is a procedure used to determine the protein  digestibility of animal by-product meals. Animal by-product meal is processed  under extreme temperature conditions that can cause the proteins to become  denatured and indigestible. Results of a pepsin digest analysis are usually  reported as a percentage of pepsin indigestible residue or percent of crude  protein that is pepsin indigestible.   
  The AFIA Feed Ingredient Guide II lists the following recommendations for animal  by-product meals:    
  
      Poultry Feathers  .  Not less than 75 percent of crude protein should be pepsin digestible.    
    Meat Meal.   Not more than 14 percent indigestible residue and not more than 11 percent of  crude protein should be pepsin indigestible.         Meat and Bone  Meal.   Not more than 14 percent indigestible residue and not more than 11  percent of crude protein should be pepsin indigestible.      
    Urease    Urease is an enzyme (present in soybeans) that acts on urea  to produce carbon dioxide and ammonia. Urease is controlled by heating to  denature the enzyme, and as such, is analyzed in soybean meal to assess if it has  been properly processed.   
    Microscopic    All microscopic identification is based upon relating the  items seen to known material. Through the use of low magnification (8 to 50  times) materials are exam- ined and identified based on physical  characteristics such as shape, color, particle size, softness, hardness, and  texture. Feed microscopy is a useful method for identifying  impurities/contaminants and evaluating the quality of incoming ingredients. It  also serves as a useful method for identifying missing ingredients in finished  feed.       M.I.U.    M.I.U. stands for moisture, impurities, and unsaponifiable  material. Fat sources should be evaluated for these components and should not  exceed the following levels: moisture less than or equal to 1 percent,  impurities less than or equal to .5 percent, unsaponifiable material less than  or equal to 1 percent.   
    Brix    Brix is a term commonly used to indicate the sugar (sucrose)  content of molasses. This analysis is per- formed based on the optical  properties of the molasses using a refractometer. Brix is expressed in degrees  and is closely related to percent sucrose. The AFIA Feed Ingredient Guide II  specifies a Brix reading of 79.5 degrees.  

        
  
     

         Other Parts to this Article    
        Evaluating Feed Components and Finished Feeds:  Part 1     
        Evaluating Feed Components and Finished Feeds:  Part 3    
   
  ]]></description><pubDate>Thu, 04 Jan 2007 00:10:00 +0000</pubDate><link>http://www.feedmachinery.com/articles/feed_ingredients/feed_evalution2/</link><guid>http://www.feedmachinery.com/articles/feed_ingredients/feed_evalution2/</guid></item><item><title><![CDATA[Evaluating Feed Components and Finished Feeds: Part 3]]></title><description><![CDATA[      Tim Herrman     Extension State Leader    Grain Science and Industry  Kansas State University   
      Laboratories    
When selecting a laboratory, price should not be the only  consideration. It is important to find out which professional association  laboratory personnel belong to and analytical techniques used. Official methods  are tested and approved by members of these professional organizations: i.e.,  Association of Official Analytical Chemists (AOAC) or American Association of  Cereal Chemists (AACC).   

  Some membership affiliations to look for include: AOAC,  AACC, American Chemical Society, American Oil Chemists Society, National  Oilseed Processors Association, American Fats and Oils Association, National  Institute of Oilseed Products, and NFIA. Also check to see if the lab  participates in check sample programs provided by the Association of American Feed  Control Officials (AAFCO), American Feed Ingredients Association, and other  professional organizations.   
           Table 2   presents a partial list of labs and mailing address,  phone number, and web address where ser- vices and prices are listed.  Laboratories and services appearing in this publication are used for  identification only. No endorsement is intended, nor is criticism implied of  laboratories not mentioned. Most labs will analyze for individual components as  well as offer special rates for grouped analyses. One such common group  analysis is proximate analysis. Proximate analysis consists of moisture, crude  protein, crude fat, crude fiber, ash, and nitrogen-free extract.   
    Analytical Variation  (AV)    
  AAFCO has established analytical variation (AV) guidelines  in order to assist control officials in making decisions regarding marginally  acceptable products (AAFCO, 2000). These variances are intended to allow for  inherit variability in sampling and laboratory analyses. They are not intended  to allow for deficiencies or excesses in a product or poor analytical techniques.   
  
        
  
    
           Table 3   shows the analytical variances for some of the  common ingredients. If the assay indicates that the ingredient is outside the  analytical variance, the feed does not conform to label requirements. The  concentration range indicates for what inclusion rate (level) the Analytical  Variation Percentages (AV%) apply; e.g., moisture AV% applies to feed containing  between 3 and 40 percent moisture. The AV% can be calculated using the  following steps:  
    Step 1:   Multiply  the expected or guaranteed value by the value derived from the formula in   Table 3   in the AV% column. Convert the AV% value to the decimal equivalent (move the  decimal two places to the right).  
    Step 2:   Add and  subtract the value obtained in Step 1 to the expected or guaranteed value.  
  Example:  
      Suppose a sample of  soybean meal was submitted for protein analysis. If the expected or guaranteed protein  content was 44%, the acceptable range would be 42.9-45.1.    
  Step 1:   
  44 x {(20 / 44 + 2) / 100} = 1.08
   
  Step 2:   
  44 - 1.08 = 42.9  
  44 + 1.08  = 45.1  
    Drug Analysis    
  The FDA&#39;s Current Good Manufacturing Practices (CGMPs)  stipulate that periodic assays of medicated feeds for drug components shall be  performed as a means of monitoring the manufacturing process. Each category II  Type A drug must be sampled and assayed three times each year. For medications  containing a combination of drugs, perform the assay on only one drug each time  and rotate the drugs analyzed. If the results of these assays are outside the  permissible limits listed in   Table 4   and   Table 5   (Feed Additive Compendium, 2001),  an investigation and corrective action must be implemented.   
                  
  The CGMPs also  stipulate that "corrective action shall include provisions for discontinuing  distribution where the medicated feed fails to meet the labeled drug potency.  Distribution and subsequent production of the particular feed shall not begin  until it has been determined that proper control procedures have been  established."  
  Many commercial feed mills and on-farm feed processors are  not required by law to conduct drug assays since they are not registered (for  further information, refer to Kansas State University Extension bulletins  MF-2042 and MF-2043 by Herrman et al., 2000). However, routine inspection of  finished feed for drug potency is a good business practice.  
  Unfortunately, the CGMPs do not provide advice on how to  investigate high or low drug potency. Typical sources (or reasons) for  out-of-tolerance assays include the following:  
 - the medicated  article has lost its drug potency,  
 - incorrect  weighing of the medicated article,  
 - poor mixing of  the medicated article into the feed,  
 - poor sampling  technique.  
  One way to perform drug assays on medicated feed is to use  the same sampling technique used to conduct mixer performance tests. A complete  description of this procedure is listed in the bulletin titled Testing Mixer Performance  (Herrman and Behnke, 2001). The procedure involves collecting samples from 10  representative locations in the mixer. Combine the samples to form a single  composite sample for the drug assay. One half of the composite sample should be  retained for a minimum of three months, in the event the first sample is out of  tolerance. Also, collect and retain a sample of the medicated article. If the  feed is out of tolerance, submit a sample of the medicated article and the  retained portion of the feed sample for drug analysis.   Note: similar procedures may be followed for identifying the source of  variation of other ingredients in complete feed.    
  The CGMPs also specify methods to avoid cross- contamination  of feed when using medicated articles to produce medicated feed. These methods  include flushing, sequencing, and equipment clean-out. Procedures to avoid drug  cross-contamination are discussed in detail in MF-2055.  
  
        
  
    
    Utilizing Assay  Results    
  After investing considerable time and capital to collect a  representative sample and have it analyzed, the feed processor must manage the  information.   
  Correct information management will assist in:  
 - detecting  ingredient/product variation,  
 - evaluating  suppliers,  
 - determining the  discount for substandard product,  
 - fine-tuning feed  rations,  
 - explaining animal  performance problems,  
 - meeting FDA CGMPs  (if feed mill is licensed).  
  A simple way to utilize information involves record- ing lab  results in table form (either by hand or on a computer spreadsheet program).  Columns in the table should include the date material was received, lab number  assigned to the sample, ingredient supplier, and assay results (e.g., protein,  moisture). Separate data sheets should be kept for each ingredient type (e.g., grain,  protein, drug). These results should be regularly compared with contract  specifications to ensure suppliers are shipping ingredients that meet or exceed  quality criteria. Summarize data by month and supplier to detect noticeable  trends.  
  The use of Statistical Process Control (SPC) to evaluate assay data provides an additional  management tool from which to control variability in finished feed, thus  improving product quality and profitability. For further information, read the  bulletin titled Statistical Process Control: Techniques for Feed Manufacturing (Herrman,  2001).  

    Summary    
  Feed ingredients should be routinely evaluated to ensure  they are safe, they contain the correct amount of the specified nutrient, and  to ensure the finished feed quality will optimize animal performance. A list of  ingredients, their important nutritional components, where they can be tested,  and how to interpret this information is provided in this bulletin. Permitted  analytical variation (PAV) guidelines are included to explain how to identify  deficiencies or excesses of an ingredient in a product. If the assay indicates  the ingredient is outside the PAV, the feed does not conform to label  requirements. Techniques for identifying the source of variation and corrective  actions are discussed.   
        
  
     

         Other Parts to this Article    
        Evaluating Feed Components and Finished Feeds: Part 1     
        Evaluating Feed Components and Finished Feeds: Part 2     
   
  ]]></description><pubDate>Thu, 04 Jan 2007 00:02:00 +0000</pubDate><link>http://www.feedmachinery.com/articles/feed_ingredients/feed_evalution3/</link><guid>http://www.feedmachinery.com/articles/feed_ingredients/feed_evalution3/</guid></item><item><title><![CDATA[Grain Grading Standards in Feed Manufacturing]]></title><description><![CDATA[          Tim Herrman     Extension State Leader    Grain Science and Industry        Gerry Kuhl     Extension Feedlot Specialist   Animal Sciences and Industry        Each year, some low quality grain enters the market channel following a growing season that is characterized by drought, extreme heat during a sensitive stage in crop development, excess moisture, an early frost, plant disease, or other malady. During years when vast geographical areas are affected by unfavorable growing conditions, a large quantity of lowgrade grain may be available for purchase and feeding. In such markets, livestock producers may realize additional profits when utilizing lowgrade grain that has been discounted.      The processing performance and feeding value may be reduced in grain that experienced significant quality deterioration due to unfavorable growing conditions. The purpose of this bulletin is to explain the different factors used to assign a grain grade and how these factors may influence the processing performance and feeding value of the low-grade grain.        Grain Grades and Standards     A set of standards established by the United States Department of Agriculture (USDA), known as the Grain Grading Standards (USDA 1995), serve as a general guideline for characterizing physical grain quality. This system was developed in 1916 to enable grain merchants to trade grain using consistent, measurable quality criteria. Since 1916, numerous changes in the grading standards have occurred to reflect changes in technology (the ability to measure quality) and customer demands.      Grain quality measurements are categorized as nongrade determining factors, grade determining factors, and special grades. Grade tables included in the back of this bulletin list the minimum and maximum criteria for each grading factor and the definition of each special grade.        Nongrade determining factors   include moisture and possibly dockage (depending on the grain). These factors are measured using approved testing equipment following official procedures. The results of these tests are included on the Grade Certificate; however, the information is not used to assign a grade. For example, an individual purchasing U.S. Number 2 corn may receive grain with any moisture content (e.g. 10 to 20%).        Grade determining factors   include test weight, damaged kernels, foreign material, broken kernels, odor, and heating. These factors are used to assign a numerical grade. Grades are assigned based on the lowest factor. For example, if a corn sample has a test weight of 56 pounds per bushel (minimum criteria for U.S. Number 1) and heat damage of 1 percent (maximum criteria for U.S. Number 4), the grade assigned to the grain would be U.S. Number 4. The presence of musty, sour, or commercially objectionable foreign odor or the presence of heating causes the grain to be assigned the title Sample Grade. Sample grade is the lowest possible category in the grading system.        Special grades   (depending on the grain) include infested, ergoty, garlicky, and smutty. These words are added to the grade designation but do not determine the numerical grade. For example, sorghum that meets or exceeds grading factors for U.S. Number 1 and contains 20 or more smut balls in a 100 gram portion would be assigned the following grade, U.S. Number 1 Sorghum, Smutty.      These characteristics provide some insight into the processing value, past or future problems related to storage, and, to a lesser extent, the nutritional value of grain. The Federal Grain Inspection Service (FGIS) and licensed inspection agencies and firms also offer informational services and tests. Quality characteristics measured by these agencies (upon request) include mycotoxins, protein and oil content, and stress crack in corn.        Nongrade Determining Factors     Moisture content was dropped as a grade determining factor in 1988 as an outgrowth of the Grain Standards Act of 1986. Because moisture level in grain is extremely important, it is measured and reported on all grade certificates. However, customers must specify the maximum (and in some cases minimum) moisture content of grain they intend to purchase since it is no longer used to assign a grade. Moisture content is important to a cattle feeder for several reasons. First, and perhaps foremost, moisture content provides an indication of how much dry matter (feeding value) is contained in the grain. A reduction in moisture content in grain results in a weight reduction referred to as shrink.   This moisture: weight relationship is expressed by the following equation:    
  % Weight Change (shrink) = Mo Mf 100 100 Mf    
  Mo = original or initial moisture content (%)  Mf = final moisture content (%) 
  
        
  
    

  The percent weight change or moisture shrink when corn dries from 17 percent to 14 percent can be calculated as follows:    
  % Weight Change (shrink) = 17 14 - 100 = 3.49 100 14    
  A shrink factor can be derived by dividing moisture shrink by the percent change in moisture content:   
(e.g. 3.49 - 3 = 1.16)   
 Many grain elevators use a fixed moisture shrink factor (e.g. 1.2) when discounting high-moisture grain to a predetermined moisture content. Moisture content in grain also determines the length of time grain can be stored. High moisture grain is more prone to experience a deterioration in quality due to mold. The relationship between grain moisture content and quality deterioration due to mold is temperature dependent as expressed in Table 1 (Sauer 1988).      The presence of mold can result in reduced grain palatability, feed refusal, and the occurrence of mycotoxins. Further information on mycotoxins, testing procedures, and animal symptoms experiencing mycotoxicosis can be found in Mycotoxins in Feed Grains and Ingredients, MF-2061, Kansas State University Cooperative Extension Service.      Grain moisture content has an inverse relationship with test weight. In other words, as moisture content increases, test weight decreases. This relationship is expressed by the following equation (Nelson 1980):    D = 701.9 + 1676M - 11,598M2 + 18,240M3   D = density in kilograms per cubic meter   This method of expressing the relationship between density and mass-moisture of grain is part of the American Society of Agricultural Engineers (ASAE) and American National Standards Institute (ANSI) Standards D241.4 Feb 93. Density expressed in kilograms per cubic meter can be converted to pounds per bushel using the constant 0.0777 as follows: D - 0.0777 = pounds per bushel.              Figure 1 presents the relationship between moisture content and test weight derived from the Nelson equation. The six lines represent grains of different test weight (e.g. corn with test weights of 48, 50, 52, 54, 56, 58, and 60 pounds per bushel at 14 percent moisture). Little change in test weight occurs between 10 to 12 percent moisture content, whereas test weight declines almost 1/2 pound per 1 percent increase in moisture greater than 14 percent.               Dockage   in feed grain is measured for barley, rye, sorghum, triticale, and wheat. Dockage is removed prior to measuring test weight for all grains listed above except sorghum. Dockage is defined in the U.S. Wheat Standards as the nonwheat material removed by an approved cleaning device (a similar definition exists for the other grains listed above). The Carter Day dockage tester is the approved cleaning device for official inspection, however, other grain cleaners are available for measuring dockage. In the absence of a mechanized method for removing dockage, hand sieves may be used as describe by the FGIS procedures.      Dockage may possess limited feeding value and hinders airflow through stored grain, which results in uneven cooling and development of hot spots. Dockage exerts a negative influence on handling grain (e.g. slows its flow through the pit and reduces leg capacity) and lowers the test weight measure of grain.        Grade Determining Factors      Test weight is a bulk density measure (weight per given volume) and is reported as pounds per Winchester bushel (bu). While low test weight grain may not translate into reduced feeding value, processing costs may increase dramatically.      Data generated at Kansas State University from 1991 to 1994 for swine indicated that the feeding value of sorghum with a test weight as low as 35 pounds per bushel was only 10 to 12 percent lower than that of 57 pounds per bushel test weight sorghum. This reduction in feeding value of light sorghum is in sharp contrast with the 30 to 50 percent discount in price received by farmers (Traylor et al 1995).      However, when comparing processing performance of high and low test weight sorghum, Kansas State University researchers report that as test weight dropped from 58 pounds per bushel to 39 pounds per bushel, grinding rate was reduced by 45 percent and grinding cost increased over 5 fold from $0.41 per ton to $2.33 per ton.      Numerous other studies evaluating the feeding value of low test weight grains on different animal species indicate that there is little or no correlation between test weight and animal performance. Perhaps the only contradiction to this was reported for barley in Idaho. Researchers discovered that, in a cattle finishing study, average daily gain and feed efficiency fell about 1 percent for each pound decrease in test weight (GPE Factsheet 2000).        Foreign material   is the nongrain material that remains in a sample after the dockage is removed. In grains and oilseeds such as corn and soybeans, dockage is not measured, thus foreign material takes on a slightly different meaning.      In corn, the foreign material is measured with broken corn and is defined as follows: all matter that passes readily through a 12/64 inch round-hole sieve and all matter other than corn that remains in the sieved sample.      Broken corn and foreign material (BCFM) generally elevates the fiber content while protein and nitrogen free extract (NFE) content is usually comparable to clean grain. Although BCFM provides limited information pertaining to the nutritional value of corn, this grading factor does indicate possible handling, storage, and processing problems. Broken kernels are more susceptible to mold invasion and insect infestation during storage. BCFM limit air flow in storage and contributes to feedbunk fines.        Damaged kernels   can include evidence of heat damage, germ damage, sprouting, mold, and insect damage. Heat damage is designated separately in all grading charts and represents the tightest standard for kernel damage. Kernels experiencing heat damage tend to possess limited nutritional value.        Heat damage   results from storing grain that possessed too high of a field moisture content, from moisture migration due to convective air currents in the bin, or from localized infestations of stored grain insects that produce heat. Any of these conditions creates an environment that favors mold growth and heating from respiration. As a consequence, the endosperm turns dark brown or black.   Drier damage may result in kernels that are puffed or swollen and materially discolored by the drier heat. This form of damage, if of similar intensity as heat damage, may be designated as such. Grain damage caused by a drier that appears less severe than heat damage is designated as damaged by heat. This form of damage is included in the total damage category.        Germ damage   is caused by heat of respiration, however, only the embryo (germ) is damaged. This form of damage in corn would result in off-color oil. Since the severity of damage is less than heat damage, there is little or no effect on the nutritional value or feed processing characteristics. Germ damage is included in the total damage category.        Sprout damage   occurs in the field when physiologically mature grain is exposed to rain and high humidity and may occur in storage in response to conditions described under heat damage. Sprouting is caused by an activation of enzymes that convert the long-chain starch molecules in the endosperm into smaller carbohydrates and simple sugars, which serve as food to the young plant. Storage proteins also are split into smaller compounds during sprouting. The feeding value of sprout damaged grain is not affected. The occurrence of sprout damaged grain may indicate other problems that a feeder should be concerned about, specifically, the presence of molds and mycotoxins.        Mold damage   may occur during the growing season or storage. Grain stored under high moisture or temperature conditions is more prone to mold problems and the development of mycotoxins. Mycotoxins are toxic metabolites produced by mold, which can cause severe animal health problems and death.        Scab damage   in wheat results from field infection by Fusarium species during flowering and kernel development. Kernels that are scab damaged have a dull, lifeless, chalky appearance and usually contain mold in the germ or in the crease. Scabby wheat may contain deoxynivalenol (DON), also called vomitoxin. Symptoms produced by DON-contaminated wheat include feed refusal, digestive disorders, diarrhea, and possibly death.    The presence of odor (designated as musty, sour, or commercially objectionable foreign) or heating causes grain to be designated as sample grade (the lowest designation in the grain grading system). All of these odors are indicative of a grain storage/transportation problem. Musty odor indicates the presence of certain grain boring insects or mold, and commercially objectionable foreign odor may result from petroleum products or excessive fumigant use. For example, sour odor may be an indication of insect infestation or fermenting/moldy grain. Rodent excrement also may cause an off-odor. Rodents, cats, and birds can potentially spread disease through feces, urine, and body parts such as feathers or hair. Feeders should thoroughly inspect grain for the cause of the odor before accepting delivery or using this grain.        Special Grades      Infested grain, while possibly possessing satisfactory feeding value and processing performance can lead to economic losses. Many grain elevator managers discount grain $0.05 per bushel to cover the cost of fumigating infested grain. Feeders should not knowingly receive infested grain without a discount and fumigation strategy in place. The use of fumigants requires personnel possess a special applicators permit and proper equipment including air quality monitoring instrumentation and personnel protective equipment.        Ergot   may occur in cultivated grasses including wheat, triticale, barley, oats, and rye. A purple-black fungal mass (sclerotium) contains alkaloids which can cause gangrene or convulsions. Ergoty grain should not be fed to livestock without a preplanned strategy to mitigate problems. Such a problem should include quantification of the amount of ergot and a method for removing or reducing the amount of ergot below the level specified in the respective grain grading standards.        Smutty grain  , while potentially not a threat to the nutritional value or animal health, may produce an off odor. Most smut problems can be controlled during production through the use of a fungicide seed treatment.   
        
  
    

    Summary      Although the U.S. Grain Grading Standards were first developed to facilitate grain merchandising through the use of uniform tests and terms, grain grades also provide valuable information to the enduser regarding feed processing performance and feeding value of low-grade grain. Occasionally, the opportunity exists to purchase low-grade grain at a substantial discount. Feeders must factor into their purchasing decision the cause of grain quality deterioration, increased cost of processing, and potential health risks to the animal. A thorough understanding of the Grain Grading Standards will enable feeders to make wise choices regarding the purchase and use of low-grade grain.          Literature Cited      GPE Factsheet 2000 In: Kansas Beef Cattle Notebook. UNN8, Cooperative Extension Service, Kansas State University, Manhattan.      Herrman, Tim, Dionisia Trigo-Stockli, John Pedersen. 1995. Mycotoxins in feed grains and ingredients. MF-2061, Cooperative Extension Service, Kansas State University, Manhattan.      Nelson, S.O. 1980. Moisture-dependent kernel and bulk-density relationships for wheat and corn. Transactions of the ASAE 23(1):139-143.      Sauer, David. 1988. Molds in stored grain In: Stored Grain Management. Cooperative Extension Service, Kansas State University, Manhattan.      Traylor, S.L., K.C. Behnke, J.D. Hancock, and T.J. Herrman. 1995. Test weight affects the milling characteristics of grain sorghum. Swine Day 1995. Kansas Agricultural Experiment Station, Manhattan.      USDA. 1995. Official United States Standards for Grain. Federal Grain Inspection Service, USDA. Washington, DC.     ]]></description><pubDate>Fri, 20 Oct 2006 08:29:00 +0000</pubDate><link>http://www.feedmachinery.com/articles/feed_ingredients/grain_grading/</link><guid>http://www.feedmachinery.com/articles/feed_ingredients/grain_grading/</guid></item><item><title><![CDATA[Statistical Process Control: Techniques for Feed Manufacturing, Part 2]]></title><description><![CDATA[  Tim Herrman   Extension State Leader   Grain Science and Industry     Procedures for Developing a Control Chart      Step 1. Collect samples or measurements during processing. This is similar to Step 1 for the frequency histogram. In some cases, multiple measurements for a particular process are collected, such as when monitoring bag weight or tracking conditioned mash temperature.   Step 2. Perform preliminary calculations with the data set. If there are multiple measurements (subsamples), calculate the average and the range of these measurements. Next, calculate the overall sum and average for these values.     Step 3. Calculate the control limits (upper control limit UCLx and lower control limit LCLx) for the mean and the upper control limit for the range (UCLR). These control limits are set at three standard deviations. A simplified method for calculating control limits involves the use of Table 1, which presents factors for calculating control limits. The A2 column provides a list of factors used to calculate the UCLx and LCLx for data sets with subsamples (Example 3). The D4 column is used to calculate the UCLR. The d2 column is used to calculate the UCLx and LCLx for data sets that contain only one measurement per sampling (Example 4).        Example 3. Bag Weight    Step 1. Collect sample data; in this example 5 bags for 20 lots of feed.   Mean Range    1) 40.00 40.20 40.05 40.00 40.10 40.07 0.20    2) 40.10 40.17 40.15 40.20 40.00 40.12 0.20    3) 39.90 39.95 39.95 40.05 40.00 39.97 0.15    4) 40.05 40.10 40.10 40.05 40.03 40.07 0.07    5) 40.00 40.10 40.10 40.05 40.10 40.07 0.10    6) 40.25 40.15 40.25 40.15 40.15 40.19 0.10    7) 40.30 40.10 40.10 40.30 40.30 40.22 0.20    8) 40.05 40.10 40.05 40.05 40.25 40.10 0.20    9) 40.10 40.10 40.10 40.20 40.20 40.14 0.10    10) 40.10 40.10 40.05 40.20 40.05 40.10 0.15    11) 40.30 40.20 40.15 40.05 40.05 40.15 0.25    12) 40.15 40.30 40.15 40.20 40.20 40.20 0.15   13) 40.00 40.05 40.05 40.05 40.00 40.03 0.05    14) 40.10 40.10 40.04 40.25 40.25 40.15 0.15    15) 40.00 40.10 40.10 40.10 40.00 40.06 0.10    16) 40.12 40.10 40.10 40.40 40.00 40.14 0.40    17) 40.12 40.27 40.25 40.10 40.15 40.18 0.17    18) 40.10 40.10 40.00 40.20 40.10 40.10 0.20    19) 40.30 40.20 40.15 40.15 40.20 40.20 0.15    20) 40.15 40.30 40.10 40.20 40.15 40.16 0.20    Total 802.42 3.29    Avg 40.12 0.16   Step 2. Calculate the averages and range for the subsamples. Then calculate the sum for the mean and range columns (802.42 and 3.29, respectively) and calculate the average by dividing the sum by the number of samples (n = 20) which results in values of 40.12 and 0.16, respectively.   Step 3. Calculate the upper and lower control limit for the range control chart. Select the factor from Table 1 under the column titled D4. (Note: The complete table is published by the American Society for Quality Control.) In this example, select the value from the row n=5, since there are five measurements for each sample collection period (the select value is 2.114). The upper control limit is derived by multiplying D4 by the average range R:    UCLR = 2.114 x 0.1645   UCLR = 0.348   Calculate the upper (UCLx) and lower control (LCLx) limits for the averages. Using Table 1, identify the value A2 based on the number (n=5) of measurements per sampling period. Multiply A2 by the average range, then add and subtract this value from the average mean to arrive at the upper and lower control limits.    A2 times R = 0.577 x 0.1645 = 0.0949    UCLx = 40.121 + 0.0949 = 40.21    LCLx = 40.121 - 0.0949 = 40.03   Step 4. Plot the data on the control chart (Figure 4).          Interpretation of the Control Chart      In Example 3, the following interpretations are included:   - The bag weight data resulted in fairly narrow upper and lower control limits; this indicates that little product is given away.   - The lower control limit of 40.03 kg indicates that there is little likelihood of under filling bags and shorting customers of product when the bagging process is under control.   - Bag weight measurements occur in nearly equal proportions above and below the mean.    - The process, while appearing to perform well, is out of control.   The control chart differentiates between normal population variation and variation due to an assignable cause. Normal variation typically occurs within upper and lower control limits. The UCLx and LCLx were plus and minus three standard deviations from the mean. (Note: Three standard deviations from the mean accounts for 99.7 percent of the variation in a population.)   Thus, there is a low probability (3 in 1,000) that a measurement will fall outside the control limits due to random chance.   For the bag weight control chart (Figure 4), the third event in the average control chart occurred below the lower control limit. Thus, the process was out of control. The cause could be due to failure to calibrate the bagging scale or, perhaps, an error by the operator. The cause and effect diagram can be used to help identify the problem, the control chart only lets us know when the event occurred. Looking at the range control chart in Example 3 (Figure 4) the 16th event resulted in an average range value that was above the upper control limit, again, indicating the process was out of control.     Additional Rules for Interpretation      In addition to assessing if points occur outside the control limits, it is important to detect whether nonrandom patterns of data occur within the control limits. Specifically, if seven consecutive data points occur all above or below the mean (even if they are within the control limits), it is correct to conclude that the process is out of control. To help understand why, consider the probability that you will get heads if you flip a coin; it is 50 percent. Now, what is the probability of flipping two heads in a row? The answer is 50 percent times 50 percent (0.5 x 0.5) or 25 percent. Carrying this calculation through seven times results in a probability of less than 1 percent of getting heads seven times in a row. At this point, we reject the possibility that this event occurred through random change.   Similar to the coin illustration, the possibility of having seven consecutive measurements in ascending or descending order also is unlikely unless there is a change in the process. Thus, if either of these events occur, the process is considered out of control.     Example 4. Individual and Range Control Charts for Finished Feed Protein Content    Step 1. Collect sample data. In this example, the value represents finished feed protein content. There are 28 total measurements.   Batch Number Protein Content Moving Range    1 17.47    2 17.95 .48   3 18.91 .96    4 18.87 .04    5 18.35 .52   6 18.44 .09    7 18.71 .27    8 18.60 .11   9 18.80 .20    10 18.84 .04    11 19.41 .57    12 18.82 .59    13 18.19 .63    14 18.75 .56    15 19.01 .26    16 18.27 .74    17 18.60 .33    18 19.46 .86    19 18.48 .98    20 18.24 .16    21 17.73 .51    22 18.40 .67    23 19.26 .86    24 18.64 .62    25 19.46 .82    26 19.23 .23    27 18.53 .70    28 18.12 .41    Total 521.54 13.21    x =18.62 MR=0.489   Step 2. Calculate the moving range for each pair of data; note the range between two measures is calculated as a positive value. Then summarize the values and calculate the mean and moving range average. Notice that the average (x) is calculated by dividing the total (521.54) by 28 and the moving range (MR) total (13.21) is divided by 27 since there are only 27 moving range values.   Step 3. Calculate the upper control limit for the range chart. Select the factor from Table 1 under the column titled D4. In this example, select the value from the row n=2 which is the smallest value in the table, since there is one measurement per event. The upper control limit is derived by multiplying D4 by the average range R:   UCLR = 3.268 x 0.489   UCLR = 1.6   Calculate the upper and lower control limits for the average control chart. Divide the moving range average by 1.128 (d2), multiply by three (the desired number of standard deviations) and then add and subtract this value from the average mean.   UCL = x + 3(MR/d2) LCL = x - 3(MR/d2)    Where x = mean of all lots   For this example, the calculations for UCL and LCL are as follows:    UCL = 18.62 + 3(.489/1.128) = 19.92    LCL = 18.62 - 3(.489/1.128) = 17.31   Step 4. Plot the data on the control chart (Figure 5).     Procedures for Developing a Pareto Chart      A Pareto chart is a special type of frequency histogram that records the most frequent problem as the first bar, the next most frequent problem as the next bar, and so on. This procedure helps prioritize problem solving activities. Steps for developing a Pareto chart are as follows:   Step 1. Categorize the type of complaint: e.g., moldy feed pellets, too many fines, etc.   Step 2. List the most serious defect first under the defect column, followed by the second, etc.   Step 3. Report the number of occurrences for each incident in the frequency column.        Step 4. Complete the cumulative complaints column by adding, in succession, the number of complaints.   Step 5. Calculate the cumulative frequency by dividing the cumulative complaints by the total number (n = 24) of complaints.   Step 6. Plot the results.     Process Improvement Using a Cause and Effect Diagram      
The cause and effect diagram shows in picture or graph form how causes relate to the stated effect or to one another. Also referred to as a fishbone diagram, the main causes or &#39;bones&#39; of the fishbone are:   
- Material   - Machine   - Environment   - Method   - Operator        For example, suppose finished product protein content is found to fluctuate by 2 percent. While in most cases the product meets label requirements for nutrient content, management is concerned about giving away protein, which is the same as giving away money. To address this problem, a team of employees, including the production supervisor, quality assurance manager, receiving technician, and lab technician, meet to solve the problem. They use the cause and effect diagram as a guide to discuss the source and solution to the problem. It is discovered that a wide range in soybean meal protein content occurs between lots. The soybean meal protein content is not identified in the warehouse, therefore, it is treated as having the same protein content by production personnel. The team decides to reformulate rations based on 1 percent soybean meal protein increments and identify the protein content of different lots of soybean meal in the warehouse. Therefore, the plant production personnel can match feed rations with the appropriate soybean meal content. The variation in finished product protein content ceases and the company reports a substantial profit increase during the next business quarter.     Summary      Statistical process control (SPC) finds many applications in the feed manufacturing industry. Examples illustrating the application of four SPC tools (frequency histogram, control chart, Pareto chart, and cause and effect diagram) are presented in this bulletin.   Additionally, a list of other ways in which SPC may be applied to control the process and improve product uniformity are presented in the bulletin. SPC relies on the application of statistical principles and procedures to improve product quality and profitability. The benefits derived from SPC in the areas of reduced internal and external failures should offset any additional costs incurred from sample collection, testing, and data analysis.  ]]></description><pubDate>Fri, 20 Oct 2006 07:38:00 +0000</pubDate><link>http://www.feedmachinery.com/articles/feed_processing/feed_spc002/</link><guid>http://www.feedmachinery.com/articles/feed_processing/feed_spc002/</guid></item></channel></rss>