Practical Utilization of NIR Technology in Poultry Feed Formulation

Practical Utilization of NIR Technology in Poultry Feed Formulation Shivaram Rao Director of Broiler Nutrition, Foster Farms, 1000 Davis Street, Livingston, CA 95334 The use of Near Infrared (NIR) technology to determine simple components such as moisture, protein, fat, and fiber of major feed ingredients and finished feeds has been around for many years (Valdes and Leeson 1992). However, the high costs involved in NIR equipment as well as the statistical expertise needed to calibrate, validate, and update equations in field conditions have been a major limiting factor in the wide use of this technology by US poultry companies. Until 2010, US poultry companies limited NIR technology application to QA and QC programs involving feed ingredients and finished feeds rather than as a tool to build or finetune specifications of feed ingredients in feed formulation software. Apparent metabolizable energy (AME or ME) is the widely used measure of energy in feed stuffs (Leeson and Valdes, 1994). Since the publication of ideal amino acid ratios by Emmert and Baker (1997), digestible amino acid (DAA) based feed formulation is becoming common as well. Overall, ME and DAA are the main drivers of poultry feed formulation. Even as late as in 2008, Steve Leeson pointed out that technology was not available to rapidly determine ME and DAA of feed ingredients in commercial systems. He states that guaranteed energy should be a component of grain trade because energy is the most expensive nutrient in a diet. Realizing the need for faster technology, researchers have developed NIR technology to measure ME (Valdes and Leeson, 1992, Leeson and Valdes, 1994) and DAA (Theo Van Kempen, 1996) in major ingredients of poultry feeds. Following their lead, several feed ingredient suppliers have developed NIR technology to determine ME and DAA which will be discussed in detail later in this article. According to Daniel Jones 1, Pioneer Hi-Bred has been developing a rapid NIR technology based tool to estimate bioavailable energy values of corn for swine and poultry. Their idea is to assist the industry to adopt and use standards based on the functional utility of the grain purchased rather than relying on the current grain physical grades to determine value in use. Their findings will soon be published in the Journal of Animal Science. One of the advantages of the NIR spectroscopy (NIRS) instrument is that a calibration developed in one NIRS can be transferred or cloned to many other NIRS instruments in the field through a process called calibration transfer (Fernandez-Ahumada, et. al., 2008). Data interpretation after scanning the feed ingredients and reporting the results in real time are the remaining obstacles to overcome. Both of these obstacles are now solved thanks to internet technology. Compared to twenty years ago, NIRS equipment remains expensive but all other costs to implement the technology can be reduced. So, after all these years, it now seems possible for practicing poultry nutritionists to apply NIR technology to improve precision poultry feed formulation in real time or near real time to lower the feed cost per unit of poultry meat produced. The purpose of this article is to provide a few examples of practical applications of NIR technology to enhance precision feed formulation in relation to ME and DAA. The examples outlined are those tested (or in the process of being field tested), verified, and successfully implemented in the field situations by the author. There currently may be other examples being 1 Daniel Jones, personal communication, 25 July 2011, daniel.jones@pioneer.com. 1

used in the industry that are unknown to the author. In addition, some ideas related to the future applications of NIR technology to take advantage of feed enzymes are discussed. In this article, the emphasis is placed on practical strategies of NIR technology utilization in poultry feed formulations. The history and principles behind NIR technology, as well as NIR application for the finished feeds are not discussed. Avoid Costly Errors by Maintaining Feed Formulas as Close to Real Time as Possible The actual ME and DAA contents of feed ingredients could vary substantially from the specifications setup (matrix) in the feed formulation software leading to formulation errors. How often do they occur in a commercial system? Could these errors be costly? In our company, one of the key performance indexes used is the broiler feed conversion. Figure 1 below shows the expected feed conversion and the actual feed conversion for a standardized 5 pound (2.27 kg) body weight. The expected line is based on approximately 0.018 units of feed conversion improvement for every year being assigned to broiler genetic improvement. Since 2008, the actual feed conversion (simple average of four complexes) was in many occasions, higher than the expected target (Figure 1) resulting in large economic losses. When causes were categorized (Table 1), we found that about 48% of the errors were major feed ingredient related and specifically to ME and DAA. Figure 1. Comparing Feed Conversion (adjusted to 5 Pound body weight) Table 1. During 2008 to 2011, Feed Ingredient Related Errors Accounted for 48% of Total Errors 1.84 1.82 1.8 1.78 1.76 1.74 1.72 1.7 1.68 1.66 Feed Conversion Expected Actual Item # Error Counts 1 Coccidiosis 4 2 Heat & cold stress 6 3 Corn ME drop b/c PAC 7 4 Corn ME drop (not related PAC*) 5 Protein (DAA) drop 2 6 Feed Mill related 2 Feed ingredient related (3+4+5) Total 23 2 11 (48%) * PAC = Proximate Analysis Content Number inside the () represents % of total Most integrated poultry companies have adopted just-in-time delivery for major feed ingredients and for corn it is usually high-speed unloading of 75 to 115 rail cars which arrive at regular intervals with limited time to unload them. In addition, the grain trade rules are somewhat outdated. Implementing an error free QA and QC system from the ME and DAA point of view for major ingredients in the USA would be prohibitively expensive. In our company, the 2

feed ingredient related errors occurred even after strictly implementing routine conventional QA and QC procedures in which we used wet chemistry analysis, followed by making appropriate adjustments in nutrient specifications in the feed formulation system. In some cases, relying on wet chemistry methods caused a delay of nearly six weeks in diagnosing the problem of low ME corn. The best option for now seems to be adopting the NIR technology to measure ME and DAA in real time (or near real time) for major feed ingredients, then to fine-tune their nutrient matrix in the feed formulation software and to reformulate the feeds if necessary. In addition to being slow, our approach of conventional wet chemistry and subsequent use of prediction equations to determine ME and DAA had another drawback. At least on two occasions (Table 1) in 2010 where corn was prematurely harvested in the field and dried postharvest, the traditional prediction equations did not show any ME reduction in the corn. Even after observing for two months of deterioration in the feed conversion, we failed to diagnose the problem but two of our feed additive suppliers (independent of each other) helped us troubleshoot. One of them used the NIR technology to find corn ME was about 90 Kcal/lb (41 Kcal/kg) lower than the prediction equation based value. In recent years, several feed ingredient suppliers have begun providing quick and reliable NIR spectroscopy (NIRS) based service to their customers in measuring the ME and DAA contents of feed ingredients. These companies first clone their master NIRS equipment in customers locations. Customers are asked to scan feed ingredient samples with NIRS, and then through internet technology and with special software, data is securely transferred, interpreted and reported back to the customers. At Foster Farms, we began using NIR technology to enhance feed formulation in 2011 with only one ingredient (corn) and are currently streamlining the process across other major ingredients. ME and DAA Determination of Feed Ingredients Using Adisseo NIRS Adisseo 2 introduced the NIRS based service to their customers in 2010 in the US. As described by Theo van Kempen (1996), the planning and research involved in developing the NIRS to measure DAA and AME of feed ingredients began in 1981. In vivo ileal AA digestibility were determined since 1981 for a large number of feed ingredients in Rhone- Poulenc s (now Adisseo) research facility in France. A subset of the same feed ingredient samples used in the in vivo ileal digestibility study were subsequently scanned using the NIRS and the results correlated with the in vivo ileal digestibility (van Kempen 1996). In addition to DAA calibration, now based on their own digestibility studies, Adisseo developed the NIRS calibration equations for AME as well. The following is a description of steps followed by our company to collaborate with Adisseo to implement the NIR technology for real time or near real term determination of ME and DAA of several feed ingredients. These steps are summarized in Figure 2 as well. Adisseo and our company mutually agreed to work together to use their NIRS service for feed formulation. We provided the details on two NIRS instruments (both Foss 5000) in our laboratory to the Adisseo technical team in France to make sure that they are compatible with the master NIRS equipment in their laboratory. Next, one of our laboratory technicians was assigned as the key contact person to work with the Adisseo s NIRS team in France. For the next 30 to 40 days, both teams worked on the preparation of standardization material, 2 4400 North Point Parkway Suite 275, Alpharetta, GA 30022, USA 3

organization, training/ support, and to standardize and validate the NIR instrument. Meanwhile, Adisseo created a private account for us on their website. We can access this website to view and download the AME and DAA reports. The report includes the AME, AMEn, total, digestible and digestibility % of the indispensible amino acids of poultry. We implemented the Adisseo NIR service during November 2010. At present, in two of our large feed mills in CA, the Adisseo NIRS analysis based AME and DAA values are being used to setup feed formulation. These feed mills produce feed 24/7 throughout the year. Each feed mill has a corn storage capacity for about a week and it will be very difficult to predict the exact date of incoming corn Figure 2. Steps to Setup Adisseo NIRS Service Qualified Adisseo Customer with NIRS (Foss or Bruker) Check NIRS instrument compatibility with Adisseo Designate key contact person (or a team) in your company to work with Adisseo NIRS team Work with the Adisseo team on preparation, organization & training / support Standardize & validate the NIR instrument ; Adisseo to create a private in their website for your company Begin scanning & uploading data to Adisseo website, login to view or download reports, (Adisseo customers without NIRS co uld also receive NIRS service to determine AME & DAA of their feed ingredients but it will take more turnaround time) being used for producing the feed. Therefore, sampling incoming corn would be less reliable to real-time feed formulation. However, once the corn is ground to be mixed, it enters rest of the feed manufacturing process within a day. Twice a day, 12 hours apart, ground corn sample are collected and scanned with NIRS. The scanned images are immediately uploaded to the Adisseo website under our private account. Within a few seconds, we receive an e-mail confirming the receipt of scanned images from one of the Adisseo s five regional centers. These centers are located at Commentry, France; Guadalajara, Mexico; Santa Maria, Brazil; Beijing, China and Singapore covering 20-24 hour per day. Usually, within two hours, we receive another e-mail informing us that the results are available with a link to their website. We then download the numbers directly onto a spreadsheet template with graphs. The decision to keep the previous corn specification in the feed formulation software or to change it to the new one is based on trend analysis of graphs. In addition to ground corn, we have now begun scanning other feed ingredients such as soybean meal, bakery meal, and DDGS using NIRS; and are compiling a database and setting up spreadsheet templates of the AME and DAA for these ingredients. Adisseo customers without an NIRS instrument can also receive NIR service but the turnaround time will be longer because the samples have to be sent to one of their regional laboratories for NIRS scanning. Determination of ME using AB Vista Aunir Service AB Vista 3 working with their affiliated company Aunir has been providing an NIRS based corn analysis report called Corn Quality Report since 2010. The unique advantage to their approach is that they consider protein digestibility and vitreouness in predicting corn ME. 3 AB Vista, Attention: Technical Service, 1350 Timberlake Manor Parkway, Suite #550,Chesterfield, Missouri, 63017, USA 4

Cowieson (2010) detailed why protein digestibility and vitreouness of corn should also be considered along with proximate composition in estimating Corn ME. It is obvious from Cowieson s (2010) article that two corns with identical starch, protein and fat contents may not necessarily have the same ME because they may have different solubilities of starch and protein in the digestive system. In practical situations, post harvest processing such as higher drying temperature can reduce protein and starch digestibility of corn (Malumba et al., 2009) which is what happened in the upper Midwest part of the USA in 2009. Our company received 2009 Midwestern corn during the earlier part of 2010. Based on the proximate analysis dependent prediction equation, the corn used in our company during the start of 2010 was estimated to have 1,510 Kcal/lb (3,322 Kcal/kg) and was formulated accordingly. However, upon experiencing significant loss in broiler feed conversion from feeds produced from two separate feed mills the diagnosis was that the Midwestern corn we used only had 1,420 Kcal/lb (3,146 Kcal/kg) of ME, a 90 Kcal/lb lower than expected. The NIR based Corn Quality Service was instrumental in troubleshooting. Setting up the Corn Quality Service system takes about four weeks. Once the customer s NIRS equipment is compatible with the AB Vista NIRS, three corn check samples will be sent by AB Vista to be scanned by the customer s NIRS equipment. The scanned images have to be e-mailed back and the check samples have to be returned as well. The AB Vista team will add the NIR scans from the customer into their program which creates a new repeatability file for that individual NIR equipment. This initial process usually takes about two to three weeks. AB Vista also provides a secure, private website to login and upload the scanned images and to view, print and download reports. After this initial setup, customers can begin using the Corn Quality Service. We are in the initial stages of utilizing the Corn Quality Report system and at present, four composite samples from 100 rail car units are scanned using NIRS. Once the NIRS scans are uploaded the turnaround time is about 36 to 48 hours. Each sample is identified with the origin of corn (geographic origin within the US), date of shipment, and feed mill using the corn. Tracking the source or origin of corn helps AB Vista to develop an online live nationwide corn ME profile which should help their customers. The completed report will contain the above sample identification as well as the following: starch (%, DM), protein (%, DM), oil (%, DM), fiber (%, DM), moisture (%), protein solubility index (%), vitreousness (%), AME @ 88% DM, AME (Kcal/kg, as is), ash (%), and phytate (%). At present, the AB Vista service only covers corn but future plans are to include other major ingredients, provide phytate, and other fiber fractions which are of interest to them as potential substrates for their enzymes. Determination of DAA with AMINORED Service from Evonik Industries Evonik Degussa GmbH 4 has been providing information on feed ingredients for several decades as AMINOdat. The AMINONIR and AMINORED, a rapid evaluation of digestibility, are their recent developments and with the combination of these two services, their customers can practically utilize NIR technology in feed formulation. Once the initial setup of customers NIRS equipment is complete, customers can scan selected feed ingredients (soybean meal, full fat soybean, and DDGS) through AMINONIR, upload results through their website and with the help of AMINORED calculators estimate the DAA. The results can be obtained in a few minutes after NIRS scanning of feed ingredients. 4 Evonik Industries, 1701 Barrett Lakes Blvd, Suite 340, Kennesaw, GA 30144 5

Although we have not used the AMINONIR at our company locations, we have been utilizing AMINORED service from Evonik to obtain the DAA profile of several ingredients. With this service, we send feed ingredient samples to EVONIK s laboratory in the USA where they use NIR technology to estimate the DAA. With AMINORED, bakery meal, meat and bone meal, poultry meal and several other feed ingredients can also be sent for the DAA estimation. This service usually takes about 10 days to two weeks. Advantages of Working with Feed Ingredient Supply Companies to Utilize NIR Technology If resources are available, an NIR technology based estimations of ME, DAA and possibly other key information required for feed formulation can be independently developed by a poultry company. However, cost of the NIR technology to improve feed formulation can be lowered if a poultry company collaborates with others as described earlier with the three examples. Some advantages of working with other companies to utilize their NIR technology calibration for feed formulation are: a) The cost of NIR technology implementation could be lowered, b) No need to have in-house technician/statistician to calibrate and validate the NIRS instrument, c) Introducing a new feed ingredient to the formula may be easier because NIRS calibrations for a large number of feed ingredients may already have been setup by the other company, d) The possibility to work with the other team to develop calibrations to new or local feed ingredients. As previously described, from poultry feed formulation point of view the current corn trade based on physical grades is outdated. Poultry companies as well as corn growers producing value added corn such as high oil corn may suffer losses under the current system. The seed company Pioneer Hi-Bred s effort to develop the NIR based tool to facilitate corn trade beyond the physical grading to one based on bioavailable energy should be encouraged because using high energy corn in feed formulation would reduce the need for expensive added fat or oil in poultry feeds. Future Directions for NIR Technology in Poultry Feed Formulation Use of enzymes in feed, especially non-starch polysaccharide (NSP) enzymes is now routinely practiced in the USA. Nutritionists should be aware of the quantity of substrate(s) available in feed for enzyme(s) being used. As described earlier, AB Vista is already determining phytate, the substrate for the phytase enzyme, in corn using the NIR technology. Blakeney and Finn (2005) demonstrated the use of NIR technology to determine non-starch polysaccharides in grains. A short list of possible applications of the NIR technology in feed formulation of the future could include: a) Quantifying enzyme specific substrate in feed ingredients and in finished feeds, b) Rapid evaluation of the magnitude of a feed enzyme effectiveness in poultry by quantifying substrate in test feed and in the ileal content of test fed birds and then comparing it to the ileal content of the control diet fed birds, c) Estimating the available minerals in feed ingredients and d) Determining the ME and DAA content of poultry manure. Measuring various compounds in poultry manure could also play a key role in waste management as well. Conclusion The NIR technology is being used in the QA and QC system of feed ingredients and finished feeds for many years. Recently, several feed ingredient suppliers are offering the NIR 6

service to determine the ME and the DAA of feed ingredients which are the two key drivers of practical feed formulation. A well-developed NIRS calibration for the ME or the DAA can then be mimicked (cloned) in several customer NIRS instruments. The NIRS technology is very compatible with the internet technology to transfer electronic data in both directions, thereby reducing the cost of NIRS data interpretation and report distribution. The NIR technology is now ready to be implemented in practical, real time or near real-time poultry feed formulation. The industry should adopt the technology responsibly and promote the interested feed ingredient suppliers and other researchers to develop more real time applications such as to understand the effectiveness of feed enzymes and to further improve feed digestibility. References Blakeney, A. B., and Flinn, P. C., 2005. Determination of non-starch polysaccharides in cereal grains with near-infared reflectance spectroscopy. Molecular Nutrition & Food Research, 49: 546 550. Cowieson, A. J., 2010. Corn nutritional value, composition analyzed. Feedstuffs June 7. pp 30-32. Emmert, J. L., and Baker, D. H., 1997. Use of the ideal protein concept for precision formulation of amino acid levels in broiler diets. J. Appl. Poultry Res. 6:462-470. Fernandez-Ahumada, E., Garrido-Varo, A., Guerrero, J. E., Perez-Marin, D., and Fearn, T., 2008. Taking NIR Calibrations of Feed Compounds from the Laboratory to the Process: Calibration Transfer between Predispersive and Postdispersive Instruments. J. Agric. Food Chem., 56:10135 10141. Leeson, S. and Valdes, E. V., 1994. Rapid indirect assays for metabolizable energy. Proceedings of the meeting Arkansas Nutrition Conference. pp 143-150. Leeson, S, 2008. Feedstuffs 2009 Reference Issue & Buyers Guide. September, Pages 53-61. Malumba, P., Janas, S., Roiseux, O., Geoges, S., Masimango, T., Sindic, M., Deroanne, C., and Bera, F., 2010. Comparative study of the effect of drying temperature and heat-moisture treatment on the physicochemical and functional properties of corn starch. Carbohydrate Polymers 79: 633-641. Valdes, E. V. and Leeson, S., 1992. Near infrared reflectance analysis as a method to measure metabolizable energy in complete poultry feeds. Poultry Sci., 71: 1179-1187. Van Kempen, T. 1996. NIR technology: Can we measure amino acid digestibility and energy values?. 12th Annual Carolina Swine Nutrition Conference. pp 34-48 7