NIR as a tool for real-time quality determination of Distiller’s Dried Grains

In a prior blog, we shared how NIR online sensors could be used to optimize biofuel processes, starting at the farm, through fuel processing, and into feed applications. In this post we take a closer look at how NIR can be used as a measurement tool for a valuable co-product of the biofuel industry: distiller’s dried grains with solubles (DDGS).  

Most ethanol plants in the United States are dry-grind facilities, which use starch from corn to produce ethanol; the remainder of the corn kernel is used to produce a variety of wet and dried distiller’s grains co-products, including DDGS. In dry-grind ethanol production, approximately 7.7 kg of DDGS is produced per bushel (25.4kg) of corn.  

The high energy, moderate protein, easily digestible phosphorus source characteristics of DDGS make it an attractive replacement for more expensive, traditional feed components, like corn, soybean meal, and mono- or dicalcium phosphate. DDGs also have a longer shelf life than normal corn or soybean meal, adding to its value.  

Selling co-products gives the ethanol producers a new revenue stream to help compensate for fluctuations in renewable fuel demand and price. However, the variability in nutrient content, digestibility, and physical characteristics (e.g., color, particle size) among DDGS sources can be a challenge when determining the economic and feeding value for livestock and poultry. Therefore, it is important to determine nutrient composition for accurate feed formulation, as well as the impact of DDGS physical characteristics on feed quality. 

Nutrient variability among distiller’s by-products has been attributed to many sources, including raw material variation (e.g., types of grains, grain variety, grain quality, and grain formula) and production factors across the process, from grinding through fermentation. 

In this blog, we present quality control of DDGS using rapid or real-time NIR measurements for proximate and color analysis.   

Proximate Analysis  

Proximate analysis of feed ingredients is a critical aspect of quality assurance. Measurements typically include moisture, crude protein, crude fiber, crude fat, and ash. Ingredient specifications serve as the basis for quality assessment, purchasing agreements, and diet formulation. Near-infrared spectroscopy (NIR) is an established technique in the feed industry for fast measurements of these sample attributes. With real-time NIR monitoring or at-line NIR access, nutritional data can be assessed at any point along the process to help plants improve their efficiency or reduce operating costs.  

For sites requiring quantification of protein from various protein-rich plant feed materials, hybrid models may be of particular interest.  Some researchers have found success combining multiple plant feed materials (e.g., corn DDGS, corn germ meal, corn gluten meal, and rapeseed meal) into a single regression model, lending some added convenience when various sample types are under investigation.  

Moisture is an especially important parameter for DDGS, considering its impact on flowability, risks of microbial spoilage, and even transportation costs.  

During feed conditioning, processors may target around 15-17% moisture. This target typically helps avoid plugs in the pellet mill. For cost-effective transportation and safer storage, a moisture content of less than 15% is the rule-of-thumb for feed ingredients.  

DDGS has shown hygroscopic tendencies during long-term storage. In one study, DDGS stored in a commercial feed mill exhibited a 30% increase in moisture content during a 13-week storage period.  

BUCHI NIR-Online instruments can easily assess moisture content throughout processing to keep products within an optimal range to support good flowability and stability. Real-time moisture data during drying can support those goals while also reducing energy costs and improving product yields.  

Color assessment 

Currently, there is no color grading system for DDGS like those existing for corn and other grain exports. Historically, color has been used subjectively to assess possible heat damage which negatively impacts amino acid digestibility. Consequently, some DDGS buyers will consider color an important quality measurement.  

Corn DDGS can vary from a light golden shade to very dark brown. An L* reading ranging from 0 (dark) to 100 (light) is used to report both redness (a*) and yellowness (b*) on the color scale. The color of DDGS may be impacted by several factors, including the amount of solubles added to grains before drying, the dryer type and temperature, and the natural color of the feedstock grain used. Bhadra et al. (2007) reported L* ranging from 36.6 to 50.2 and b* ranging from 5.2 to 10.8, respectively.  

Both the BUCHI at-line ProxiMate NIR and online NIR-Online™ systems can be configured with a visible detector, enabling rapid or real-time analysis of color at any point along the process chain. Visible and NIR data are collected in parallel, keeping testing time short. The visible signal may also be combined mathematically with NIR signal to improve the performance of some parameter measurements.  

In a typical NIR application for measuring DDGs quality parameters, spectral data is correlated with reference values (obtained by validated reference methods) to build a calibration. Calibration development requires the application of chemometrics, in which information is extracted from chemical systems using statistical and data-driven techniques. Chemometrics requires advanced analytical training in the use of software, which most operators do not have. The BUCHI ProxiMate NIR offers a solution to this challenge with its auto-calibration feature, known as AutoCal®. The NIR Online™  sensor has a similar provision for continuous production systems. 

Once spectral data have been collected, and reference measurements added, calibrations for all parameters of interest (e.g., crude protein, fiber, moisture ash) are obtained simultaneously with a single push of the AutoCal® button. Next to proximate analysis, both the ProxiMate NIR and NIR Online™ systems have capabilities to monitor color changes in DDGS and other by-products of biofuel production. This color determination is considered a primary measurement that does not necessitate calibration development. 

In terms of standing up to harsh conditions, the NIR ProxiMate and NIR Online™ units are developed with a minimal number of moving parts and up to IP69/X9K ingress protection ratings.

Video illustrating the use of NIR-Online in a production facility

To conclude… 

NIR spectroscopy has been embraced as a fast, non-destructive, and cost-effective technique for analyzing the quality and nutritional value of food, feed and processing co-products, such as DDGS. The BUCHI ProxiMate NIR and NIR Online™ spectrometers can be calibrated for use in monitoring DDGS based on proximate composition and color changes. Suspect materials can be sent to a laboratory for more thorough testing using standard reference methods. Once calibrations have been developed, the amount of material that needs to be tested by rigorous chemical methods is greatly reduced, saving labor, time, and energy resources. Materials that fail to meet the minimum quality standard can then be diverted for additional processing to increase their value.  

We hope you enjoyed reading this blog. Get more information from these references: 

U.S. Grains Council (USGC) 2012, DDGS User Handbook, 3rd Edition, viewed 31 August 2021,  <https://grains.org/wp-content/uploads/2018/01/Complete-2012-DDGS-Handbook.pdf>. 

Shapley 2011, Ethanol from Corn, viewed 16 August 2021,   <http://butane.chem.uiuc.edu/pshapley/Environmental/L8/4.html>  

Badhra (2007) Characterization of Chemical and Physical Properties of Distillers Dried Grain with Solubles (DDGS) for Value Added Uses. American Society of Agricultural and Biological Engineers International Meeting, Technical Papers (14). DOI: 10.13031/2013.22861

Fan X, Tang S, Li G, Zhou X (2016) Non-Invasive Detection of Protein Content in Several Types of Plant Feed Materials Using a Hybrid Near Infrared Spectroscopy Model. PLoS ONE 11(9): e0163145. https://doi.org/10.1371/journal.pone.0163145&nbsp;

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