Development of NIRS Method for Predicting Starch Content in Sorghum Grains
The potential for using Near-Infrared Spectroscopy (NIRS) as a way of quantifying starch in Sorghum grains was investigated. Spectral data from 41 ground Sorghum grain samples was obtained with an Antaris II FT-NIR analyzer (Thermo Scientific Inc., Madison, WI, USA) in reflectance mode. Each sample was averaged over 32 scans at a resolution of 4 cm-1 in the wavelength range of 4,000-10,000cm-1 (1,000-2,500nm.). The starch content for these samples was obtained through a Megazyme total starch assay kit, with two replicates for each sample. Of these 41 samples, 9 were randomly selected for validation, with the other 32 being used for calibration. Chemometric analysis was conducted by TQ Analyst 8.6.12 software (Thermo Scientific Inc., Madison, WI). The method of Partial Least Squares was used for model development. Model performance was evaluated in terms of the coefficient of determination (R2), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). The Chauvenet test was used to eliminate outliers defined as the points at distances in the principle component space greater than 3.0. After eliminating outlier data, it was found that the calibration model has an R-value of 0.9224, translating to an R2-value of 0.8508, meaning there is a strong correlation between starch content and NIR absorptions. These results show that the NIRS model is promising for starch content analysis and can be used for the biofuel industry in the future.
Many growing concerns such as petroleum price instability and dependence on imported oil have encouraged Renewable Energy acts to be put into motion by both the European Union and the United States. The European Union Renewable Energy Directive from 2009 set the following goals to be reached by the year 2020: having 20% renewable energy of total energy consumption, and a 10% fraction of renewable energy (ethanol) in the transport sector (Council of the European Union, 2009). The United States also adopted a renewable energy policy through the Renewable Fuel Standard (RFS) program. The RFS requires that renewable fuel be blended into transportation fuel in increasing amounts each year, escalating to 36 billion gallons by 2022 (Environmental Protection Agency, 2007). In order to meet these requirements potential energy crops such as wheat, rye, barley, and sorghum are being integrated into the current ethanol production process.
Sorghum is a promising energy crop due to its ability to tolerate drought, soil toxicities, a wide range of temperatures, and high altitudes (Stroade & Boland, 2013). In addition, it has short growing seasons, high
Figure 1. Sorghum Crop
Figure 2. Simplified Plant to Ethanol Process
Near-infrared spectroscopy (NIRS) is a spectroscopic method that uses the near-infrared region of the electromagnetic spectrum (from about 800 nm to 2500 nm) and is widely applied
Figure 3. Light Spectrum
Many other studies have done research on cereal grains using NIRS. Kim and Williams were successful in proving NIRS as accurate when determining starch content and energy in canadian wheat, barley, and corn (Kim & Williams, 1990). Pohl and Senn investigated the potential of near-infrared spectroscopy for determining fermentable substance and ethanol yield in wheat, rye, and triticale grains and concluded that NIRS is an appropriate and useful tool for these predictions (Pohl & Senn, 2011). A study was also on whether NIRS can be used to predict the ethanol yield of dry-grind maize, and it was proven that a NIRS model could in fact be used to predict maize grain ethanol yield potential (Hao, Thelen, & Gao, 2012). Considering several other studies have had success in using NIRS as a method for cereal grain analysis, it is thought a similar model can be produced for sorghum grains. Therefore, the main objective of this project is to develop a NIRS model that can be used as a fast method for predicting the starch content in sorghum grains for further research and industry use.
Several varieties of sorghum grain samples were received from a supplier in Texas. 41 of these grain samples were then scanned by an Antaris II FT-NIR analyzer
Figure 1. NIR Analyzer
Figure 2. Inner workings of the NIR Analyzer
Grain samples were then ground down to a 0.5mm diameter particle size using a UDY Cyclone Mill.
Figure 3. UDY Cyclone Mill
In order to calculate starch content, the moisture content of each sample must be known. Therefore, the moisture content of each sample was determined after milling. Two replicates were weighed out for each sample and placed in sterilized pans. These pans were then placed in a 130°C oven for 3 hours. After 3 hours the pans were taken out of the oven and placed in desiccators to cool for 20 minutes. Once cool, the pans were weighed again and the following formula was used to calculate the moisture content for each sample: MC = (Wet Weight Total-Dry Weight Total)/(Wet Weight Flour)X100%
Figure 4. Oven full of samples being tested for moisture content
The next procedure to be carried out was starch content analysis. A total starch assay kit from Megazyme was used to quantify the starch for each sample, with two replicates per sample. The main enzymes used were alpha-amylase and amyloglucosidase. The final part of the procedure requires that the samples be run through a spectrophotometer, which measured the absorbencies at a wavelength of 510nm. These absorbencies were then entered into a Mega-Calc spreadsheet along with the samples weight and moisture content. The spreadsheet then returns the starch content as a percentage of the dry weight base for the sample.
Once all of the data was collected, a chemometric analysis was conducted using TQ Analyst 8.6.12 software (Thermo Scientific Inc., Madison, WI). The pathlength was calibrated using standard normal variate (SNV) and multiplicative signal correction (MSC). The Savitzky-Golay filter was used to reduce random noise. First and second derivatives were used as a pretreatment method to resolve spectra peak overlap and eliminate linear baseline drift. The first derivative was the rate of change of absorbance with respect to wavelength, whereas the second derivative corresponded to the curvature or concavity of the graph. First and second spectra formats were compared. To avoid bias in the subset and achieve a calibration set and validation set with a ratio of 3, all 41 sorghum grain samples were sorted ascendingly by measured value. One in every five samples was randomly assigned for validation (9 total) , with remaining samples as calibration samples (32). A full spectra range from 4,000 to 10,000 cm-1 was used unless otherwise specified. The method of partial least squares (PLS) was used for model development. Model performance was evaluated in terms of the coefficient of determination (R2), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and ratio of standard deviation of calculated set (SDy) to RMSEP (RPD).The Chauvenet test was used to eliminate outliers defined as the points at distances in the principle component space greater than 3.0. Predicted residual error sum of squares (PRESS) diagnostic function was used to determine the number of factors necessary for calibration.
Results and Discussion
Figure 1. Grain Spectra
Figure 2. Flour Spectra
Figure 3. Moisture Content of flour samples
Figure 4. Starch Content of flour samples
Figure 5. Grain Calibration Model
Figure 6. Flour Calibration Model
There was hope that a NIRS model could be derived for sorghum grain as a non-destructive method for starch analysis, however the data shows that the samples may not be uniform enough to acquire an accurate reading. On the other hand, even though there is a noticeable amount of background noise in the NIR spectra, the data from the calibration model for sorghum grain flour shows a strong correlation between starch content and NIR absorptions. These results demonstrate that the NIRS model for sorghum grain flour is promising for starch content analysis and can be used for the biofuel industry in the future.
For future research, more sorghum grain and flour samples will be analyzed and added to the starch calibration models. The additional samples will increase the accuracy of the models and decrease the background noise . In addition, we believe the R2 value will increase as the number of sorghum samples increases.
- University of Kentucky
- Biosystems Engineering, Junior, 2017
- Flaherty, Kentucky
- Outdoor sports/ activities, cooking, baking
Me and one of my dogs
- Kaelin Saul, IGERT Fellow and Graduate Student, Department of Biological and Agricultural Engineering, Kansas State University
- Dr. Donghai Wang, Professor, Department of Biological and Agricultural Engineering, Kansas State University
- This material is based upon work supported by National Science Foundation Grant: REU Site: Summer Academy in Sustainable Bioenergy; NSF Award No.: SMA-1359082.