Grain breeders and dealers rely heavily on the compositional properties of many different types of grain, including: soybeans, corn, rice, and wheat etc. The most common variables assessed for commercial and nutritive properties of grains are oil and protein content, both of which are highly variable. Content assessment is a frequent occurrence in a seeds lifetime: grain breeders assess content individually, per seed, to select the best line for crop production; dealers on the other hand will likely assess content in bulk each time the grain changes hands to set a price point.
This invention uses a new, untapped type of spectroscopy to assess the protein and oil content of grains. It is completely unaffected by moisture content, is significantly more robust in the presence of contaminants, and is dramatically more accurate and reproducible. This technology is a type of spectroscopy that uses Raman principles. A beam of light is passed through the sample of interest; some of the light is scattered, and some is passed through; the light that passes through is collected and analyzed.
The different resultant wavelengths indicate the presence or absence of certain bonds (all parts of organic molecules). The spectra are compared to the necessary proprietary calibration set from UIUC researchers to quantitatively indicate molecule-of-interest presence.
Applications
This technology is applicable in any grain testing environment where NIR can be used, such as grain breeding and dealing facilities. It can detect with very high sensitivity and specificity many organic compounds (for example, it has to potential to differentiate between individual amino acids) and therefore has significant potential for analysis of animal feedstocks.
Benefits
This technology solves many of the problems posed by NIR, the conventional method of grain testing.
- More robust and less susceptible to operator error and machine abuse
- Less susceptible to dust and contaminant fouling, both of which are ubiquitous in the operating environment of such devices.
- Not susceptible to variation in atmospheric water, or grain moisture content
- Significantly more accurate, sensitive, and reproducible than NIR