Corn is a complex raw material composed mainly of starch, with smaller amounts of protein, oil, fiber, and moisture. Its chemical composition and physical properties vary and strongly influence processing efficiency, product yield, and quality in milling, feed, and ethanol production.

Therefore, fast and reliable analysis of corn quality is essential to optimize processing and ensure consistent final products.

Perten NIR Analyzers

The DA analyser series from Perten comprises diode array NIR instruments specifically designed for analysis in the food and agricultural industries, including the monitoring of critical parameters in corn. Advanced technology delivers easy-to-use instruments for operators while providing reliable and highly reproducible results.

DA 7250™ is a fast, accurate, and versatile near-infrared benchtop analyser designed for use in both laboratory and in-process environments. Leveraging diode array technology, it enables rapid multi-component analysis in less than 10 seconds. In addition to high analytical speed, the instrument requires little to no sample preparation.

DA 7350 in-line and DA 7440 on-line analysers are based on the same NIR technology platform as DA 7250 and are engineered to deliver real-time measurements directly within the production environment. This common technology platform enables straightforward calibration transfer between instruments, ensuring consistent performance across applications. DA 7440 is typically installed above the product stream or conveyor belt for at-line or on-line analysis. In contrast, the DA 7350 is designed for true in-line measurements and can be directly installed in pipes or similar process equipment, allowing continuous monitoring of moving samples.

Method

Thousands of corn samples were collected both on the Perten NIR bench top instruments and the Process instruments from multiple places around the word. Variation in sample temperature is also part of the database to compensate for temperature differences in the samples from cold to hot.

Some of the samples only had reference values form moisture, while other also had protein, oil, starch and some also density. The Reference methods that are typically used for corn are Oven for Moisture. For Protein both Kjeldahl and Dumas are widely used. Soxhlet extraction is the traditional standard for oil. The Ewers polarimetric method is the traditional ISO/ICC standard and most used for starch determination.

Most of the calibrations were developed to model the relationships between the collected NIR spectra and reference chemistry results using advanced algorithms such as Partial Least Square (PLS), Artificial Neural Network (ANN), and PerkinElmer's Honigs Regression™ (HR).

The corn calibrations can be deployed across all DA-series instruments, providing flexibility and consistency in quality monitoring throughout production lines.  

Results and Discussions

Statistics of developed calibrations are summarized in the tables 1. N is number of calibration samples, correlation strength, is denoted R and range the variability of each parameter. The differences between the predictions from the DA series and the reference method are of the same magnitude as typical differences between two different reference labs. Reference vs Predicted for each parameter are presented in the graphs.

Table 1. Corn calibrations expressed in Asis

PerkinElmer recommends that you validate calibration models on a regular basis or whenever there are changes in sample variation or process environment, in accordance with ISO 12099 guidelines for application of Near Infrared Spectroscopy.

Conclusion

In summary it is concluded that the DA analyzers can accurately and with a wide range determine several parameters in corn, with high accuracy. NIR is an indirect method, and therefore, the actual accuracy will depend on the accuracy of the reference method which is used for comparison. For most products and parameters, a typical accuracy would be 1-1.5 times the accuracy of the reference method.