Near-infrared (NIR) analysis has become an essential quality control tool for cereal operators worldwide, but can it do more to address the unprecedented challenges in cereal quality across the supply chain?

How has near-infrared analysis of cereals become such a powerful tool, and what should we consider when deciding analytical operations for the future?

Looking inside the kernel – the evolution of near-infrared transmittance

Near-infrared (NIR) utilizes the natural electromagnetic spectrum defined by wavelengths between 700 nm and 2500 nm. NIR is an accurate and rapid analysis method, well-suited for the quantitative determination of the major constituents in most types of food and agricultural products. In particular, it provides a stable platform for robust analytical solutions that can be used in harsh environments subject to vibration, dust, and fluctuations in humidity and temperature.

NIR can be performed in transmission or reflection. In transmission, an infrared spectrum can be obtained by passing infrared light through a sample and determining what fraction is absorbed by the sample. Alternatively, light can be reflected from the sample, and the absorption properties can be extracted from the reflected light.

The detail about transmittance is relevant for whole grains, for example, when testing parameters such as moisture, which can be unevenly distributed in the kernel and may affect the result if only the surface is measured. Transmittance ensures that the grains are measured in depth to provide enough data for accurate measurement.

Figure 1: Changes in NIR grain analysis seen through the well-known Infratec™ grain analyzer.

 

Data collection lays the groundwork for an increasingly diverse range of applications. The first applications for NIR-based grain analyzers were for wheat, corn, barley, soybeans, and rice for parameters such as moisture, protein, and oil content.

In 1996, a powerful form of application modeling called Artificial Neural Network models (ANN) was introduced to handle the range and complexity of data. ANN models have contributed to highly stable performance regardless of weather and region. Today, well-established data models for the latest generation Infratec™ instruments contain up to 50,000 samples or more, representing over 35 years of seasonal variations.

Transferability

Transferability means that measurements done with different instruments on the same sample give identical results. An indication of transferability therefore helps users of NIR analysis equipment to understand the reliability of the measurements popping up on the screens of the instruments in use across the organization.

Transferability is affected by factors related to both instrumentation and the application model.

On an instrument level, the repeatability of measurements, the accuracy of measurements, and comparisons from one instrument unit to another are important. On an application model level, the variables can include factors such as the number and source of NIR measurements used to create the model, not to mention the number of reference tests and potential error between those reference tests.

The application model makes a ‘prediction’ of the result based on the data used in the model. The term “standard error of prediction” (SEP) is therefore used to sum up the potential error against reference results determined by the actual chemical properties of the sample. SEP is typically stated in technical documentation such as application notes. Another useful tool in any application note is a graph showing results from the NIR instrument plotted against the corresponding reference results for a number of samples. This provides a source for performance-related statistics, for example, SEP, see figure 2.

Figure 2: Typical Graph from Application Note

NIR in-line

Transferability has provided a cornerstone for the latest evolution of NIR grain analysis, where “in-line” sensors continuously measure in the grain handling process. For instance, this could be as grain is transported into silos upon receipt, as it is blended before loading onto a ship, as it is received at the malt factory, or as it is mixed before entering the flour milling process.

 

The big advantage of in-line analysis compared to tests with a traditional benchtop analyzer is that measurements are taken automatically every few seconds. The FOSS product portfolio provides an example of this logical evolution in the form of the Infratec™ grain analyzer and the ProFoss™ 2 whole grain in-line solution. Infratec™ is widely recognized for its reliable measurements.

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