| Producto | Tipo de muestra | Parámetro |
| Semillas oleaginosas | Harina de soja | Grasa, Humedad, Proteína |
| Soja | Grasa, Humedad, Proteína | |
| Canola (colza) | Grasa, Humedad, Proteína | |
| Girasol molido | Grasa, Humedad | |
| Semillas de mostaza | Grasa, Humedad | |
| Grano | Maiz | Grasa, Humedad, Proteína, Almidón |
| Cebada | Humedad, Proteína |
|
| Cebada maltera | Humedad, Proteína, Proteína Soluble, Extracto | |
| Cebada Verde | Humedad | |
| Trigo, trigo duro |
Humedad, Proteína, Almidón, Glúten húmedo, Zeleny |
|
| Harina de trigo | Humedad, Proteína, Cenizas, Absorción de agua, Gluten húmedo | |
|
Harina integral |
Humedad, Proteína, Cenizas | |
| Trigo duro | Humedad | |
| Harina de trigo duro | Humedad, Proteína, Cenizas | |
| Trigo sarraceno | Humedad | |
| Centeno | Humedad, Proteína | |
| Harina de centeno y mixta | Humedad, Proteína, Cenizas | |
| Avena | Humedad, Proteína | |
| Sorgo | Humedad, Proteína, Almidón | |
| Triticale | Humedad, Proteína | |
| Arroz | Arroz integral | Humedad, Proteína, Amilosa |
| Arroz blanco | Humedad, Proteína, Amilosa | |
| Arroz rugoso | Humedad | |
| Judías y legumbres | Guisantes | Humedad, Proteína |
| Garbanzos | Humedad | |
| Habas | Humedad, Proteína | |
| Lentejas | Humedad, Proteína | |
| Altramuces | Humedad, Proteína |
Flotas de instrumentos de alto rendimiento
Mientras evolucionaban la estabilidad y la gama de los instrumentos individuales, muchos manipuladores de grano y cereal descubrieron que conectando los instrumentos analíticos en redes de grano podían recopilar valiosos datos de varios analizadores en un solo lugar. Un tiempo después, se desarrolló un software de red que no sólo permitía a los manipuladores de grano recopilar datos, sino también configurar los instrumentos a distancia; por ejemplo, cuando se disponía de actualizaciones de los modelos de aplicación para adaptarse a la última temporada de cultivo, podían enviarse a varios instrumentos de una sola vez desde un único escritorio.
Como atestiguará cualquiera que haya intentado ocuparse de varios instrumentos sin este tipo de conectividad, mantenerlos todos controlados y actualizados puede ser una tarea que lleve mucho tiempo, sobre todo en distintas ubicaciones geográficas. La posibilidad de hacerlo una sola vez desde un ordenador puede considerarse un ahorro de miles de horas de trabajo.
Reliability is the sum of many things
In step with developments in networks, the reliability of results on an instrument-to-instrument basis was also greatly improved in the late nineties. This brings us to a final ‘need to know’ concept called transferability.
Transferability means that measurements done with different instruments on the same sample give identical results. An indication of transferability therefore helps users of the 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 applications 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.
The topic of SEP can easily become rather detailed, but what is critical for your operations is that you have a correct and reliable statement about what to expect in terms of potential error. Imagine, for example, that you are running a population of instruments across different sites and that you are grading grain for payment according to a target of 10.5 protein. Firstly, it is important to know that the instrument is correct and measurement is correct and the potential error is low. Secondly, it is essential that the performance is consistent wherever and whenever measurements are made. Only in this way can you ensure that payment tests will be reliable and trustworthy across all sites.
Figure 2: Typical plot from application note
In-line NIR – the new frontier
Transferability has provided a cornerstone for the latest evolution of the NIR grain analysis where ‘in-line’ sensors measure continuously in the grain handling process. For instance, this could be as grain is transported into the silo at receival, as it is blended before loading onto a ship, as it is received at the malting plant or as it is blended before heading into 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 analyser and the ProFoss™ 2 whole grain in-line solution. The Infratec is widely recognized for its reliable measurements due to attributes already covered in this article. It therefore makes perfect sense that the analytics packages used for the ProFoss are based on data used to develop analytics packages for the Infratec. Furthermore, networking and connectivity software makes it simple to check the performance of the in-line sensor against the rock-solid Infratec.
In-line analysis allows key control parameters to be more closely monitored allowing tighter control against targets. For example, assume that you are building a 20,000 ton ship load of grain to a target of 10.5% protein. You are using two grades of wheat and there is a price difference of EUR 10 between the grades. The real-time data offers insight that allows you to use 15% more wheat from the lower grade wheat and save 15% high quality wheat. Given the 20,000 load, 15% equals 3,000 tons of grain at EUR 10 higher price, resulting in a saving of EUR 30,000. Read more.
The potential of NIR grain analysis
The advances made by NIR technology offer a useful checklist when deciding future analytical strategy.
The power of NIR transmittance technology to get a clear picture of what is going on inside the grain kernel is an obvious requirement for any whole grain solution. Similarly, the ability of an instrument to perform perfectly in the non-lab like conditions at the weighbridge is another. These hardware aspects are nothing however without robust application models based on sufficiently rich pool of data from both reference tests and NIR tests. And the availability of networking software and connectivity are essential for running any number of instruments from a single central location.
Pulling all the threads together is the concept of transferability and its promise of identical measurements wherever analysis is performed. This provides a secure foundation for any analytical strategy involving multiple instruments across sites and locations. Furthermore, transferability in combination with advanced software, lays the ground for the exploitation of in-line analysis by making it simple to ensure in-line performance aligned with that of well-proven benchtop NIR solutions.
