Gelymar: sorting ingredients in less time and at lower cost with NIR technology

Most food companies still depend on external laboratory analysis for quality control of ingredients and raw materials, but this method is costly in time and money.

How can this process be optimized with NIR technology?

Here’s the case of Gelymar, a leading company in Chile in textural solutions for the dairy, meat and personal care industries.

Gelymar supplies, among other ingredients, carrageenan, a type of natural polysaccharide obtained from seaweed that is used as a thickener and stabilizer in the food industry.

Until now, to classify the different types of carrageenan according to their commercial group, the company always used laboratory tests for viscosity and gel strength analysis and, depending on the ratio of these physicochemical parameters, characterized the samples as IOTA, KAPPA or LAMBDA.

In addition to establishing the type of commercial carrageenan, this classification allows to attribute a specific functionality for its use in specific applications: meat industry, confectionery, dairy sector, etc...

The main disadvantage of this traditional methodology is that it takes a long time to obtain laboratory results: from 4 to 24 hours.

To optimize these processes, Gelymar and Chemometric Brain have worked on the creation and validation of a qualitative model that, in a matter of seconds, predicts and classifies the different types of carrageenans that the company supplies to its customers.

The model developed has 940 spectra and 358 batches belonging to 44 different carrageenans and establishes 3 confidence zones. By validating the classification using Chemometric Brain with the corresponding laboratory tests, the success rate was 100%.

From now on, the model is available to Gelymar to continue measuring and validating new samples with Chemometric Brain. Thus, in the medium term, the company will be able to use only NIR to classify its samples and progressively dispense with laboratory analysis.

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