Sauces and dressings: detect minimal quality deviations in secondary properties and avoid claims

Do you operate in the sauces and dressings segment? Then you surely know that achieving the right texture and viscosity in your products is not easy and depends on many factors.

Sometimes, as light deviation in the ingredients blend can affect the homogeneity of the final products. And this can lead to customer complaints.

Are traditional quality controls sufficient to avoid such incidents? We help you find out.

With aACCP-based quality control system, you can analyze the physicochemical properties that pose the main risks, and those agreed in the specifications.

This helps us to establish whether an ingredient or product is within agreed quality standards but does not allow us to identify changes in properties that pose secondary hazards. Any change in secondary properties can have a major impact on the functionality and performance of the final product. In addition, this control system relies on a small sample size to make decisions about the over all quality of a product.

Therefore, it is clear to us that traditional quality control, highly dependent on external laboratories, is not sufficient to control all risks.

This is whereNIR technology comes in. By developing qualitative models based on product fingerprinting, it is possible to assess not only whether a product meets specifications, but also to identify minor changes in secondary properties.

Learn more in1 minute with this recent case study.

A company specializing in ingredient blends for different segments of the food industry applied Chemometric Brain software to analyze a blend of xanthan gum and guargum used as a thickener in sauces and dressings.

Although all the batches tested were within the reference values, between 7000 cPcs and10000 cPcs, two of them were close to the maximum value of 10000 cPcs, when all previous batches were between 7000 cPcs and 8000 cPcs. If this had been the only test, they would have passed.

However, this company uses NIR and Chemometric Brain for qualitative model development, which it applies to each batch of final product. Once applied to this product, a problem was quickly identified: samples were falling outside the previously defined confidence zone with "good samples" of the same product.

Based on this negative result in the qualitative test, the R&D team set out to find the cause of this deviation. The investigation clarified the causes of the problem and how to reprocess these batches to avoid throwing away the product. Once reprocessed, these were re-validated against existing qualitative models and this time they were correctly placed in the confidence zone.

This quality control philosophy expands control beyond the major risks and ensures the identification of minimal deviations that would otherwise go undetected because they meet the agreed specifications.  

Ultimately, even if the product was within the agreed quality standards, its application would have generated differences in the final product and this would have triggered a complaint from the customer.

In short, ensuring product quality with this solution saves time and cost in claims for problems that a traditional quality system is blind to.

Find out more about this innovative approach to quality control in other success stories or ask us.

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