[Alain Stevens – Petfood Advisor]

Hello everyone,

Many pet owners now rely on pet food rating apps to compare kibble and wet food. The process feels reassuring: scan a barcode, receive a score instantly, and assume the application has fully analyzed the product.

But in reality, most scoring systems do not truly “understand” the food itself.

What they mainly analyze are the numbers and words printed on the packaging.

That difference is extremely important.

A pet food label only provides a limited snapshot of the product. Protein percentages, fat levels, ash content, fibers, and ingredient lists offer useful information, but they do not reveal the full nutritional reality of what your dog or cat will actually digest every day.

For example, two foods may both display 30% protein on the bag while having completely different biological quality. One product may contain highly digestible animal proteins adapted to carnivores, while another may rely heavily on lower-value protein concentrates. To a simplified scoring algorithm, those differences are often difficult to evaluate properly.

Most apps also cannot measure how ingredients behave after industrial processing. Cooking temperatures, extrusion methods, fat oxidation, nutrient degradation, and ingredient stability all influence the final nutritional value of the food. Yet these factors are rarely visible through a barcode scan.

Another issue comes from marketing language itself. Labels are designed to sell products. Terms such as “fresh meat,” “natural,” “premium,” or “grain-free” may influence scoring systems even though those claims do not automatically guarantee nutritional quality.

In some cases, algorithms reward or penalize certain ingredients without understanding the complete formulation behind the recipe. A single ingredient taken out of context can create a misleading impression about the overall food quality.

Estimated carbohydrates create another layer of complexity. Many manufacturers do not directly display carbohydrate levels on the packaging, forcing apps and online charts to calculate approximate values using formulas. Depending on the method used, the final result may vary significantly between platforms.

There is also the question of the animal itself.

A food suitable for a highly active dog may not be appropriate for a sterilized indoor cat. A senior animal with specific health issues may require nutritional compromises that generic scoring systems interpret negatively. Yet many apps still attempt to assign one universal score to every pet, regardless of age, activity, metabolism, or medical history.

Social media has amplified this phenomenon. Simplified ratings spread quickly because they are easy to share and easy to understand. A red “bad score” or a green “excellent score” generates emotional reactions instantly. Unfortunately, nutrition is far more complicated than a color code.

This is why more pet owners are beginning to look beyond simplified ABCDE rankings and search for tools that explain products instead of judging them with a single number.

Understanding pet food requires more than reading a label. It requires context, analysis, and critical thinking about the real nutritional needs of the animal behind the bowl.

[Auteur]

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