Depuis la base produits
Tu vois tout de suite quelles catégories sont les plus légères en moyenne et lesquelles montent davantage en calories.
Short answer
Calories per 100 g are a practical way to compare energy density across broad groups of foods. Lower-energy-density categories often make it easier to build filling meals for fewer calories, but category averages hide huge variation inside the category and should never replace reading the actual product page.
The category calorie map groups foods by category and reports the average kcal per 100 g for each group in the current sample. That makes it easy to scan for broad patterns. Some categories cluster at the lower end because they contain more water and less fat. Others rise quickly because they are built from concentrated starches, fats, or both.
Per-100-g values are useful because they put foods on common ground. They are also blunt. A person does not eat 100 g of olive oil the way they eat 100 g of soup.
Research on dietary energy density shows that people tend to eat a fairly consistent weight or volume of food. When meals are built from lower-energy-density foods, total calorie intake often falls without the person feeling like the plate suddenly became tiny.
That is why category averages can be useful when you are trying to make a meal more filling for the same calories. They help you find better starting points, not perfect foods.
Averages hide spread. One category can contain plain yogurt, sweetened yogurt, and protein puddings with very different calorie profiles. The same issue shows up in ready meals, cereals, breads, sauces, and restaurant-style products.
Data quality matters too. Label revisions, product reformulation, and restaurant variation can all shift the real number away from the stored number. Use the ranking to narrow the search, then open the product page and check the details.
The best use of this page is directional. If you want lower-calorie lunches, start with categories that run lighter on average, then look for products with enough protein, fiber, or volume to make the meal satisfying. If you are trying to raise calories, do the opposite.
Par catégorie
Si tu veux aller vite vers des produits moins caloriques, partir de la catégorie aide souvent plus qu'une recherche trop large.
0 kcal / 100 g. 4 produits dans l'échantillon.
1 kcal / 100 g. 1 produits dans l'échantillon.
1 kcal / 100 g. 14 produits dans l'échantillon.
1 kcal / 100 g. 88 produits dans l'échantillon.
1 kcal / 100 g. 127 produits dans l'échantillon.
1 kcal / 100 g. 2 produits dans l'échantillon.
1 kcal / 100 g. 2 produits dans l'échantillon.
1 kcal / 100 g. 9 produits dans l'échantillon.
1 kcal / 100 g. 13 produits dans l'échantillon.
2 kcal / 100 g. 2 produits dans l'échantillon.
2 kcal / 100 g. 1 produits dans l'échantillon.
2 kcal / 100 g. 59 produits dans l'échantillon.
No. A lower average calorie density can make meal planning easier, but it does not tell you everything about protein, fiber, sodium, ingredients, or how filling a specific product will be for you.
Because it gives a standard unit for side-by-side comparison. It is imperfect for foods eaten in tiny amounts, but it is still one of the cleanest ways to compare labels across many products.
Because averages hide variation inside the category. A category page is a map, not a promise about every individual product.
Rolls BJ. The relationship between dietary energy density and energy intake.
Explains why energy density is a useful lens for appetite and calorie intake.
Rolls BJ. Dietary energy density: Applying behavioural science to weight management.
Practical review on using lower-energy-density foods to build satisfying diets.
Urban LE, et al. Food Label Accuracy of Common Snack Foods.
Helpful reminder that stored calorie values and measured values do not always match perfectly.
Urban LE, et al. Accuracy of Stated Energy Contents of Restaurant Foods.
Supports caution when interpreting averages for food groups with large recipe variation.
