Archives in Food and Nutrition

All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.
Reach Us +1 (629)348-3199

Short Communication - Archives in Food and Nutrition (2022) Volume 5, Issue 4

Aspects and methodologies for characterising caloric density.

Birsen Elibol*

Department of Medical Biology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey

*Corresponding Author:
Birsen Elibol
Department of Medical Biology
Faculty of Medicine, Bezmialem Vakif University
Istanbul, Turkey

Received: 02-Aug-2022, Manuscript No. AAAFN-22-74660; Editor assigned: 04-Aug-2022, PreQC No. AAAFN-22-74660(PQ); Reviewed: 17-Aug-2022, QC No. AAAFN-22-74660; Revised: 19-Aug-2022, Manuscript No. AAAFN-22-74660(R); Published: 25-Aug-2022, DOI:10.35841/aaafn-5.4.117

Citation: Elibol B. Aspects and methodologies for characterising caloric density. Arch Food Nutr. 2022;5(4):117

Visit for more related articles at Archives in Food and Nutrition


Nutrient profiling is a process for rating, ranking, or categorising foods based on their nutritional content. Dietary protein profile models give assessments of overall nutritional composition based on a balance of helpful and tolerable nutrients. Protein, dietary fibre, and a range of vitamins and minerals are among the beneficial nutrients to encourage, whereas free or added sugars, saturated fat, and salt are nutrients to restrict.

Given that most foods include many nutrients, adopting an appropriate analytical method to estimate the total nutritional content of different foods presents a scientific as well as a linguistic issue. assist in identifying foods that are nutrientdense, inexpensive, and sustainable With the addition of food costs in nutrient density estimates, researchers have been able to develop new measures of affordability and find which foods deliver the most nutrients per penny. This econometric nutrition profile technique was among the first to investigate the relationships between nutrient density, energy density, and energy cost. Recent research has investigated nutrient profiling in a new direction, investigating the relationship between dietary nutrient density and carbon footprint, as estimated by greenhouse gas emissions via life-cycle analysis.

Nutritional identification techniques

The goal of reinforcement nutrient density ratings is to capture all of the nutritional properties of a specific diet. Those that are filling and nutrient-dense obtain high marks, whereas foods that supply calories but few nutrients receive lower marks. Balanced nutritional profile models shift the emphasis from "bad" nutrients to "outstanding" and "superior" diets by incorporating many beneficial nutrients to promote [1]. Nutrient profiling is an example of a positive and effective technique to communicate important nutritional information about meals and drinks to the customer. Nutrient profiling must adhere to scientific principles in order to remain a science. So far, there has been no consistency of the techniques for designing, testing, and validating dietary protein profile models.

Nutrient-rich foods index

The Nutrient-Rich Foods (NRF) 4 Test was calculated in strict accordance with regulatory requirements established by the US Food and Drug Administration (FDA) [2]. The selection of beneficial nutrients, in particular, adhered to federal laws and criteria. The FDA classifies foods as "healthy" based on their protein, fibre, vitamins A and C, calcium, and iron levels. Foods that contain more than defined levels of fat, saturated fat, trans fat, cholesterol, or salt are forbidden from direct influence nutrition and health claims by the FDA [3]. The 2005 Dietary Guidelines recommended additional (NRF) nutrients. Overall nutritional density score calculated from numerous nutrients. Protein (50 g); fibre (25 g); vitamins A (5000 IU), C (60 mg), and E (30 IU); calcium (1000 mg); iron (18 mg); potassium (3500 mg); and magnesium were assigned DVs based on a 2000-kcal diet (400 mg). Food nutrient contents were converted to percent DVs per reference quantity and then limited at 100% DV such that meals having extremely significant doses of a particular nutrient did not receive an abnormally elevated index score [4]. Maximum suggested values for nutrients to restrict were 20 g of saturated fat, 125 g of total sugar, 50 g of added sugar, and 2400 mg of salt. Initially, all scores were computed per 100 kcal.

Validation of nutrient profile models

Choosing the best nutrient profile model from among multiple alternatives is another scientific challenge. In some validation studies, food rankings generated by different models were compared with mean ratings for the same foods generated by health professionals or by expert panels. Only 3 published, fully transparent models have been validated with respect to objective diet quality measures: the French SAIN/LIM, the British FSA-Ofcom mode. NRF indexes that included both beneficial nutrients and nutrients to restrict fared better than indexes that solely contained nutrients to limit [5]. The LIM score predicted 32% of the variation in HEI. The usage of 6 or 9 helpful nutrients explained the most variance in HEI index performance actually fell with the addition of more vitamins and minerals. Previous research found that increasing the number of elements in a nutritional profile model above 10 gave little or no incremental help in predicting overall diet quality.


LED meals improve appetite control in women attempting weight loss and the effect is sustainable. Consumption of LED meals likely contributed to weight loss in the SW program. This study was registered at as NCT02012426


  1. Bolles RC, Hayward L, Crandall C. Conditioned taste preferences based on caloric density. J Exp Psychol: Animal Behavior Processes. 1981(1):59.
  2. Indexed at, Google Scholar, Cross Ref

  3. Pellegrini N, Fogliano V. Cooking, industrial processing and caloric density of foods. Curr Opin Food Sci. 2017;14:98-102.
  4. Indexed at, Google Scholar, Cross Ref

  5. Angioloni A, Collar C. Physicochemical and nutritional properties of reduced-caloric density high-fibre breads. LWT - Food Sci Technol. 2011;44(3):747-58.
  6. Indexed at, Google Scholar, Cross Ref

  7. Scourboutakos MJ, L'Abbé MR. Restaurant menus: calories, caloric density, and serving size. Am J Prev Med. 2012;43(3):249-55.
  8. Indexed at, Google Scholar, Cross Ref

  9. Xu JL, Riccioli C, Sun DW. Development of an alternative technique for rapid and accurate determination of fish caloric density based on hyperspectral imaging. Int. J. Food Eng. 2016;190:185-94.
  10. Indexed at, Google Scholar, Cross Ref

Get the App