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Applied Sciences
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Assessment of Food and Nutrition: Theory, Methods and Applications
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Assessment of Food and Nutrition: Theory, Methods and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 8257

Special Issue Editors

Dr. Laurencia Govender

E-Mail Website
Guest Editor
Dietetics and Human Nutrition Department, University of KwaZulu-Natal, Durban 4041, South Africa
Interests: public health nutrition; infant and young child nutrition; community nutrition; clinical nutrition; food security; the potential of provitamin A-biofortified and underutilized crops in alleviating malnutrition
Dr. Lize Havemann-Nel

E-Mail Website
Guest Editor
Centre of Excellence for Nutrition, North-West University, Potchefstroom 2531, South Africa
Interests: infant and young child nutrition; behaviour change communication; nutrition education; public health nutrition; community nutrition
Dr. Kirthee Pillay

E-Mail Website
Guest Editor
Dietetics and Human Nutrition Department, University of KwaZulu-Natal, Durban 4041, South Africa
Interests: public health nutrition; child health and nutrition; clinical nutrition

Special Issue Information

Dear Colleagues,

Globally, the triple burden of malnutrition co-exists and is on the rise. Although enough food is produced in many countries, access to adequate nutritious foods, unhealthy food environments and poor dietary behaviors remains a problem. Good nutrition is critical for optimal health, growth, and development, especially in vulnerable population groups. Thus, it is important to determine the types and quality of the foods consumed and whether these foods meet nutritional requirements. This Special Issue invites you to submit reviews or original research on the assessment of food and nutrition.

The research presented in this Special Issue could possibly aid in the development food security and nutrition policies and better equip healthcare professionals to provide appropriate nutritional advice to vulnerable individuals and population groups.

Dr. Laurencia Govender
Dr. Lize Havemann-Nel
Dr. Kirthee Pillay
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • dietary intake
  • dietary assessment
  • food intake
  • nutrition
  • nutritional analysis
  • dietary patterns
  • food and nutrition security

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Published Papers (3 papers)

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Research

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11 pages, 634 KiB  
Article
Accuracy of Assessing Weight Status in Adults by Structured Observation
by Tânia Jorge, Sofia Sousa, Isabel do Carmo, Nuno Lunet and Patrícia Padrão
Appl. Sci. 2023, 13(14), 8185; https://doi.org/10.3390/app13148185 - 14 Jul 2023
Cited by 2 | Viewed by 1787
Abstract
The assessment of weight status is important in many epidemiological studies, but its direct measurement is not always possible. Self-reported weight and height are often used, although previous research reported low accuracy. This study aimed to test the ability of trained observers to [...] Read more.
The assessment of weight status is important in many epidemiological studies, but its direct measurement is not always possible. Self-reported weight and height are often used, although previous research reported low accuracy. This study aimed to test the ability of trained observers to accurately estimate weight status in adults using structured observation. A cross-sectional study was conducted. For each participant, height and weight were estimated in categories, and weight status was recorded using Stunkard’s body figures, by two trained observers. Height and weight were also measured, using standardized procedures. Subjects were classified according to World Health Organization body mass index (BMI) cut-offs from objective measurements and from the BMI assigned to each body figure. Sensitivity, specificity, and likelihood ratios were calculated to assess the accuracy of estimating weight status by observation. Kappa was used to test inter-observer reliability. A total of 127 participants were assessed, 70 women and 57 men, aged between 19 and 89 years (mean ± standard deviation: 50.3 ± 16.3 years). Most participants were overweight or obese (64.3% women; 78.9% men). The sensitivity and specificity of overweight/obesity status identification were 72.8% and 78.4%, respectively. Observers’ gender, participants’ gender, and participants’ age were significantly associated with the estimation of overweight/obesity. The agreement between observers was moderate for BMI estimates (κ = 0.52) but substantial when distinguishing normal weight from overweight/obesity (κ = 0.67). Trained observers were able to distinguish normal weight from overweight/obesity with high sensitivity and specificity, and substantial interrater reliability. This innovative methodology showed potential for improvement through enhanced training techniques. The use of structured observation may be a useful and accurate alternative to self-reported weight status assessment, whenever anthropometric measurement is not achievable. Full article
(This article belongs to the Special Issue Assessment of Food and Nutrition: Theory, Methods and Applications)
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17 pages, 5107 KiB  
Article
Modeling and Optimization with Artificial Intelligence in Nutrition
by Vesna Knights, Mirela Kolak, Gordana Markovikj and Jasenka Gajdoš Kljusurić
Appl. Sci. 2023, 13(13), 7835; https://doi.org/10.3390/app13137835 - 3 Jul 2023
Cited by 14 | Viewed by 4138
Abstract
The use of mathematical modeling and optimization in nutrition with the help of artificial intelligence is indeed a trendy and promising approach to data processing. With the ever-increasing amount of data being generated in the field of nutrition, it has become necessary to [...] Read more.
The use of mathematical modeling and optimization in nutrition with the help of artificial intelligence is indeed a trendy and promising approach to data processing. With the ever-increasing amount of data being generated in the field of nutrition, it has become necessary to develop new tools and techniques to help process and analyze these data. The paper presents a study on the development of a neural-networks-based model to investigate parameters related to obesity and predict participants’ health outcomes. Improvement techniques of model performances are made (classification performance by reducing overfitting, capturing non-linear relationships, and optimizing the learning process). Predictions are also made with the random forest model to compare the performance of accuracy and prediction scores of two different models. The dataset contains data relating to the obesity of 200 participants in a weight loss program. Information is collected on their basic anthropometric data, as well as biochemical data, which are significant parameters closely related to obesity. It is important to note that weight loss is not always linear and can vary based on individual factors; so, a prediction is made on supervised learning based on patient data (before the diet regime, during the regime, and reaching the desired weight). The dataset is trained on individuals features such as age; gender; body mass index; and biochemical attributes such as MCHC (Mean Corpuscular Hemoglobin Concentration), cholesterol, glucose, platelets, leukocytes, ALT (alanine aminotransferase), triglycerides, TSH (thyroid stimulating hormone), and magnesium. The results of the developed neural network model show high accuracy, low loss in training, high-precision predictions during evaluation of the model, and improved performance over other machine learning models. Calculations are conducted in Anaconda/Python. Overall, the combination of mathematical modeling, optimization, and AI offers a powerful set of tools for analyzing and processing nutrition data. As our understanding of the relationship between diet and health continues to evolve, these techniques will become increasingly important for developing personalized dietary recommendations and optimizing population-level dietary guidelines. Full article
(This article belongs to the Special Issue Assessment of Food and Nutrition: Theory, Methods and Applications)
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Review

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12 pages, 1210 KiB  
Review
The Relationship between Dietary Intake and Adiposity in South African Female Adolescents: A Systematic Review
by Nokuthula Vilakazi, Sithabile Mathunjwa, Heather Legodi and Pedro Terrence Pisa
Appl. Sci. 2023, 13(19), 10813; https://doi.org/10.3390/app131910813 - 28 Sep 2023
Viewed by 1804
Abstract
The prevalence of obesity has increased significantly in developing nations over the past decade, particularly among adolescent girls. To assess the scale of this epidemic among female adolescents in South Africa, a systematic review was undertaken to investigate the connection between diet and [...] Read more.
The prevalence of obesity has increased significantly in developing nations over the past decade, particularly among adolescent girls. To assess the scale of this epidemic among female adolescents in South Africa, a systematic review was undertaken to investigate the connection between diet and obesity. Multiple databases (Google Scholar, Science Direct, Cochrane Library, PubMed, and Web of Science) were searched to identify studies investigating the associations between diet and various adiposity indices as outcomes. Of the 56 studies identified, 7 met the inclusion criteria. The age range of participants spanned from 11 to 21 years. Tabulation was used to report the data, study by study. The consumption of nutrients from animal sources exhibited a positive correlation with higher BMI-for-age Z scores (p = 0.02). Eating habits such as sporadic family meals (p ≤ 0.02), irregular breakfast consumption (p ≤ 0.05), and a high energy intake derived from fat were linked to an increased risk of adiposity. Additionally, factors such as socioeconomic status and residential location revealed associations with certain dietary intakes and adiposity. As more studies identify the causative role of diet in obesity, there is an urgent need for policy intervention and strategies to address the growing non-communicable disease burden in South Africa. Full article
(This article belongs to the Special Issue Assessment of Food and Nutrition: Theory, Methods and Applications)
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