Validity of image-assisted and image-based dietary assessment methods relies on the accuracy of portion size estimation based on food images. However, little is known on the ability of nutrition professionals in assessing dietary intake based on digital food images. This study aims to examine the ability of nutrition professionals in reviewing food images with regard to food item identification and portion size estimation. Thirty-eight nutritionists, dietitians, and nutrition researchers participated in this study. Through an online questionnaire, participants' accuracy in identifying food items and estimating portion sizes of two sets of digital food images presenting a meal on a plate (Image PL) and in a bowl (Image BW) were tested. Participants reported higher accuracy in interpreting Image BW compared to Image PL, both in terms of accuracy in food identification (75.3 ± 17.6 vs. 68.9 ± 17.1%) and percentage difference in portion size estimation (44.3 ± 16.6 vs. 47.6 ± 21.2%). Weight of raw vegetables was significantly underestimated (-45.1 ± 22.8% vs. -21.2 ± 37.4%), while drink was significantly overestimated (40.1 ± 45.8% vs. 26.1 ± 32.2) in both images. Less than one-third of the participants estimated portion size within 10% of actual weight for Image PL (23.7%) and Image BW (32.3%). Accuracy of nutrition professionals in reviewing food images could be further improved with training on better perception of portion sizes from images.
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http://dx.doi.org/10.3390/nu10080984 | DOI Listing |
Appl Microbiol Biotechnol
January 2025
Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224, Warsaw, Poland.
Bacteriophage infections in bacterial cultures pose a significant challenge to industrial bioprocesses, necessitating the development of innovative antiphage solutions. This study explores the antiphage potential of indigo carmine (IC), a common FDA-approved food additive. IC demonstrated selective inactivation of DNA phages (P001, T4, T1, T7, λ) with the EC values ranging from 0.
View Article and Find Full Text PDFNutrients
January 2025
Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
View Article and Find Full Text PDFNutrients
January 2025
School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW 2308, Australia.
Background/objectives: For low- and middle- income country (LMIC) settings, a global nutrition transition is rapidly emerging as diets shift, resulting in a dual burden of malnutrition. High quality dietary intake data for these populations is essential to understand dietary patterns contributing to these nutrition issues. New technology is emerging to address dietary assessment challenges; however, it is unknown how researchers conducting studies with LMIC populations or under-served groups in high-income countries adopt technology-assisted methods.
View Article and Find Full Text PDFNutrients
January 2025
Department of Psychology, Toronto Metropolitan University, 350 Victoria St., Toronto, ON M5B 2K3, Canada.
Background/objectives: Appearance-related social media, such as "thinspiration" and "fitspiration" posts, have been shown to contribute to poor body image and disordered eating. Food-related social media is becoming increasingly popular; however, far less is known about its relationship to body image and disordered eating.
Methods: The current review searched PsycNet and PubMed (Medline) for all the literature examining food-related social media and its relationship with body image and/or disordered eating outcomes.
Nutrients
January 2025
Graduate School of Bioscience and Biotechnology, Chubu University, 1200, Matsumoto-cho, Kasugai-city 487-8501, Aichi, Japan.
Background/objectives: Facial bone density, including the jawbone, declines earlier than that of the lumbar spine and calcaneus. Calcium maltobionate is reported to mitigate bone resorption and maintain bone density of the lumbar spine in post-menopausal women, but its effects on facial bone density remain understudied. Therefore, this study compared variations in facial bone mineral density with variations in calcaneal bone mineral density and bone resorption markers among healthy women, examining differences between pre- and post-menopause and the effects of continuous calcium maltobionate intake.
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