• Ayob Ainaa Fatehah, Bee Koon Poh, Safii Nik Shanita, and Jyh Eiin Wong.
• Nutritional Sciences Programme & Center for Community Health, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur 50300, Malaysia. ainaa.ayob@gmail.com.
• Nutrients. 2018 Jul 27; 10 (8).

AbstractValidity 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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