Evaluating missing value imputation methods for food composition databases

Ispirova, Gordana; Eftimov, Tome; Seljak, Barbara Koroušić

doi:10.1016/j.fct.2020.111368

Cited by 40 publications

(17 citation statements)

References 15 publications

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“…Regression methods are a powerful tool in statistics that are widely used to predict the values of dependent variables using information concerning independent variables. Three papers [ 21 , 25 , 37 ] reported applying regression methods to the FCDB. Two papers [ 25 , 37 ] related concepts from traditional Chinese medicine to food composition.…”

Section: Resultsmentioning

confidence: 99%

Statistical Methods for the Analysis of Food Composition Databases: A Review

et al. 2022

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Evidence-based knowledge of the relationship between foods and nutrients is needed to inform dietary-based guidelines and policy. Proper and tailored statistical methods to analyse food composition databases (FCDBs) could assist in this regard. This review aims to collate the existing literature that used any statistical method to analyse FCDBs, to identify key trends and research gaps. The search strategy yielded 4238 references from electronic databases of which 24 fulfilled our inclusion criteria. Information on the objectives, statistical methods, and results was extracted. Statistical methods were mostly applied to group similar food items (37.5%). Other aims and objectives included determining associations between the nutrient content and known food characteristics (25.0%), determining nutrient co-occurrence (20.8%), evaluating nutrient changes over time (16.7%), and addressing the accuracy and completeness of databases (16.7%). Standard statistical tests (33.3%) were the most utilised followed by clustering (29.1%), other methods (16.7%), regression methods (12.5%), and dimension reduction techniques (8.3%). Nutrient data has unique characteristics such as correlated components, natural groupings, and a compositional nature. Statistical methods used for analysis need to account for this data structure. Our summary of the literature provides a reference for researchers looking to expand into this area.

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Section: Resultsmentioning

confidence: 99%

Statistical Methods for the Analysis of Food Composition Databases: A Review

et al. 2022

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“…In addition, missingness also excluded biotin and folate from the analysis, which are both vital B-vitamins that are sourced from food [ 27 ]. Methods to impute missing values in food composition data have been investigated [ 28 , 29 , 30 ] and further research in this area could facilitate the completeness of FCDBs.…”

Section: Discussionmentioning

confidence: 99%

Identifying Nutrient Patterns in South African Foods to Support National Nutrition Guidelines and Policies

et al. 2021

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Food composition databases (FCDBs) provide the nutritional content of foods and are essential for developing nutrition guidance and effective intervention programs to improve nutrition of a population. In public and nutritional health research studies, FCDBs are used in the estimation of nutrient intake profiles at the population levels. However, such studies investigating nutrient co-occurrence and profile patterns within the African context are very rare. This study aimed to identify nutrient co-occurrence patterns within the South African FCDB (SAFCDB). A principal component analysis (PCA) was applied to 28 nutrients and 971 foods in the South African FCDB to determine compositionally similar food items. A second principal component analysis was applied to the food items for validation. Eight nutrient patterns (NPs) explaining 73.4% of the nutrient variation among foods were identified: (1) high magnesium and manganese; (2) high copper and vitamin B12; (3) high animal protein, niacin, and vitamin B6; (4) high fatty acids and vitamin E; (5) high calcium, phosphorous and sodium; (6) low moisture and high available carbohydrate; (7) high cholesterol and vitamin D; and (8) low zinc and high vitamin C. Similar food patterns (FPs) were identified from a PCA on food items, yielding subgroups such as dark-green, leafy vegetables and, orange-coloured fruit and vegetables. One food pattern was associated with high sodium levels and contained bread, processed meat and seafood, canned vegetables, and sauces. The data-driven nutrient and food patterns found in this study were consistent with and support the South African food-based dietary guidelines and the national salt regulations.

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“…Another consideration is that not all foods have nutrient values for every nutrient of interest and often rely on complex imputations for estimates 86 . For example, nutrients like vitamin D are often incomplete 87 .…”

Section: Disentangling Sources Of Error For Nutrient Estimationmentioning

confidence: 99%

Enhancing Food Intake Tracking in Long-Term Care with Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology

Pfisterer¹,

Amelard²,

Boger³

et al. 2021

Preprint

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Half of long-term care (LTC) residents are malnourished increasing hospitalization, mortality, morbidity, with lower quality of life. Current tracking methods are subjective and time consuming. This paper presents the automated food imaging and nutrient intake tracking (AFINI-T) technology designed for LTC. We propose a novel convolutional autoencoder for food classification, trained on an augmented UNIMIB2016 dataset and tested on our simulated LTC food intake dataset (12 meal scenarios; up to 15 classes each; top-1 classification accuracy: 88.9%; mean intake error: -0.4 mL±36.7 mL). Nutrient intake estimation by volume was strongly linearly correlated with nutrient estimates from mass (r 2 0.92 to 0.99) with good agreement between methods (σ = −2.7 to −0.01; zero within each of the limits of agreement). The AFINI-T approach is a deep-learning powered computational nutrient sensing system that may provide a novel means for more accurately and objectively tracking LTC resident food intake to support and prevent malnutrition tracking strategies.

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Evaluating missing value imputation methods for food composition databases

Cited by 40 publications

References 15 publications

Statistical Methods for the Analysis of Food Composition Databases: A Review

Statistical Methods for the Analysis of Food Composition Databases: A Review

Identifying Nutrient Patterns in South African Foods to Support National Nutrition Guidelines and Policies

Enhancing Food Intake Tracking in Long-Term Care with Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology

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