Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.4K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

8.1K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
8.1K
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

13.7K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
13.7K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.7K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.7K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.0K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

192
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
192

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Large language models in food and nutrition science: Opportunities, challenges, and the case of FoodyLLM.

Current research in food science·2026
Same author

Measuring biological age: Insights from omics studies.

Ageing research reviews·2025
Same author

Informatics for Food Processing.

ArXiv·2025
Same author

Applicability Assessment of Technologies for Predictive and Prescriptive Analytics of Nephrology Big Data.

Proteomics·2025
Same author

Beyond Landscape Analysis: DynamoRep Features For Capturing Algorithm-Problem Interaction In Single-Objective Continuous Optimization.

Evolutionary computation·2025
Same author

IsoFoodTrack: a comprehensive database and management system based on stable isotope ratio analysis for combating food fraud.

Frontiers in nutrition·2025
Same journal

Preclinical Toxicological Evaluation of 7-Methoxy-3-Phenyl-4H-Chromen-4-one: Acute and Sub-acute Oral Toxicity with Histopathological Correlation.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association·2026
Same journal

Redox-dependent cytoprotection by zerumbone via Nrf2-Keap1 mediated restoration of antioxidant and apoptotic balance in Mancozeb-exposed cells.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association·2026
Same journal

Dihydroberberine in Metabolic Disorders: Bioavailability, Molecular Mechanisms, Toxicology, and Future Perspectives.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association·2026
Same journal

RIFM fragrance ingredient safety assessment, linoleic acid, CAS Registry Number 60-33-3.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association·2026
Same journal

ORGAN-SPECIFIC RESPONSES OF AGE-DEPENDENT VULNERABILITY TO DEOXYNIVALENOL IN FEMALE MICE.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association·2026
Same journal

Update to RIFM fragrance ingredient safety assessment, β-naphthyl isobutyl ether, CAS Registry Number 2173-57-1.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association·2026
See all related articles

Related Experiment Video

Updated: Dec 22, 2025

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

10.4K

Evaluating missing value imputation methods for food composition databases.

Gordana Ispirova1, Tome Eftimov2, Barbara Koroušić Seljak3

  • 1Computer Systems Department, Jožef Stefan Institute, Jamova Cesta 39, 1000, Ljubljana, Slovenia; Jožef Stefan International Postgraduate School, Jamova Cesta 39, 1000, Ljubljana, Slovenia.

Food and Chemical Toxicology : an International Journal Published for the British Industrial Biological Research Association
|May 8, 2020
PubMed
Summary
This summary is machine-generated.

Missing data in food composition databases (FCDBs) hinder dietary assessments. Advanced imputation methods like Random Forest and KNN offer more accurate solutions than traditional mean/median fill-ins.

Keywords:
Food composition databasesMissing dataMissing-data imputationNutrient valuesfood composition data

More Related Videos

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
04:46

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Published on: September 18, 2018

7.6K
PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
08:43

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

12.8K

Related Experiment Videos

Last Updated: Dec 22, 2025

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

10.4K
'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
04:46

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Published on: September 18, 2018

7.6K
PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
08:43

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

12.8K

Area of Science:

  • Nutrition Science
  • Data Science
  • Bioinformatics

Background:

  • Missing data are prevalent in research, including nutrition.
  • Incomplete food composition databases (FCDBs) limit dietary assessment accuracy.
  • Traditional methods like mean/median imputation introduce significant errors.

Purpose of the Study:

  • To evaluate advanced statistical imputation techniques for missing data in FCDBs.
  • To compare the performance of NMF, MICE, MissForest, and KNN against traditional methods.
  • To improve the completeness and reliability of FCDBs for dietary analysis.

Main Methods:

  • Utilized Non-Negative Matrix Factorization (NMF).
  • Employed Multiple Imputations by Chained Equations (MICE).
  • Applied Nonparametric Missing Value Imputation using Random Forest (MissForest) and K-Nearest Neighbors (KNN).
  • Compared these with fill-in with mean and fill-in with median approaches.
  • Used data from national FCDBs collected by EuroFIR.

Main Results:

  • Advanced imputation methods demonstrated superior performance compared to traditional approaches.
  • NMF, MICE, MissForest, and KNN provided more accurate data imputation in FCDBs.
  • The study confirmed the limitations of mean/median imputation for FCDBs.

Conclusions:

  • State-of-the-art imputation techniques significantly improve the quality of FCDBs.
  • Accurate imputation is crucial for reliable dietary assessments.
  • The findings support the adoption of advanced methods for managing missing data in nutritional databases.