INTEGRATION OF LINEAR REGRESSION AND KNN REGRESSION FOR PREDICTING FOOD SHELF-LIFE BASED ON NUTRITIONAL COMPOSITION

In this research, a combination of Linear Regression and K-Nearest Neighbors (KNN) was utilized to analyze the relationship between nutritional composition (carbohydrates, fats, and proteins) and shelf-life of food products. The primary objective was to enhance the predictive accuracy of food expiration dates using machine learning techniques.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-14
DOI
https://doi.org/10.5281/zenodo.15028140
Primary Topic
Nutritional Studies and Diet
Type
article
Field-Weighted Citation Impact
0.00
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article

INTEGRATION OF LINEAR REGRESSION AND KNN REGRESSION FOR PREDICTING FOOD SHELF-LIFE BASED ON NUTRITIONAL COMPOSITION

Zenodo (CERN European Organization for Nuclear Research)
Nutritional Studies and Diet
article

INTEGRATION OF LINEAR REGRESSION AND KNN REGRESSION FOR PREDICTING FOOD SHELF-LIFE BASED ON NUTRITIONAL COMPOSITION

article en

Abstract

In this research, a combination of Linear Regression and K-Nearest Neighbors (KNN) was utilized to analyze the relationship between nutritional composition (carbohydrates, fats, and proteins) and shelf-life of food products. The primary objective was to enhance the predictive accuracy of food expiration dates using machine learning techniques.

Zenodo (CERN European Organization for Nuclear Research)
Zero hunger
Openalex Percentile: Top 47%
Nutritional Studies and Diet
DOIOpen Access PDFOpenAlex
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INTEGRATION OF LINEAR REGRESSION AND KNN REGRESSION FOR PREDICTING FOOD SHELF-LIFE BASED ON NUTRITIONAL COMPOSITION · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS