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Record W4389429131 · doi:10.1038/s41538-023-00237-8

Nutritional-environmental trade-offs in potato storage and processing for a sustainable healthy diet

2023· article· en· W4389429131 on OpenAlexafffund
A. I. Payne, Ebenezer Miezah Kwofie, Prince Agyemang, Jamie Baum

Bibliographic record

Venuenpj Science of Food · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaMcGill UniversityUniversity of Arkansas
KeywordsMicronutrientNutrientFood fortificationFood processingFood sciencePopulationEnvironmental healthSustainabilityAgricultural scienceNutrient densityAgricultural economicsBusinessToxicologyBiologyMedicineEconomics

Abstract

fetched live from OpenAlex

Over the last decade, poor diets and limited access to nutritious foods have been critical drivers of micronutrient deficiency in human health. However, food fortification at an industrialized scale in developed countries has helped eliminate deficiency-related diseases. In developing countries, fortified foods and biofortified materials have been delivered to nutrient-deficient communities. While these strategies have produced significant and acclaimed results, reports from the Food and Agricultural Organization suggest that over a quarter of the world's population suffers from micronutrient deficiency. This implies that there are still declines in micronutrients in food products at different nodes along the food value chain (FVC). Hence, this study sets out to track micronutrient leakages at specific nodes of the FVC using potato household storage, processing, and consumption in the United States as a case study. The experiment was laid out in a full factorial design with three storage conditions (cupboard at (17.5-22.4 °C, 32.7-48% RH), refrigerator at (-1.8 - 0.89 °C, 37.5-66.1% RH) and ideal condition at (7.2-11.1 °C, 85.0-92.4% RH)), two storage times (2 weeks (±3 days) and 5 weeks (±3 days)) and three household processing pathways (boiling in water, baking at 204 °C, and frying in vegetable oil at 149-204 °C). Additionally, we explored the dynamics of optimal household storage and processing pathways by placing a high, low, or equal priority on environmental sustainability or nutrient retention. The results show that storing potatoes for 5 weeks (±3 days) and processing through boiling (in water at 100 °C), baking (at 204 °C), and frying (in vegetable oil at 149-204 °C) are associated with 33.5%, 40.3% and 15.0% greater nutrient loss than a similar processing scenario after 2 weeks (±3 days) of storage. Additionally, storing and processing potatoes after 5 weeks (±3 days) results in approximately 2.2 ± 0.7 times more damage to human health, ecosystem safety, and resource availability than storing and processing potatoes after 2 weeks (±3 days), averaged between the different storage conditions. Storing and processing after 5 weeks (±3 days) results in approximately 53.6 ± 10.3 times more damage to human health, species disappearing per year, and USD loss than freshly purchased and processed potatoes. Perhaps the most significant finding from the study is that storing potatoes in cupboards and boiling (BL-CP pathway) is optimal for achieving a sustainable healthy diet, as it yields the optimal combination of nutrient retention and low environmental damage. Insights from the study could be translated to support consumer decision-making as they weigh the value of environmental sustainability against nutrition in the context of household potato storage and processing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.245
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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