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Record W4408592488 · doi:10.1093/jaoacint/qsaf026

A Contemporary Look at the Precision of Modern Analytical Methods in Food Analysis and the Relevance of the Horwitz Equation

2025· article· en· W4408592488 on OpenAlexaff
Stefan Ehling, Joseph J. Thompson, Karen J Schimpf, Lawrence H Pacquette, Philip A Haselberger

Bibliographic record

VenueJournal of AOAC International · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsAbbott (Canada)
Fundersnot available
KeywordsRelevance (law)Computer sciencePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The Horwitz equation models an empirically observed relationship between inter-laboratory relative standard deviation RSDR and analyte concentration expressed as a mass fraction. The Horwitz ratio (HorRat) is the ratio of observed RSDR to the corresponding calculated RSDR from the Horwitz equation. The empirical acceptable range is 0.5 to 2.0 for a successful multi-laboratory method validation trial. OBJECTIVE: This work examines data from multi-laboratory trials on food analyses conducted between 2011 and 2017 for trends in analytical method precision and the applicability and relevance of the Horwitz model. METHODS: Data on method precision from 20 multi-laboratory trials consisting of 961 data points were analyzed. The scope was limited to methods employing modern chromatographic and spectroscopic techniques and to well-defined small-molecule analytes and elements. Within-laboratory and inter-laboratory precision and their ratio, HorRat, goodness of fit to the Horwitz model, and variation of precision across the analytical range were examined. RESULTS: The variance of inter-laboratory precision is largely (86%) independent of concentration and remains unexplained by the Horwitz equation. Only 52% of all data points fell within the Horwitz band (0.5-2.0), with 46% falling under 0.5, indicating substantially better inter-laboratory precision than predicted by the Horwitz equation at all concentration levels. Near-constant precision was confirmed across the analytical range of methods, even near the limit of quantitation. CONCLUSION: The analysis of the data in scope demonstrates that the analytical method precision routinely achievable with modern chromatographic and spectroscopic techniques, proper laboratory controls, and training is much better than that predicted by the Horwitz equation. HorRat has lost its relevance as a method performance criterion for judging the success of a multi-laboratory trial. HIGHLIGHTS: Recent data do not follow the Horwitz model. HorRat values <0.5 can be routinely achieved. Method precision is mostly independent of analyte concentration. Method-related factors have greater impact on precision.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.098

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.325
Teacher spread0.289 · 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 teacher head, 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

Citations3
Published2025
Admission routes1
Has abstractyes

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