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Record W2974780160 · doi:10.1111/1750-3841.14778

Noninvasive, Nondestructive Measurement of Tomato Concentrate Spoilage in Large‐Volume Aseptic Packages

2019· article· en· W2974780160 on OpenAlexaff
Michele N. Martin, Bruce J. Balcom, Michael J. McCarthy, Matthew P. Augustine

Bibliographic record

VenueJournal of Food Science · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsFood spoilageMaterials scienceMagnetNuclear magnetic resonanceDetectorOpticsPhysics

Abstract

fetched live from OpenAlex

Low frequency nuclear magnetic resonance (NMR) is used to noninvasively and nondestructively detect spoiled tomato concentrate stored in >200 L metal-lined containers. It is shown that longitudinal and transverse NMR relaxation times change as the tomato concentrate spoils. A rapid, viscosity-dependent spoilage detection method that takes advantage of the inherent inhomogeneity in single-sided NMR instruments is proposed. Here, the effective transverse magnetization decay rate is used as a parameter to determine tomato concentrate spoilage. Three different low frequency, single-sided NMR instruments are described and compared to determine the optimum sensor for spoiled tomato concentrate detection in large-format, metal-lined, aseptic containers. The most effective NMR sensor for this application is temperature stable and has large magnetic field gradients and a homogeneous magnetic field region offset >0.5 cm from the magnet surface. PRACTICAL APPLICATION: This manuscript describes a noninvasive and nondestructive tomato concentrate spoilage detector for application to large-format, sealed, commercial storage bins.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.292
Teacher spread0.281 · 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 designBench or experimental
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

Citations15
Published2019
Admission routes1
Has abstractyes

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