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Record W4410966167 · doi:10.3390/biophysica5020021

A Novel Linear Evaluation of Chromatographic Peak Features in Pharmacopoeias Using an Inverse Fourier Transform Algorithm

2025· article· en· W4410966167 on OpenAlex
Shuping Chen, Sai Huang, Baoling Zheng

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiophysica · 2025
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsSciencetech (Canada)
FundersXiamen University
KeywordsInverseFourier transformPharmacopoeiaMathematicsChromatographyAlgorithmChemistryMathematical analysis

Abstract

fetched live from OpenAlex

The system suitability testing of chromatography is an indispensable procedure in pharmaceutical analysis, and it must comply with rules in related pharmacopoeias. An inverse Fourier transform algorithm was developed to accurately evaluate chromatographic features versus a standard Gaussian peak shape. The regular chromatogram is considered a pseudo-frequency spectrum and can be converted to a nominal time signal via inverse Fourier transformation. The system suitability parameters of peak width, theoretical plate number, tailing factor, and noise testing were evaluated using linear regressions directly and compared with the compendial rules. This novel method is simple, accurate, robust, reliable, and efficient for the evaluation of chromatographic peak features.

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.

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)0.000
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 score0.223
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.030
GPT teacher head0.321
Teacher spread0.291 · 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