Integrated Smartphone-App-Chip System for On-Site Parts-Per-Billion-Level Colorimetric Quantitation of Aflatoxins
Bibliographic record
Abstract
We demonstrate herein an integrated, smartphone-app-chip (SPAC) system for on-site quantitation of food toxins, as demonstrated with aflatoxin B1 (AFB1), at parts-per-billion (ppb) level in food products. The detection is based on an indirect competitive immunoassay fabricated on a transparent plastic chip with the assistance of a microfluidic channel plate. A 3D-printed optical accessory attached to a smartphone is adapted to align the assay chip and to provide uniform illumination for imaging, with which high-quality images of the assay chip are captured by the smartphone camera and directly processed using a custom-developed Android app. The performance of this smartphone-based detection system was tested using both spiked and moldy corn samples; consistent results with conventional enzyme-linked immunosorbent assay (ELISA) kits were obtained. The achieved detection limit (3 ± 1 ppb, equivalent to μg/kg) and dynamic response range (0.5-250 ppb) meet the requested testing standards set by authorities in China and North America. We envision that the integrated SPAC system promises to be a simple and accurate method of food toxin quantitation, bringing much benefit for rapid on-site screening.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".