Peer-reviewedCC BY-NC-ND 4.0Scientific Reports
An integrated machine learning framework for accurate, real-time and non-destructive vitamin C quantification from Vis/NIR spectral data
Related
- NephroTox AI: an explainable machine learning framework with uncertainty quantification for drug-induced nephrotoxicity prediction
- Quadruple isotope dilution versus conventional calibration methods for accurate urinary hippuric acid quantification with a synthesized isotopically labelled material
- Accurate quantification in proteomics with QuantUMS
- DeepSAP: an integrated generative and predictive deep-learning framework for controllable design of self-assembling peptides.
- Multimodal Sensor Fusion for Non-Destructive Tea Quality Evaluation: Deep Learning-Enabled Methods, Applications, and Challenges
BibTeX
@article{pourali2026integrated,
title = {{An integrated machine learning framework for accurate, real-time and non-destructive vitamin C quantification from Vis/NIR spectral data}},
author = {Saleh Yasour Pourali and Amir Hossein Afkari-Sayyah and Ebrahim Taghinezhad and Ali Motevali},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76924-9},
url = {https://www.nature.com/articles/s41598-026-76924-9},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - An integrated machine learning framework for accurate, real-time and non-destructive vitamin C quantification from Vis/NIR spectral data
AU - Saleh Yasour Pourali
AU - Amir Hossein Afkari-Sayyah
AU - Ebrahim Taghinezhad
AU - Ali Motevali
PY - 2026
DA - 2026/10/24
JO - Scientific Reports
DO - 10.1038/s41598-026-76924-9
UR - https://www.nature.com/articles/s41598-026-76924-9
SN - 2045-2322
ER -