ArticleFood chemistry: X2024
Characterization of saffron from different origins by HS-GC-IMS and authenticity identification combined with deep learning.
Article in Food chemistry: X, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Effects of cultivar and stir-frying on the volatile flavor characteristics of white and yellow mustard seeds.Food chemistry: X · 2026Article
- HS-SPME-GC-MS Coupled with Chemometrics for Detecting HFCS and Invert Sugar Adulteration in Coriander Honey.Foods (Basel, Switzerland) · 2026Article
- Quantitative analysis and visualization of live spotted seabass (Food chemistry: X · 2026Article
- Comprehensive volatile profiling and metabolomics analysis of cowpea.Food chemistry: X · 2026Article
- Differential Analysis of Non-Volatile and Volatile Organic Compounds inMolecules (Basel, Switzerland) · 2025Article
- Comparative Analysis of Dried Water Bamboo Shoots Using Different Drying Methods: Physicochemical Properties and Flavor.Foods (Basel, Switzerland) · 2025Article
- Honey adulteration detection using the HS-SPME-IMS technique combined with chemometric analysis.Food chemistry: X · 2025Article
- Volatile Fingerprinting and Regional Differentiation of Safflower (Foods (Basel, Switzerland) · 2025Article
- Comparative analysis of key flavor compounds in various baijiu types usingFood chemistry: X · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rising demand of saffron, it is essential to standardize the confirmation of its origin and identify any adulteration to maintain a good quality led market product. However, a rapid and reliable strategy for identifying the adulteration saffron is still lacks. Herein, a combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and convolutional neural network (CNN) was developed. Sixty-nine volatile compounds (VOCs) including 7 groups of isomers were detected rapidly and directly. A CNN prediction model based on GC-IMS data was proposed. With the merit of minimal data prepossessing and automatic feature extraction capability, GC-IMS images were directly input to the CNN model. The origin prediction results were output with the average accuracy about 90 %, which was higher than traditional methods like PCA (61 %) and SVM (71 %). This established CNN also showed ability in identifying counterfeit saffron with a high accuracy of 98 %, which can be used to authenticate saffron.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.