ArticleCurrent research in food science2022
Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification.
Article in Current research in food science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Chemical Profiling and Quality Assessment of Food Products Employing Magnetic Resonance Technologies.Foods (Basel, Switzerland) · 2025Review
- Deep Learning-Enhanced Spectroscopic Technologies for Food Quality Assessment: Convergence and Emerging Frontiers.Foods (Basel, Switzerland) · 2025Review
- The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud Detection.Foods (Basel, Switzerland) · 2025Review
- Rapid detection of honey adulteration using machine learning on gas sensor data.NPJ science of food · 2025Article
- Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning.Food chemistry: X · 2025Article
- Physicochemical and antioxidant properties of Apis cerana honey from Lombok and Bali Islands.PloS one · 2024Article
- Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors.Molecules (Basel, Switzerland) · 2023Article
- Non-Targeted Detection and Quantification of Food Adulteration of High-Quality Stingless Bee Honey (SBH) via a Portable LED-Based Fluorescence Spectroscopy.Foods (Basel, Switzerland) · 2023Article
- Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches.Foods (Basel, Switzerland) · 2023Review
- Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning.Foods (Basel, Switzerland) · 2023Article
- Physicochemical Composition of Local and Imported Honeys Associated with Quality Standards.Foods (Basel, Switzerland) · 2023Article
- Primary Structure of Glycans by NMR Spectroscopy.Chemical reviews · 2023Review
- Off-Line SPE LC-LRMS Polyphenolic Fingerprinting and Chemometrics to Classify and Authenticate Spanish Honey.Molecules (Basel, Switzerland) · 2022Article
- Physicochemical Properties of a New Green Honey from Banggi Island, Sabah.Molecules (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nuclear magnetic resonance (NMR) is a powerful analytical tool which can be used for authenticating honey, at chemical constituent levels by enabling identification and quantification of the spectral patterns. However, it is still challenging, as it may be a person-centric analysis or a time-consuming process to analyze many honey samples in a limited time. Hence, automating the NMR spectral analysis of honey with the supervised machine learning models accelerates the analysis process and especially food chemistry researcher or food industry with non-NMR experts would benefit immensely from such advancements. Here, we have successfully demonstrated this technology by considering three major sugar adulterants, i.e., brown rice syrup, corn syrup, and jaggery syrup, in honey at varying concentrations. The necessary supervised machine learning classification analysis is performed by using logistic regression, deep learning-based neural network, and light gradient boosting machines schemes.
Indexed as
Identifiers
What Socratic 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 Socratic graph.