ArticleFood chemistry2022
The application of machine-learning and Raman spectroscopy for the rapid detection of edible oils type and adulteration.
Article in Food chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 117 citations in OpenAlex.
- Unveiling key peak features for olive oil authentication utilizing Raman spectroscopy and chemometrics.NPJ science of food · 2026Article
- Machine learning-assisted Raman spectroscopy for non-destructive analysis of crude palm oil quality.NPJ science of food · 2026Article
- Recent Advances in Raman Spectral Classification with Machine Learning.Sensors (Basel, Switzerland) · 2026Review
- Rapid classification and quality assessment of citrus essential oils via machine-learning-assisted Raman spectroscopy.Current research in food science · 2026Article
- Rapid Authentication of Plant-Based Milk Alternatives by Coupling Portable Raman Spectroscopy With Machine Learning.Journal of AOAC International · 2026Article
- Trends on vibrational spectroscopy tools in the agri-food sector.Analytical and bioanalytical chemistry · 2026Review
- Identification, Geographical Traceability, and Thermal Oxidation and Photodegradation Studies of Camellia Oil Based on Raman Spectroscopy.Molecules (Basel, Switzerland) · 2025Article
- The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud Detection.Foods (Basel, Switzerland) · 2025Review
- Fatty acids and fatty alcohol esters as novel markers of authenticity and extraction method of commercial avocado oil.Food chemistry: X · 2025Article
- Using visible and NIR hyperspectral imaging and machine learning for nondestructive detection of nutrient contents in sorghum.Scientific reports · 2025Article
- Nondestructive Detection of Corky Disease in Symptomless 'Akizuki' Pears via Raman Spectroscopy.Sensors (Basel, Switzerland) · 2024Article
- Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties.Annals of biomedical engineering · 2023Article
- Exploring Strategies to Mitigate the Lightness Effect on the Prediction of Soybean Oil Content in Blends of Olive and Avocado Oil Using Smartphone Digital Image Colorimetry.Foods (Basel, Switzerland) · 2023Article
- A Review of Recent Advances for the Detection of Biological, Chemical, and Physical Hazards in Foodstuffs Using Spectral Imaging Techniques.Foods (Basel, Switzerland) · 2023Review
- Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems.Sensors (Basel, Switzerland) · 2023Article
- Metabolomics integrated with machine learning to discriminate the geographic origin of Rougui Wuyi rock tea.NPJ science of food · 2023Article
- Enhancing the classification metrics of spectroscopy spectrums using neural network based low dimensional space.Earth science informatics · 2023Article
- A Coding Basis and Three-in-One Integrated Data Visualization Method 'Ana' for the Rapid Analysis of Multidimensional Omics Dataset.Life (Basel, Switzerland) · 2022Article
- High Precisive Prediction of Aflatoxin BFoods (Basel, Switzerland) · 2022Article
- Distinguishing between Decaffeinated and Regular Coffee by HS-SPME-GC×GC-TOFMS, Chemometrics, and Machine Learning.Molecules (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Raman spectroscopy is an emerging technique for the rapid detection of oil qualities. But the spectral analysis is time-consuming and low-throughput, which has limited the broad adoption. To address this issue, nine supervised machine learning (ML) algorithms were integrated into a Raman spectroscopy protocol for achieving the rapid analysis. Raman spectra were obtained for ten commercial edible oils from a variety of brands and the resulting spectral dataset was analyzed with supervised ML algorithms and compared against a principal component analysis (PCA) model. A ML-derived model obtained an accuracy of 96.7% in detecting oil type and an adulteration prediction of 0.984 (R
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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.