ArticleDigital Chemical Engineering2022
Integration of surface-enhanced Raman spectroscopy (SERS) and machine learning tools for coffee beverage classification.
Article in Digital Chemical Engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples.ACS omega · 2026Article
- Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples.bioRxiv : the preprint server for biology · 2026Article
- Rapid Authentication of Plant-Based Milk Alternatives by Coupling Portable Raman Spectroscopy With Machine Learning.Journal of AOAC International · 2026Article
- Integration of Nanoengineering with Artificial Intelligence and Machine Learning in Surface-Enhanced Raman Spectroscopy (SERS) for the Development of Advanced Biosensing Platforms.Advanced sensor research · 2025Article
- MXene-based SERS spectroscopic analysis of exosomes for lung cancer differential diagnosis with deep learning.Biomedical optics express · 2025Article
- Flavor Wheel Development from a Machine Learning Perspective.Foods (Basel, Switzerland) · 2024Article
- Direct Glycan Analysis of Biological Samples and Intact Glycoproteins by Integrating Machine Learning-Driven Surface-Enhanced Raman Scattering and Boronic Acid Arrays.ACS measurement science au · 2024Article
- Artificial Q-Grader: Machine Learning-Enabled Intelligent Olfactory and Gustatory Sensing System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Surface-Enhanced Raman Spectroscopy-Based Detection of Micro-RNA Biomarkers for Biomedical Diagnosis Using a Comparative Study of Interpretable Machine Learning Algorithms.Applied spectroscopy · 2024Article
- Enhancing the classification metrics of spectroscopy spectrums using neural network based low dimensional space.Earth science informatics · 2023Article
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Authors and funding
4 authors.
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Abstract
Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for molecule identification. However, profiling complex samples remains a challenge because SERS peaks are likely to overlap, confounding features when multiple analytes are present in a single sample. In addition, SERS often suffers from high variability in signal enhancement due to nonuniform SERS substrate. The machine learning classification techniques widely used for facial recognition are excellent tools to overcome the complexity of SERS data interpretation. Herein, we reported a sensor for classifying coffee beverages by integrating SERS, feature extractions, and machine learning classifiers. A versatile and low-cost SERS substrate, called nanopaper, was used to enhance Raman signals of dilute compounds in coffee beverages. Two classic multivariate analysis techniques, Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC), were used to extract the significant spectral features, and the performance of various machine learning classifiers was evaluated. The combination of DAPC with Support Vector Machine (SVM) or K-Nearest Neighbor (KNN) shows the best performance for classifying coffee beverages. This user-friendly and versatile sensor has the potential to be a practical quality-control tool for the food industry.
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