ArticleCurrent research in food science2024
Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy.
Article in Current research in food science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Food Intelligent Quality and Safety Analysis: From Data-Driven to Data-Mechanism Hybrid-Driven Paradigm.Foods (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Driven Dairy Quality Assessment: From Advanced Sensing Technologies to Explainable Intelligence.Foods (Basel, Switzerland) · 2026Review
- Rapid Evaluation of Wet Gluten Content in Wheat Using Hyperspectral Technology Combined with Machine Learning Algorithms.Foods (Basel, Switzerland) · 2025Article
- Deep Learning-Enhanced Spectroscopic Technologies for Food Quality Assessment: Convergence and Emerging Frontiers.Foods (Basel, Switzerland) · 2025Review
- Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine Learning.Molecules (Basel, Switzerland) · 2025Review
- Advances in Surface-Enhanced Raman Spectroscopy for Urinary Metabolite Analysis: Exploiting Noble Metal Nanohybrids.Biosensors · 2024Review
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Authors and funding
6 authors.
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Abstract
Discriminant analysis of similar food samples is an important aspect of achieving food quality control. The effective combination of Raman spectroscopy and machine learning algorithms has become an extremely attractive approach to develop intelligent discrimination techniques. Feature spectral analysis can help researchers gain a deeper understanding of the data patterns in food quality discrimination. Herein, this work takes the discrimination of three brands of dairy products as an example to investigate the Raman spectral feature based on the support vector machines (SVM), extreme learning machines (ELM) and convolutional neural network (CNN) algorithms. The results show that there are certain differences in the optimal spectral feature interval corresponding to different machine learning algorithms. Selecting the appropriate spectral feature interval can maintain high recognition accuracy and improve the computational efficiency of the algorithm. For example, the SVM algorithm has a recognition accuracy of 100% in the 890-980 cm
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