JiangTao Wang at Yanshan University, China
Mr. JiangTao Wang is a master’s student in the Department of Instrument Science and Engineering at Yanshan University, China. His research centers on spectral detection technologies such as SERS, Raman, and FTIR, with a focus on food safety and trace antibiotic detection. Supported by the Hebei Natural Science Foundation, he is developing innovative methods that integrate nanomaterials and spectroscopy for enhanced analytical performance. Mr. Wang has completed multiple interdisciplinary projects involving deep learning and spectroscopic fusion techniques, with one paper accepted in Food Chemistry. His expertise spans nanomaterial synthesis, spectroscopy, and data modeling for advanced sensing applications.
Professional Profile:
ORCID
Summary of Suitability for Best Researcher Award
Mr. Jiangtao Wang, a master’s student at Yanshan University, has shown exceptional promise and productivity in advanced spectral detection research. His work focuses on combining nanomaterials, deep learning, and vibrational spectroscopy (SERS, Raman, FTIR) to address critical challenges in food safety and analytical chemistry. Through two completed projects and one ongoing, he has innovated a Kolmogorov-Arnold neural network (CKAN) coupled with a hybrid SERS substrate, achieving breakthrough sensitivity in detecting antibiotic residues in milk. He also proposed novel mid-level fusion approaches for Raman–IR spectral data, enhancing adulteration detection accuracy.
🎓 Education
💼 Research Experience
🔬 Completed Projects:
🧪 CKAN Model + Hybrid SERS Substrate
Project: “Synergistic Analysis Based on Chemometrics and Deep Learning: An Innovative Kolmogorov-Arnold Neural Network (CKAN) Model Combined with Ternary Hybrid SERS Substrate (Au@mSiO₂(YSN)-Fe₃O₄@MoS₂-rGO) for Highly Sensitive Detection of Trace Quinolone Antibiotics in Milk”
📊 Spectroscopy Fusion for Food Safety
Project: “Optimization of Convenient Infrared Spectroscopy Detection Methods and Infrared-Raman Fusion Spectroscopic Detection for Milk Powder Adulteration”
⚗️ Ongoing Research:
Developing a SERS detection method for specific antibiotics in milk with photocatalytic self-cleaning capabilities.
🔬 Areas of Expertise
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🌈 Spectral Detection Technologies
(SERS, Raman, FTIR)
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🧫 Nanomaterial Synthesis
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📈 Data Modeling & Chemometrics
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⚙️ Photocatalytic Self-Cleaning Surfaces
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🧠 Deep Learning for Analytical Chemistry
🏆 Achievements
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📄 One accepted paper in Food Chemistry
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📑 One paper under review
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📚 Developed a novel hybrid substrate for trace antibiotic detection
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💡 Proposed an integrated Raman–IR fusion strategy for food adulteration analysis
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💻 Applied advanced deep learning (CKAN model) in spectroscopy
🌟 Honors & Recognition
📚Publication Top Note
Synergistic analysis based on chemometrics and deep learning: An innovative Kolmogorov-Arnold neural network (CKAN) model combined with ternary hybrid SERS substrate (Au@mSiO₂(YSN)-Fe₃O₄@MoS₂-rGO) for highly sensitive detection of trace quinolone antibiotics in milk