AI in Optical Spectroscopy Analysis

Topic Information

Dear Colleagues,

Spectroscopic techniques are widely employed in the field of analytical chemistry. Common modalities, including ultraviolet/visible/near-infrared/mid-infrared spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy, terahertz spectroscopy, fluorescence spectroscopy, and their corresponding imaging modes, have been extensively investigated for chemical analysis across diverse application domains such as food, agriculture, pharmacy, and medicine. These techniques operate on distinct principles and working mechanisms. Notably, compared to traditional wet chemistry methods, spectroscopic techniques offer advantages such as rapid analysis, non-destructive or minimally invasive measurement, low cost, and environmental friendliness.

Based on specific instruments, these spectroscopic techniques can generate large volumes of data. The methods for interpreting this data vary. Direct analysis of certain spectral features can provide intuitive information about the sample. However, for more complex qualitative analyses (e.g., classification) and quantitative analyses (e.g., determining component concentrations), appropriate spectral data analysis methods are essential for processing the acquired data. The required processing techniques also differ depending on the specific type of spectral data. Chemometric methods have achieved significant success in the field of spectral data analysis.

The development of artificial intelligence (AI), particularly machine learning and deep learning, has provided a continuous stream of inspiration and solutions for spectral data analysis. The application of AI in spectroscopic analysis primarily addresses three major challenges inherent in traditional methods: cumbersome data processing, difficulty in feature extraction, and limited model generalization capability. Consequently, AI enhances the accuracy and robustness of spectroscopic detection.

Despite their clear advantages, spectroscopic techniques are susceptible to variations in equipment (e.g., parameters, models), environmental conditions, and sample characteristics. These factors have largely confined their use to research settings, leaving a gap towards practical deployment and application. Furthermore, for quantitative analysis of components, obtaining reference values entails substantial cost. Optimizing and improving methodologies to reduce the waste associated with repetitive measurements are also necessary. Therefore, the primary objective and direction of current research is to leverage the strengths of AI to overcome the challenges hindering the practical application of spectroscopic techniques.

The primary objective of this Topic is to address the challenges in applying spectroscopic techniques by integrating artificial intelligence algorithms, tailored to the characteristics of different spectroscopic modalities. Building upon the substantial body of existing research, the aim is to progressively advance these techniques from feasibility studies and laboratory research towards practical deployment and application.

Dr. Chu Zhang
Dr. Jiyu Peng
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.6	6.3	2011	18.8 Days	CHF 2600	Submit
AI Chemistry aichem	-	-	2026	15.0 days *	CHF 1000	Submit
Analytica analytica	3.6	3.7	2020	19 Days	CHF 1200	Submit
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Chemosensors chemosensors	3.7	7.3	2013	19.1 Days	CHF 2000	Submit
Foods foods	5.1	8.7	2012	15 Days	CHF 2900	Submit
Plants plants	4.1	7.6	2012	16.5 Days	CHF 2700	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit

* Median value for all MDPI journals in the second half of 2025.

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