Self-Driving Spectroscopy for Food Safety, Industrial and Cultural Heritage Applications
A special issue of Journal of Experimental and Theoretical Analyses (ISSN 2813-4648).
Deadline for manuscript submissions: 31 July 2026 | Viewed by 99
Special Issue Editor
Interests: infrared spectroscopy and imaging; spectral library matching; forensic automotive paint analysis; chemometrics; bioinformatics; swellable pH-sensitive polymers for optical sensing; chemical data science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The focus of this Special Issue is self-driving laboratories (SDLs) that automate the collection, analysis, and interpretation of infrared and Raman spectral data. SDLs are advanced research environments that couple experimental hardware with artificial intelligence (AI) to optimize data collection, data preprocessing, and decision making. SDLs enable closed-loop workflows where the system formulates experiments, runs them, collects data, and refines hypotheses autonomously—often 10-100 times faster than traditional methods. SDL strategies discussed in this Special Issue are specific to the context of food safety challenges and industrial and cultural heritage applications. An area of particular interest is the use of digitally blended data to augment experimental data in the calibration or classification of data. Advantages of incorporating simulated data in analyses include adequate sample representation in all regions of the spectral property calibration, a better understanding of the calibration relationship through the identification of sample outliers in experimental data that can be difficult to detect because of swamping, the establishment of the limits of detection for adulterants, and overcoming challenges in the classification of spectra that are near the limit of determination. Papers that discuss these applications or recent methodological developments in SDLs are welcome.
Prof. Dr. Barry K. Lavine
Guest Editor
Manuscript Submission Information
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Keywords
- self-driving laboratories
- infrared and Raman spectral data
- vibrational spectroscopic imaging
- digitally blended and simulated data
- calibration
- pattern recognition
- limits of detection
- limits of determination
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