Abstract
Optical spectrometers are fundamental across numerous disciplines. However, miniaturized versions, while essential for in situ measurements, are often restricted to coarse identification of signature peaks and are inadequate for metrological purposes. Here we introduce a new class of spectrometer, which uses the convolution theorem as its unique mathematical foundation. Our ‘convolutional spectrometer’ offers unmatched performance for miniaturized systems and distinct structural and computational simplicity, featuring a centimetre-scale footprint for the fully packaged unit, low cost (~US$10) and a 2,400 cm−1 (approximately 500 nm) bandwidth in the near-infrared region. We achieve excellent precision in resolving complex spectra with subsecond sampling and processing time, demonstrating wide near-infrared spectroscopic applications from industrial and agricultural analysis to healthcare monitoring. Specifically, our spectrometer system classifies diverse solid samples, including plastics, pharmaceuticals, coffee, flour and tea, with 100% success rate, and quantifies concentrations of aqueous and organic solutions with detection accuracy surpassing commercial benchtop spectrometers. We also realize the non-invasive sensing of human biomarkers, such as skin moisture (mean absolute error = 2.49%), blood alcohol (1.70 mg dl−1), blood lactate (0.81 mmol l−1) and blood glucose (0.36 mmol l−1), highlighting the potential of this new class of spectrometers for low-cost, high-precision, portable and/or wearable spectral metrology.
| Original language | English |
|---|---|
| Pages (from-to) | 664-672 |
| Number of pages | 9 |
| Journal | Nature Photonics |
| Volume | 20 |
| Issue number | 6 |
| Early online date | 15 Apr 2026 |
| DOIs | |
| Publication status | Published - 30 Jun 2026 |
| Externally published | Yes |
Data Availability Statement
All data supporting this study are included within the main text and/or Supplementary Information. Source data are available in the University of Cambridge Repository at https://doi.org/10.17863/CAM.126013. Source data are provided with this paper.Funding
This research was mainly supported by GlitterinTech Limited, but also received support from the UK EPSRC through project QUDOS (EP/T028475/1, to R.P. and Q.C.) and European Union’s Horizon 2020 Research and Innovation programme through project INSPIRE (101017088, to R.P. and Q.C.). We thank B. Liu, M. Wu, Y. Wen, J. Wang, Y. Liang, K. Qiu and Y. Chen for their assistance in experiments. We also thank J. Cao and L. Xu for contributions to figure preparation.
| Funders | Funder number |
|---|---|
| GlitterinTech Limited | |
| Engineering and Physical Sciences Research Council | EP/T028475/1 |
| Horizon 2020 Framework Programme | 101017088 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
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