Machine learning picks the wavelengths that shape mid-infrared laser design

Mid-infrared spectroscopy is quietly becoming one of the most powerful tools for reading the chemistry of biological materials, from food safety screening to protein quality assessment. But the lasers at the heart of these instruments face a fundamental design question: which wavelengths should they emit? A team of Norwegian and Italian researchers has now tackled this problem head-on, using machine learning to determine which wavelengths matter most when building tunable mid-infrared lasers tailored to specific analytical tasks. Their findings, published in Frontiers in Photonics, could reshape how compact, application-specific laser spectrometers are designed in the coming decade.

The research, led by Miriam Aledda and Achim Kohler at the Norwegian University of Life Sciences in Ås, together with collaborators at the University of Rome La Sapienza, the Norwegian food research institute Nofima, and the University of L’Aquila, addresses a challenge that has grown more pressing as laser technology has advanced. Quantum cascade lasers and interband cascade lasers have emerged over the past decade as key light sources for mid-infrared sensing, offering high intensity, excellent signal-to-noise ratios, and exceptional spectral resolution. Unlike traditional broadband Fourier-transform infrared spectrometers, which collect data across the entire mid-infrared range from 4000 to 400 wavenumbers, these lasers can be engineered to emit precisely in narrow spectral windows. That precision is their greatest strength and their greatest design constraint.

The team focused on a particularly important spectral region: 1700 to 1600 wavenumbers, which corresponds to the amide I band. This band, arising from the stretching vibration of carbon-oxygen double bonds in the peptide backbone of proteins, is exquisitely sensitive to protein secondary structure and conformational changes. It is also the region where a tunable interband cascade laser developed by the German company nanoplus currently operates, capable in principle of emitting at more than 1000 discrete frequencies through temperature and current modulation. The researchers wanted to know: if you can only access a limited number of wavelengths, which ones should you choose?

To answer this, the team assembled three datasets of protein hydrolysates derived from salmon and poultry by-products, materials obtained through routine food industrial processes. Protein hydrolysates are produced by enzymatically breaking down protein-rich raw materials into smaller peptides and free amino acids, and their average molecular weight is a crucial quality parameter that determines functional properties, bioavailability, and sensory characteristics. The datasets comprised 95 salmon hydrolysate samples, 133 poultry samples measured in five technical replicates, and 286 poultry samples measured in three technical replicates, with average molecular weights ranging from roughly 1885 to 7111 grams per mole as determined by size-exclusion chromatography. The researchers then applied a battery of variable selection methods to identify which wavelengths in the mid-infrared spectrum carry the most predictive information for estimating average molecular weight.

The methods tested span several families of machine learning approaches. Sparse Partial Least Squares, an embedded technique that performs variable selection and dimensionality reduction simultaneously, was applied with varying degrees of sparsity tailored to different spectral ranges. The Successive Projection Algorithm, a forward selection method designed to minimise collinearity among selected variables, was also included. The Covariance Selection family, comprising the original CovSel, its group-based extension g-CovSel, and a newly implemented window-based variant called w-CovSel, formed another major component of the comparison. Principal Variable Selection and its supervised extension Principal Variable Regression rounded out the set. Each method operates on different principles, from maximising covariance with the response variable to selecting groups or narrow windows of consecutive variables, and each carries different implications for laser hardware design.

The results revealed a striking hierarchy of predictive power across spectral regions. When the researchers benchmarked performance using broadband partial least squares regression on the full mid-infrared range, they obtained coefficients of determination of 0.78 for the fish dataset, 0.85 for the first poultry dataset, and 0.84 for the second. Narrowing to the fingerprint region from 1800 to 800 wavenumbers improved these values for two of the three datasets, and restricting further to the amide I range from 1700 to 1600 wavenumbers maintained high performance, with the second poultry dataset actually improving to 0.90. Remarkably, even randomly selecting ten wavelengths within the amide I range produced coefficients of determination of 0.79, 0.75, and 0.84 across the three datasets, approaching the performance of broadband models. This finding underscores how information-dense the amide I region is for protein characterisation.

Perhaps the most surprising result concerned the set of biochemically motivated features drawn from the literature. The researchers identified nine wavenumbers that prior studies had flagged as particularly relevant for protein and peptide characterisation, including signals at 1740, 1645, 1630, 1548, 1515, 1452, 1400, 1081, and 1045 wavenumbers. When these were used alone for regression, performance dropped considerably, with coefficients of determination of only 0.58, 0.74, and 0.62 across the three datasets. This suggests that variable selection based purely on literature-derived biochemical knowledge is not the best approach for laser design, especially when the number of accessible wavelengths is limited. The data-driven methods, by contrast, consistently identified wavelengths that outperformed the literature-based selection.

Among the data-driven approaches, the group-based and window-based Covariance Selection methods proved particularly suited to laser design constraints. The g-CovSel method, which selects groups of variables rather than individual wavelengths, achieved the best overall performance on the fish dataset with a coefficient of determination of 0.87 when all five selected groups were combined. The newly proposed w-CovSel method, designed specifically to select narrow windows of three consecutive variables, mimics the fine tuning capability of interband cascade lasers, which can achieve sub-wavenumber precision around discrete emission points. When the researchers applied w-CovSel within the amide I range, selecting up to five narrow intervals, prediction ability was very good, consistent with previous findings that three fine-tuned single emission points already yield strong results. The team also found that the first interval selected by w-CovSel within the amide I range for the second poultry dataset alone achieved a coefficient of determination of 0.84, demonstrating that even a single narrow tuning window can carry substantial predictive information.

The implications extend beyond protein hydrolysates. The researchers note that the same variable selection strategies could inform the design of other advanced optical systems, including hyperspectral imaging platforms, Raman spectroscopy setups, and atomic force microscopy-based spectroscopic tools, wherever spectral sparsity, tuning precision, and data interpretability are critical. As tunable laser technology continues to mature, the ability to systematically identify which wavelengths matter most for a given analytical task will be essential for building compact, high-performance instruments that bypass the optical complexity of traditional broadband spectrometers. The study demonstrates that machine learning is not merely a tool for analysing spectroscopic data after the fact, but a genuine design partner for the engineers building the next generation of mid-infrared laser instrumentation.

Subject of Research: Wavelength selection for laser design in mid-infrared spectroscopy

Article Title: Wavelength selection for laser design in mid-infrared spectroscopy

Article References: Aledda, M., Marini, F., Kafle, B., Erdem, M. C., Karki, P., Liland, K. H., Zimmermann, B., Afseth, N. K., Biancolillo, A., Tafintseva, V., Tøndel, K., Shapaval, V., & Kohler, A. (2026). Wavelength selection for laser design in mid-infrared spectroscopy. Frontiers in Photonics, 7, Article 1696425. https://doi.org/10.3389/fphot.2026.1696425

Image Credits: AI Generated

DOI: 10.3389/fphot.2026.1696425

Keywords: Wavelength, selection, laser, design, mid-infrared, spectroscopy, scientific research