Low-power swept-source Raman spectroscopy.

'Molecular fingerprinting' with Raman spectroscopy can address important problems-from ensuring our food safety, detecting dangerous substances, to supporting disease diagnosis and management. However, the broad adoption of Raman spectroscopy demands low-cost, portable instruments that are sensitive and use lasers that are safe for human eye and skin. This is currently not possible with existing Raman spectroscopy approaches. Portability has been achieved with dispersive Raman spectrometers, however, fundamental entropic limits to light collection both limits sensitivity and demands high-power lasers and cooled expensive detectors. Here, we demonstrate a swept-source Raman spectrometer that improves light collection efficiency by up to 1000× compared to portable dispersive spectrometers. We demonstrate high detection sensitivity with only 1.5 mW average excitation power and an uncooled amplified silicon photodiode. The low optical power requirement allowed us to utilize miniature chip-scale MEMS-tunable lasers with close to eye-safe optical powers for excitation. We characterize the dynamic range and spectral characteristics of this Raman spectrometer in detail, and use it for fingerprinting of different molecular species consumed everyday including analgesic tablets, nutrients in vegetables, and contaminated alcohol. By moving the complexity of Raman spectroscopy from bulky spectrometers to chip-scale light sources, and by replacing expensive cooled detectors with low-cost uncooled alternatives, this swept-source Raman spectroscopy technique could make molecular fingerprinting more accessible.

Optical Detection of Degraded Therapeutic Proteins.

The quality of therapeutic proteins such as hormones, subunit and conjugate vaccines, and antibodies is critical to the safety and efficacy of modern medicine. Identifying malformed proteins at the point-of-care can prevent adverse immune reactions in patients; this is of special concern when there is an insecure supply chain resulting in the delivery of degraded, or even counterfeit, drug product. Identification of degraded protein, for example human growth hormone, is demonstrated by applying automated anomaly detection algorithms. Detection of the degraded protein differs from previous applications of machine-learning and classification to spectral analysis: only example spectra of genuine, high-quality drug products are used to construct the classifier. The algorithm is tested on Raman spectra acquired on protein dilutions typical of formulated drug product and at sample volumes of 25 µL, below the typical overfill (waste) volumes present in vials of injectable drug product. The algorithm is demonstrated to correctly classify anomalous recombinant human growth hormone (rhGH) with 92% sensitivity and 98% specificity even when the algorithm has only previously encountered high-quality drug product.