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Near-infrared (NIR) spectroscopy is widely used for the classification of materials and the quantification of their properties. Today, there is a high demand for extending the use of this technique to portable applications, and eventually, the integration with consumer appliances and smartphones. To reach this goal, the overall size of the NIR sensor, its production cost, robustness, and resistance to vibrations are of particular importance. This paper describes an approach to spectral sensing in the NIR (850–1700 nm) using a handheld sensor module based on a fully integrated multipixel detector array with a footprint of around 2×2 mm2. The capabilities of the spectral sensor module were recently evaluated in two application cases: Quantification of the fat percentage in raw milk and the classification of plastic types. Fat quantification was achieved with a root mean square error (RMSE) of prediction of 0.14% and classification of plastic types was achieved with a prediction accuracy on unknown samples of 100%. The results demonstrate the feasibility of the direct NIR sensing approach used by the integrated sensor, which has potential to be used in a variety of applications.

A static filter array is developed based on Fabry–Perot cavities operating in the mid-infrared range from 2 to 4.5 µm, matching the absorption region of several gases relevant for air quality monitoring. The filters include two distributed Bragg reflectors and a high-index tuning element with varying thickness in between. The filters display high peak transmittance (>80%) and controllable peak width. The robustness, ease of fabrication and the possibility to tune the optical response to a specific application make the integrated filter arrays suitable for compact sensing systems.

Illicit-drug production, trafficking and seizures are on an all-time high. This consequently raises pressure on investigative authorities to provide rapid forensic results to assist law enforcement and legal processes in drug-related cases. Ideally, every police officer is equipped with a detector to reliably perform drug testing directly at the incident scene. Such a detector should preferably be small, portable, inexpensive and shock-resistant but should also provide sufficient selectivity to prevent erroneous identifications. This study explores the concept of on-site drugs-of-abuse detection using a 1.8 × 2.2 mm2 multipixel near-infrared (NIR) spectral sensor that potentially can be integrated into a smartphone. This integrated sensor, based on an InGaAs-on-silicon technology, exploits an array of resonant-cavity enhanced photodetectors without any moving parts. A 100% correct classification of 11 common illicit drugs, pharmaceuticals and adulterants was achieved by chemometric modelling of the response of 15 wavelength-specific pixels. The performance on actual forensic casework was investigated on 246 cocaine-suspected powders and 39 MDMA-suspected ecstasy tablets yielding an over 90% correct classification in both cases. These findings show that presumptive drug testing by miniaturized spectral sensors is a promising development ultimately paving the way for a fully integrated drug-sensor in mobile communication devices used by law enforcement.

We present a novel approach to NIR spectral sensing using a handheld module based on a miniaturized integrated detector array. Applicability of the sensor is demonstrated in monitoring moisture in grains and classification of plastics.

We demonstrate a compact and robust near-infrared (900-1700nm) spectral sensor consisting of 16 resonant-cavity-enhanced photodetectors. We achieve a tunable spectral response with narrow linewidth and high responsivity and demonstrate the sensing performance on various applications.