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Consumables-from food to pharmaceuticals and supplements-are becoming increasingly vulnerable to various modes of counterfeiting due to the growing complexity of their supply chain. Mislabeling, re-branding, and false advertising are prevalent in this sector. Existing physical authentication techniques fail to adequately verify integrity of these products and protect the end-users. In this paper, we aim at addressing this critical problem through the development of a novel authentication solution. It builds on the chemical analysis properties of a powerful spectroscopy technique, namely, nuclear quadrupole resonance (NQR) that is quantitative, non-invasive, low-cost, and amenable for miniaturization (to handheld form factors). The method is sensitive to small variations in the solid-state chemical structure of a sample, which change the NQR signal properties. These attributes can be unique for various manufacturers, enabling their use as manufacturer-specific watermarks. However, NQR spectroscopy only works reliably (i.e., provides good sensitivity) on compounds that contain certain nuclear isotopes. We take advantage of the intrinsic properties of NQR-sensitive isotopes to use them as extrinsic tags in NQR-insensitive products. The NQR spectra of these extrinsic tags act as unique watermarks that can be analyzed using machine learning methods to authenticate any consumable with high confidence. In particular, we use support vector machines to classify the measured spectra and confirm the identity of items under test. We have assessed this approach on a variety of consumables utilizing semi-custom equipment and verified that it results in high (>95%) classification accuracy. In order to prove the unclonability of such extrinsic tags, we have also performed a mathematical analysis that proves the randomness of the extrinsic tag and confirms its robustness to bruteforce attacks.

Summary As the industry grows, adulteration of many products by mislabelling, re‐branding and false advertising is becoming prevalent practice. Existing solutions for analysis often require extensive sample preparation or are limited in terms of detecting different types of integrity issues. We describe a novel authentication method based on Nuclear Quadrupole Resonance (NQR) spectroscopy which is quantitative, non‐invasive and non‐destructive. It is sensitive to small deviation in the solid‐state chemical structure of a product, which changes the NQR signal properties. These characteristics are unique for different manufacturers, resulting in manufacturer‐specific watermarks. We show that nominally identical dietary supplements from different manufacturers can be accurately classified based on features from NQR spectra. Specifically, we use a machine learning‐based classification called support vector machines (SVMs) to verify the authenticity of products under test. This approach has been evaluated on three products using semi‐custom hardware and shows promising results, with typical classification accuracy of over 95%.

Face masks are a primary preventive measure against airborne pathogens. Thus, they have become one of the keys to controlling the spread of the COVID-19 virus. Common examples, including N95 masks, surgical masks, and face coverings, are passive devices that minimize the spread of suspended pathogens by inserting an aerosol-filtering barrier between the user’s nasal and oral cavities and the environment. However, the filtering process does not adapt to changing pathogen levels or other environmental factors, which reduces its effectiveness in real-world scenarios. This paper addresses the limitations of passive masks by proposing ADAPT, a smart IoT-enabled “active mask”. This wearable device contains a real-time closed-loop control system that senses airborne particles of different sizes near the mask by using an on-board particulate matter (PM) sensor. It then intelligently mitigates the threat by using mist spray, generated by a piezoelectric actuator, to load nearby aerosol particles such that they rapidly fall to the ground. The system is controlled by an on-board micro-controller unit that collects sensor data, analyzes it, and activates the mist generator as necessary. A custom smartphone application enables the user to remotely control the device and also receive real-time alerts related to recharging, refilling, and/or decontamination of the mask before reuse. Experimental results on a working prototype confirm that aerosol clouds rapidly fall to the ground when the mask is activated, thus significantly reducing PM counts near the user. Also, usage of the mask significantly increases local relative humidity levels.

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