Development and Consumer Perception of a Snack Machine Producing Customized Spoonable and Drinkable Products Enriched in Dietary Fiber and Protein.

The aim of the study was to evaluate consumer perceptions toward customized snacks produced with a Healthy Snack Machine (HSM) prototype, at-site of the purchase and consumption. The present study had a multi-disciplinary approach including both snack product and HSM development (hardware and user interface). Snack development included both instrumental (viscosity, colloidal stability) and sensory characterization (by trained sensory (N = 10) and consumer (N = 55) panels) of spoonable and drinkable, oat- and dairy-based snack products, fortified with protein and/or dietary fiber. The protein and fiber addition reduced viscosity in spoonable products but did not affect the consistency of drinkable samples. Oat-based samples differed from dairy-based in multiple attributes in sensory profiling. In consumer sample testing, sample odor and taste were the most and least preferred aspects, respectively. In the snack machine testing, a qualitative consumer study (N = 33) showed that the HSM was easy to use, the user interface was clear, the ordering process was quick, and the participants were interested in using the HSM in the future. The snack choices (spoonable/drinkable and dairy/oat base) made by the consumers were distributed equally, but the berry-flavor was preferred over cocoa and vanilla. The most common HSM usage scenarios were "between work/school and hobbies" and "in transit from one place to another".

SmartTags: IoT Product Passport for Circular Economy Based on Printed Sensors and Unique Item-Level Identifiers.

In this paper, we present a method that facilitates Internet of Things (IoT) for building a product passport and data exchange enabling the next stage of the circular economy. SmartTags based on printed sensors (i.e., using functional ink) and a modified GS1 barcode standard enable unique identification of objects on a per item-level (including Fast-Moving Consumer Goods-FMCG), collecting, sensing, and reading of parameters from environment as well as tracking a products' lifecycle. The developed ontology is the first effort to define a semantic model for dynamic sensors, including datamatrix and QR codes. The evaluation of decoding and readability of identifiers (QR codes) showed good performance for detection of sensor state printed over and outside the QR code data matrix, i.e., the recognition ability with image vision algorithm was possible. The evaluation of the decoding performance of the QR code data matrix printed with sensors was also efficient, i.e., the QR code ability to be decoded with the reader after reversible and irreversible process of ink (dis)appearing was preserved, with slight drop in performance if ink density is low.