Zhan, K. / Jiang, Y. / Qin, P. / Chen, Y. / Heinke, L. (2023)

Advanced Materials Inetrfaces 2023, 10, 28, 2300329

Abstract

The unambiguous detection and classification of volatile organic compounds (VOCs) are crucial in many fields. For using VOC-sensing to explore the alteration and spoilage of food, very inexpensive sensors are desired. Simple colorimetric sensors seem highly attractive for these applications. Here, a label-free, colorimetric sensor array made of metal-organic-framework-based (MOF-based) Fabry-Pérot (FP) films is presented where the signal read-out is performed either by their optical spectra or by pictures taken with a smartphone camera. Exposing the FP-MOF-films to various VOCs causes a reversible shift of the photonic pattern, where the magnitude of the shift depends on the VOC type, its concentration, and the MOF structure. The application of machine- learning- algorithms on the sensor data allows to identify the VOCs with a high classification accuracy (92% at 100 ppm). It is shown that the sensor array read-out can also be performed with a common smartphone camera, also precisely classifying the VOC analytes. Moreover, fresh and spoiled food, like milk and meat, is distinguished by its head space. Thus, the study presents a very inexpensive platform of small colorimetric sensors that allow determining the quality, alteration, and spoilage of food, and it may contribute to realizing smart labels and intelligent packaging.

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