A Colorimetric Label‐Free Sensor Array of Metal–Organic‐Framework‐Based Fabry–Pérot Films for Detecting Volatile Organic Compounds and Food Spoilage

Abstract

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.

Keywords

• artificial nose
• colorimetric array
• machine learning
• metal−organic frameworks
• optical sensors