A concept for sensor system developments using raw-milk monitoring as a case study

Abstract

Abstract. In this work, we present a concept for a raw-milk monitoring sensor system aiming at demonstrating a generalized approach for low-cost gas sensor system development in future. These systems are expected to be comparatively less expensive than conventional gas chromatography (GC) systems and can therefore likewise be used by farmers to monitor on-site storage as well as by dairy companies for the inspection of incoming milk and can thus play a significant role in counteracting the waste of milk and its products. This generalizable method is based on three steps: identification of potential milk degradation markers, quantification of these markers, and characterization of metal oxide semiconductor (MOS) sensors for these markers. In the first step, gas chromatography–mass spectrometry (GC-MS) and GC–flame ionization detector (GC-FID)/olfactometry (O) were used to tentatively identify 14 volatile substances in the headspace concentrations above the raw milk. From this, 3-methylbutan-1-ol, hexan-1-ol, pentan-1-ol, acetic acid, and additionally ethanol and ethyl acetate were selected by cross-referencing our results with literature data. In addition, hexanal, 2-methyl-1-propanol, limonene, nonanal, 2-ethylhexan-1-ol, butanoic acid, hexanoic acid, octanoic acid, methyl hexadecanoate, and decanoic acid were identified but not selected as potential markers due to their properties being incompatible with gas mixing apparatus (GMA). In the second step, a proton transfer reaction–MS (PTR-MS) analysis was used to determine the concentration in the headspace, which is in the parts per billion (ppb) range. Investigations of good milk samples and bad milk samples from alpine farms showed that ethanol, 3-methylbutan-1-ol, pentan-1-ol, and hexan-1-ol offered an increasing trend from good to bad milk samples. To enable more precise differentiation, further investigations with a higher sample size are necessary to reveal the feasibility of these markers within the complex matrix of raw milk. In the third step, these selected and literature-confirmed markers were presented to a commercially available sensor, run in a temperature-cycled operation and characterized by a self-developed system. When using ethanol, pentan-1-ol, and hexan-1-ol, a regression model with an accuracy of 42.9 ppb using partial least-squares regression (PLSR) analysis could be established, enabling such sensors to be used in raw-milk monitoring systems in the future.