Application of a Novel S3 Nanowire Gas Sensor Device in Parallel with GC-MS for the Identification of Rind Percentage of Grated Parmigiano Reggiano

Abstract

Parmigiano Reggiano cheese is one of the most appreciated and consumed foods worldwide, especially in Italy, for its high content of nutrients and taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed to investigate the potentiality of this tool to distinguish rind percentages in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples, in terms of percentage, seasoning and rind working process, were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them to sensors responses. Data analysis consisted of two stages: Multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results were promising, in terms of correct classification of the samples. The correct classification rate (%) was higher for ANNs than PLS-DA, with correct identification approaching 100 percent.

Innovative Sensor Approach to Follow <em>Campylobacter jejuni </em>Development

“…S3 device used in the present work has been completely designed and constructed at SENSOR Laboratory (University of Brescia, Italy) in collaboration with NASYS S.r.l., a spin-off of the University of Brescia. It has been described in other works [20][21][22][23]. Briefly, the tool comprises three parts: pneumatic components, that transfer VOCs from the headspace of samples to the sensing chamber; electronic boards, that manage the acquisition and transmission of the data from the device to the dedicated Web-App and allow the synchronization between S3 and the auto-sampler; sensing chamber, that can host from five to ten different MOX gas sensors and is thermostated and isolated in order to avoid any influence of the surrounding environment.…”

Discrimination of Quality and Geographical Origin of Extra Virgin Olive Oil by S3 Device with Metal Oxides Gas Sensors

“…The complex set of data acquired from the S3 sensors response to the volatile organic compounds originating from the EVOO headspace were analyzed through a multivariate analysis PCA and ANN. The measurements were performed in an autosampler HT2800T (HTA srl, Brescia, Italy) [5,6]. For this, 5 mL of olive oil samples were enclosed in 20 mL vials and placed randomly in the autosampler.…”

Nanomaterial Gas Sensors for Online Monitoring System of Fruit Jams

“…Ample interest has been demonstrated by the numerous scientific publications that are distributed both between classes of foods such as meats, vegetables, cereals, etc., but also between raw materials and finished and packaged products, following the entire production chain from the fields to the fork. Applications take into consideration geographical origins, production anomalies, supply chain checks or possible chemical and physical contamination of the matrix [17][18][19].…”