Next Article in Journal
Use of Computing Devices as Sensors to Measure Their Impact on Primary and Secondary Students’ Performance
Previous Article in Journal
SemanticDepth: Fusing Semantic Segmentation and Monocular Depth Estimation for Enabling Autonomous Driving in Roads without Lane Lines
Open AccessArticle

Meat and Fish Freshness Assessment by a Portable and Simplified Electronic Nose System (Mastersense)

1
Department of Food, Environmental, and Nutritional Sciences (DeFENS), Università degli Studi di Milano, via G. Celoria 2, 20133 Milan, Italy
2
Senior S.r.l., via Molino 2, 21052 Busto Arsizio, Italy
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3225; https://doi.org/10.3390/s19143225
Received: 22 June 2019 / Revised: 17 July 2019 / Accepted: 18 July 2019 / Published: 22 July 2019
(This article belongs to the Section Chemical Sensors)
The evaluation of meat and fish quality is crucial to ensure that products are safe and meet the consumers’ expectation. The present work aims at developing a new low-cost, portable, and simplified electronic nose system, named Mastersense, to assess meat and fish freshness. Four metal oxide semiconductor sensors were selected by principal component analysis and were inserted in an “ad hoc” designed measuring chamber. The Mastersense system was used to test beef and poultry slices, and plaice and salmon fillets during their shelf life at 4 °C, from the day of packaging and beyond the expiration date. The same samples were tested for Total Viable Count, and the microbial results were used to define freshness classes to develop classification models by the K-Nearest Neighbours’ algorithm and Partial Least Square–Discriminant Analysis. All the obtained models gave global sensitivity and specificity with prediction higher than 83.3% and 84.0%, respectively. Moreover, a McNemar’s test was performed to compare the prediction ability of the two classification algorithms, which resulted in comparable values (p > 0.05). Thus, the Mastersense prototype implemented with the K-Nearest Neighbours’ model is considered the most convenient strategy to assess meat and fish freshness. View Full-Text
Keywords: electronic nose; food quality; MOS sensors; K-Nearest Neighbours’ algorithm (K-NN); Partial Least Square-Discriminant Analysis (PLS-DA) electronic nose; food quality; MOS sensors; K-Nearest Neighbours’ algorithm (K-NN); Partial Least Square-Discriminant Analysis (PLS-DA)
Show Figures

Graphical abstract

MDPI and ACS Style

Grassi, S.; Benedetti, S.; Opizzio, M.; di Nardo, E.; Buratti, S. Meat and Fish Freshness Assessment by a Portable and Simplified Electronic Nose System (Mastersense). Sensors 2019, 19, 3225.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop