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Electronic Nose Technology in Quality Assessment: Predicting Volatile Composition of Danish Blue Cheese During Ripening
Author(s) -
Trihaas Jeorgos,
Tempel Tatjana,
Nielsen Per Vággemose
Publication year - 2005
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/j.1365-2621.2005.tb11444.x
Subject(s) - electronic nose , ripening , chemistry , flavor , aroma , food science , cheese ripening , gas chromatography , chromatography , artificial intelligence , computer science
This work describes for the 1st time the use of an electronic nose (e‐nose) for the determination of changes of blue cheeses flavor during maturation. Headspace analysis of Danish blue cheeses was made for 2 dairy units of the same producer. An e‐nose registered changes in cheeses flavor 5, 8, 12, and 20 wk after brining. Volatiles were collected from the headspace and analyzed by gas chromatography‐mass spectrometry (GC‐MS). Features from the chemical sensors of the e‐nose were used to model the volatile changes by multivariate methods. Differences registered during ripening of the cheeses as well as between producing units are described and discussed for both methods. Cheeses from different units showed significant differences in their e‐nose flavor profiles at early ripening stages but with ripening became more and more alike. Prediction of the concentration of 25 identified aroma compounds by e‐nose features was possible by partial least square regression (PLS‐R). It was not possible to create a reliable predictive model for both units because cheeses from 1 unit were contaminated by Geotrichum candidum , leading to unstable ripening patterns. Correction of the e‐nose features by multiple scatter correction (MSC) and mean normalization (MN) of the integrated GC areas made correlation of the volatile concentration to the e‐nose signal features possible. Prediction models were created, evaluated, and used to reconstruct the headspace of unknown cheese samples by e‐nose measurements. Classification of predicted volatile compositions of unknown samples by their ripening stage was successful at a 78% and 54% overall correct classification for dairy units 1 and 2, respectively. Compared with GC‐MS, the application of the rapid and less demanding e‐nose seems an attractive alternative for this type of investigation.