Premium
Prediction of Microbial and Sensory Quality of Cold Smoked Atlantic Salmon ( Salmo salar ) by Electronic Nose
Author(s) -
Olafsdottir Gudrun,
Chanie Eric,
Westad Frank,
Jonsdottir Rosa,
Thalmann Claudia R.,
Bazzo Sandrine,
Labreche Said,
Marcq Pauline,
Lundby Frank,
Haugen John Erik
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.tb08330.x
Subject(s) - food spoilage , electronic nose , odor , salmo , food science , partial least squares regression , modified atmosphere , sensory analysis , cold storage , vacuum packing , chemistry , environmental science , shelf life , fishery , biology , mathematics , statistics , bacteria , horticulture , fish <actinopterygii> , genetics , organic chemistry , neuroscience
Quality changes of cold smoked salmon from 4 different smokehouses in Europe were monitored by a prototype gas‐sensor array system, the FishNose. Samples were stored in different packaging (vacuum and Modified Atmosphere Packaging [MAP]) for up to 4 wk under controlled storage conditions at 5 °C and 10 °C. Quality criteria based on sensory attributes (sweet/sour, off, and rancid odor), and total viable counts and lactic acid bacteria counts were established and used for classification of samples based on the responses of the FishNose. The responses of the gas‐sensors correlated well with sensory analysis of spoilage odor and microbial counts suggesting that they can detect volatile microbially produced compounds causing spoilage odors in cold‐smoked salmon during storage. The system is therefore ideal for fast quality control related to freshness evaluation of smoked salmon products. Partial least squares (PLS) regression models based on samples from single producer showed better performance than a global model based on products from different producers to classify samples of different quality.