Chemical composition, antioxidant activity and sensory evaluation of five different species of brown edible seaweeds. Food Research International, 66. pp. 36-44. ISSN 0963-9969 Available from: http://eprints.uwe.ac.uk/24525 We recommend you cite the published version. The publisher's URL is: http://dx.
A technique was developed to predict the freshness of packaged sliced chicken breast employing a nondestructive visible and short-wavelength near infrared (SW-NIR) spectroscopy method. Spectra were recorded at 0, 7 and 14 days using a camera, spectral filter (400-1000 nm) and a halogen flood lighting system which were developed and calibrated for the purpose. Physicochemical, biochemical and microbiological properties such as moisture (x w ), water activity (a w ), pH, total volatile basic nitrogen (TVB-N), ATP breakdown compounds (K 1 values) and mesophilic bacteria (cfu g -1 ) were determined to predict freshness degradation. The spectra obtained were related to the storage time of the samples. The best wavelengths for modeling freshness were 413, 426, 449, 460, 473, 480, 499, 638, 942, 946, 967, 970 and 982 nm. A linear correlation was found between the visible and SW-NIR spectroscopy and parameters such as microbiological counts, K 1 and T-VBN indexes.
Consumers want fresh food with a long shelf-life, which in 2010, resulted in an important increase in packaged and processed food. This is especially important for fishery products due to their highly perishable nature. One problem is that it is not possible to measure freshness in packaged food only using the visible spectrum. Moreover, the detection of freshness is a complex problem as fish has different tissues with different biodegradation processes. Therefore, it would be especially interesting to have a non-destructive method to evaluate the shelf-life of packed processed fish. This paper proposes a method for detecting expired packaged salmon. Firstly, this method uses hyperspectral imaging spectroscopy (HIS) using visible and SW-NIR wavelengths. Secondly, a classification of different salmon tissues is carried out by image segmentation. Finally, classifications of expired or non expired salmon are realized with the PLS-DA statistical multivariate method due to the large amount of captured data. In a similar way, spectral data and the physicochemical, biochemical and microbiological properties of salmon are correlated using partial least squares (PLS). The result obtained has a classification success rate of 82.7% in cross-validation from real commercial samples of salmon. Therefore, this is a promising technique for the non-destructive detection of expired packaged smoked salmon.
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