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The performances of three non-destructive sensors, based on different principles, bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time domain reflectometry (TDR), were studied to discriminate between unfrozen and frozen-thawed fish. Bigeye tuna (Thunnus obesus) was selected as a model to evaluate these technologies. The addition of water and additives is usual in the fish industry, thus, in order to have a wide range of possible commercial conditions, some samples were injected with different water solutions (based on different concentrations of salt, polyphosphates and a protein hydrolysate solution). Three different models, based on partial least squares discriminant analysis (PLS-DA), were developed for each technology. This is a linear classification method that combines the properties of partial least squares (PLS) regression with the classification power of a discriminant technique. The results obtained in the evaluation of the test set were satisfactory for all the sensors, giving NIR the best performance (accuracy = 0.91, error rate = 0.10). Nevertheless, the classification accomplished with BIA and TDR data resulted also satisfactory and almost equally as good, with accuracies of 0.88 and 0.86 and error rates of 0.14 and 0.15, respectively. This work opens new possibilities to discriminate between unfrozen and frozen-thawed fish samples with different non-destructive alternatives, regardless of whether or not they have added water. [ABSTRACT FROM AUTHOR]

The common octopus (Octopus vulgaris) is a highly valued cephalopod species which is marketed with different grades of processing, such as frozen, cooked or even canned, and is likely to be mislabeled. Some molecular methods have been developed for the authentication of these products, but they are either labor-intensive and/or require specialized equipment and personnel. This work describes a newly designed rapid, sensitive and easy-to-use method for the detection of Octopus vulgaris in food products, based on Recombinase Polymerase Amplification (RPA) and a detection using a Lateral Flow assay (LFA). After studying several gene markers, a system of primers and nfo-probe was designed in the COI (Cytochrome Oxidase I) region and was successfully tested in 32 reference samples (covering 14 species) and 32 commercial products, after optimization. The method was also validated in a ring trial with eight European laboratories and represents a useful tool for food authenticity control at all levels of the value chain. [ABSTRACT FROM AUTHOR]

Cephalopods are very relevant food resources. The common cuttlefish (Sepia officinalis) is highly appreciated by consumers and there is a lack of rapid methods for its authentication in food products. We introduce a new minor groove binding (MGB) TaqMan real-time PCR (Polymerase Chain Reaction) method for the authentication of S. officinalis in food products to amplify a 122 base pairs (bp) fragment of the mitochondrial COI (Cytochrome Oxidase I) region. Reference and commercial samples of S. officinalis showed a threshold cycle (Ct) mean of 14.40, while the rest of the species examined did not amplify, or showed a significantly different Ct (p < 0.001). The calculated efficiency of the system was 101%, and the minimum DNA quantity detected was 10−4 ng. No cross-reactivity was detected with any other species, thus, the designed method differentiates S. officinalis from other species of the genus Sepia and other cephalopod species and works for fresh, frozen, grilled, cooked and canned samples of Sepia spp. The method has proved to be reliable and rapid, and it may prove to be a useful tool for the control of fraud in cuttlefish products. [ABSTRACT FROM AUTHOR]

The performances of three non-destructive sensors, based on different principles, bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time domain reflectometry (TDR), were studied to discriminate between unfrozen and frozen-thawed fish. Bigeye tuna ( Thunnus obesus ) was selected as a model to evaluate these technologies. The addition of water and additives is usual in the fish industry, thus, in order to have a wide range of possible commercial conditions, some samples were injected with different water solutions (based on different concentrations of salt, polyphosphates and a protein hydrolysate solution). Three different models, based on partial least squares discriminant analysis (PLS-DA), were developed for each technology. This is a linear classification method that combines the properties of partial least squares (PLS) regression with the classification power of a discriminant technique. The results obtained in the evaluation of the test set were satisfactory for all the sensors, giving NIR the best performance (accuracy = 0.91, error rate = 0.10). Nevertheless, the classification accomplished with BIA and TDR data resulted also satisfactory and almost equally as good, with accuracies of 0.88 and 0.86 and error rates of 0.14 and 0.15, respectively. This work opens new possibilities to discriminate between unfrozen and frozen-thawed fish samples with different non-destructive alternatives, regardless of whether or not they have added water.