The demand for analytical methods for fish product authenticity has increased dramatically, particularly in rapid and non- destructive food authentication. Near-infrared (NIR) handheld devices can meet this need for fast, reliable, non-destructive and in situ analysis. Aim of this study was to verify fish fillets species by using a pocket-sized NIR sensor, called SCiO, a handheld NIR spectrometer that can easily scan solid and liquid samples. The species were chosen from among the most commonly sold on the market as fresh. Samples were divided in two groups, one for calibration and one for validation. The first was performed to set up the instrument and the second to cross-validate model performance. The fish species were correctly identified with a global accuracy between 93.97% and 96.58% and were all confirmed by a validated method based on genetic marker. The samples correspond to the declaration, suggesting this method as a good screening approach to avoid fish frauds with good accuracy.

A New Approach Against Food Frauds: The Portable Near-Infrared Device for Fish Fillets Identification / Sciuto, Simona; Esposito, Giovanna; Dell’Atti, Luana; Rossi, Francesco; Vittoria Riina, Maria; Merlo, Gabriele; Magnani, Luca; Benso, Alfredo; Maria Bozzetta and Pier Luigi Acutis, Elena. - In: SCHOLARLY JOURNAL OF FOOD AND NUTRITION. - ISSN 2638-6070. - (2021), pp. 442-447. [10.32474/SJFN.2021.04.000177]

A New Approach Against Food Frauds: The Portable Near-Infrared Device for Fish Fillets Identification

Francesco Rossi;Alfredo Benso;
2021

Abstract

The demand for analytical methods for fish product authenticity has increased dramatically, particularly in rapid and non- destructive food authentication. Near-infrared (NIR) handheld devices can meet this need for fast, reliable, non-destructive and in situ analysis. Aim of this study was to verify fish fillets species by using a pocket-sized NIR sensor, called SCiO, a handheld NIR spectrometer that can easily scan solid and liquid samples. The species were chosen from among the most commonly sold on the market as fresh. Samples were divided in two groups, one for calibration and one for validation. The first was performed to set up the instrument and the second to cross-validate model performance. The fish species were correctly identified with a global accuracy between 93.97% and 96.58% and were all confirmed by a validated method based on genetic marker. The samples correspond to the declaration, suggesting this method as a good screening approach to avoid fish frauds with good accuracy.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2913533