Country of origin label monitoring of musky and common octopuses (Eledone spp. and Octopus vulgaris) by means of a portable near-infrared spectroscopic device. (August 2022)

• Record Type: Journal Article
• Authors: Varrà, Maria Olga; Ghidini, Sergio; Fabrile, Maria Pia; Ianieri, Adriana; Zanardi, Emanuela
• Is Part Of: Food control. Volume 138(2022)
• Publication Date: 2022-08
• Subjects: Rapid methods -- Machine learning -- Chemometrics -- Food authenticity -- Geographical origin -- Cephalopods; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2022.109052
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: Modern analytical techniques using miniaturized and portable near infrared (NIR) spectroscopy instruments are particularly suited for assessing the authenticity of fishery products since meeting the requirements of rapidity, eco-friendliness, cost-effectiveness, and easiness of application. The objective of the present study was to verify the suitability of use of a portable and ultra-compact NIR spectrometer combined with machine learning to characterize the geographic origin of two octopus species. Replicate NIR spectra (908.1–1676.2 nm) of 118 musky and 29 common octopus specimens ( Eledone spp. and Octopus vulgaris ) from Portuguese Atlantic or Spanish Mediterranean fishing areas were recorded, pre-processed and elaborated via the following classification algorithms: orthogonal partial least square discriminant analysis (OPLS-DA), logistic regression (LR), random forest (RF), support vector machine (SVM), and multilayer perceptron-artificial neural network (MLP-ANN). When 7-fold cross validation was performed on 75% of data, the results showed that linear tools (OPLS-DA and LR) were the most powerful and stable techniques in recognizing the origin of both octopus species (mean sensitivity, specificity, accuracy, and precision values above 98%). During the external validation phase OPLS-DA, SVM, and MLP-ANN performed better for common octopuses, while LR and MLP-ANN for musky octopuses. The achieved outcomes suggest the combination of portable NIR spectroscopy and machine learning as a promising plan of action to be adopted for the creation of an integrated analytical platform with capabilities for automated data recording, processing, and reporting, which may be helpful for on-site and in-line monitoring of fishery products. Highlights: Untargeted authentication of cephalopods was investigated. The geographic origin of musky and common octopuses was assessed by NIR spectroscopy. The performances of a miniaturized and portable instrument were evaluated. Five different machine learning algorithms were compared.