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Poster.-- 20th Meeting of the International Council for Near-Infrared Spectroscopy (ICNIRS) - NIR2021, A handheld near infrared (NIR) spectroscopy device, with a wavelength range from 900 nm to 1650 nm and coupled with two Partial Least-Squares Discriminant Analysis (PLS-DA) models, has been used to demonstrate its applicability as a proof of concept for quality monitoring of bigeye tuna (Thunnus obesus). First, a classification model was created to discriminate between injected and non-injected tuna samples. Then, a second classification model was developed to discriminate between non-injected and each water and additives treatment used. The results were promising, showing both models good results in the validation dataset. The first model, with 8 latent variables (LV), had an error-rate of 0.08 and an accuracy value of 0.93. It showed a good discrimination between injected and non-injected samples. The second model, with 10 LV, presented an error rate of 0.15 and an accuracy of 0.88. The discrimination between treatments was good even when protein hydrolysate solutions were used (sensitivity = 0.81; specificity = 0.99; precision = 0.87), a case which is typically hard to detect with accurate destructive analysis. This work opens new possibilities for onsite inspection in the fish industry, where NIR could be used as a complementary tool for the detection of water solutions in tuna, The research leading to these results has received funding from the Interreg Atlantic Area Project EAPA_87/2016, SEATRACES: Smart Traceability and Labeling ToolBox for a Sustainable Seafood Production. The authors acknowledge the Basque Government - Department of Economic Development, Sustainability and Environment – Directorate of Quality and Food Industries for the scholarship of S. Nieto-Ortega. B. Teixeira acknowledges the Portuguese Foundation for Science and Technology (FCT), the European Social Fund (FSE) and the Ministry of Education and Science for supporting a grant (Ref. SFRH/BPD/92929/2013). A. Velasco acknowledges the Spanish Ministry of Science, innovation and technology for the contract of technical staff (PTA2016-12254-I)

Poster.-- 20th Meeting of the International Council for Near-Infrared Spectroscopy (ICNIRS) - NIR2021 No

17th Scandinavian Symposium on Chemometrics 2021, Comwell Hvide Hus, Aalborg, Denmark, September 6-9, 2021, Fish is susceptible to be affected by practices that can lead to mislabelling [1], as the non-reported water addition during harvest, processing, and storage to compensate moisture losses [2]. Though this water addition must be included in the label if it makes up more than 5 % of the weight of the product [3], this declaration is not always added, leading to mislabelling problems. Non-destructive technologies, such as Time Domain Reflectometry (TDR), in combination with chemometrics, have emerged as powerful tools for the assessment of food quality parameters. TDR can probe the dynamics of dipolar molecules like water subjected to an alternating electric field, allowing a precise characterization of the dielectric properties of the sample. Changes in the amount of water in the muscle are expected to be detected by this technology [4]. In this work, a TDR sensor, (RFQ-SCAN®, Sequid company), coupled with two different classifiers (PLS-DA and SIMCA), have been used to detect the injection of different water solutions in fresh bigeye tuna (Thunnus obesus). A total of 11 dorsal and ventral loins were purchased in July 2018 and July 2019 (7 loins in 2018 and 4 loins in 2019). They were sectioned in portions of 500 g weight, and randomly distributed for the control and different injection treatments. The different treatments consisted of the injection of water with different additives: A. 3 % salt; B. 3 % salt + 3 % polyphosphates; C. 3 % salt + 5 % polyphosphates; D. 3 % salt + 5 % hydrolysate prepared from four-spot megrim Lepidorhombus boscii; E. a polyphosphate commercial solution. TDR signals were pre-processed using several techniques: SNV with and without detrend, Savitzky-Golay 1st and 2º derivatives and different combinations. All of them were also mean centred. Then, data were divided into a calibration and a validation dataset using the Duplex algorithm [5]. Both classifiers separated correctly control samples, with a precision of 0.94 in PLS-DA and 0.91 in SIMCA. However, they failed in the differentiation of some of the treatments, such as A and C (0.00 and 0.25 of precision with PLS-DA and 0.33 and 0.57 in SIMCA respectively). The precision in separation of treatments B, D and E gave good results with PLS-DA (0.75, 0.80 and 0.75 respectively) but poorer results with SIMCA (0.67, 0.67 and 0.50). As a conclusion, both classifiers work properly to separate control from the injected samples, but PLS-DA worked better to differentiate some of the injected treatments

Poster.-- 9th International Symposium on Recent Advances in Food Analysis, November 5-8 2019, Prague

4 Pags., 2 figs., This work has been carried out in the frame of the European project InsideFood (Integrated sensing and imaging devices for designing, monitoring and controlling microstructure of foods). The aim of this project is to provide technological solutions for exploring the microstructure of foods, by the combination and application of different non-destructive techniques such as X-ray CT, OCT, MRI, NMR, TRS and SRS, aimed at online sensing of food microstructure, water status, texture and optical properties. Magnetic Resonance Imaging (MRI) has been used in order to detect watercore disorder in three different varieties of apples, which are non commercialized due to their watercore development problems. These three varieties are: Ascara2, Rebellón and Tempera, which were grown under favourable conditions for the development of such disorder. At first step, MRI has been used for screening the apples population in order to detect those ones which had watercore problems. Then, an image analysis has been done in all these apples, in order to check the evidences of such disorder. Several methods have been used: Texture analysis, Principal Components Analysis (PCA) and Cluster Analysis