1,720,971 research outputs found
Near infrared spectral fingerprinting: A tool against origin-related fraud in the sector of processed anchovies
Full text linkIn the present study near-infrared (NIR) spectroscopy was used to assess the geographical traceability of salted ripened anchovies, whose raw product originated from fishing areas of Morocco, Spain, Tunisia, and Croatia. Two different products were tested: semi-finished and finished salted anchovies. The development and optimization of combined discrimination models based on orthogonal partial least square-discriminant analysis successfully led to the identification of the geographical origin of both anchovy datasets with >98% sensitivity, >99% specificity, and >99% accuracy on average. While NIR absorption bands related to proteins and degradation compounds highly characterized samples from Morocco and those of unsaturated lipids and derivatives globally contradistinguished anchovies from Tunisia, absorptions of both protein and lipid compounds were responsible for the discrimination of samples from Croatia and Spain. The proposed method is particularly helpful to guarantee the authenticity of salted ripened anchovies and, thus, to deter commercial frauds throughout the fish value chain and ensure traceability along the whole food chain
Analisi chemiometrica degli isotopi stabili e delle terre rare per l’autenticazione d’origine geografica e del metodo di produzione di branzini (Dicentrarchus Labrax L.).
No full textMulti-elemental composition of botanical preparations and probabilistic evaluation of toxic metals and metalloids intake upon dietary exposure
No full textThe aim of this study was to evaluate the inorganic elemental composition (49 elements) of 29 botanical preparations obtained from fruits, leaves, peels, seeds, roots, fungi, and spirulina by using inductively coupled-mass spectrometry and a mercury analyzer. Simultaneously, the risk associated with the chronic dietary exposure to 12 toxic metals and metalloids among the European population was evaluated by using a probabilistic approach based on Monte Carlo simulations. The analysis revealed worrying intake levels of Al, As, and Ni, primarily stemming from the consumption of spirulina-, peel-, and leaf-based botanicals by younger age groups. The intake of As from all analyzed botanicals posed a significant risk for infants, yielding margins of exposure (MOEs) below 1, while those deriving from peel-based botanicals raised concerns across all age groups (MOEs = 0.04-2.3). The consumption of peel-based botanicals contributed substantially (13-130%) also to the tolerable daily intake of Ni for infants, toddlers, and children, while that of spirulina-based botanicals raised concerns related to Al intake also among adults, contributing to 11-176% of the tolerable weekly intake of this element. The findings achieved underscore the importance of implementing a monitoring framework to address chemical contamination of botanicals, thus ensuring their safety for regular consumers
Feasibility of Near-Infrared Spectroscopy in the Classification of Pig Lung Lesions
No full textRespiratory diseases significantly affect intensive pig farming, causing production losses and increased antimicrobial use. Accurate classification of lung lesions is crucial for effective diagnostics and disease management. The integration of non-destructive and rapid techniques would be beneficial to enhance overall efficiency in addressing these challenges. This study investigates the potential of near-infrared (NIR) spectroscopy in classifying pig lung tissues. The NIR spectra (908-1676 nm) of 101 lungs from weaned pigs were analyzed using a portable instrument and subjected to multivariate analysis. Two distinct discriminant models were developed to differentiate normal (N), congested (C), and pathological (P) lung tissues, as well as catarrhal bronchopneumonia (CBP), fibrinous pleuropneumonia (FPP), and interstitial pneumonia (IP) patterns. Overall, the model tailored for discriminating among pathological lesions demonstrated superior classification performances. Major challenges arose in categorizing C lungs, which exhibited a misclassification rate of 30% with N and P tissues, and FPP samples, with 30% incorrectly recognized as CBP samples. Conversely, IP and CBP lungs were all identified with accuracy, precision, and sensitivity higher than 90%. In conclusion, this study provides a promising proof of concept for using NIR spectroscopy to recognize and categorize pig lungs with different pathological lesions, offering prospects for efficient diagnostic strategies
Country of origin label monitoring of musky and common octopuses (Eledone spp. and Octopus vulgaris) by means of a portable near-infrared spectroscopic device
Full text linkModern 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
Isotope Fingerprinting as a Backup for Modern Safety and Traceability Systems in the Animal-Derived Food Chain
Full text linkIn recent years, due to the globalization of food trade and certified agro-food products, the authenticity and traceability of food have received increasing attention. As a result, opportunities for fraudulent practices arise, highlighting the need to protect consumers from economic and health damages. In this regard, specific analytical techniques have been optimized and implemented to support the integrity of the food chain, such as those targeting different isotopes and their ratios. This review article explores the scientific progress of the last decade in the study of the isotopic identity card of food of animal origin, provides the reader with an overview of its application, and focuses on whether the combination of isotopes with other markers increases confidence and robustness in food authenticity testing. To this purpose, a total of 135 studies analyzing fish and seafood, meat, eggs, milk, and dairy products, and aiming to examine the relation between isotopic ratios and the geographical provenance, feeding regime, production method, and seasonality were reviewed. Current trends and major research achievements in the field were discussed and commented on in detail, pointing out advantages and drawbacks typically associated with this analytical approach and arguing future improvements and changes that need to be made to recognize it as a standard and validated method for fraud mitigation and safety control in the sector of food of animal origin
Advances in troubleshooting fish and seafood authentication by inorganic elemental composition
Full text linkThe demand for fish and seafood is growing worldwide. Meanwhile, problems related to the integrity and safety of the fishery sector are increasing, leading legislators, producers, and consumers to search for ways to effectively protect themselves from fraud and health hazards related to fish consumption. What is urgently required now is the availability of reliable, truthful, and reproducible methods assuring the correspondence between the real nature of the product and label declarations accompanying the same product during its market life. The evaluation of the inorganic composition of fish and seafood appears to be one of the most promising strategies to be exploited in the near future to assist routine and official monitoring operations along the supply chain. The present review article focuses on exploring the latest scientific achievements of using the multi-elemental composition of fish and seafood as an imprint of their authenticity and traceability, especially with regards to the geographical origin. The scientific literature of the last 10 years focusing on the analytical determination and statistical elaboration of elemental data (alone or in combination with methodologies targeting other compounds) to verify the identity of fishery products is summarized and discussed
Filling gaps in animal welfare assessment through metabolomics
Full text linkSustainability has become a central issue in Italian livestock systems driving food business operators to adopt high standards of production concerning animal husbandry conditions. Meat sector is largely involved in this ecological transition with the introduction of new label claims concerning the defense of animal welfare (AW). These new guarantees referred to AW provision require new tools for the purpose of authenticity and traceability to assure meat supply chain integrity. Over the years, European Union (EU) Regulations, national, and international initiatives proposed provisions and guidelines for assuring AW introducing requirements to be complied with and providing tools based on scoring systems for a proper animal status assessment. However, the comprehensive and objective assessment of the AW status remains challenging. In this regard, phenotypic insights at molecular level may be investigated by metabolomics, one of the most recent high-throughput omics techniques. Recent advances in analytical and bioinformatic technologies have led to the identification of relevant biomarkers involved in complex clinical phenotypes of diverse biological systems suggesting that metabolomics is a key tool for biomarker discovery. In the present review, the Five Domains model has been employed as a vademecum describing AW. Starting from the individual Domains—nutrition (I), environment (II), health (III), behavior (IV), and mental state (V)—applications and advances of metabolomics related to AW setting aimed at investigating phenotypic outcomes on molecular scale and elucidating the biological routes most perturbed from external solicitations, are reviewed. Strengths and weaknesses of the current state-of-art are highlighted, and new frontiers to be explored for AW assessment throughout the metabolomics approach are argued. Moreover, a detailed description of metabolomics workflow is provided to understand dos and don'ts at experimental level to pursue effective results. Combining the demand for new assessment tools and meat market trends, a new cross-strategy is proposed as the promising combo for the future of AW assessment
Classification of transformed anchovy products based on the use of element patterns and decision trees to assess traceability and country of origin labelling
No full textQuadrupole inductively coupled plasma mass spectrometry (Q-ICP-MS) and direct mercury analysis were used to determine the elemental composition of 180 transformed (salt-ripened) anchovies from three different fishing areas before and after packaging. To this purpose, four decision trees-based algorithms, corresponding to C5.0, classification and regression trees (CART), chi-square automatic interaction detection (CHAID), and quick unbiased efficient statistical tree (QUEST) were applied to the elemental datasets to find the most accurate data mining procedure to achieve the ultimate goal of fish origin prediction. Classification rules generated by the trained CHAID model optimally identified unlabelled testing bulk anchovies (93.9% F-score) by using just 6 out of 52 elements (As, K, P, Cd, Li, and Sr). The finished packaged product was better modelled by the QUEST algorithm which recognised the origin of anchovies with F-score of 97.7%, considering the information carried out by 5 elements (B, As, K. Cd, and Pd). Results obtained suggested that the traceability system in the fishery sector may be supported by simplified machine learning techniques applied to a limited but effective number of inorganic predictors of origin
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