Origin determination of the Eastern oyster (Crassostrea virginica) using a combination of whole-body compound-specific isotope analysis and heavy metal analysis

Exploring the feasibility of multi-elements coupled with chemometrics for discriminating the geographical origins of oysters (Crassostrea ariakensis)

This study explored the efficacy of multi-elements combined with chemometrics to discriminate the geographical origins of oysters (Crassostrea ariakensi). We determined 52 elements in 166 samples from four regions along the southeast coast of China. Significant regional variations of 51 elements were revealed (P < 0.05), while the principal component analysis (PCA) provided no clear regional delineations. The training models (n = 117) established on linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), and random forest (RF) uniformly achieved 100% predictive accuracy. The cross-validation accuracies of the final models (n = 166) derived from LDA, PLS-DA, and RF were 100%, 100%, and 99.4%, respectively. Even with the models simplified to 8 elements (Zn, Al, K, Cd, Cu, Rb, B, and Ag), high predictive and cross-validation accuracies were maintained, underscoring the robustness and algorithm flexibility of elemental profiling for accurately identifying the geographical origins of oysters.

Combined Use of Fatty Acid Profiles and Elemental Fingerprints to Trace the Geographic Origin of Live Baits for Sports Fishing: The Solitary Tube Worm (Diopatra neapolitana, Annelida, Onuphidae) as a Case Study

Diopatra neapolitana Delle Chiaje, 1841 (Annelida, Onuphidae) is one of the most exploited polychaete species in European waters, particularly in Ria de Aveiro, a coastal lagoon in mainland Portugal, where the overexploitation of this resource has led to a generalized decline of local populations. In an attempt to reduce the impact of harvesting, several management actions were implemented, but illegal poaching still fuels a parallel economy that threatens the sustainable use of this marine resource. The present study evaluated the combination of fatty acid profiles and elemental fingerprints of the whole body and jaws, respectively, of D. neapolitana collected from four harvesting locations within Ria de Aveiro in order to determine if their geographic origin could be correctly assigned post-harvesting. Results showed that both fatty acid profiles and elemental fingerprints differ significantly among locations, discriminating the geographic origin with higher accuracy when combining these two natural barcodes than when employing each individually. The present work can, therefore, contribute to the implementation of an effective management plan for the sustainable use of this marine resource, making it possible to detect if D. neapolitana was sourced from no-take zones and if it was collected from the place of origin claimed by live bait traders.

Explainable machine learning for predicting the geographical origin of Chinese Oysters via mineral elements analysis

The traceability of geographic origin is essential for guaranteeing the quality, safety, and protection of oyster brands. However, the current outcomes of traceability lack credibility as they do not adequately explain the model's predictions. Consequently, we conducted a study to evaluate the efficacy of utilizing explainable machine learning combined with mineral elements analysis. The study findings revealed that 18 elements have the ability to determine regional orientation. Simultaneously, individuals should pay closer attention to the potential risks associated with oyster consumption due to the regional differences in essential and toxic elements they contain. Light gradient boosting machine (LightGBM) model exhibited indistinguishable performance, achieving flawless accuracy, precision, recall, F1 score and AUC, with values of 96.77%, 96.43%, 98.53%, 97.32% and 0.998, respectively. The SHapley Additive exPlanations (SHAP) method was used to evaluate the output of the LightGBM model, revealing differences in feature interactions among oysters from different provinces. Specifically, the features Na, Zn, V, Mg, and K were found to have a significant impact on the predictive process of the model. Consistent with existing research, the use of explainable machine learning techniques can provide insights into the complex connections between important product attributes and relevant geographical information.

A Simple Laser-Induced Breakdown Spectroscopy Method for Quantification and Classification of Edible Sea Salts Assisted by Surface-Hydrophilicity-Enhanced Silicon Wafer Substrates

Salt, one of the most commonly consumed food additives worldwide, is produced in many countries. The chemical composition of edible salts is essential information for quality assessment and origin distinction. In this work, a simple laser-induced breakdown spectroscopy instrument was assembled with a diode-pumped solid-state laser and a miniature spectrometer. Its performances in analyzing Mg and Ca in six popular edible sea salts consumed in South Korea and classification of the products were investigated. Each salt was dissolved in water and a tiny amount of the solution was dropped and dried on the hydrophilicity-enhanced silicon wafer substrate, providing homogeneous distribution of salt crystals. Strong Mg II and Ca II emissions were chosen for both quantification and classification. Calibration curves could be constructed with limits-of-detection of 87 mg/kg for Mg and 45 mg/kg for Ca. Also, the Mg II and Ca II emission peak intensities were used in a k-nearest neighbors model providing 98.6% classification accuracy. In both quantification and classification, intensity normalization using a Na I emission line as a reference signal was effective. A concept of interclass distance was introduced, and the increase in the classification accuracy due to the intensity normalization was rationalized based on it. Our methodology will be useful for analyzing major mineral nutrients in various food materials in liquid phase or soluble in water, including salts.

Quantitative determination of heavy metal contaminants in edible soft tissue of clams, mussels, and oysters

Aquatic environments are important sources of healthy and nutritious foods; however, clams, mussels, and oysters (the bivalves most consumed by humans) can pose considerable health risks to consumers if contaminated by heavy metals in polluted areas. These organisms can accumulate dangerously high concentrations of heavy metals (e.g., Cd, Hg, Pb) in their soft tissues that can then be transferred to humans following ingestion. Monitoring contaminants in clams, mussels and oysters and their environments is critically important for global human health and food security, which requires reliable measurement of heavy-metal concentrations in the soft tissues. The aim of our present paper is to provide a review of how heavy metals are quantified in clams, mussels, and oysters. We do this by evaluating sample-preparation methods (i.e., tissue digestion / extraction and analyte preconcentration) and instrumental techniques (i.e., atomic, fluorescence and mass spectrometric methods, chromatography, neutron activation analysis and electrochemical sensors) that have been applied for this purpose to date. Application of these methods, their advantages, limitations, challenges and expected future directions are discussed.