Bay scallops (Argopecten irradians; A. irradians) are shellfish with high nutritional and economic value. However, nutritional studies on A. irradians with different shell colors are limited. This study examines the hazardous, nutritional, and taste-contributing compounds during the growth of A. irradians with different shell colors. During the growth of A. irradians, the hazardous contents were below the standard limit. Changes in the nutritional and taste-contributing compounds between months were more significant than shell color. Bay scallops had more fats, total fatty acids, and taste-contributing compounds in August and more proteins, essential fatty acids, vitamin D, vitamin B₁₂, Cu, and Zn in September and October. In October, the golden shell color strain had more proteins, essential fatty acids, vitamin D, vitamin B₁₂, Cu, and Zn, while the purple shell color strain had more taste-contributing compounds. A. irradians had better taste in August and higher nutritional value in September and October. In October, the golden shell color strain has higher nutritional value, and the purple shell color strain has better commercial value and taste. The correlation analysis indicates that the nutritional quality of bay scallops is affected by age (months), shell color, and seawater environment.

In addition to its flavor and nutritional value, the origin of kelp has become a crucial factor influencing consumer choices. Nevertheless, research on kelp’s origin traceability by volatile organic compound (VOC) analysis is lacking, and the application of deep learning in this field remains scarce due to its black-box nature. To address this gap, we attempted to identify the origin of kelp by analyzing its VOCs in conjunction with explainable deep learning. In this work, we identified 115 distinct VOCs in kelp samples using gas chromatography coupled with ion mobility spectroscopy (GC-IMS), of which 68 categories were discernible. Consequently, we developed a comprehensible one-dimensional convolutional neural network (1D-CNN) model that incorporated 107 VOCs exhibiting significant regional disparities (p < 0.05). The model successfully discerns the origin of kelp, achieving perfect metrics across accuracy (100%), precision (100%), recall (100%), F1 score (100%), and AUC (1.0). SHapley Additive exPlanations (SHAP) analysis highlighted the impact of features such as 1-Octen-3-ol-M, (+)-limonene, allyl sulfide-D, 1-hydroxy-2-propanone-D, and (E)-2-hexen-1-al-M on the model output. This research provides deeper insights into how critical product features correlate with specific geographic information, which in turn boosts consumer trust and promotes practical utilization in actual settings.

Heavy metal concentrations (Fe, Mn, Cu, Zn, As, iAs, Cr, Cd, Pb and Hg) in five commercially important bivalves from the coastal areas of Yellow sea and Bohai sea, China were determined, and their potential human health risks were evaluated. Results showed that there was a significant difference among these metal contents (p<0.05) in different bivalve species, except for Hg. The contents of all metals were below the maximum allowable limit regulated by China Food Standard Agency. The health risks from most bivalves’ consumption were safe with the THQ being much lower than 1.0, except for Cd in some scallop samples. The average EDI/PTDI of Cd from Chlamys farreri consumption was 92%, and the average EDI of Cu and Cd in Crassostrea gigas occupied 61% and 62% of PTDI. Thus a moderate consumption of C. farreri and C. gigas was suggested to reduce the potential health risk of heavy metals.

In this work, a rapid method for the simultaneous determination of N and S in seafood was established based on a solid sampling absorption-desorption system coupled with a thermal conductivity detector. This setup mainly includes a solid sampling system, a gas line unit for controlling high-purity oxygen and helium, a combustion and reduction furnace, a purification column system for moisture, halogen, SO₂, and CO₂, and a thermal conductivity detector. After two stages of purging with 20 s of He sweeping (250 mL/min), N₂ residue in the sample-containing chamber can be reduced to <0.01% to improve the device’s analytical sensitivity and precision. Herein, 100 s of heating at 900 °C was chosen as the optimized decomposition condition. After the generated SO₂, H₂O, and CO₂ were absorbed by the adsorption column in turn, the purification process executed the vaporization of the N-containing analyte, and then N₂ was detected by the thermal conductivity cell for the quantification of N. Subsequently, the adsorbed SO₂ was released after heating the SO₂ adsorption column and then transported to the thermal conductivity cell for the detection and quantification of S. After the instrumental optimization, the linear range was 2.0–100 mg and the correlation coefficient (R) was more than 0.999. The limit of detection (LOD) for N was 0.66 μg and the R was less than 4.0%, while the recovery rate ranged from 95.33 to 102.8%. At the same time, the LOD for S was 2.29 μg and the R was less than 6.0%, while the recovery rate ranged from 92.26 to 105.5%. The method was validated using certified reference materials (CRMs) and the measured N and S concentrations agreed with the certified values. The method indicated good accuracy and precision for the simultaneous detection of N and S in seafood samples. The total time of analysis was less than 6 min without the sample preparation process, fulfilling the fast detection of N and S in seafood. The establishment of this method filled the blank space in the area of the simultaneous and rapid determination of N and S in aquatic product solids. Thus, it provided technical support effectively to the requirements of risk assessment and detection in cases where supervision inspection was time-dependent.