Residue analysis and dietary risk assessment of abamectin in fresh corn, bitter melon, and Fritillaria

To clarify the residue behavior and possible dietary risk of abamectin in fresh corn, bitter melon, and Fritillaria, a method was developed for the simultaneous determination of abamectin residues in fresh corn, bitter melon, and Fritillaria by QuEChERS (quick, easy, cheap, effective, rugged, safe) ultra-performance liquid chromatography-tandem mass spectrometry. The mean recovery of abamectin in fresh corn, bitter melon, and Fritillaria was 86.48%-107.80%, and the relative standard deviation was 2.07%-10.12%. The detection rates of abamectin residues in fresh corn, bitter melon, and Fritillaria were 62.50%, 87.50%, and 80.00%, respectively. The residues of abamectin in fresh corn, bitter melon, and Fritillaria were not more than 0.020, 0.019, and 0.087 mg/kg, respectively. Based on these results, dietary risk assessment showed that the risk content of abamectin residues in long- and short-term dietary exposure for Chinese consumers was 61.57% and 0.41%-1.11%, respectively, indicating that abamectin in fresh corn, bitter melon, and Fritillaria in the market would not pose a significant risk to consumers.

Sensory Evaluation and Model Prediction of Vacuum-Packed Fresh Corn during Long-Term Storage

The postharvest shelf life of fresh corn largely depends on the packaging method and storage temperature. This study investigated the effect of vacuum packaging (VP) with high-barrier (HB) or ordinary (OR) nylon/nylon/polypropylene (PP) composite films and the impact of storage temperature (4, 25, and 38 °C) on the shelf life of fresh corn. The sensory quality and color changes of the corn were evaluated, indicating a significant improvement in the glossiness (GL), sourness (SO), and color changes compared to corn packaged using OR films. The results showed that the HB films preserved corn freshness under refrigerated and normal temperature storage conditions, delaying color changes and SO development. A shelf-life model was established based on the Arrhenius equation. The predicted values of the corn at different temperatures were compared with the experimental data, indicating that the model could accurately predict the shelf life. The shelf life observed via sensory evaluation was more than 50% shorter than the results obtained by instrumental measurements. Therefore, sensory evaluation could be applied to determine shelf life and avoid food waste.

Hyperspectral detection of fresh corn peeling damage using germinating sparse classification method

Peeling damage reduces the quality of fresh corn ear and affects the purchasing decisions of consumers. Hyperspectral imaging technique has great potential to be used for detection of peeling-damaged fresh corn. However, conventional non-machine-learning methods are limited by unsatisfactory detection accuracy, and machine-learning methods rely heavily on training samples. To address this problem, the germinating sparse classification (GSC) method is proposed to detect the peeling-damaged fresh corn. The germinating strategy is developed to refine training samples, and to dynamically adjust the number of atoms to improve the performance of dictionary, furthermore, the threshold sparse recovery algorithm is proposed to realize pixel level classification. The results demonstrated that the GSC method had the best classification effect with the overall classification accuracy of the training set was 98.33%, and that of the test set was 95.00%. The GSC method also had the highest average pixel prediction accuracy of 84.51% for the entire HSI regions and 91.94% for the damaged regions. This work represents a new method for mechanical damage detection of fresh corn using hyperspectral image (HSI).