Effect of salinity stress on respiratory metabolism, glycolysis, lipolysis, and apoptosis in Pacific oyster (Crassostrea gigas) during depuration stage.

BACKGROUND: Depuration is an important process performed to ensure the safety of oyster consumption, and the effect of salinity stress on physiological and ecological characteristics of oyster remains unknow. In this study, the simulated depuration of Crassostrea gigas was performed with the salinities varying from ±10% to ±20% away from that of production area (26, 28, 32, 35, and 38 g L-1 ), as well as respiratory metabolism, glycolysis, lipolysis, and apoptosis were analyzed. RESULTS: (i) The oxygen consumption rate, ammonia discharge rate and enzyme activities related to respiratory metabolism were decreased significantly at salinities of 38 g L-1 , indicating that salinity stress triggered the abnormal respiratory metabolism of C. gigas, further, glycolysis was enhanced. (ii) Glycogen decomposition, lactic acid increase, and fatty acid composition modifications were caused by adenosine monophosphate (AMP)-activated protein kinase (AMPK) -mediated during salinity stress. (iii) There was a clear decrease of the condition index and meat yield of C. gigas after 72 h of depuration, especially in salinity 38 g L-1 . (iv) Salinity stress would lead to the increase of cytochrome c levels, then cause apoptosis of C. gigas, while heat shock protein 70 (HSP70) would interfere with this process. CONCLUSION: Salinity stress had a significant effect on the physiological and ecological response of C. gigas during the depuration process, including respiratory metabolism, glycolysis, lipolysis, and apoptosis. In general, the low depuration salinity fluctuation (±10%) is helpful to maintain quality of C. gigas, as well as the optimal depuration time was 48 h. © 2021 Society of Chemical Industry.

Temperature effects on the nutritional quality in Pacific oysters (Crassostrea gigas) during ultraviolet depuration.

BACKGROUND: Oysters are mainly consumed in the raw form, so it is important to get rid of bacteria and other harmful substances. Ultraviolet (UV) sterilization depuration is a commonly used method and does not produce chemical residues or act directly on shellfish, resulting in minimal adverse effects on flavor. This study simulated the industrial depuration process using UV sterilization to depurate Pacific oysters (Crassostrea gigas). The effects of different temperatures (15, 20, and 25 °C) on the quality and taste components of C. gigas were investigated by measuring changes in physiological and biochemical indexes in C. gigas tissue samples. RESULTS: At the end of depuration, no oyster mortality occurred, but it was up to 55% at 25 °C at 84 h. Glycogen content decreased the most at 25 °C at 48 h. The fatty acid content was higher at 20 and 25 °C. Succinic acid content decreased significantly and was higher at 20 and 25 °C at 48 h with no significant difference. Total free amino acid (FAA) content was significantly higher at 20 °C, however, there were no significant differences in nucleotide content at any temperature at 48 h. Adenylate energy charge (AEC) values decreased, with higher values at 15 and 25 °C, and equivalent umami concentration (EUC) values increased, with higher values at 20 and 25 °C. CONCLUSION: Considering the changes in flavor substances and mortality rate, 20 °C is the appropriate temperature for UV sterilization depuration of C. gigas to produce better edible quality. © 2021 Society of Chemical Industry.

Evaluation indicators of Ruditapes philippinarum nutritional quality.

To access the nutritional quality of the Ruditapes philippinarum, a comprehensive quality evaluation procedure is always important to be established. In this study, fifteen nutritional quality evaluation indicators of R. philippinarum from 7 months were analyzed, and the most important indicators were determined using a combination of multiple chemometric methods such as correlation analysis (CA), principal component analysis (PCA), and system cluster analysis (SCA). Significant differences in nutritional quality were observed across the 7 months, as per the ANOVA results (P < 0.05). The coefficient of variation values for the fifteen evaluation indicators for R. philippinarum across 7 months was 1.67-43.47%. The CA results revealed that some indicators were correlated to each other within a certain range. Four principal components with eigen-values > 1 were obtained with PCA, and a cumulative contribution of 92.11% was achieved. In addition, four essential quality indicators were extracted using SCA. Using these four indicators, a simple and efficient procedure can be applied for quality control in aquaculture.

Efficient Production of High-Quality Infrared-Assisted Spouted Bed-Dried Areca taro Based on the Drying Temperature and Cutting Size Control.

The purpose of this study was to apply infrared-assisted spouted bed drying (IRSBD) technology for Areca taro drying and to investigate the effects of different parameters on its drying quality. Specifically, in order to determine the suitable conditions for IRSBD, the effects of different drying temperatures (45 °C, 50 °C, 55 °C, and 60 °C) and cutting sizes (6 × 6 × 6 mm, 8 × 8 × 8 mm, 10 × 10 × 10 mm, and 12 × 12 × 12 mm) on the drying characteristics, temperature uniformity, and quality properties (including colour, rehydration ratio, total phenol content, total flavonoid content, and antioxidant activity) of Areca taro were studied. The results showed that the optimal drying condition was the sample with a cutting size of 10 × 10 × 10 mm and drying at 50 °C, which yielded the dried sample with the best colour, highest total phenol and flavonoid contents, maximum antioxidant capacity, and rehydration ratio.

Recent Advances in Astaxanthin as an Antioxidant in Food Applications.

In recent years, astaxanthin as a natural substance has received widespread attention for its potential to replace traditional synthetic antioxidants and because its antioxidant activity exceeds that of similar substances. Based on this, this review introduces the specific forms of astaxanthin currently used as an antioxidant in foods, both in its naturally occurring forms and in artificially added forms involving technologies such as emulsion, microcapsule, film, nano liposome and nano particle, aiming to improve its stability, dispersion and bioavailability in complex food systems. In addition, research progress on the application of astaxanthin in various food products, such as whole grains, seafood and poultry products, is summarized. In view of the characteristics of astaxanthin, such as insolubility in water and sensitivity to light, heat, oxygen and humidity, the main research trends of astaxanthin-loaded systems with high encapsulation efficiency, good stability, good taste masking effect and cost-effectiveness are also pointed out. Finally, the possible sensory effects of adding astaxanthin to food aresummarized, providing theoretical support for the development of astaxanthin-related food.

Recent Advances in Lycopene for Food Preservation and Shelf-Life Extension.

In recent years, there has been increasing concern about the safety of additives used to extend the shelf-life of food products. As a result, lycopene, a natural phytochemical compound, has attracted attention, as it has been demonstrated to be a potential alternative to traditional artificial antioxidants, with significant health benefits when applied to food preservation. Based on this, this review introduces the specific forms of lycopene currently used as an antioxidant in foods, both in its naturally occurring forms in fruits and vegetables and in artificially added forms involving technologies such as composite coating, active film packaging, emulsion, and microcapsules. In addition, it also provides a comprehensive summary of the effects and progress of lycopene in the preservation of different types of food products, such as meat, seafood, oil, dairy products, fruits, and vegetables, in the last decade. At last, it also points out the limitations of lycopene, including its insolubility in water, dark color, and high sensitivity to heat or light, as well as the potential solutions to load lycopene on suitable carriers, such as combining lycopene with antimicrobial substances or other actives, in order to broaden its applications as an antioxidant in future foods.

Deep Learning-Based Intelligent Apple Variety Classification System and Model Interpretability Analysis.

In this study, series networks (AlexNet and VGG-19) and directed acyclic graph (DAG) networks (ResNet-18, ResNet-50, and ResNet-101) with transfer learning were employed to identify and classify 13 classes of apples from 7439 images. Two training datasets, model evaluation metrics, and three visualization methods were used to objectively assess, compare, and interpret five Convolutional Neural Network (CNN)-based models. The results show that the dataset configuration had a significant impact on the classification results, as all models achieved over 96.1% accuracy on dataset A (training-to-testing = 2.4:1.0) compared to 89.4-93.9% accuracy on dataset B (training-to-testing = 1.0:3.7). VGG-19 achieved the highest accuracy of 100.0% on dataset A and 93.9% on dataset B. Moreover, for networks of the same framework, the model size, accuracy, and training and testing times increased as the model depth (number of layers) increased. Furthermore, feature visualization, strongest activations, and local interpretable model-agnostic explanations techniques were used to show the understanding of apple images by different trained models, as well as to reveal how and why the models make classification decisions. These results improve the interpretability and credibility of CNN-based models, which provides guidance for future applications of deep learning methods in agriculture.

An Intelligent Method for Predicting Pacific Oyster (Crassostrea gigas) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information.

To achieve a non-destructive and rapid detection of oyster freshness, an intelligent method using deep learning fused with malondialdehyde (MDA) and total sulfhydryl groups (SH) information was proposed. In this study, an "MDA-SH-storage days" polynomial fitting model and oyster meat image dataset were first built. AleNet-MDA and AlxNet-SH classification models were then constructed to automatically identify and classify four levels of oyster meat images with overall accuracies of 92.72% and 94.06%, respectively. Next, the outputs of the two models were used as the inputs to "MDA-SH-storage days" model, which ultimately succeeded in predicting the corresponding MDA content, SH content and storage day for an oyster image within 0.03 ms. Furthermore, the interpretability of the two models for oyster meat image were also investigated by feature visualization and strongest activations techniques. Thus, this study brings new thoughts on oyster freshness prediction from the perspective of computer vision and artificial intelligence.

The flavor profile changes of Pacific oysters (Crassostrea gigas) in response to salinity during depuration.

The effect of salinity on taste and odor characteristics of Pacific oyster (Crassostrea gigas) during depuration was investigated in this study. In combination with free amino acids (FAAs), 5'-nucleotides, and organic acids, electronic tongues were measured to evaluate the changes in taste-related compounds. Gas chromatography-ion mobility spectrometry (GC-IMS) and electronic nose were used to analyze the odor compounds of C. gigas at different depuration salinities. The results showed that bitter substances in C. gigas significantly decreased as salinity decreased. The equivalent umami concentration (EUC) was highest at a salinity of 29 g/L. The GC-IMS results were consistent with the electronic-nose test results. After low-salinity depuration, aldehyde and ketone levels were significantly reduced, and furan concentrations increased. In addition, multivariate analysis was used to determine the correlation between each component and flavor profile differences due to depuration at various salinities. Overall, salinity of 29 g/L could be optimal for oyster depuration.

A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning.

A generic intelligent tomato classification system based on DenseNet-201 with transfer learning was proposed and the augmented training sets obtained by data augmentation methods were employed to train the model. The trained model achieved high classification accuracy on the images of different quality, even those containing high levels of noise. Also, the trained model could accurately and efficiently identify and classify a single tomato image with only 29 ms, indicating that the proposed model has great potential value in real-world applications. The feature visualization of the trained models shows their understanding of tomato images, i.e., the learned common and high-level features. The strongest activations of the trained models show that the correct or incorrect target recognition areas by a model during the classification process will affect its final classification accuracy. Based on this, the results obtained in this study could provide guidance and new ideas to improve the development of intelligent agriculture.

Effects of Mixing Ratio and pH on the Electrostatic Interactions of Hydrolyzed Alaska Pollock Protein and κ-Carrageenan.

In order to widen the application of Alaska Pollock salt-soluble protein and improve the properties of κ-carrageenan (KC), we evaluated whether the electrostatic interaction between KC and hydrolyzed protein (HP) improved the solubility of salt-soluble proteins and assessed acid hydrolysis-induced changes in the viscosity of KC. Moreover, we determined the effects of pH, KC, and HP mixing weight ratio (R, from 1:8 to 8:1) on the properties of mixtures. ζ-Potential results demonstrated that there were electrostatic interactions between KC and HP. Results of solution transmission and precipitation diagram analysis demonstrated that increasing the ratio of KC in KC-HP mixtures expanded the pH range of HP water solubility and prevented HP from precipitating, even at pH values around the pI. The highest viscosity of the solution measured by rheology analysis occurred at pH 3.5, and the ratio of KC:HP (R) was 1:1. These findings suggested that Alaska Pollock protein could be modified appropriately for use in acidic solutions and semisolid foods; moreover, KC-HPs have a potential application as thickeners in acid condition. PRACTICAL APPLICATION: These findings suggested that Alaska Pollock protein could be modified appropriately for use in acidic solutions and semisolid foods and that KC-HP may have applications as a new acid thickener.