Shelf-life Prediction of Chilled Penaeus vannamei Using Grey Relational Analysis and Support Vector Regression

Grey Correlation Analysis of Drying Characteristics and Quality of Hypsizygus marmoreus (Crab-Flavoured Mushroom) By-Products

The physical properties and nutritional quality of H. marmoreus by-products (HMB) dried by different methods were comprehensively evaluated by a rigorous statistical method of grey correlation analysis. The results indicated that different drying methods had significant impacts on the characteristics of HMB. Heat pump drying (HPD) was conducive to the preservation of protein and reducing sugar, and hot air drying (HAD) maintained a high content of total flavonoids. The highest fat, polysaccharide, and total phenolic contents were obtained by heated vacuum freeze-drying (H-VFD) treatment. The unheated vacuum freeze-drying (UH-VFD) treatment achieved bright colour, lacunose texture profile, and looser organization structure. The grey correlation analysis showed that UH-VFD and H-VFD had higher-weighted correlation degrees than HPD and HAD. HMB had many higher nutritional components than commodity specifications, especially protein, fat, polyphenols, and amino acids, and had potential applications in the food industry as functional foods and nutraceutical agents.

Construction of a photosynthetic rate prediction model for greenhouse strawberries with distributed regulation of light environment

In winter and spring, for greenhouses with larger areas and stereoscopic cultivation, distributed light environment regulation based on photosynthetic rate prediction model can better ensure good crop growth. In this paper, strawberries at flowering-fruit stage were used as the test crop, and the LI-6800 portable photosynthesis system was used to control the leaf chamber environment and obtain sample data by nested photosynthetic rate combination experiments under temperature, light and CO2 concentration conditions to study the photosynthetic rate prediction model construction method. For a small-sample, nonlinear real experimental data set validated by grey relational analysis, a photosynthetic rate prediction model was developed based on Support vector regression (SVR), and the particle swarm algorithm (PSO) was used to search the influence of the empirical values of parameters, such as the penalty parameter C, accuracy ε and kernel constant g, on the model prediction performance. The modeling and prediction results show that the PSO-SVR method outperforms the commonly used algorithms such as MLR, BP, SVR and RF in terms of prediction performance and generalization on a small sample data set. The research in this paper achieves accurate prediction of photosynthetic rate of strawberry and lays the foundation for subsequent distributed regulation of greenhouse strawberry light environment.

Comparison of freshness prediction method for salmon fillet during different storage temperatures

BACKGROUND

Many new forecasting models have been applied to fish freshness prediction like support vector regression (SVR) and radial basis function neural network (RBFNN). In this study, RBFNN, SVR, and Arrhenius models were established and compared for predicting and evaluating the quality of salmon fillets during storage at different temperatures, based on thiobarbituric acid (TBA), total volatile basic nitrogen (TVB-N), total viable counts (TVCs), K value, and sensory assessment (SA).

RESULTS

The TBA, TVB-N, TVC, and K values increased during storage whereas SA decreased. Residuals of the three models are random and irregular, indicating that these models were suitable for predicting the freshness of salmon fillets. The RBFNN predicted quality of salmon fillets stored at different temperatures with relative errors all within ±5% (except for the TVC value at day 6). Relative errors of the SVR model for predicting TVB-N and K value were within 10%, while the relative errors of the Arrhenius model fluctuated greatly (ranging from ±0.46 to ±38.29%) and most of it exceeded 10%. RBFNN model had the best predictive performance by comparing the residual and relative errors of the three models.

CONCLUSION

RBFNN is a promising method for predicting the freshness of salmon fillets stored at -2 to 10 °C in the cold chain. © 2021 Society of Chemical Industry.