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Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.

Hybrid flow-shop scheduling based on the parallel sequential movement mode (HFSP-PSMM) is an extended application of hybrid flow-shop scheduling that ensures that the equipment works continuously during the processing cycle. However, current research has only investigated the flow-shop scheduling of single-equipment processing, and ignores the effect of auxiliary time. Therefore, this paper investigates a multi-equipment hybrid flow-shop scheduling problem based on the parallel sequential movement mode and considers the setup time and handling time. The mathematical model of the HFSP-PSMM was developed with the handling and makespan numbers as the optimization objectives. The NSGA-II-V based on the NSGA-II was designed by combining the problem characteristics. New crossover, mutation, and selection strategies were proposed and variable neighborhood search operations were implemented for the optimal set of Pareto solutions. Finally, through an algorithm comparison, performance testing, and an example simulation, the effectiveness of the NSGA-II-V for solving the HFSP-PSMM was verified.

Over the past several decades, vegetation restoration has been carried out extensively in South China. Theoretically, the process of vegetation restoration is usually accompanied by changes in soil properties. However, the effects of vegetation restoration on soil hydraulic properties are poorly documented in humid subtropical China. In this study, we compared soil hydraulic properties across three undisturbed subtropical forests, i.e., Pinus massoniana forest (PF), mixed Pinus massoniana/broad-leaved forest (MF), and monsoon evergreen broad-leaved forest (BF), which represented a vegetation restoration sequence in South China. Our results showed that vegetation restoration decreased the bulk density while increasing the total porosity and the soil organic matter (SOM). The clay content and capillary porosity of soil in the middle- and late-recovery-stage forests were significantly higher than those in the early stage, which was consistent with the soil water-holding capacity. The saturated hydraulic conductivity (KS) values of BF were always significantly higher than those of the other forests. In the whole soil profile, the water-holding capacity and KS in the topsoil (above 30 cm depth) were significantly higher than those in the deep soil for all forests. Further analyses indicated that the SOM was the main factor that affected KS, and the relationship of them could be fitted by a linear equation. Overall, our study revealed vegetation restoration ameliorates soil hydraulic properties in humid subtropical China. And the role of SOM in improving soil hydraulic properties should be emphasized in future forest ecosystem management.