Numerical studies of reactive polymer flows in porous materials

This dissertation presents a new numerical model of reactive polymer flow in heterogeneous porous media. A moment representation of the log-normal polymer molecular weight distribution is used to model polymer as a multi-component species. Three leading moments are used to simulate the polymer transport and reaction processes in a two-dimensional porous medium. A new operator splitting technique that allows the moment equations for polymerization to be incorporated into a finite-difference transport model is developed. The novelty of this approach is the use of two different dependent variables (for the transport versus reaction parts of the problem). It is significant from a physical standpoint because previous techniques did not allow us to observe the full evolution of polymer molecular weights in space and time. In this dissertation, two types of flows are examined. The first is the injection of a polymerizing fluid into a heterogeneous material containing low viscosity fluid (e.g., air or water). Simulations show that, depending on the Damkohler number, preferential loss of material permeability can occur in either low or high permeability regions. Because this effect dictates subsequent flow patterns, this result suggests that front stability can be controlled through proper design of the flow dynamics versus reaction dynamics. The formation of steady viscous fingers was observed, which is a fundamentally different phenomenon than previously observed transient viscous fingering formed during displacements. It is affected by the competition between reaction and convection, which allows the behavior to be correlated with the Damkohler number. A critical Damkohler number exists, above which steady-state conditions are not observed. The critical Damkohler number is affected by the Peclet number and permeability field. The second type of simulation is the injection of polymerizing fluids under conditions that lead to viscous instabilities. Results show the Damkohler number again to be a critical parameter. In both cases, the scale and structure of the material heterogeneities have a significant effect on the resulting flow. These research results provide important information for various polymer processing applications

Technical exploration of the origins, storage periods and species identification of Boletus bainiugan

To prevent fraud in Boletus bainiugan commodities, this study provides the market with two fast and stable identification models for accurate identification of Boletus bainiugan origins, storage periods and species. Partial least squares discrimination analysis (PLS-DA), Support vector machine (SVM), Residual convolutional neural network (ResNet) and Data-driven soft independent modeling of class analogy (DD-SIMCA) models were built by combining with Fourier transform near-infrared spectroscopy (FT-NIR). The results show that the ResNet model is significant in solving the Boletus bainiugan origin identification problems. The ResNet model had the best performance and highest accuracy compared to the PLS-DA and SVM models. The DD-SIMCA model was the preferred method for the one-class classification problem, achieving an accuracy of over 96% for the Boletus bainiugan storage period and species identification. Non-target class classification accuracy reached 100%. In summary, FT-NIR combined with ResNet and DD-SIMCA models were able to solve the related identification problems of Boletus bainiugan with more satisfactory results

Research Progress on Elements of Wild Edible Mushrooms

Wild edible mushrooms are distributed all over the world and are delicious seasonal foods, rich in polysaccharides, amino acids, vitamins, and other components. At the same time, they contain many essential trace elements and are highly enriched in heavy metals (compared to green plants and cultivated edible mushrooms). Consumers may be exposed to health risks due to excessive heavy metals in the process of consumption. This is also one of the important factors affecting the import and export of edible mushrooms, which is of great concern to consumers and entry and exit inspection and quarantine departments. In this paper, the contents of four essential trace elements of iron, manganese, zinc, and copper and four harmful heavy metals of cadmium, lead, mercury, and arsenic in nearly 400 species of wild edible mushrooms from 10 countries are reviewed. It was found that the factors affecting the elemental content of edible mushrooms are mainly divided into internal and external factors. Internal is mainly the difference in species element-enrichment ability, and external is mainly environmental pollution and geochemical factors. The aim is to provide a reference for the risk assessment of edible mushrooms and their elemental distribution characteristics

Simulation of suitable habitats and geographic tracing based on medicinal and edible plants with Gastrodia elata Bl. as an example

Gastrodia elata Bl. (G. elata) is a herb with important medicinal and edible values. Wild G. elata has been subjected to overharvesting and habitat fragmentation due to people’s pursuit of wild quality, resulting in resource depletion. Furthermore, yield cannot keep up with demand, and the contradiction between supply and demand has become increasingly prominent, giving rise to unscrupulous merchants selling counterfeit and shoddy herbs and confusing origins. Therefore, it is necessary to find methods to predict suitable habitats for G. elata to standardize cultivation and authenticate the origins. The research utilized the MaxEnt model and ATR-FTIR spectra to explore the distribution of G. elata’s suitable habitats and to authenticate G. elata from various origins, followed by the Mantel test to analyze the association between environmental variables and infrared spectra. Under the current climate scenario, G. elata’s suitable habitats were primarily found in provinces such as Yunnan, Sichuan, Guizhou, Tibet, and Shaanxi, among which Yunnan had the largest suitable habitat (21.75 %). The ResNet model based on simultaneous 3DOCS spectral data had the best authentication performance, with 100 % accuracy in both the train and test sets. The infrared spectra of G. elata samples from different origins significantly correlate with Prec09, demonstrating that this environmental variable may affect the accumulation of gastrodin components in G. elata. This research gives a scientific basis for the artificial cultivation of G. elata, avoiding resource waste (both human and material) and losses caused by blind introduction. It is conducive to exploring and protecting wild G. elata resources

Feature Fusion of ICP-AES, UV-Vis and FT-MIR for Origin Traceability of Boletus edulis Mushrooms in Combination with Chemometrics

Origin traceability is an important step to control the nutritional and pharmacological quality of food products. Boletus edulis mushroom is a well-known food resource in the world. Its nutritional and medicinal properties are drastically varied depending on geographical origins. In this study, three sensor systems (inductively coupled plasma atomic emission spectrophotometer (ICP-AES), ultraviolet-visible (UV-Vis) and Fourier transform mid-infrared spectroscopy (FT-MIR)) were applied for the origin traceability of 184 mushroom samples (caps and stipes) in combination with chemometrics. The difference between cap and stipe was clearly illustrated based on a single sensor technique, respectively. Feature variables from three instruments were used for origin traceability. Two supervised classification methods, partial least square discriminant analysis (FLS-DA) and grid search support vector machine (GS-SVM), were applied to develop mathematical models. Two steps (internal cross-validation and external prediction for unknown samples) were used to evaluate the performance of a classification model. The result is satisfactory with high accuracies ranging from 90.625% to 100%. These models also have an excellent generalization ability with the optimal parameters. Based on the combination of three sensory systems, our study provides a multi-sensory and comprehensive origin traceability of B. edulis mushrooms

Species discrimination and total polyphenol prediction of porcini mushrooms by fourier transform mid-infrared (FT-MIR) spectrometry combined with multivariate statistical analysis

The plateau specialty agricultural products, wild porcini mushrooms, have great value both as a superb cuisine and as a potential medication. Due to quality different between species added with the fraud behavior in sales process, make poor quality or poisonous sample inflow into the market, which pose a health risk for consumers, but also disrupted the mushroom market. Traditional analysis way is time-consuming and laborious. Therefore, the aim of this study is to develop a way using fourier transform mid-infrared (FT-MIR) spectrometry and data fusion strategies for the fast and accurate species discrimination and predict amount of total polyphenol in four porcini mushrooms. The t-distributed stochastic neighbor embedding based on mid-level data fusion showed two species of Boletus edulis and B. umbriniporus have been identified. The order of correct rate of PLS-DA models was mid-level data fusionq (100%) > mid-level data fusione (97.06%) = mid-level data fusionv (97.06%) = stipes (97.06%) > low-level data fusion (94.12%) > caps (91.18%). The order of correct rate of grid-search support vector machine models was low-level data fusion (100%) > caps (94.12%) > stipes (91.18%), and the order of particle swarm optimization support vector machine was low-level data fusion (100%) > caps (97.06%) > stipes (88.24%). The mid-level data fusionq and low-level data fusion had best discrimination accuracy (100%) allowing each mushroom classed into its real species, which could be used for accurate discrimination of samples. B. edulis mushrooms had highest total polyphenol, with 14.76 mg/g dw and 17.33 in caps and stipes mg/g dw, respectively. The phenols were easier to accumulate in the caps in Leccinum rugosiceps (1.03) and B. tomentipes (1.19), and the opposite phenomenon is observed in B. edulis (0.85) and B. umbriniporus (0.95). The correlation coefficient and residual predictive deviation of best prediction model were 86.76% and 2.40%, respectively, indicating that that there is good relevance between FT-MIR and total polyphenol content, which could be used to predict roughly polyphenols content in mushrooms

Discrimination of Boletaceae mushrooms based on data fusion of FT-IR and ICP–AES combined with SVM

In this study, the individual and data fusion of Fourier transform infrared (FT-IR) spectroscopy and inductively coupled plasma atomic emission spectrometry (ICP–AES) were used for the discrimination of five species of Boletaceae mushrooms with the aid of support vector machine (SVM). First, the original FT-IR spectra of 230 samples with different species were preprocessed and optimized by second derivative (2D), Savitzky–Golay filter (15:1) and standardized normal variate. Second, the datasets of FT-IR spectra and ICP–AES were integrated, and the low-level data fusion strategy was used to classify different species mushrooms. Third, the latent variables of elements concentration and FT-IR spectra were extracted by partial least square discriminant analysis and two datasets were fused into a new matrix. Finally, the classification models were established by SVM. Compared with single spectroscopic technique, the mid-level data fusion strategy can provide better result. Especially, the accuracy of correct classification of samples in calibration and test sets were 100.00% and 98.68%, respectively. The results demonstrated that the mid-level data fusion of FT-IR and ICP–AES can provide higher synergic effect for the discrimination of different species Boletaceae mushrooms, which could be benefited for the further authentication and quality control of edible mushrooms