Valorization of groundnut shell via pyrolysis: Product distribution, thermodynamic analysis, kinetic estimation, and artificial neural network modeling

Biotechnological trends and optimization of Arachis hypogaea residues valorization: A bibliometric analysis and comprehensive review

This review thoroughly explores the valorization of Arachis hypogaea (groundnut) residues for producing high-value bioproducts such as biofuels, biocatalysts, biochar, and nanomaterials through processes like pyrolysis, gasification, and enzymatic conversion. Optimization techniques, including Response Surface Methodology (RSM), Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), have led to significant enhancements in bioproduct yields. A detailed bibliometric analysis spanning from 2000 to the present highlights key research trends and influential contributors, reflecting the increasing global focus on groundnut residue valorization. The study emphasizes the environmental and economic benefits, such as improved waste management, reduced greenhouse gas emissions, and contributions to a circular bioeconomy. It advocates for policy frameworks that support these biotechnological advancements and recommends further research on process scalability, long-term stability, and life cycle assessments to ensure the environmental and economic viability of groundnut residue utilization in sustainable development.

Prediction of MSW pyrolysis products based on a deep artificial neural network

Pyrolysis is a promising method for recovering resources and energy products from municipal solid waste (MSW). Predicting MSW pyrolysis products is crucial for establishing an efficient pyrolysis system for resource recovery. In this study, a database was established based on MySQL to record relevant information on MSW pyrolysis, which includes the MSW ultimate analysis results, proximate analysis results, parameters of pyrolysis operation and yields of pyrolysis products, etc. Based on the database and with help of a deep artificial neural network (ANN) which contains 10 hidden layers, a prediction model was successfully established to predict the yield of char, liquid and gas products from MSW pyrolysis. The results showed that the coefficients of determination for predicting the yields of char, liquid and gas from the MSW pyrolysis are 0.841, 0.84, and 0.85, respectively; these values demonstrate an accuracy comparable to that achieved for product prediction from single biomass, indicating a successful model performance. The results also show that ash content and temperature are the most important input factors influencing the outputs, namely, yields of char, liquid and gas. The results of this study can help to achieve a more efficient design of the pyrolysis system and improve the recovery of the desired pyrolysis products.

Comparative study for sorption of arsenic on peanut shell biochar and modified peanut shell biochar

In this study, arsenite [As(III)] and arsenate [As(V)] removal efficiency of peanut shell biochar (PSB) and modified peanut shell biochar (MPSB) was compared in aqueous solutions. The modification was carried out with KMnO₄ and KOH. Sorption efficiency of MPSB was relatively higher than PSB at pH 6 for As(III) (86%) and for As(V) (91.26%) for initial concentration of 1 mg/L, adsorbent dose of 0.5 g/L and 240 min equilibrium time at 100 rpm. Freundlich isotherm and pseudo-second order kinetic model suggested possible multilayer chemisorption. Fourier transform infrared spectroscopy showed that -OH, C-C, CC and C-O-C groups contributed significantly in adsorption for both PSB and MPSB. Thermodynamic study showed that the adsorption process was spontaneous and endothermic. Regeneration studies revealed that PSB and MPSB can be successfully used for three cycles. This study established that peanut shell is a low-cost, environment friendly and efficient biochar for removal of arsenic from water.

Neural network in food analytics

Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.

A cationic amino acid polymer nanocarrier synthesized in supercritical CO2 for co-delivery of drug and gene to cervical cancer cells

The present study was undertaken to investigate the ability of a drug curcumin-loaded polymer to inhibit the growth of cervical cancer cells by enhancing the anti-cancer efficiency of curcumin. We synthesized poly(methacryloyl beta-alanine) (PMBA) as a nanocarrier by radical polymerization in supercritical CO₂. The results showed that the curcumin encapsulated and folic acid (FA)-treated PMBA (Poly@Cur-FA) for 24 h activated the reactive oxygen species-mediated programmed cell death machinery in HeLa cells. This remarkable effect of Poly@Cur-FA treatment was visualized using different fluorescent probes, which demonstrated that the Poly@Cur-FA treatment disrupted the cell membrane, as also supported by scanning electron microscopy observations. The effect of Poly@Cur-FA dispersion on the cells was observed under a transmission electron microscope. Further, the HeLa cells were treated with the polymer encapsulated curcumin and Bcl2 siRNA (Pol-Cur-siRNA) for 24 h, which effectively suppressed the Bcl2 and simulated the autophagic pathway. This co-delivery system was designed to inhibit curcumin efflux and can enhance the treatment efficacy by targeting multiple signaling pathways, including cell cycle, apoptotic, and autophagic pathways. Collectively, the Pol-Cur-siRNA system appears to offer an efficient combinational therapeutic strategy that might overcome the problems associated with the chemosensitivity against the standard synthetic anti-cancer drugs. To support the experimental data, an artificial neural network model was developed to foresee the drug and gene release behaviors.

Predicting the quality of soybean seeds stored in different environments and packaging using machine learning

The monitoring and evaluating the physical and physiological quality of seeds throughout storage requires technical and financial resources and is subject to sampling and laboratory errors. Therefore, machine learning (ML) techniques could help optimize the processes and obtain accurate results for decision-making in the seed storage process. This study aimed to analyze the performance of ML algorithms from variables monitored during seed conditioning (temperature and packaging) and storage time to predict the physical and physiological quality of stored soybean seeds. Data analysis was performed using the Artificial Neural Networks, decision tree algorithms REPTree and M5P, Random Forest, and Linear Regression. In predicting seed quality, the combination of the input variables temperature and storage time for REPTree and Random Forest algorithms outperformed the linear regression, providing higher accuracy indices. Among the most important results, it was observed for apparent specific mass that T + P + ST, T + ST, P + ST, and ST had the highest r means and the lowest MAE means, however, Person's r coefficient for these inputs was 0.63 and the MAE between 9.59 to 10.47. The germination results for inputs T + P + ST and T + ST had the best results (r = 0.65 and r = 0.67, respectively) in the ANN, REPTree, M5P and RF models. Using computational intelligence algorithms is an excellent alternative to predict the quality of soybean seeds from the information of easy-to-measure variables.