Increasing demand for protein diversity has prompted widespread investigation into alternative protein foods, yet these products frequently exhibit suboptimal flavor, texture, and mouthfeel. In this review, sustainability is emphasized as a driving factor for pursuing novel molecular interventions that address persistent sensory limitations. Detailed attention is given to recent advances in precision fermentation, gene editing, and innovative structuring techniques that act directly on plant protein matrices, aiming to minimize off-flavors, reduce astringency, and improve texture. Emphasis is placed on bridging fundamental molecular insights with practical outcomes: enzymatic treatments and microbial pathways that alter volatile and phenolic profiles, as well as processing conditions that enable fibrous network formation and enhanced creaminess. Various case studies demonstrate how integrated approaches—combining flavor modulation with textural refinements—can produce more acceptable meat and dairy analogs. Although considerable progress has been made, continuing knowledge gaps remain in understanding protein–polyphenol interactions, lipid oxidation pathways, and consumer-driven design principles. By synthesizing cutting-edge studies and proposing directions for future work, this review underscores the significance of deploying biochemical, genetic, and engineering perspectives in tandem. It is anticipated that a broader uptake of these strategies will lead to alternative proteins that meet both sustainability goals and consumer expectations for taste and sensory quality.

Extensive research is currently dedicated to creating biodegradable packaging materials that match the qualities of traditional synthetic packaging materials. Among these options, polylactic acid (PLA) is noteworthy. PLA is a renewable‐source‐derived thermoplastic polymer with excellent barrier properties, mechanical strength, and a strong safety profile. To enhance food product shelf life, active packaging materials, incorporating functional ingredients like antimicrobials, have gained prominence. Peppermint essential oil is one such active ingredient, offering potential improvements in preserving food freshness and safety. This study's objective is to craft antimicrobial, biodegradable food packaging materials by blending peppermint oil into PLA films. Various peppermint oil concentrations (1.25%, 1.875%, and 2.5% w/v) were blended with PLA to assess their impact on opacity, water vapor permeability (WVP), mechanical and thermal properties, and antimicrobial characteristics. Higher peppermint oil concentrations increased opacity, making them advantageous for light‐sensitive food items. These films reduced WVP without affecting PLA's thermal stability. Antimicrobial effectiveness was evaluated against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), showing inhibition with 1.875% and 2.5% w/v peppermint oil concentrations. Compared to control, PLA films with peppermint oil extended the shelf life of packaged chicken breast meats from 3 to 8 days. As a result, incorporating peppermint oil into PLA films presents a promising solution for advanced antimicrobial and biodegradable food packaging.Highlights: The incorporation of peppermint oil resulted in higher opacity values. Peppermint incorporated PLA films showed antimicrobial activity. Peppermint oil addition decreased water vapor permeability of PLA films. Peppermint oil added PLA films increased the shelf life of chicken meat up to 8 days.

The global atmospheric concentration of carbon dioxide (CO₂) has exceeded 420 ppm. Adsorption-based carbon capture technologies, offer energy-efficient, sustainable solutions. Relying on classical adsorption models like Langmuir to predict CO₂ uptake presents limitations due to the need for case-specific parameter fitting. To address this, the present study introduces a universal machine learning (ML) framework using multiple algorithms—Generalized Linear Model (GLM), Feed-forward Multilayer Perceptron (DL), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosted Trees (GBT)—to reliably predict CO₂ adsorption capacities across diverse zeolite structures and conditions. By compiling over 5700 experimentally measured adsorption data points from 71 independent studies, this approach systematically incorporates critical factors including pore size, Si/Al ratio, cation type, temperature, and pressure. Rigorous Cross-Validation confirmed superior performance of the GBT model (R² = 0.936, RMSE = 0.806 mmol/g), outperforming other ML models and providing comparable performance with classical Langmuir model predictions without separate parameter calibration. Feature importance analysis identified pressure, Si/Al ratio, and cation type as dominant influences on adsorption performance. Overall, this ML-driven methodology demonstrates substantial promise for accelerating material discovery, optimization, and practical deployment of zeolite-based CO₂ capture technologies.