This exploratory product development study compared the final product performance of a laboratory prepared soft candy (LPS) containing separately optimized pulsed electric field (PEF) modified starch and PEF-ultrasound (PEF+US) treated ginger with three commercially available ginger candies. The LPS showed the highest measured total phenolic content (62.01mg GAE/g), gingerol concentration (867.59µg/g), and in vitro radical scavenging activity with 51.53% DPPH inhibition and 52.31% ABTS inhibition. It also showed lower hardness, gumminess, and chewiness, greater adhesiveness, and higher springiness than the commercial products, while a semi trained panel of 10 assessors assigned it the highest mean overall acceptability score. FTIR revealed a distinct matrix fingerprint, while HPLC and colorimetric assays confirmed the higher bioactive content of the LPS. These findings characterize the performance of the complete LPS formulation relative to the commercial products, while the contributions of the individual pretreatments and formulation components were not assessed separately.
Background Oxygen ingress and distribution significantly impact wine quality, affecting oxidation, colour stability, and aroma development. Quantifying effective oxygen diffusion coefficients (Deff) in wine-like matrices is essential for optimizing storage conditions and antioxidant strategies. Objective Ascorbic acid and inactive dry yeasts are among the approaches used to limit oxygen ingress in wine systems. This work evaluates these strategies by measuring Deff in synthetic wine matrices containing ascorbic acid or inactive dry yeasts (Longevity® and Glutastar®), and by assessing the VisiSens 2D imaging system as a non-intrusive tool for spatiotemporal monitoring of oxygen diffusion dynamics. Methods Synthetic wines (10% ethanol, pH 3.5, Fe(III) 3 ppm, Cu(II) 0.3 ppm, potassium tartrate) were prepared with either ascorbic acid or inactivated yeasts. Samples were placed in sealed bottles with a sensor foil strip spanning the liquid and headspace. The VisiSens 2D system recorded oxygen saturation at multiple depths over time at 20 ± 1 °C. Deff maps were generated from measured oxygen profiles in liquid and gas phases. Results Ascorbic acid caused rapid, uniform depletion of dissolved O₂ and suppressed diffusion, especially near the air–liquid interface. Inactive yeasts reduced oxygen more gradually and exhibited spatial heterogeneity: Longevity® showed delayed localized diffusion; Glutastar® produced complex diffusion peaks mid-depth. Headspace diffusion was similarly constrained under antioxidant treatments. Gas–liquid oxygen transfer rates mirrored these patterns. Conclusions The VisiSens 2D imaging system effectively quantifies oxygen diffusion and gradients. Inactivated dry yeasts offer a promising, more gradual alternative to chemical antioxidants for managing oxygen in wine-like systems, particularly relevant for low-sulfite or natural wine production.
Natural polysaccharides are increasingly valued as structure-programmable biopolymers that bridge texture design and nutritional functionality. However, conventional extraction routes relying on prolonged heating, harsh chemical conditions, or multi-step purification often compromise molecular integrity, limit yield, and incur high energy consumption, ultimately constraining the functional performance of the final products. This review systematically examines four representative physical field-assisted extraction technologies (ultrasound-, microwave-, pulsed electric field-, and high-pressure processing-assisted extraction) for polysaccharide extraction and functional enhancement. We analyze their working mechanisms, extracted polysaccharides’ structure-function relationships, synergies with multi-field cascades and green solvents, and establish a comparative framework for application-oriented technology selection. Physical field-assisted extraction enables in-situ structural modulation during extraction, outperforming conventional methods. Synergistic strategies further boost extraction efficiency and functional outcomes. Despite advances, industrial scale-up faces challenges in energy uniformity, equipment design, and process economics. Future directions include AI-driven precision optimization, function-oriented cascades, and circular economy integration. These technologies redefine polysaccharide extraction paradigms for sustainable production of high-value, functionally tailored biopolymers.
Non-continuous rice-water contact (NRWC) cooking has emerged as a promising strategy for regulating starch leaching and improving the quality attributes of cooked rice. However, the mechanisms by which different spatial separation architectures influence starch migration and structural evolution remain poorly understood. This study aimed to investigate the effect of emerging non-continuous rice-water contact strategies on the starch leaching behavior and morphological properties in cooked rice. Cooked rice and rice soup were prepared using various spatial separation cooking methods, including full immersion, percolation, steam-lift cooking, and microwave-assisted steam-lift cooking (MW-SC). The microstructural characteristics, particle size distribution, starch-protein framework, short-range ordered structure, crystal structure, thermal property, and water migrations were analyzed. Compared to the control rice treated with traditional immersion, MW-SC significantly intensified starch leaching and water content. Examination of rice structure revealed the cooking rice treated by MW-SC displayed a three-dimensional porous network characterized by pronounced surface roughness, with decreased average particle size. Confocal laser scanning microscopy analysis demonstrated that the starch-protein framework was pronounced damaged. Crucially, while the crystalline polymorph and functional groups of rice starch remain intact, MW-SC induces a marked reduction in relative crystallinity and short-range order. Macroscopically, the hardness, and adhesiveness of cooking rice was reduced by MW-SC with water absorption and starch solubilization. Our findings establish a direct causal link between spatial separation architectures and starch leaching behavior, providing a mechanistic basis for next-generation cooking appliances designed to precisely engineer rice texture and nutritional profile.
The bioactivity and Traditional Chinese Medicine (TCM) attributes of durian (Durio zibethinus L.) have attracted worldwide research attention. Compared to conventional thermal methods, non‑thermal technologies are increasingly being explored to better preserve its thermolabile bioactive compounds. Techniques such as pulsed electric fields, high‑pressure processing, and modified atmosphere packaging can inactivate microorganisms and enzymes without substantial heat, thus maintaining key functional components. These include sulfur-containing volatiles potentially linked to its “warm” TCM property and flavonoid glycosides relevant to its “blood-nourishing” effect. Additionally, waste valorization strategies enable more sustainable utilization of durian by-products. This review summarizes recent advances in non‑thermal processing of durian with emphasis on retaining its bioactivity and TCM-related properties, discusses the valorization of processing waste, and suggests future research directions.
To enhance the molding accuracy and printability of brown rice flour gel (BRFG) in 3D food printing, this study systematically investigated the rheological properties of BRFG at various concentrations (20%, 24%, 28%, 32%, and 36%) and evaluated its printing performance under different process parameters (layer height, nozzle height, nozzle diameter, and printing speed). Rheological tests revealed that a moderate consistency coefficient (K), power-law index (n), and high energy storage modulus (G ') synergistically enhanced the printability of BRFG, with the optimal concentration identified as 28%. The printing speed and nozzle diameter significantly impacted dimensional accuracy of BRFG in the X-Y plane, while dimensional deviation in the Zdirection was influenced by layer height and nozzle height. Furthermore, the nozzle diameter served as the primary factor influencing hardness, whereas cohesiveness exhibited no statistically significant dependence on the process parameters. The BRFG printed at the optimal process parameters (a layer height of 1.2 mm, a printing speed of 16 mm/s, a nozzle height of 1.4 mm, and a nozzle diameter of 1.4 mm) exhibited superior dimensional fidelity and texture attributes. This study provides a theoretical foundation for the 3D printing application of BRFG-based foods.
Conventional lipid extraction methods face three critical limitations: reliance on toxic organic solvents (hexane/chloroform), inability to process wet biomass without energy-intensive drying, and thermal degradation of omega-3 polyunsaturated fatty acids. This review critically assesses Subcritical fluid technology (SFT) as a sustainable alternative. SFT utilizes non-toxic solvents (water, ethanol, dimethyl ether) at moderate temperatures (100-250 degrees C) and pressures (10-100 bar) below critical points, offering tunable polarity (epsilon = 80 -> 25), gas-like diffusivities, and direct wet biomass processing. These properties enable selective lipid extraction with yields exceeding 90%, preservation of thermolabile bioactives (EPA/DHA retention >90%), and elimination of toxic residues. SFT demonstrates superior environmental, economic (energy savings 20-40%), and quality outcomes across microalgae, marine by-products, and oilseeds. This review provides a comprehensive framework, positioning SFT as a potential cornerstone for sustainable high-value lipid production across food, nutraceutical, and pharmaceutical industries, while identifying future research priorities in scalability and circular bioeconomy integration.
The by-products of vegetable oil extraction, oilseed meals, which contain high levels of protein, are becoming attractive raw materials for the sustainable production of bioactive antioxidant peptides. They are becoming known for their ability to neutralize free radicals, chelate pro-oxidant metals, and interfere with lipid peroxidation mechanisms, supporting both food preservation and human health applications. This review presents a combined view of the nutritional and structural foundations of proteins in oilseed meal, the molecular determinants that affect peptide antioxidant activity (molecular weight, hydrophobicity, and aromatic or metal-binding residues), and the mechanisms underlying radical-scavenging and chain-breaking antioxidant activity. Various processing strategies for generating antioxidant peptides from oilseed meals are discussed, including fermentation-based methods, structure-modifying pretreatments, membrane-based separations, and emerging green bioprocessing technologies. Structure-opening pre-treatments such as ultrasound, microwave, and high hydrostatic pressure are highlighted for their ability to enhance hydrolysis efficiency. In addition, green technologies, including subcritical water hydrolysis (SCW), natural deep eutectic solvents (NADES), and the pulsed electric field (PEF) treatments, are discussed as innovative approaches to improve protein accessibility and peptide release. The generated peptides are further purified and characterized using chromatographic techniques and LC-MS/MS-based proteomic analysis. Unlike previous reviews that broadly discuss plant protein hydrolysates, this review specifically emphasizes oilseed meal valorization pathways, peptide structure-function relationships, and emerging green processing technologies within a circular bioeconomy framework. Overall, valorizing oilseed meals into antioxidant peptides represents a promising strategy for developing clean-label functional ingredients aligned with the principles of the circular bioeconomy.
Peeling is an important step in tomato processing, as it affects product quality, operational efficiency, and the environment. Conventional peeling methods (such as steam and lye) are effective but face with some limitations, such as the generation of large amounts of alkaline wastewater, high energy requirements, and quality degradation. However, recent innovations in tomato peeling are shifting towards nutrient-saving and environmentally friendly methods. This review critically analyzes the emerging laboratory and pilot-scale “green peeling” technologies (e.g. catalytic infrared heating, pulsed electric fields, cold plasma, etc.) and their hybrid methods. We discussed the peeling mechanism and the effects of these innovative technologies on product quality. In addition, we discussed the current integration of near-infrared spectroscopy and machine learning for more rapid, automated and energy-optimised processing of tomatoes. However, challenges remain in terms of scalability, cost, and enzyme recycling. Future research should focus on nano-enzyme carriers and supercritical CO₂ pretreatment to improve peeling efficiency and quality.
Ultrasonication has emerged as a prominent non-thermal processing technology in the food industry due to its capacity to enhance mass transfer through acoustic cavitation, thereby facilitating cell disruption and the release of intracellular bioactive compounds. Ultrasound-Assisted Extraction (UAE) offers notable advantages over conventional techniques, such as shortened processing times, reduced solvent consumption, and improved recovery under comparatively mild conditions. Despite these benefits, standalone ultrasonication may encounter limitations related to matrix heterogeneity, limited penetration in compact plant tissues, scale-up complexity, and variable selectivity. These challenges underscore the need for integrated strategies capable of further intensifying extraction performance. Hybrid Ultrasound-Based Extraction (HUBE) techniques represent a strategic advancement that combines UAE with complementary physical and biochemical approaches to overcome the constraints of single-method systems. This state-of-the-art review provides a comprehensive overview of hybrid configurations integrating ultrasound with various extraction techniques. Across diverse food matrices and target compounds, hybrid ultrasound systems consistently demonstrated greater performance compared to conventional extraction methods. These improvements are reflected in enhanced recovery efficiency, accelerated kinetics, improved preservation of thermolabile bioactive, and reduced solvent and energy requirements. This review further highlights the evolution from conventional and single-technique ultrasound extraction toward integrated hybrid platforms and affirms their transformative potential in efficient and sustainable, extraction of compound within modern food processing systems.
Pulsed electric field (PEF) technology was employed as a green and efficient non-thermal pretreatment to enhance the acid hydrolysis of starch for the preparation of starch nanoparticles (SNPs) with tailored porous structures. Under optimized conditions (PEF pretreatment: 8 kV/cm, 35 degrees C for 10 min; followed by acid hydrolysis with 6.32 M H2SO4 at 35 degrees C for 1.5 days), PEF pretreatment removed channel proteins to drastically improve acid permeability through granular pores while concurrently disrupting hydrogen bonds in amorphous regions to promote molecular rearrangement. The resulting SNPs exhibited a uniform spherical morphology with an average particle size of 214.08 nm, high crystallinity (39.60%), and a specific surface area of 1.97 m2/g. Nitrogen adsorption-desorption analysis confirmed a well-defined mesoporous structure characterized by H3-type hysteresis loop and cylindrical pore channels, which facilitated efficient acid diffusion and homogeneous hydrolysis. FTIR and XRD results demonstrated that PEF pretreatment synergistically enhanced both short and long range structural order while preserving chemical integrity. The obtained SNPs were further applied as effective stabilizers in oil-in-water Pickering emulsions, showing excellent emulsification performance, storage stability, and environmental tolerance. This work provides a sustainable and precise strategy for fabricating starch-based nanoparticles with controlled porosity and enhanced functionality, demonstrating great potential for applications in the food, packaging, and biomedical fields. Data Availability: The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.
Near-infrared (NIR) spectroscopy has become a key enabling technology for rapid and nondestructive analysis in the food industry; however, the intrinsic complexity of NIR spectral data, arising from overlapping absorption bands, scattering effects, and matrix-dependent variability, requires advanced data-driven strategies for reliable interpretation. This review critically examines the integration of NIR spectroscopy with machine-learning approaches for food quality evaluation, authentication, and process monitoring, with emphasis on methodological rigor, robustness, and industrial applicability. Fundamental aspects of NIR spectral generation, instrumentation, and measurement configurations are discussed alongside preprocessing strategies, highlighting matrix-dependent selection, risks of over-preprocessing, and interactions with modern machine-learning and deep-learning models. A systematic comparative framework is presented to evaluate classical chemometric and advanced machine-learning algorithms in terms of data requirements, nonlinearity handling, interpretability, computational cost, and suitability for industrial deployment. Applications across major food sectors, including meat and fish, dairy, cereals and grains, fruits and vegetables, and processed or fermented foods, are reviewed with explicit distinction between laboratory-scale studies and industrial-scale implementations. Particular attention is given to external validation, batch effects, instrument transferability, model drift, and performance metrics relevant to quantitative NIR applications. Emerging trends such as deep learning, transfer learning, portable and inline NIR devices, process analytical technology (PAT), and Industry 4.0 integration are evaluated alongside practical challenges related to data scarcity, sensor stability, and regulatory acceptance. Overall, this review provides a structured and application-oriented perspective on advancing NIR–machine learning systems toward robust, interpretable, and regulatory-compliant food quality control.
Drying is a crucial preservation method for vegetables, and pretreatments can markedly affect both drying kinetics and final product quality. This study evaluated the convective drying behavior of beetroot cubes subjected to four conditions: control (BC), ethanol immersion (BE), ultrasound in water (UC), and combined ethanol immersion plus ultrasound (UE), at 60, 70, and 80 degrees C. Moisture loss was monitored gravimetrically and modeled using the Page equation, which provided an excellent fit (R & sup2; > 0.99) for all treatments. Effective moisture diffusivity (Fick's model) was on the order of 10(-7) m & sup2; s(-1) and increased with temperature and pretreatments, with UE showing the highest D-eff values. Arrhenius analysis revealed apparent activation energies of 16.08, 17.08, 19.43, and 24.70 kJ mol(-1) for BC, BE, UC, and UE, respectively, indicating that structural modifications induced by pretreatments enhanced temperature sensitivity and diffusion of bound water. Time-domain NMR relaxometry provided complementary information on water populations and mobility during drying, supporting the kinetic and diffusivity results. Overall, the combination of ethanol and ultrasound (UE) significantly intensified drying, reduced drying time by 35%, and improved mass transfer, demonstrating a promising strategy for optimizing vegetable dehydration and advancing mechanistic understanding of water dynamics in plant tissues.
Drying represents a critical operation in food, agricultural, and material processing systems; commonly modeled using Fick’s laws of diffusion. However, many real-world drying processes exhibit non-Fickian behavior, characterized by nonlinear moisture dynamics, internal resistance, and structural transformations, that classical models are unable to capture with sufficient accuracy.To address these limitations, fractional calculus has emerged as a robust mathematical framework for modeling anomalous diffusion; employing non-integer order derivatives that naturally incorporate memory effects and non-local transport phenomena. This review synthesizes recent advances in the application of fractional diffusion models to drying systems. Theoretical foundations of key fractional operators, including Caputo, Riemann-Liouville, and Grünwald-Letnikov, are examined, along with their physical interpretations in materials exhibiting structural heterogeneity, time-dependent moisture resistance, and coupled heat-mass transfer. Case studies involving fruits, vegetables, grains, hydrogels, and porous food matrices are analyzed; demonstrating the enhanced fitting and predictive capabilities of fractional models compared to classical approaches. Mathematical and computational techniques used to solve fractional differential equations are reviewed; encompassing analytical methods, finite difference and finite element schemes, and emerging tools such as physics-informed neural networks (PINNs). Challenges in parameter estimation are also addressed, with attention to the role of optimization algorithms and machine learning techniques in model calibration. By linking theoretical advancements with practical applications, this review highlights the increasing relevance of fractional diffusion frameworks in drying science; and outlines future directions in intelligent drying technologies and hybrid modeling strategies.
This study explores the intricate relationship between the physical attributes of Naga king chilli and their mass and volume, exploiting this correlation to assess chilli quality and devise innovative post-harvest machinery. Chilli grading plays a pivotal role in post-harvest activities in the pickle industry. This assessment is particularly crucial when dealing with Naga king chilli, as it necessitates size and mass-based categorization to ensure optimal processing standards. A prediction method of the mass and volume of Naga king chilli based on a computer vision system was introduced in this study. The work aimed to predict the mass and volume of Naga king chilli as functions of its physical properties, measured using image processing techniques, using single and multiple variable regression models such as linear, quadratic, rational, and exponential. Various mass and volume models are studied to determine their relationships with physical characteristics such as length, width, perimeter, projected area, and elongation ratio. In a single variable mass and volume models, the length-based rational and quadratic models showed the best fit, with R² values of 0.89 and 0.85 and RMSE values of 0.053 and 0.075, respectively. In addition, for multivariable models, the exponential model for both mass and volume were the most suitable regression model, with R² values of 0.92 and 0.90 and RMSE values of 0.046 and 0.062, respectively. The findings of the study revealed that the multivariable model has a better fit than a single variable.
Lotus root starch (LRS) is rich in bioactive compounds but has a low native resistant starch (RS) content, limiting its nutritional functionality. This study aimed to enhance the yield of type 3 resistant starch (RS3) from LRS using ultrasound-assisted enzymatic treatment. The process parameters, including pullulanase dosage, hydrolysis time, ultrasonic power density, and treatment time, were optimized through single-factor and orthogonal experiments. Under optimal conditions (36 NPUN/g pullulanase, 24 h hydrolysis, 20 W/L ultrasonic power, 15 min treatment), the RS3 content reached 24.34%. Structural and physicochemical analyses revealed that ultrasound treatment significantly increased amylose content, promoted the formation of a more ordered crystalline structure, and improved thermal stability. The resulting RS3 exhibited reduced solubility and swelling power, along with enhanced resistance to digestion. In vivo studies using a type 2 diabetes mellitus (T2DM) mouse model demonstrated that dietary supplementation with ultrasound-treated RS3 (U-RS3) effectively alleviated hyperglycemia, dyslipidemia, and body weight loss. These findings suggest that ultrasound-assisted enzymatic treatment is an effective strategy for producing high-quality lotus root RS3, suggesting its potential as a functional food ingredient for glycemic and lipid management.
Amla pomace, a by-product of juice processing, is abundant in phytochemicals and fiber but remains largely unutilized. Because it contains more moisture, drying is necessary to prevent microbial spoilage, improve stability, and increase shelf life, making it suitable for use in valueadded foods and nutraceutical products. The drying behaviour of amla pomace dried in a computer-controlled tray drier at different drying temperatures, DT (50, 60, and 70 degrees C) and air velocities, AV (0.9, 1.0, and 1.1 m/s) was therefore explored. Drying of amla pomace was observed to follow falling rate period pattern over the DT and AV range. Five thin-layer drying models were evaluated for amla pomace, and the Page model showed the best fit with the highest R2 and lowest chi 2 and RMSE values. An ANN model with a 3-3-4-1 topology predicted the drying behaviour of amla pomace with greater accuracy than the page model. Furthermore, the effective moisture diffusivity (Deff) and activation energy (Ea) were estimated using linearization and DAnlinfit methods, with latter method predicted both Deff and Ea at lower RMSE and higher R2 values. Also, the mass transfer coefficient and Gibbs free energy increased with increasing DT, while enthalpy followed converse trend, as determined by the DA-nlinfit method. Further, rise in DT adversely affected the phytochemical attributes and colour quality of pomace powder, although AV helps in retention of all these quality attributes of amla pomace powder. Nonetheless, phenolic content, total flavonoid content and DPPH assay was found to be maximum at 60 degrees C and 1.1 m/s, while colour attributes and ascorbic acid content was most preserved at 50 degrees C and 1.1 m/ s. Additionally, microstructural analysis confirmed that higher DT lagged behind in preserving the cellular matrix of pomace.
This study presents a concise and improved approach for estimating the ripeness parameters of Kepok bananas (Musa balbisiana BBB) using a Partial Least Squares Regression (PLSR) method. The proposed technique integrates RGB reflectance and fluorescence maging to obtain comprehensive color and texture information for objective fruit maturity estimation, specifically to determine the optimum harvest maturity. Comparative models were developed using individual and combined image datasets to evaluate their performance in predicting Total Soluble Solids (TSS) and firmness, two key indicators of banana maturity. Results revealed that fluorescence and combined RGB reflectance-fluorescence imaging yielded the highest accuracy, with R2training = 0.9231, R2test = 0.9256 for firmness and R2training = 0.8623, R2test = 0.8908 for TSS. The optimal PLSR models utilized 12 and 9 selected features, respectively, achieving RMSE values of 0.3341 (firmness) and 2.0829 (TSS). These outcomes confirm that integrating RGB reflectance-fluorescence imaging with PLSR modeling enhances the accuracy and reliability of nondestructive Kepok banana maturity assessment.
Secondary extraction of flavonoids from propolis residue following supercritical CO2 extraction was investigated, with concurrent development of an on-line and real-time flavonoid quantification monitoring method. Ultrasonic-assisted ethanol extraction (UAE) parameters were optimized by single-factor experiments and response surface methodology (RSM), with comparative assessment against traditional extraction without ultrasound assistance. Under optimal UAE conditions, an offline quantitative detection model for the content of flavonoids was established firstly by ultraviolet-visible (UV-Vis) spectroscopy coupled with machine learning algorithms. Secondly, an online detection model was obtained by calibrating the offline model with direct standardization (DS) algorithm. The optimal extraction conditions were determined to be: ultrasonic frequency of 28 kHz, power density of 136 W/L, extraction time of 15 min, temperature of 43 degrees C, solid-liquid ratio of 1:10 (g/mL), and ethanol concentration of 75 %. Under these optimized parameters, the extraction yield reached 9.41 mg/mL. Comparative analysis revealed that the ultrasonic method significantly outperformed both conventional extraction techniques, with low-speed agitation yielding 7.52 mg/mL and high-speed agitation yielding 8.26 mg/mL (p < 0.05). offline models, support vector regression (SVR) with normalized spectra and synergy interval partial least squares (Si-PLS) feature selection demonstrated superior performance (calibration: Rc=0.9914, RMSEC=0.3224; prediction: Rp=0.9926, RMSEP=0.3003)The statement "superior performance" refers specifically to the comparative results within this evaluation framework. The model built using Support Vector Regression (SVR) on normalized spectra with Synergy Interval Partial Least Squares (Si-PLS) for feature selection demonstrated superior predictive accuracy and robustness compared to the other modeling methods evaluated in the study, primarily Partial Least Squares (PLS) and Backpropagation Neural Network (BPNN). The DS-corrected online migration model achieved optimal real-time quantification (Rp=0.9660, RMSEP=0.7773). This study confirms UAE as an efficient low-temperature method for flavonoid recovery from propolis residue. Integration of offline UV-Vis modeling with DS spectral correction enables robust online flavonoid monitoring during extraction.
The potato (Solanum tuberosum L.) is one of the most widely cultivated crops worldwide, and its starch has attracted growing interest as a renewable and biodegradable biopolymer for applications in food, packaging, pharmaceuticals, and emerging material systems. The functional behavior of potato starch is governed by intrinsic structural features, such as granule size, crystalline organization, and the amylose-to-amylopectin ratio, which collectively influence gelatinization, viscosity development, and retrogradation. Despite these favorable attributes, native potato starch shows limited resistance to heat, shear, and acidic conditions, which restricts its direct use in many industrial processes. This narrative review provides an integrated overview of potato starch functionality, major modification strategies, sustainability aspects, and industrial applications. Physical, chemical, and enzymatic modification methods are discussed in relation to their effects on starch structure and the resulting functional performance. Particular attention is given to emerging green modification approaches that seek to enhance functionality while reducing chemical use and energy demand. A comparative analysis highlights the differences in effectiveness, scalability, and industrial feasibility between conventional and alternative modification technologies. Sustainability considerations are examined through biodegradation behavior and life cycle perspectives, emphasizing the importance of balancing functional improvement with environmental performance. Industrial case studies demonstrate the application of modified potato starch in various food systems, thermoplastic materials, packaging, and other value-added products, where processing conditions and formulation choices significantly influence the outcome. Overall, this review highlights the structure-function relationships and practical considerations that support the continued development and industrial application of potato starch as a biopolymer.
