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Advances in Processing, Starch Functionality, and Integrated Quality Design for...
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Advances in Processing, Starch Functionality, and Integrated Quality Design for Grain-Based Foods

A Special Issue of Foods (ISSN 2304-8158) belonging to the section "Grain".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1101

Editors

Prof. Dr. Zhongquan Sui

E-Mail Website
Guest Editor
Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
Interests: carbohydrate chemistry; starch modification; functional characteristics
Special Issues, Collections and Topics in MDPI journals
Dr. Mengting Ma

E-Mail Website
Guest Editor
Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
Interests: starch; structure–properties–functionality; starch modification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cereal-based foods are staple components of diets worldwide, with their quality critically determined by the behavior of starch and its interactions with other grain constituents such as proteins, lipids, and dietary fiber. Processing operations, including milling, cooking, baking, extrusion, and fermentation, profoundly modify starch structure and its interplay with other components, thereby governing the integrated quality attributes of the final product, from digestibility and texture to flavor and appearance. Meeting consumer demand for healthier yet sensorially appealing staple foods requires a systematic understanding of how diverse processing methods can be designed to simultaneously optimize nutritional and sensory attributes. This Special Issue, entitled “Advances in Processing, Starch Functionality, and Integrated Quality Design for Grain-Based Foods”, aims to bridge the gap between processing technologies, starch functionality, and holistic quality optimization. We welcome contributions that elucidate how traditional or emerging processing techniques modulate starch digestibility, pasting, retrogradation, and textural performance in cereal-based systems. Research on the interactions between starch and other grain components (proteins, lipids, fiber) during processing is of particular interest, as these interactions critically influence the multi-faceted quality of products such as rice, bread, noodles, baked goods, and fermented cereal products. We especially encourage studies that move beyond single-property optimization and embrace a multi-objective quality design perspective. This includes, but is not limited to, the use of in vitro digestibility models, instrumental texture analysis, sensory evaluation, and computational approaches such as response surface methodology, kinetic modeling, machine learning, and artificial intelligence-driven multi-objective optimization. Submissions that demonstrate data-driven strategies to balance starch digestibility with sensory acceptability, or that integrate predictive modeling into the design of both modern and traditional cereal processing, are highly valued. This Special Issue of Foods aims to compile innovative reviews and original research that collectively advance our ability to tailor starch functionality and overall product quality through intelligent processing, addressing the evolving demands for nutrition, pleasure, and sustainability in cereal-based food systems. Dr. Mengting Ma Prof. Dr. Zhongquan Sui Guest Editors

Prof. Dr. Zhongquan Sui
Dr. Mengting Ma
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • grain-based foods
  • cereal processing
  • sensory quality
  • starch functionality
  • in vitro digestibility
  • multi-objective optimization
  • artificial intelligence in food processing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

34 pages, 7541 KB  
Article
Synergistic Optimization of In Vitro Digestibility and Sensory Quality of Moderately Milled Rice Based on the RiceMambaOpt Model
by Zijun Li, Zhihong Wen, Mengting Ma, Wenshu Niu, Zhongquan Sui and Harold Corke
Foods 2026, 15(16), 2812; https://doi.org/10.3390/foods15162812 - 12 Aug 2026
Viewed by 326
Abstract
To address over-processing and limited process-control precision in rice manufacturing, this study developed an artificial intelligence (AI)-assisted optimization framework for rice milling. A data-driven predictive model was constructed to characterize the nonlinear relationships between milling conditions and the in vitro starch-digestibility and sensory [...] Read more.
To address over-processing and limited process-control precision in rice manufacturing, this study developed an artificial intelligence (AI)-assisted optimization framework for rice milling. A data-driven predictive model was constructed to characterize the nonlinear relationships between milling conditions and the in vitro starch-digestibility and sensory attributes of rice. Across the nine physicochemical, in vitro starch-digestibility, and sensory indicators, RiceMambaOpt achieved a mean coefficient of determination (R2) of 0.975. Explainability analyses were used to examine process–quality relationships and characterize nonlinear trade-offs among appearance, texture, and starch-digestibility attributes across rice cultivars with different genetic backgrounds. A target-oriented inverse optimization procedure was then developed, which estimates feasible process parameters subject to process-feasibility constraints, tailored to differentiated orientations such as low rapidly digestible starch (RDS) content or high palatability. On the independent 100-sample test set, the inverse predictions achieved a milling-time MAE of 0.960 s with an R2 of 0.986 and a milling-speed MAE of 19.522 r/min with an R2 of 0.851. In a prospective experimental validation, 10 of the 12 prespecified target quality profiles (83.3%) were attained across 36 independently milled samples, with an RDS mean absolute error of 1.8 percentage points and a joint normalized root-mean-square error of 6.7%. For the Qiuguang cultivar, the model identified a representative processing condition of 38.5 s and 1020 r/min, corresponding to a predicted in vitro RDS content of 21.6% and a palatability score of 26.5. The results demonstrate the feasibility of combining predictive modeling with process-feasibility-constrained inverse optimization and provide a computational approach for investigating moderate rice-milling conditions. Full article
(This article belongs to the Special Issue Advances in Processing, Starch Functionality, and Integrated Quality Design for Grain-Based Foods)
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20 pages, 1196 KB  
Article
Physical, Chemical, and Bioactive Properties of Bread Made from Particle-Size-Fractionated Wheat Flour
by Samson Adeoye Oyeyinka, Oluseyi Moses Ajayi, Olayemi Eyituoyo Dudu, Jill Ellis and Oluwafemi Ayodeji Adebo
Foods 2026, 15(15), 2644; https://doi.org/10.3390/foods15152644 - 28 Jul 2026
Viewed by 493
Abstract
Bran enrichment improves the nutritional value of wheat-based bread but often impairs appearance, structure and sensory acceptance. This study investigated the role of controlled particle-size fractionation of brown wheat flour (BWF, equivalent to whole wheat flour) into <500 µm and <250 µm fractions [...] Read more.
Bran enrichment improves the nutritional value of wheat-based bread but often impairs appearance, structure and sensory acceptance. This study investigated the role of controlled particle-size fractionation of brown wheat flour (BWF, equivalent to whole wheat flour) into <500 µm and <250 µm fractions in balancing bioactive enhancement and functional potential with acceptable product quality. White wheat flour (WWF) served as a refined control. Bread samples were prepared from WWF, BWF and their two bran-rich fractions, and assessed for appearance, color, physicochemical properties, texture, total phenolic content (TPC), ABTS antioxidant activity, individual phenolics, sensory attributes, correlations and chemometric patterns (using Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminatory Analysis (OPLS-DA)). Flour particle size strongly influenced bread color, texture and crumb structure: BWF bread samples were darkest, firmest and most chewy, whereas <250 µm bread was lighter and softer. TPC and ABTS activity were highest in BWF, followed by <500 µm and <250 µm, mirroring phenolic retention. Specifically, total phenolic content reached 1.58 mg GAE/g and ABTS antioxidant activity 19.49% in BWF, compared with 0.96 mg GAE/g and 18.31% in the <500 µm fraction, and 0.87 mg GAE/g and 17.67% in the <250 µm fraction. Bread hardness decreased from 5437 g in BWF to 4878 g in the <500 µm fraction and 4386 g in the <250 µm fraction. Key phenolic acids, chlorogenic, p-coumaric, sinapic and trans-ferulic acids, showed strong positive correlations with hardness and chewiness and negative correlations with appearance and overall acceptability. PCA and OPLS-DA confirmed distinct biochemical separation among bread types, with the PCA model explaining 73% of the total variance (PC1 = 57% and PC2 = 16%), and phenolic subclass distribution was identified as the main discriminant factor. Bread from the <500 µm fraction preserved phenolics but reduced sensory quality, while finer fractions (<250 µm) improved acceptability at the cost of some flavonoids and antioxidant capacity. Fractionation of flour, therefore, provides a practical strategy to optimize bioactive–sensory trade-offs in fiber-enriched bread samples. This study contributes to efforts in achieving sustainable development goals aimed at improving nutrition and food security. Full article
(This article belongs to the Special Issue Advances in Processing, Starch Functionality, and Integrated Quality Design for Grain-Based Foods)
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