Better Design for Formulation Optimization of Grain Foods

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Grain".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 1723

Special Issue Editors


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Guest Editor
Department of Food and Nutrition, University of Helsinki, P.O. Box 66, FI-00014 Helsinki, Finland
Interests: food science; food technology; whole grains; product development

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Guest Editor
College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
Interests: innovative agriculture; novel agricultural product development; agricultural application of nuclear technology; whole-grains; lipids; proteins; flavour; food processing; food nutrition and health
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Guest Editor
Hainan Institute, Zhejiang University, Sanya 572025, China
Interests: rice; molecular mechanism; abiotic stress; nanotechnology
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Guest Editor
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: food chemistry; food technology; food analysis; grain foods; grain flavor; metabolomics

Special Issue Information

Dear Colleagues,

Grain-based foods are made from various cereal grains like wheat, oats, rice, barley, rye, millet, sorghum, corn, and other cereal grains. These foods offer a rich array of essential nutrients, including carbohydrates, proteins, dietary fiber, as well as a wide spectrum of vitamins and minerals, which contribute significantly to global health maintenance. The grain-based food industry is currently under mounting pressure to innovate and produce products that incorporate healthier components, such as whole grains and dietary fiber, while reducing the presence of less-healthy elements like fats, sugars, and salt. A diet rich in whole grains and fiber has been increasingly recognized as a protective measure against the development of diet-related ailments such as cardiovascular disease, obesity, and type 2 diabetes. This underscores the need to enhance the formulation of grain foods to promote health and well-being for the world's growing population.

This Special Issue of Foods will cover recent studies carried out on the formulation optimization of grain foods. The studies may focus on understanding the physical and chemical properties of cereal grains, selecting grain types or mixtures to optimize end-product nutrition and health benefits, customizing grain-based product’s appearance and texture, partially substituting refined flour with whole grains or cereal side streams, using bioprocessing method to deliver clean-label grain foods, developing innovative whole-grain products, and grain foods reformulation with lower salt, sugar, and fat contents.

Dr. Yaqin Wang
Dr. Zhen Yang
Dr. Meng Jiang
Dr. Chenguang Zhou
Guest Editors

Manuscript Submission Information

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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

  • wholegrain
  • cereal fiber
  • nutrition and health
  • formulation
  • optimization
  • bioprocessing
  • fermentation
  • gut microbiota
  • clean label

Published Papers (2 papers)

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Research

18 pages, 3156 KiB  
Article
A Comprehensive Study on the Influence of Superheated Steam Treatment on Lipolytic Enzymes, Physicochemical Characteristics, and Volatile Composition of Lightly Milled Rice
by Chenguang Zhou, Bin Li, Wenli Yang, Tianrui Liu, Haoran Yu, Siyao Liu and Zhen Yang
Foods 2024, 13(2), 240; https://doi.org/10.3390/foods13020240 - 11 Jan 2024
Viewed by 722
Abstract
Enzyme inactivation is crucial for enhancing the shelf life of lightly milled rice (LMR), yet the impact of diverse superheated steam (SS) treatment conditions on lipolytic enzyme efficiency, physicochemical properties, and volatile profiles of LMR remains unclear. This study investigated varying SS conditions, [...] Read more.
Enzyme inactivation is crucial for enhancing the shelf life of lightly milled rice (LMR), yet the impact of diverse superheated steam (SS) treatment conditions on lipolytic enzyme efficiency, physicochemical properties, and volatile profiles of LMR remains unclear. This study investigated varying SS conditions, employing temperatures of 120 °C, 140 °C, and 160 °C and exposure times of 2, 4, 6, and 8 min. The research aimed to discern the influence of these conditions on enzyme activities, physicochemical characteristics, and quality attributes of LMR. Results indicated a significant rise in the inactivation rate with increased treatment temperature or duration, achieving a notable 70% reduction in enzyme activities at 120 °C for 6 min. Prolonged exposure to higher temperatures also induced pronounced fissures on LMR surfaces. Furthermore, intensive SS treatment led to a noteworthy 5.52% reduction in the relative crystallinity of LMR starch. GC/MS analysis revealed a consequential decrease, ranging from 44.7% to 65.7%, in undesirable odor ketones post-SS treatment. These findings underscore the potential of SS treatment in enhancing the commercial attributes of LMR. Full article
(This article belongs to the Special Issue Better Design for Formulation Optimization of Grain Foods)
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21 pages, 4974 KiB  
Article
Deep-Learning-Based Model Predictive Control of an Industrial-Scale Multistate Counter-Flow Paddy Drying Process
by Ye Zhang, Zhuangdong Fang, Changyou Li and Chengjie Li
Foods 2024, 13(1), 43; https://doi.org/10.3390/foods13010043 - 21 Dec 2023
Viewed by 706
Abstract
In practical industrial-scale paddy drying production, manual empirical operation is still widely used for process control. This often leads to poor uniformity in the moisture content distribution of discharged grains, affecting product quality. Model Predictive Control (MPC) is considered the most effective control [...] Read more.
In practical industrial-scale paddy drying production, manual empirical operation is still widely used for process control. This often leads to poor uniformity in the moisture content distribution of discharged grains, affecting product quality. Model Predictive Control (MPC) is considered the most effective control method for paddy drying, but its implementation in industrial-scale drying is hindered by its high computational cost. This study aims to address this challenge by proposing a deep-learning-based model predictive control (DL-MPC) strategy for paddy drying. By establishing a mapping relation between the inlet and outlet paddy moisture content and paddy flow velocity, a DL-MPC strategy suitable for multistage counter-flow paddy drying systems is proposed. DL-MPC systems are developed using long short-term memory (LSTM) neural networks and trained using datasets from single-drying-stage and multistage drying systems. Simulation and analysis are conducted, followed by verification experiments on a 5HNH-15 multistage counter-flow paddy dryer. The results show that the DL-MPC system significantly improves computational speed while achieving satisfactory control performance. The predicted paddy flow velocity exhibits a smooth variation and matches field data obtained from multiple transition points, confirming the effectiveness of the designed DL-MPC system. The mean absolute error between the predicted and actual paddy moisture content under the DL-MPC system is 0.190% d.b., further supporting the effectiveness of the control system. Full article
(This article belongs to the Special Issue Better Design for Formulation Optimization of Grain Foods)
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