Grain-Based Products: Innovative Processing Technologies and Quality: Second Volume

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 2236

Special Issue Editor


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Guest Editor
School of Food Science and Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, China
Interests: cereal foods; cereal protein; frozen dough
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Special Issue Information

Dear Colleagues,

Volume I (https://www.mdpi.com/journal/foods/special_issues/grain_product) of this Special Issue was incredibly successful. We would like to express our gratitude to everyone involved for their participation.

Grain-based products play an important role in our diet and provide carbohydrates, proteins, lipids, micro-nutrients (vitamins and minerals), and other phytochemicals (phenolic compounds) for both children and adults. Wheat, maize, and rice are the major food grains. Oat, sorghum, millet, and barley are minor crops with various food uses. The food industry is becoming increasingly competitive and must develop high-quality food products. It is important to explore novel technologies in order to discover grain-based foods with potential health benefits. The goal of grain-based product processing is to enhance the health aspects, nutrition, flavor and taste, preservation, stabilization, and security of food, as well as to ensure more diversity in the acceptability and preference of consumers. We invite submissions to this Special Issue on the aspects of innovative processing technologies that can be used for grain-based products and their effects on improving the quality of grain-based foods, including textural and sensory properties, nutrition, structural components, and shelf life.

Prof. Dr. Xiaona Guo
Guest Editor

Manuscript Submission Information

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Keywords

  • grain
  • food quality
  • processing technology
  • nutrition
  • structure components
  • preservation

Published Papers (2 papers)

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Research

20 pages, 6338 KiB  
Article
The Effect of Carboxymethyl Cellulose Sodium on the Proofing Tolerance and Quality of Frozen Dough Steamed Bread
by Si-Fan Liu, Ke-Xue Zhu and Xiao-Na Guo
Foods 2024, 13(6), 870; https://doi.org/10.3390/foods13060870 - 13 Mar 2024
Viewed by 542
Abstract
This study investigated the effects of dough proofing degree (1.1, 1.3, 1.5, and 1.7 mL/g) and carboxymethyl cellulose sodium (CMC-Na) on the quality of frozen dough steamed bread (FDSB). As the dough proofing degree was increased from 1.1 to 1.7 mL/g, the specific [...] Read more.
This study investigated the effects of dough proofing degree (1.1, 1.3, 1.5, and 1.7 mL/g) and carboxymethyl cellulose sodium (CMC-Na) on the quality of frozen dough steamed bread (FDSB). As the dough proofing degree was increased from 1.1 to 1.7 mL/g, the specific volume of FDSB initially increased and then decreased, with the maximum at 1.3 mL/g, and then dramatically decreased at 1.5 and 1.7 mL/g, accompanied by a harder texture and secession of crust and crumb, which were the detrimental effects brought by over-proofing. The optimal amount of CMC-Na effectively alleviated the deterioration associated with over-proofing, and the proofing tolerance of FDSB was increased from 1.3 mL/g to 1.7 mL/g. Fermentation analysis showed that CMC-Na significantly improved the extensibility and gas-holding capacity of the dough by increasing the maximum height of the dough (Hm) and the emergence time (T1) of Hm. Frequency sweep tests indicated that CMC-Na improved the plasticity of proofed dough by increasing loss factor tan δ. Significant reductions were found in peak viscosity and complex modulus G* in pasting properties tests and temperature sweep measurements, respectively, suggesting that CMC-Na influenced starch gelatinization and dough stiffening during steaming, which promoted the extension of the network structure, thus facilitating gas expansion and diffusion. These property changes theoretically explained the improvement in the proofing tolerance of FDSB by CMC-Na. Full article
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20 pages, 2880 KiB  
Article
Modeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain
by Kannapot Kaewsorn, Thitima Phanomsophon, Pisut Maichoon, Dharma Raj Pokhrel, Pimpen Pornchaloempong, Warawut Krusong, Panmanas Sirisomboon, Munehiro Tanaka and Takayuki Kojima
Foods 2023, 12(24), 4516; https://doi.org/10.3390/foods12244516 - 18 Dec 2023
Cited by 1 | Viewed by 1406
Abstract
If a non-destructive and rapid technique to determine the textural properties of cooked germinated brown rice (GBR) was developed, it would hold immense potential for the enhancement of the quality control process in large-scale commercial rice production. We combined the Fourier transform near-infrared [...] Read more.
If a non-destructive and rapid technique to determine the textural properties of cooked germinated brown rice (GBR) was developed, it would hold immense potential for the enhancement of the quality control process in large-scale commercial rice production. We combined the Fourier transform near-infrared (NIR) spectral data of uncooked whole grain GBR with partial least squares (PLS) regression and an artificial neural network (ANN) for an evaluation of the textural properties of cooked germinated brown rice (GBR); in addition, data separation and spectral pretreatment methods were investigated. The ANN was outperformed in the evaluation of hardness by a back extrusion test of cooked GBR using the smoothing combined with the standard normal variate pretreated NIR spectra of 188 whole grain samples in the range of 4000–12,500 cm−1. The calibration sample set was separated from the prediction set by the Kennard–Stone method. The best ANN model for hardness, toughness, and adhesiveness provided R2, r2, RMSEC, RMSEP, Bias, and RPD values of 1.00, 0.94, 0.10 N, 0.77 N, 0.02 N, and 4.3; 1.00, 0.92, 1.40 Nmm, 9.98 Nmm, 1.6 Nmm, and 3.5; and 0.97, 0.91, 1.35 Nmm, 2.63 Nmm, −0.08 Nmm, and 3.4, respectively. The PLS regression of the 64-sample KDML GBR group and the 64-sample GBR group of various varieties provided the optimized models for the hardness of the former and the toughness of the latter. The hardness model was developed by using 5446.3–7506 and 4242.9–4605.4 cm−1, which included the amylose vibration band at 6834.0 cm−1, while the toughness model was from 6094.3 to 9403.8 cm−1 and included the 6834.0 and 8316.0 cm−1 vibration bands of amylose, which influenced the texture of the cooked rice. The PLS regression models for hardness and toughness had the r2 values of 0.85 and 0.82 and the RPDs of 2.9 and 2.4, respectively. The ANN model for the hardness, toughness, and adhesiveness of cooked GBR could be implemented for practical use in GBR production factories for product formulation and quality assurance and for further updating using more samples and several brands to obtain the robust models. Full article
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