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Yagihara S, Watanabe S, Abe Y, Asano M, Shimizu K, Saito H, Maruyama Y, Kita R, Shinyashiki N, Kundu SK. Universal Behavior of Fractal Water Structures Observed in Various Gelation Mechanisms of Polymer Gels, Supramolecular Gels, and Cement Gels. Gels 2023; 9:506. [PMID: 37504385 PMCID: PMC10379185 DOI: 10.3390/gels9070506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/26/2023] [Accepted: 06/16/2023] [Indexed: 07/29/2023] Open
Abstract
So far, it has been difficult to directly compare diverse characteristic gelation mechanisms over different length and time scales. This paper presents a universal water structure analysis of several gels with different structures and gelation mechanisms including polymer gels, supramolecular gels composed of surfactant micelles, and cement gels. The spatial distribution of water molecules was analyzed at molecular level from a diagram of the relaxation times and their distribution parameters (τ-β diagrams) with our database of the 10 GHz process for a variety of aqueous systems. Polymer gels with volume phase transition showed a small decrease in the fractal dimension of the hydrogen bond network (HBN) with gelation. In supramolecular gels with rod micelle precursor with amphipathic molecules, both the elongation of the micelles and their cross-linking caused a reduction in the fractal dimension. Such a reduction was also found in cement gels. These results suggest that the HBN inevitably breaks at each length scale with relative increase in steric hindrance due to cross-linking, resulting in the fragmentation of collective structures of water molecules. The universal analysis using τ-β diagrams presented here has broad applicability as a method to characterize diverse gel structures and evaluate gelation processes.
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Affiliation(s)
- Shin Yagihara
- Department of Physics, School of Science, Tokai University, Hiratsuka-shi 259-1292, Japan
| | - Seiei Watanabe
- Course of Physics, Graduate School of Science, Tokai University, Hiratsuka-shi 259-1292, Japan
| | - Yuta Abe
- Department of Physics, School of Science, Tokai University, Hiratsuka-shi 259-1292, Japan
| | - Megumi Asano
- Department of Physics, School of Science, Tokai University, Hiratsuka-shi 259-1292, Japan
| | - Kenta Shimizu
- Course of Physics, Graduate School of Science, Tokai University, Hiratsuka-shi 259-1292, Japan
| | - Hironobu Saito
- Department of Physics, School of Science, Tokai University, Hiratsuka-shi 259-1292, Japan
| | - Yuko Maruyama
- Department of Physics, School of Science, Tokai University, Hiratsuka-shi 259-1292, Japan
| | - Rio Kita
- Department of Physics, School of Science, Tokai University, Hiratsuka-shi 259-1292, Japan
- Micro/Nano Technology Center, Tokai University, Hiratsuka-shi 259-1292, Japan
| | - Naoki Shinyashiki
- Department of Physics, School of Science, Tokai University, Hiratsuka-shi 259-1292, Japan
- Micro/Nano Technology Center, Tokai University, Hiratsuka-shi 259-1292, Japan
| | - Shyamal Kumar Kundu
- Department of Physics, School of Basic and Applied Sciences, Galgotias University, Greater Noida 201306, India
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Heat treatment in the presence of arginine increases the emulsifying properties of soy proteins. Food Chem X 2023; 17:100567. [PMID: 36845474 PMCID: PMC9945471 DOI: 10.1016/j.fochx.2023.100567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 12/21/2022] [Accepted: 01/05/2023] [Indexed: 01/09/2023] Open
Abstract
This study aimed to improve the emulsifying properties of commercial soy protein isolates (CSPIs). CSPIs were thermally denatured without additives (CSPI_H) and with arginine (CSPI_A), urea (CSPI_U), and guanidine hydrochloride (CSPI_G), which improve protein solubility to prevent aggregation. These additives were removed by dialysis, and the samples were lyophilized. CSPI_A resulted in high emulsifying properties. FT-IR analysis showed that the β-sheet content in CSPI_A was reduced compared to that of untreated CSPI (CSPI_F). Fluorescence analysis showed that the tryptophan-derived emission peak of CSPI_A shifted between CSPI_F and CSPI_H which was exposed to hydrophobic amino acid chains with aggregation. As a result, the structure of CSPI_A became moderately unfolded and exposed the hydrophobic amino acid chains without aggregation. The CSPI_A solution had a more reduced oil-water interface tension than other CSPIs. These results support that CSPI_A attaches efficiently to the oil-water interface and produces small, less flocculated emulsions.
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Xia Y, Kuda T, Nakamura S, Yamamoto M, Takahashi H, Kimura B. Effects of soy protein and β-conglycinin on microbiota and in vitro antioxidant and immunomodulatory capacities of human faecal cultures. Food Hydrocoll 2022. [DOI: 10.1016/j.foodhyd.2022.107516] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Nakamura S, Kuda T, Midorikawa Y, Takamiya D, Takahashi H, Kimura B. Detection and isolation of β-conglycinin-susceptible gut indigenous bacteria from ICR mice fed high-sucrose diet. FOOD BIOSCI 2021. [DOI: 10.1016/j.fbio.2021.100994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Nakamura S, Kuda T, Midorikawa Y, Takahashi H, Kimura B. Typical gut indigenous bacteria in ICR mice fed a soy protein-based normal or low-protein diet. Curr Res Food Sci 2021; 4:295-300. [PMID: 33997796 PMCID: PMC8102713 DOI: 10.1016/j.crfs.2021.04.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 03/31/2021] [Accepted: 04/01/2021] [Indexed: 01/08/2023] Open
Abstract
For patients with inflammatory bowel disease, cow’s milk allergy, and lactose intolerance, soymilk is a potential alternative to cow’s milk. In this study, we aimed to identify the effects of a soy protein-based low-protein diet on the body and organ weights and the gut microbiome of six-week-old mice fed a diet containing 20% (SP) or 5% (LP) soy protein for 14 days via 16S rRNA (V4) amplicon sequencing. Body weight gain (growth) and liver, spleen, and fat tissue weight were significantly suppressed by the LP diet. Operational taxonomic unit numbers and α-diversity were lower in the LP group than in the SP group. A principal coordinate analysis revealed differences in the gut microbiome compositions of SP and LP mice. The abundances of caecal Roseburia sp., Alistipes sp., and bacteria from the family Muribaculaceae were lower in the LP group than in the SP group. In contrast, the abundance of Desulfovibrionaceae, which is positively correlated with inflammation, was higher in the LP group than in the SP group. These results differed from the effects of a milk casein-based low-protein diet (reported previously). Based on these findings, we conclude that the undesirable effects of a low-protein diet and/or protein deficiency are related to changes in the gut microbiome composition and may differ depending on the kind of proteins used. Six-week-old ICR mice were fed a diet containing 20% (SP) or 5% (LP) soy protein for 14 days. Body weight gain and liver, spleen, and fat tissue weight were significantly suppressed by the LP diet. Caecal Roseburia sp., Alistipes sp., and bacteria from the family Muribaculaceae was lower in the LP. Desulfovibrionaceae, which is positively correlated with inflammation, was higher in the LP group.
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Affiliation(s)
- Saori Nakamura
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology, 4-5-7 Among Konan, Minato-ku, Tokyo, 108-8477, Japan
| | - Takashi Kuda
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology, 4-5-7 Among Konan, Minato-ku, Tokyo, 108-8477, Japan
| | - Yuko Midorikawa
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology, 4-5-7 Among Konan, Minato-ku, Tokyo, 108-8477, Japan
| | - Hajime Takahashi
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology, 4-5-7 Among Konan, Minato-ku, Tokyo, 108-8477, Japan
| | - Bon Kimura
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology, 4-5-7 Among Konan, Minato-ku, Tokyo, 108-8477, Japan
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Wang C, Zhou S, Du Q, Qin W, Wu D, Raheem D, Yang W, Zhang Q. Shelf life prediction and food safety risk assessment of an innovative whole soybean curd based on predictive models. Journal of Food Science and Technology 2019; 56:4233-4241. [PMID: 31477994 DOI: 10.1007/s13197-019-03893-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 06/08/2019] [Accepted: 06/24/2019] [Indexed: 11/29/2022]
Abstract
The aim of the present study is to predict the shelf life and evaluate the risk profile of an innovative whole soybean curd (WSC). Two main spoilage strains were isolated from spoiled WSC and identified as B. subtilis and B. cereus. The origin analysis confirmed that B. subtilis and B. cereus originated from soybean materials and survived in soybean curd. For microbial contamination analysis, thermotolerant coliforms, E. coli and S. aureus were not detected in soybean curd. The predicted shelf life of WSC and okara-filtered curd that was stored at 10 °C were 141.95 h (5.91 d) and 206.25 h (8.59 d), respectively. Moreover, the models applied in this study exhibited great fitting goodness and the predicted growth parameters were fail-safe. To conclude, introduction of okara into soybean curd reinforced the initial contamination level but didn't significantly increase the risk profile of WSC.
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Affiliation(s)
- Chenzhi Wang
- 1College of Food Science, Sichuan Agricultural University, No. 46, Xinkang Road, Ya'an, 625014 Sichuan China
| | - Siyi Zhou
- 1College of Food Science, Sichuan Agricultural University, No. 46, Xinkang Road, Ya'an, 625014 Sichuan China
| | - Qinling Du
- 1College of Food Science, Sichuan Agricultural University, No. 46, Xinkang Road, Ya'an, 625014 Sichuan China
| | - Wen Qin
- 1College of Food Science, Sichuan Agricultural University, No. 46, Xinkang Road, Ya'an, 625014 Sichuan China.,2Institute of Food Processing and Safety, College of Food Science, Sichuan Agricultural University, Ya'an, 625014 Sichuan China
| | - Dingtao Wu
- 1College of Food Science, Sichuan Agricultural University, No. 46, Xinkang Road, Ya'an, 625014 Sichuan China.,2Institute of Food Processing and Safety, College of Food Science, Sichuan Agricultural University, Ya'an, 625014 Sichuan China
| | - Dele Raheem
- 5Northern Institute of Environmental and Minority Law, Arctic Centre, University of Lapland, 96101 Rovaniemi, Finland
| | - Wenyu Yang
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture/Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, 611130 Sichuan China.,4College of Agronomy, Sichuan Agricultural University, Chengdu, 611130 Sichuan China
| | - Qing Zhang
- 1College of Food Science, Sichuan Agricultural University, No. 46, Xinkang Road, Ya'an, 625014 Sichuan China.,Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture/Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, 611130 Sichuan China
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Classification of Soymilk and Tofu with Diffuse Reflection Light Using a Deep Learning Technique. AGRIENGINEERING 2019. [DOI: 10.3390/agriengineering1020017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Tofu is an ancient soybean product that is produced by heating soymilk containing a coagulation agent. Owing to its benefits to human health, it has become popular all over the world. An important index that determines the final product’s (tofu’s) quality is firmness. Coagulants such as CaSO4 and MgCl2 affect the firmness. With the increasing demand for tofu, a monitoring methodology that ensures high-quality tofu is needed. In our previous paper, an opportunity to monitor changes in the physical properties of soymilk by studying its optical properties during the coagulation process was implied. To ensure this possibility, whether soymilk and tofu can be discriminated via their optical properties should be examined. In this study, a He–Ne laser (Thorlabs Japan Inc., Tokyo, Japan, 2015) with a wavelength of 633 nm was emitted to soymilk and tofu. The images of the scattered light on their surfaces were discriminated using a type of deep learning technique. As a result, the images were classified with an accuracy of about 99%. We adjusted the network architecture and hyperparameters for the learning, and this contributed to the successful classification. The construction of a network that is specific to our task led to the successful classification result. In addition to this monitoring method of the tofu coagulation process, the classification methodology in this study is worth noting for possible use in many relevant agricultural fields.
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Li C, Wu X, Mu D, Zhao Y, Luo S, Zhong X, Jiang S, Li X, Zheng Z. Effect of Partial Hydrolysis with Papain on the Characteristics of Transglutaminase-Crosslinked Tofu Gel. J Food Sci 2018; 83:3092-3098. [PMID: 30461022 DOI: 10.1111/1750-3841.14403] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Revised: 10/29/2018] [Accepted: 11/01/2018] [Indexed: 11/27/2022]
Abstract
The effects of partial enzymatic hydrolysis of soymilk on the characteristics of transglutaminase (TG)-crosslinked tofu gel were studied. SDS-PAGE showed that the molecular weight of the partially hydrolyzed soybean protein was reduced to that of a digested peptide (less than 43.0 kDa) when papain was added at more than 50 μL/100 mL soymilk. The content of free sulfhydryls, β-sheets, and random coils in papain-treated soymilk increased. When TG was added to soy milk after papain treatment and tofu gel was formed, its storage modulus increased from 957.44 to 1241.39 Pa. The gel strength, water-holding capacity, and nonfreezing water content of the tofu gel were greater than those without enzyme treatment. Scanning electron microscopy revealed that limited papain hydrolysis stimulated TG-catalyzed cross-linking of soymilk to form a dense gel network structure, whereas an extended enzymatic hydrolysis of soymilk did not promote crosslinking by TG. PRACTICAL APPLICATION: This work investigated the effect of partial hydrolysis on TG cross-linked tofu gel. Partial hydrolysis of soybean protein with papain can promote TG cross-linking reaction, thus form a dense network structure, increase gel strength, and water-holding capacity. Therefore, it can be used to produce a good gel product with higher gel strength, springiness, water-holding capacity, and a more dense microstructure.
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Affiliation(s)
- Chuanyun Li
- School of Food Science and Engineering, Hefei Univ. of Technology, Hefei, 230009, China
| | - Xuefeng Wu
- School of Food Science and Engineering, Hefei Univ. of Technology, Hefei, 230009, China.,Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei Univ. of Technology, Hefei, 230009, China
| | - Dongdong Mu
- School of Food Science and Engineering, Hefei Univ. of Technology, Hefei, 230009, China.,Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei Univ. of Technology, Hefei, 230009, China
| | - Yanyan Zhao
- School of Food Science and Engineering, Hefei Univ. of Technology, Hefei, 230009, China.,Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei Univ. of Technology, Hefei, 230009, China
| | - Shuizhong Luo
- School of Food Science and Engineering, Hefei Univ. of Technology, Hefei, 230009, China.,Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei Univ. of Technology, Hefei, 230009, China
| | - Xiyang Zhong
- School of Food Science and Engineering, Hefei Univ. of Technology, Hefei, 230009, China.,Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei Univ. of Technology, Hefei, 230009, China
| | - Shaotong Jiang
- School of Food Science and Engineering, Hefei Univ. of Technology, Hefei, 230009, China.,Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei Univ. of Technology, Hefei, 230009, China
| | - Xingjiang Li
- School of Food Science and Engineering, Hefei Univ. of Technology, Hefei, 230009, China.,Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei Univ. of Technology, Hefei, 230009, China
| | - Zhi Zheng
- School of Food Science and Engineering, Hefei Univ. of Technology, Hefei, 230009, China.,Key Laboratory for Agricultural Products Processing of Anhui Province, Hefei Univ. of Technology, Hefei, 230009, China
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