Evaluation of Short-Season Soybean (Glycine max (L.) Merr.) Breeding Lines for Tofu Production
"> Figure 1
<p>Mean values for temperature (columns) and total precipitation (blue lines) from May to October between 2018 and 2022. Error bars on columns are ±SE.</p> "> Figure 2
<p>Mean values of yield (<b>a</b>), maturity (<b>b</b>), plant height (<b>c</b>), and lodging (<b>d</b>) of all tested genotypes in each year for a given MG. Error bars are ±SE.</p> "> Figure 3
<p>Mean values of tofu-related traits, including stones (%), Brix (%), dry matter (% of soy milk), and tofu texture firmness (N force), using GDL or MgCl<sub>2</sub> coagulants. Error bars are ±SE.</p> "> Figure 4
<p>GGEBiplot visualization of trait association for all genotypes tested in 2018. Traits comprised yield, maturity, plant height, lodging, seed quality, stone seeds (STONES), seed weight, protein, oil, carbohydrates (CARBO), sugar, sucrose, raffinose and stachyose (RAFFSTACH), soymilk Brix, soymilk dry matter, and tofu texture firmness using GDL and MgCl<sub>2</sub> (TEXTURE GDL and TEXTURE MGCL2, respectively). The circles represent genotypes in MG0 (blue circles) and MG00 (green circles). The genotype names are presented in <a href="#app1-seeds-03-00028" class="html-app">Table S2</a>.</p> "> Figure 5
<p>GGEBiplot visualization of trait association for all genotypes tested in 2019. Traits comprised yield, maturity, plant height, lodging, seed quality, stone seeds (STONES), seed weight, protein, oil, carbohydrates (CARBO), sugar, sucrose, raffinose and stachyose (RAFFSTACH), soymilk Brix, soymilk dry matter, and tofu texture firmness using GDL and MgCl<sub>2</sub> (TEXTURE GDL and TEXTURE MGCL2, respectively). The circles represent genotypes in MG0 (blue circles), MG00 (green circles), and MG000 (brown circles). The genotype names are presented in <a href="#app1-seeds-03-00028" class="html-app">Table S2</a>.</p> "> Figure 6
<p>GGEBiplot visualization of trait association for all genotypes tested in 2020. Traits comprised yield, maturity, plant height, lodging, seed quality, stone seeds (STONES), seed weight, protein, oil, carbohydrates (CARBO), sugar, sucrose, raffinose and stachyose (RAFFSTACH), soymilk Brix, soymilk dry matter, and tofu texture firmness using GDL and MgCl<sub>2</sub> (TEXTURE GDL and TEXTURE MGCL2, respectively). The circles represent genotypes in MG0 (blue circles) and MG00 (green circles). The genotype names are presented in <a href="#app1-seeds-03-00028" class="html-app">Table S2</a>.</p> "> Figure 7
<p>GGEBiplot visualization of trait association for all genotypes tested in 2021. Traits comprised yield, maturity, plant height, lodging, seed quality, stone seeds (STONES), seed weight, protein, oil, carbohydrates (CARBO), sugar, sucrose, raffinose and stachyose (RAFFSTACH), soymilk Brix, soymilk dry matter, and tofu texture firmness using GDL and MgCl<sub>2</sub> (TEXTURE GDL and TEXTURE MGCL2, respectively). The circles represent genotypes in MG0 (blue circles), MG00 (green circles), and MG000 (brown circles). The genotype names are presented in <a href="#app1-seeds-03-00028" class="html-app">Table S2</a>.</p> "> Figure 8
<p>GGEBiplot visualization of trait association for all genotypes tested in 2022. Traits comprised yield, maturity, plant height, lodging, seed quality, stone seeds (STONES), seed weight, protein, oil, carbohydrates (CARBO), sugar, sucrose, raffinose and stachyose (RAFFSTACH), soymilk Brix, soymilk dry matter, and tofu texture firmness using GDL and MgCl<sub>2</sub> (TEXTURE GDL and TEXTURE MGCL2, respectively). The circles represent genotypes in MG0 (blue circles) and MG00 (green circles). The genotype names are presented in <a href="#app1-seeds-03-00028" class="html-app">Table S2</a>.</p> "> Figure 9
<p>GGEBiplot visualization of trait by MG in all years and for all the genotypes tested. Traits comprised yield, maturity, plant height, lodging, seed quality, stone seeds (STONES), seed weight, protein, oil, carbohydrates (CARBO), sugar, sucrose, raffinose and stachyose (RAFFSTACH), soymilk Brix, soymilk dry matter, and tofu texture firmness using GDL and MgCl<sub>2</sub> (TEXTURE GDL and TEXTURE MGCL2, respectively). Mg0, Mg00, and Mg000 refer to the different maturity groups.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Field Trials
2.2. Agronomic Traits
2.3. Seed Composition
2.4. Assessment of Tofu-Related Quality
2.5. Statistical Analysis
3. Results and Discussion
3.1. Agronomy Traits
3.2. Seed Composition and Tofu-Related Traits
3.3. GGEbiplot Analysis
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- FAO. Agricultural Market Information Systems (AMIS). License: CC BY-NC-SA 3.0 IGO. 2024. Available online: https://www.fao.org/statistics/statistics/statistical-capacity-development/agricultural-market-information-system/en (accessed on 20 March 2024).
- Dilawari, R.; Kaur, N.; Priyadarshi, N.; Prakash, I.; Patra, A.; Mehta, S.; Singh, B.; Jain, P.; Islam, M.A. Soybean: A Key Player for Global Food Security. In Soybean Improvement: Physiological, Molecular and Genetic Perspectives; Wani, S.H., Sofi, N.u.R., Bhat, M.A., Lin, F., Eds.; Springer International Publishing: Cham, Switzerland, 2022; pp. 1–46. [Google Scholar] [CrossRef]
- Neupane, A.; Bulbul, I.; Wang, Z.; Lehman, R.M.; Nafziger, E.; Marzano, S.-Y.L. Long term crop rotation effect on subsequent soybean yield explained by soil and root-associated microbiomes and soil health indicators. Sci. Rep. 2021, 11, 9200. [Google Scholar] [CrossRef] [PubMed]
- Ali, F.; Tian, K.; Wang, Z.-X. Modern techniques efficacy on tofu processing: A review. Trends Food Sci. Technol. 2021, 116, 766–785. [Google Scholar] [CrossRef]
- Meng, S.; Chang, S.; Gillen, A.M.; Zhang, Y. Protein and quality analyses of accessions from the USDA soybean germplasm collection for tofu production. Food Chem. 2016, 213, 31–39. [Google Scholar] [CrossRef] [PubMed]
- Rotundo, J.L.; Miller-Garvin, J.E.; Naeve, S.L. Regional and Temporal Variation in Soybean Seed Protein and Oil across the United States. Crop Sci. 2016, 56, 797–808. [Google Scholar] [CrossRef]
- Skurray, G.; Cunich, J.; Carter, O. The effect of different varieties of soybean and calcium ion concentration on the quality of tofu. Food Chem. 1980, 6, 89–95. [Google Scholar] [CrossRef]
- Goel, R.; Kaur, A.; Singh, J. Varietal evaluation of soybean for tofu making. Asian J. Dairy Food Res. 2018, 37, 81–84. [Google Scholar] [CrossRef]
- Mujoo, R.; Trinh, D.T.; Ng, P.K.W. Characterization of storage proteins in different soybean varieties and their relationship to tofu yield and texture. Food Chem. 2003, 82, 265–273. [Google Scholar] [CrossRef]
- Toda, K.; Ono, T.; Kitamura, K.; Hajika, M.; Takahashi, K.; Nakamura, Y. Seed Protein Content and Consistency of Tofu Prepared with Different Magnesium Chloride Concentrations in Six Japanese Soybean Varieties. Breed. Sci. 2003, 53, 217–223. [Google Scholar] [CrossRef]
- Ort, N.W.W.; Morrison, M.J.; Cober, E.R.; McAndrew, D.; Lawley, Y.E. A comparison of soybean maturity groups for phenology, seed yield, and seed quality components between eastern Ontario and southern Manitoba. Can. J. Plant Sci. 2022, 102, 812–822. [Google Scholar] [CrossRef]
- Ndatsu, Y.; Olekan, A.A. Effects of Different Types of Coagulants on the Nutritional Quality Tofu Produced in the Northern Part of Nigeria. World J. Dairy Food Sci. 2012, 7, 135–141. [Google Scholar] [CrossRef]
- Shurtleff, W.; Aoyagi, A. Tofu & Soymilk Production: The Book of Tofu, Volume II, A Craft and Technical Manual, 3rd ed.; Soyinfo Center Publishing: Lafayette, CA, USA, 2000. [Google Scholar]
- Zhang, Q.; Wang, C.; Li, B.; Li, L.; Lin, D.; Chen, H.; Liu, Y.; Li, S.; Qin, W.; Liu, J.; et al. Research progress in tofu processing: From raw materials to processing conditions. Crit. Rev. Food Sci. Nutr. 2018, 58, 1448–1467. [Google Scholar] [CrossRef]
- Zhang, Q.; Zhou, S.; Chen, J.; Qin, W.; Liu, J.; Yang, W.; Zhang, L. Fabrication of whole soybean curd using three soymilk preparation techniques. LWT 2019, 104, 91–99. [Google Scholar] [CrossRef]
- Frégeau-Reid, J.A.; Cober, E.R. A small-scale tofu test for soybean breeding programs. Can. J. Plant Sci. 2019, 99, 50–55. [Google Scholar] [CrossRef]
- Mullin, W.J.; Fregeau-Reid, J.A.; Butler, M.; Poysa, V.; Woodrow, L.; Jessop, D.B.; Raymond, D. An interlaboratory test of a procedure to assess soybean quality for soymilk and tofu production. Food Res. Int. 2001, 34, 669–677. [Google Scholar] [CrossRef]
- Patterson, H.D.; Williams, E.R.; Hunter, E.A. Block designs for variety trials. J. Agric. Sci. 1978, 90, 395–400. [Google Scholar] [CrossRef]
- Yan, W. GGEbiplot—A Windows Application for Graphical Analysis of Multienvironment Trial Data and Other Types of Two-Way Data. Agron. J. 2001, 93, 1111–1118. [Google Scholar] [CrossRef]
- Cober, E.R.; Morrison, M.J. Soybean Yield and Seed Composition Changes in Response to Increasing Atmospheric CO2 Concentration in Short-Season Canada. Plants 2019, 8, 250. [Google Scholar] [CrossRef]
- Nakagawa, A.C.S.; Ario, N.; Tomita, Y.; Tanaka, S.; Murayama, N.; Mizuta, C.; Iwaya-Inoue, M.; Ishibashi, Y. High temperature during soybean seed development differentially alters lipid and protein metabolism. Plant Prod. Sci. 2020, 23, 504–512. [Google Scholar] [CrossRef]
- Islam, N.; Krishnan, H.B.; Natarajan, S. Quantitative proteomic analyses reveal the dynamics of protein and amino acid accumulation during soybean seed development. Proteomics 2022, 22, 2100143. [Google Scholar] [CrossRef]
- Bennett, J.O.; Krishnan, A.H.; Wiebold, W.J.; Krishnan, H.B. Positional Effect on Protein and Oil Content and Composition of Soybeans. J. Agric. Food Chem. 2003, 51, 6882–6886. [Google Scholar] [CrossRef]
- Chachalis, D.; Smith, M.L. Imbibition behavior of soybean (Glycine max (L.) Merrill) accessions with different testa characteristics. Seed Sci. Technol. 2000, 28, 321–331. [Google Scholar]
- Gerna, D.; Clara, D.; Antonielli, L.; Mitter, B.; Roach, T. Seed Imbibition and Metabolism Contribute Differentially to Initial Assembly of the Soybean Holobiont. Phytobiomes J. 2024, 8, 21–33. [Google Scholar] [CrossRef] [PubMed]
- Jang, S.J.; Sato, M.; Sato, K.; Jitsuyama, Y.; Fujino, K.; Mori, H.; Takahashi, R.; Benitez, E.R.; Liu, B.; Yamada, T.; et al. A Single-Nucleotide Polymorphism in an Endo-1,4-β-Glucanase Gene Controls Seed Coat Permeability in Soybean. PLoS ONE 2015, 10, e0128527. [Google Scholar] [CrossRef] [PubMed]
- Koizumi, M.; Kikuchi, K.; Isobe, S.; Ishida, N.; Naito, S.; Kano, H. Role of seed coat in imbibing soybean seeds observed by micro-magnetic resonance imaging. Ann. Bot. 2008, 102, 343–352. [Google Scholar] [CrossRef] [PubMed]
- Meyer, C.J.; Steudle, E.; Peterson, C.A. Patterns and kinetics of water uptake by soybean seeds. J. Exp. Bot. 2007, 58, 717–732. [Google Scholar] [CrossRef] [PubMed]
- Shi, Y.-g.; Yang, Y.; Piekoszewski, W.; Zeng, J.-h.; Guan, H.-n.; Wang, B.; Liu, L.-l.; Zhu, X.-q.; Chen, F.-l.; Zhang, N. Influence of four different coagulants on the physicochemical properties, textural characteristics and flavour of tofu. Int. J. Food Sci. Technol. 2020, 55, 1218–1229. [Google Scholar] [CrossRef]
- Poysa, V.; Woodrow, L.; Yu, K. Effect of soy protein subunit composition on tofu quality. Food Res. Int. 2006, 39, 309–317. [Google Scholar] [CrossRef]
- Stanojevic, S.P.; Barac, M.B.; Pesic, M.B.; Vucelic-Radovic, B.V. Assessment of Soy Genotype and Processing Method on Quality of Soybean Tofu. J. Agric. Food Chem. 2011, 59, 7368–7376. [Google Scholar] [CrossRef] [PubMed]
- Wu, C.; Hua, Y.; Chen, Y.; Kong, X.; Zhang, C. Effect of 7S/11S ratio on the network structure of heat-induced soy protein gels: A study of probe release. RSC Adv. 2016, 6, 101981–101987. [Google Scholar] [CrossRef]
- Ono, T.; Onodera, Y.; Chen, Y.; Nakasato, K. Tofu structure is regulated by soymilk protein composition and coagulant concentration. In Chemistry, Texture, and Flavor of Soy; ACS Symposium Series; American Chemical Society: Washington, DC, USA, 2010; pp. 219–229. [Google Scholar]
- Chen, R.; Chang, S.K.C.; Gillen, A.M.; Chen, P.; Zhang, B. Relationships between protein and other chemical composition and texture of tofu made from soybeans grown in different locations. J. Food Sci. 2024, 89, 1428–1441. [Google Scholar] [CrossRef]
- Onodera, Y.; Ono, T.; Nakasato, K.; Toda, K. Homogeneity and Microstructure of Tofu Depends on 11S/7S Globulin Ratio in Soymilk and Coagulant Concentration. Food Sci. Technol. Res. 2009, 15, 265–274. [Google Scholar] [CrossRef]
- Kurasch, A.K.; Hahn, V.; Miersch, M.; Bachteler, K.; Würschum, T. Analysis of tofu-related traits by a bench-scale tofu production method and their relationship with agronomic traits in European soybean. Plant Breed. 2018, 137, 271–282. [Google Scholar] [CrossRef]
- Wilcox, J.R.; Cavins, J.F. Backcrossing High Seed Protein to a Soybean Cultivar. Crop Sci. 1995, 35, 1036–1041. [Google Scholar] [CrossRef]
- Cober, E.R.; Voldeng, H.D. Developing High-Protein, High-Yield Soybean Populations and Lines. Crop Sci. 2000, 40, 39–42. [Google Scholar] [CrossRef]
- Gupta, S.K.; Manjaya, J.G. Advances in improvement of soybean seed composition traits using genetic, genomic and biotechnological approaches. Euphytica 2022, 218, 99. [Google Scholar] [CrossRef]
- Prenger, E.M.; Yates, J.; Mian, M.A.R.; Buckley, B.; Boerma, H.R.; Li, Z. Introgression of a High Protein Allele into an Elite Soybean Cultivar Results in a High-Protein Near-Isogenic Line with Yield Parity. Crop Sci. 2019, 59, 2498–2508. [Google Scholar] [CrossRef]
- Sebolt, A.M.; Shoemaker, R.C.; Diers, B.W. Analysis of a Quantitative Trait Locus Allele from Wild Soybean That Increases Seed Protein Concentration in Soybean. Crop Sci. 2000, 40, 1438–1444. [Google Scholar] [CrossRef]
- Wang, J.; Mao, L.; Zeng, Z.; Yu, X.; Lian, J.; Feng, J.; Yang, W.; An, J.; Wu, H.; Zhang, M.; et al. Genetic mapping high protein content QTL from soybean ‘Nanxiadou 25’ and candidate gene analysis. BMC Plant Biol. 2021, 21, 388. [Google Scholar] [CrossRef] [PubMed]
- Clemente, T.E.; Cahoon, E.B. Soybean Oil: Genetic Approaches for Modification of Functionality and Total Content. Plant Physiol. 2009, 151, 1030–1040. [Google Scholar] [CrossRef]
- Kim, H.K.; Kang, S.T. Identification of Quantitative Trait Loci (QTLs) Associated with Oil and Protein Contents in Soybean (Glycine max L.). LJournal Life Sci. 2004, 14, 453–458. [Google Scholar]
- Hou, Z.; Liu, B.; Kong, F. Chapter Two—Regulation of flowering and maturation in soybean. In Advances in Botanical Research; Lam, H.-M., Li, M.-W., Eds.; Academic Press: Cambridge, MA, USA, 2022; Volume 102, pp. 43–75. [Google Scholar]
- Cober, E.R.; Molnar, S.J.; Charette, M.; Voldeng, H.D. A New Locus for Early Maturity in Soybean. Crop Sci. 2010, 50, 524–527. [Google Scholar] [CrossRef]
- Wang, L.; Fang, C.; Liu, J.; Zhang, T.; Kou, K.; Su, T.; Li, S.; Chen, L.; Cheng, Q.; Dong, L.; et al. Identification of major QTLs for flowering and maturity in soybean by genotyping-by-sequencing analysis. Mol. Breed. 2020, 40, 99. [Google Scholar] [CrossRef]
MG/Year | Seed Weight (g/100 Seeds) | Seed Quality (1–5 Score *) | Protein (% DM) | Oil (% DM) | Carbohydrates (% DM) | Sugar (% DM) | Sucrose (% DM) | Raffinose and Stachyose (% DM) |
---|---|---|---|---|---|---|---|---|
MG0 | ||||||||
2018 | 20.3 ± 0.3 | 2.4 ± 0.1 | 43.6 ± 0.2 | 20.6 ± 0.1 | 17.4 ± 0.1 | 11.6 ± 0.1 | 5.9 ± 0.1 | 4.8 ± 0.0 |
2019 | 17.8 ± 0.3 | 2.3 ± 0.1 | 40.5 ± 0.3 | 20.9 ± 0.2 | 20.0 ± 0.1 | 12.7 ± 0.1 | 7.3 ± 0.1 | 4.3 ± 0.0 |
2020 | 21.9 ± 0.5 | 1.0 ± 0.0 | 44.7 ± 1.0 | 19.5 ± 0.5 | 19.1 ± 0.3 | 13.1 ± 0.1 | 7.0 ± 0.2 | 4.4 ± 0.1 |
2021 | 18.8 ± 0.4 | 3.2 ± 0.1 | 41.2 ± 0.4 | 21.9 ± 0.3 | 18.3 ± 0.1 | 11.4 ± 0.1 | 6.0 ± 0.1 | 4.8 ± 0.0 |
2022 | 22.3 ± 0.3 | 2.4 ± 0.1 | 44.4 ± 0.2 | 19.5 ± 0.1 | 18.0 ± 0.1 | 11.6 ± 0.1 | 6.5 ± 0.1 | 4.4 ± 0.0 |
MG00 | ||||||||
2018 | 24.2 ± 0.4 | 2.3 ± 0.1 | 44.1 ± 0.2 | 20.7 ± 0.1 | 16.8 ± 0.1 | 11.1 ± 0.1 | 5.7 ± 0.1 | 4.8 ± 0.0 |
2019 | 16.6 ± 0.2 | 2.1 ± 0.0 | 39.0 ± 0.2 | 21.9 ± 0.1 | 20.3 ± 0.1 | 12.5 ± 0.1 | 7.6 ± 0.1 | 4.1 ± 0.0 |
2020 | 21.1 ± 0.3 | 2.2 ± 0.1 | 44.1 ± 0.5 | 20.1 ± 0.2 | 17.1 ± 0.2 | 10.6 ± 0.1 | 5.9 ± 0.1 | 4.4 ± 0.0 |
2021 | 17.0 ± 0.1 | 1.6 ± 0.1 | 42.9 ± 0.3 | 20.9 ± 0.2 | 19.0 ± 0.1 | 11.5 ± 0.1 | 6.2 ± 0.1 | 4.4 ± 0.0 |
2022 | 20.6 ± 0.4 | 1.4 ± 0.1 | 42.1 ± 0.3 | 20.9 ± 0.2 | 18.9 ± 0.1 | 12.0 ± 0.1 | 6.9 ± 0.1 | 4.3 ± 0.0 |
MG000 | ||||||||
2019 | 15.8 ± 0.2 | 2.3 ± 0.1 | 38.8 ± 0.3 | 21.9 ± 0.2 | 20.4 ± 0.2 | 12.5 ± 0.1 | 7.4 ± 0.1 | 4.1 ± 0.0 |
2021 | 16.7 ± 0.2 | 1.9 ± 0.1 | 43.1 ± 0.4 | 20.9 ± 0.2 | 19.1 ± 0.1 | 11.5 ± 0.1 | 6.1 ± 0.1 | 4.4 ± 0.0 |
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Hadinezhad, M.; Lackey, S.; Cober, E.R. Evaluation of Short-Season Soybean (Glycine max (L.) Merr.) Breeding Lines for Tofu Production. Seeds 2024, 3, 393-410. https://doi.org/10.3390/seeds3030028
Hadinezhad M, Lackey S, Cober ER. Evaluation of Short-Season Soybean (Glycine max (L.) Merr.) Breeding Lines for Tofu Production. Seeds. 2024; 3(3):393-410. https://doi.org/10.3390/seeds3030028
Chicago/Turabian StyleHadinezhad, Mehri, Simon Lackey, and Elroy R. Cober. 2024. "Evaluation of Short-Season Soybean (Glycine max (L.) Merr.) Breeding Lines for Tofu Production" Seeds 3, no. 3: 393-410. https://doi.org/10.3390/seeds3030028