Abstract
In developing a simple, reliable, small-scale method to assess silken tofu quality in our soybean improvement program, we examined two processing methods and two coagulants, glucono-δ-lactone (GDL) or nigari (magnesium chloride) in two experiments. Silken tofu was prepared from a commercial soybean variety (expt 1) or seven soybean varieties (V1–V7) which were grown and harvested together (expt 2). The soybeans were soaked overnight (the soak method, with 55 g soybeans) or ground dry first (the dry method, with 60 g soybeans) before processing. The quality of the silken tofu was evaluated and compared among varieties and coagulant-processing methods and their interactions. Moisture and protein content in soymilk and soybean seeds, soymilk yield and protein and solid recovery in soymilk were determined. Compared with the dry method, the soak method allowed faster soymilk extraction, produced soymilk with lower solid and higher protein content and firmer silken tofu with either GDL or nigari as coagulant. Depending on whether nigari or GDL was used as coagulant, the soak method also produced silken tofu with the highest or the lowest water loss which correlated strongly and negatively with tofu hardness (r = −0.93***). Differences were detected among varieties for the key quality attributes. Taken together, the soak method with GDL as coagulant would be the preferred combination to use to assess tofu quality.
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Acknowledgments
Work reported here was conducted under the auspices of the Australian Soybean Breeding Program funded in part by the Grains Research and Development Corporation, Australia.
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Funding: Grains Research and Development Corporation, Australia (grant number GRDC CSP00157).
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Aijun Yang declares that she has no conflict of interest. Andrew James declares that he has no conflict of interest.
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This article does not contain any studies with human participants or animals performed by any of the authors.
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Yang, A., James, A.T. Comparison of Two Small-Scale Processing Methods for Testing Silken Tofu Quality. Food Anal. Methods 9, 385–392 (2016). https://doi.org/10.1007/s12161-015-0205-8
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DOI: https://doi.org/10.1007/s12161-015-0205-8