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<p><sub>D</sub>-tagatose, a low-calorie sweetener with well-documented health benefits, has gained increasing attention. Traditional chromatographic methods for quantifying <sub>D</sub>-tagatose are costly, complex, and dependent on column separation efficiency and sample pretreatment. Developing reliable alternative detection methods is crucial to promote <sub>D</sub>-tagatose production and application. This study developed and optimized a rapid and cost-effective spectrophotometric method (SM) for quantifying <sub>D</sub>-tagatose based on ketose transformation under acidic conditions and visualization with tryptophan-<sub>L</sub>-cysteine. Under optimal conditions (62% sulfuric acid reacting for 90&#xa0;min), interference from aldoses was negligible, and absorbance from <sub>D</sub>-sorbose was reduced to ~ 20% of <sub>D</sub>-tagatose. The absorbance at 518&#xa0;nm showed a linear relationship to <sub>D</sub>-tagatose concentration (<Emphasis Type="BoldItalic">y</Emphasis> = 0.0042<Emphasis Type="BoldItalic">x</Emphasis> + 0.0636) in the range of 25 ~ 200&#xa0;µg/mL, with a detection limit of 0.62&#xa0;µg/mL and a deviation &lt; 6% compared to the chromatographic method. Validating in catalytic systems, SM-determined results in arginine- and enzyme-catalyzed isomerization matched chromatography results (deviation &lt; 5%). In NaOH-mediated reactions, the formed <sub>D</sub>-sorbose led to elevated SM-determined results. Therefore, univariate linear regression models were proposed to correct the SM-determined results (<Emphasis Type="BoldItalic">x</Emphasis>), reflecting the relationship with <sub>D</sub>-tagatose concentration (<Emphasis Type="BoldItalic">y</Emphasis>) as described by <Emphasis Type="BoldItalic">y</Emphasis> = 0.7584<Emphasis Type="BoldItalic">x</Emphasis> ~ 0.8225<Emphasis Type="BoldItalic">x</Emphasis>. Finally, bivariate linear regression established the relationship (<Emphasis Type="BoldItalic">z</Emphasis> = 0.154 + 0.418<Emphasis Type="BoldItalic">x</Emphasis> + 0.995<Emphasis Type="BoldItalic">y</Emphasis>) between the SM-determined results (<Emphasis Type="BoldItalic">z</Emphasis>) and the authentic concentrations of <sub>D</sub>-sorbose (<Emphasis Type="BoldItalic">x</Emphasis>) and <sub>D</sub>-tagatose (<Emphasis Type="BoldItalic">y</Emphasis>), whilst quantifying their respective contributions. The optimized SM and regression equations provide a new perspective for high-throughput determination of <sub>D</sub>-tagatose in practical catalytic systems, offering a convenient solution for rapid quantification of <sub>D</sub>-tagatose.</p>

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A quick and simple spectrophotometric method with high resolution for quantitative determination of D-tagatose in chemical/enzymatic-based catalytic systems

  • 期刊论文

  • 发表日期：

    2025年6月4日

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  • Suotao Cao,
  • Guangzhen Wang,
  • Yawen Lu,
  • Yiming Sun,
  • Dongfeng Wang,
  • Ying Xu,
  • Mingming Wang

摘要

D-tagatose, a low-calorie sweetener with well-documented health benefits, has gained increasing attention. Traditional chromatographic methods for quantifying D-tagatose are costly, complex, and dependent on column separation efficiency and sample pretreatment. Developing reliable alternative detection methods is crucial to promote D-tagatose production and application. This study developed and optimized a rapid and cost-effective spectrophotometric method (SM) for quantifying D-tagatose based on ketose transformation under acidic conditions and visualization with tryptophan-L-cysteine. Under optimal conditions (62% sulfuric acid reacting for 90 min), interference from aldoses was negligible, and absorbance from D-sorbose was reduced to ~ 20% of D-tagatose. The absorbance at 518 nm showed a linear relationship to D-tagatose concentration (y = 0.0042x + 0.0636) in the range of 25 ~ 200 µg/mL, with a detection limit of 0.62 µg/mL and a deviation < 6% compared to the chromatographic method. Validating in catalytic systems, SM-determined results in arginine- and enzyme-catalyzed isomerization matched chromatography results (deviation < 5%). In NaOH-mediated reactions, the formed D-sorbose led to elevated SM-determined results. Therefore, univariate linear regression models were proposed to correct the SM-determined results (x), reflecting the relationship with D-tagatose concentration (y) as described by y = 0.7584x ~ 0.8225x. Finally, bivariate linear regression established the relationship (z = 0.154 + 0.418x + 0.995y) between the SM-determined results (z) and the authentic concentrations of D-sorbose (x) and D-tagatose (y), whilst quantifying their respective contributions. The optimized SM and regression equations provide a new perspective for high-throughput determination of D-tagatose in practical catalytic systems, offering a convenient solution for rapid quantification of D-tagatose.