Novel and Facile Colorimetric Detection of Reducing Sugars in Foods via In Situ Formed Gelatin-Capped Silver Nanoparticles

The evolution of green technology for the simple and ecological formation of silver nanoparticles (AgNPs) inspired the present work for simple and efficient detection of reducing sugars (RS) in foods. The proposed method relies on gelatin as the capping and stabilizing agent and the analyte (RS) as the reducing agent. This work may attract significant attention, especially in the industry, for testing the sugar content using gelatin-capped silver nanoparticles as it not only detects the sugar in food, but also determines the content (%), which could be an alternative technique to the conventionally used DNS colorimetric method. For this purpose, a certain amount of maltose was mixed with a gelatin-silver nitrate. Different conditions that may affect the color changes at 434 nm owing to the in situ formed AgNPs, such as gelatin-silver nitrate ratio, PH, time, and temperature, have been investigated. The 1:3 mg/mg ratio of gelatin-silver nitrate dissolved in 10 mL distilled water was most effective in color formation. The development of AgNPs color increases within 8-10 min at PH 8.5 as the selected optimum value and at the optimum temperature of 90 degrees C for the evolution of the gelatin-silver reagent's redox reaction. The gelatin-silver reagent showed a fast response (less than 10 min) with a detection limit for maltose at 46.67 mu M. In addition, the selectivity of maltose was checked in the presence of starch and after its hydrolysis with alpha-amylase. Compared with the conventionally used dinitrosalicylic acid (DNS) colorimetric method, the proposed method could be applied to commercial fresh apple juice, watermelon, and honey to prove its viability for detecting RS in fruits; the total reducing sugar content was 287, 165, and 751 mg/g, respectively.

Automated summary

The use of gelatin-capped silver nanoparticles is proposed as a simple and efficient method for detecting reducing sugars in foods. This method not only detects the sugar content, but also determines the content (%) as an alternative to conventional colorimetric methods. The proposed method showed promising results in detecting reducing sugars in fruits such as apple juice, watermelon, and honey.