Quantitative detection of umami substances using FT-IR spectroscopy and wavelength optimization

Umami is one of the five basic tastes, primarily represented by monosodium glutamate (MSG) and disodium 5′-inosinate (IMP). The current primary methods for detecting MSG and IMP are expensive and complex, limiting their widespread applications. Hence, there is a need to explore novel and more affordable methods to characterize umami taste substances. FT-IR was used to detect umami substances, MSG, and its mixture with IMP. Uniform spectral spacing method (USS) was combined separately with a continuous projection algorithm (SPA), competitive adaptive weighting algorithm (CARS), and uninformed variable elimination method (UVE) to simplify partial least squares regression (PLSR) and principal component regression (PCR) prediction models. The results demonstrated that the optimal model for MSG solution detection was the USS-CARS-PCR quantitative simplified model based on 17 feature wavelengths with $R_c^{\text{2}}$=0.97, ${\text{RMSE}}_c$=0.23 g/L, $R_p^{\text{2}}$ =0.96, and ${\text{RMSE}}_p$=0.27 g/L. For MSG and IMP mixture detection, the optimal model was the full wavelength model with $R_c^{\text{2}}$=0.97, ${\text{RMSE}}_c$=0.11 g/L, $R_p^{\text{2}}$=0.98, and ${\text{RMSE}}_p$=0.07 g/L. These findings indicate the feasibility of using FT-IR spectroscopy for rapid and quantitative detection of umami substances, providing a theoretical basis for detecting complex umami substances in food using FT-IR technology.