Modeling method and miniaturized wavelength strategy for near-infrared spectroscopic discriminant analysis of soy sauce brand identification

Abstract

The identification of soy sauce brands can avoid adulteration and fraud, which is meaningful for food safety screening. Using visible and near-infrared (Vis-NIR) spectroscopy combined with k-nearest neighbor (kNN), the four-category discriminant models of soy sauce brands were established. The soy sauce of three brands (identification) and the other ten brands (interference) were collected, and a total of four categories of samples were obtained. The spectral datasets of two measurement modals (1 mm, 10 mm) were obtained. Based on moving-window (MW) waveband screening and wavelength step-by-step phase-out (WSP), the MW-WSP-kNN algorithm was proposed and applied to the wavelength optimization for the four-category discriminant analysis. Using calibration-prediction-validation experiment design, various high accuracy models with a small number of wavelengths located in NIR region were determined. In the independent validation, for the 1 mm measurement modal, the selected thirty-five dual-wavelength models and one three-wavelength model were located in NIR combined and overtone frequency regions respectively, all achieved 100% total recognition accuracy rate (RARTotal); for the 10 mm measurement modal, the selected seven three-wavelength models located in NIR overtone frequency region all reached more than 96.8% RARTotal, and the optimal RARTotal was 97.8%. The results showed the feasibility of small number of wavelengths' NIR spectroscopy applied to multi-category discriminant of soy sauce brands, with the advantages of rapid, simple and miniaturized. The proposed various small number of wavelengths' models provided a valuable reference for the design of small dedicated spectrometer with different measurement modals. The integrated optimization method and wavelength selection strategy here are also expected to be applied to other fields.