Temperature-perturbed two-dimensional generalized correlation characteristic slice spectra combined with multivariate...

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Food Science

Background:

• Milk adulteration poses a significant threat to public health and the economy.
• Accurate and efficient detection methods for adulterated milk are crucial for quality control.

Purpose of the Study:

• To develop an improved method for detecting adulterated milk with higher discrimination accuracy.
• To leverage temperature-perturbed generalized two-dimensional (2D) correlation spectroscopy for milk analysis.

Main Methods:

• Infrared attenuated total reflection spectra of pure and urea-tainted milk samples were collected at varying temperatures.
• Synchronous 2D infrared correlation spectra were generated using temperature as a perturbation.
• Characteristic spectral slices were extracted and analyzed using N-way partial least squares discriminant analysis (NPLS-DA) models.

Main Results:

• The proposed 2D correlation characteristic slice spectra method achieved 100% discrimination accuracy for a fused three-brand model.
• This method demonstrated superior performance compared to using 2D correlation spectra (98.8%) and 1D infrared spectra (82.7%).
• The approach effectively compressed data and extracted key characteristic information.

Conclusions:

• Temperature-perturbed generalized 2D correlation characteristic slice spectra offer a highly accurate method for detecting adulterated milk.
• This technique provides a robust and efficient solution for milk quality and safety assurance.
• The method's ability to extract characteristic information improves the reliability of adulterant detection.