Cheddar cheese classification based on flavor quality using a novel extraction method and Fourier transform infrared spectroscopy

Raw (A and C) and derivatized (B and D) spectra of Cheddar cheese (–––) and Cheddar cheese extract (- - -). Certain important functional groups and their region of absorbance are highlighted. Spectral data were obtained in the mid-infrared region (4,000 to 700cm⁻¹) at resolution of 8cm⁻¹ by co-adding 128 scans. Cheddar cheese spectra were obtained by pressing 0.5g of cheese on a diamond attenuated total reflectance (ATR) crystal. Extracts were scanned by drying 10μL of the extract on a zinc selenide ATR crystal.

Soft independent modeling of class anology (SIMCA) classification plot of samples from the 2 production plants. The mid-infrared spectra were transformed into their second derivative, mean-centered and normalized prior multivariate analysis. The samples clustered distinctly indicating marked difference in the chemical composition of samples from the 2 production plants. The 95% probability cloud indicates the probability of a sample belonging to the cluster in which it is located.

Soft independent modeling of class analogy (SIMCA) classification plot of samples from plant 1. The samples were projected against the first 3 principal components (PC) that explained the largest amount of variance among the samples. All 5 samples formed distinct clusters in 3-dimensional space based on their flavor quality descriptors (1=fermented; 2=unclean; 3=slight sour; 4=good Cheddar; and 5=slight burnt).

Soft independent modeling of class analogy (SIMCA) classification plot of samples from plant 2. The orientation of the clusters in the SIMCA plot correlated with the flavor of the cheese samples. Samples 6 and 14=good Cheddar; sample 7=fermented; samples 8, 9, 10, and 11=sour and slight acid; sample 12=slight low flavor and slight sour; sample 13=low flavor; and sample 15=sulfide. PC=principal components.

Discriminating power plot for classification of Cheddar cheese samples. The regions of the Fourier transform infrared spectra that contribute to the discrimination of the cheese samples based on their flavor are highlighted. The greater the discriminating power at a particular wavenumber, the greater the difference between the samples in the functional group associated with that wavenumber.