Turmeric sourced from six retailers was processed into a powder and adulterated with metanil yellow (MY) at concentrations of 0.0 to 30% (w/w). A handheld near-infrared spectrometer was used to obtain spectral scans of the samples, which were preprocessed using Savitzky-Golay first-derivative (SG1) approximation using 61 smoothing points and second-order polynomial. The preprocessed spectra were analyzed using principal component analysis (PCA) followed by classification by soft independent modeling class analogy (SIMCA) and were used to group the adulterated turmeric powder samples according to the source (i.e., processor) of adulteration. Results showed the first principal component (PC1) of PCA models was sensitive to adulteration level, but when coupled with SIMCA, unadulterated and adulterated samples could be classified according to their source despite having high levels of MY. At 5% level of significance, all of the samples were correctly classed for origin during validation. Some samples were classified under two groups, indicating possible inherent similarities. When the PCA model was built using only unadulterated samples, the PCA-SIMCA model could not classify the adulterated samples but could classify those with very low levels (≤2%, w/w) of MY, allowing for segregation of adulterated samples but not identification of sources. The combination of near-infrared and PCA-SIMCA modeling is a great tool not only to detect adulterated turmeric powder but also, potentially, to deter it in the future because the source of adulterated food can be traced back to the source of adulteration.
PC1 of PCA models was sensitive to metanil yellow adulteration levels.
PCA-SIMCA can be used to identify source or processor of adulterated turmeric powder.
Depending on calibration set used, PCA-SIMCA can be used to segregate adulterated samples.