Application of mid-infrared spectroscopy and PLS-Kernel calibration for quick detection of pork in higher value meat mixes

Analytical methods for meat-species identification and detection of adulteration are always needed for quality control and the safety of consumers. A novel application of PLS-Kernel with MIR for quick detection of pork in beef mixes down to 1.4 wt% is outlined. PLS-Kernel algorithm showed an excellent performance for handling many variables spectral data, which makes it suitable for food analysis by IR. Raw spectral data indicated a nonlinear relationship between pork level and IR band intensities of proteins and fats in the mixes. The ratios $${{\text{A}}_{\text{1654}\ \text{c}{{\text{m}}^{-1}}}}\ \left( \text{amid-I} \right)/{{\text{A}}_{\text{1745}\ \text{c}{{\text{m}}^{-1}}}}$$ (C=O band of ester), $${{\text{A}}_{\text{154}0\ \text{c}{{\text{m}}^{-1}}}}\ \left( \text{amid-II} \right)/{{\text{A}}_{\text{1745}\ \text{c}{{\text{m}}^{-1}}}}$$ , and $$\left( {{\text{A}}_{\text{1395}\ \text{c}{{\text{m}}^{-1}}}}\,+\,{{\text{A}}_{\text{145}0\ \text{c}{{\text{m}}^{-1}}}} \right)/{{\text{A}}_{\text{1175}\ \text{c}{{\text{m}}^{-1}}}}$$ were correlated with pork level to establish a useful analytical signal for detection purposes. Chemometric analysis was carried out on 900–1900 cm−1 as an informative spectral range for protein. PLS-Kernel model is recommended over PCR and PLS for handling many variables in IR data due to the lower computation cycles and less-memory storage. The proposed method would replace advanced techniques for quick pork detection in minced meat mixes.