Rapid discrimination of pork in Halal and non-Halal Chinese ham sausages by Fourier transform infrared (FTIR) spectroscopy and chemometrics.

Rapid discrimination of pork in Halal and non-Halal Chinese ham sausages was developed by Fourier transform infrared (FTIR) spectrometry combined with chemometrics. Transmittance spectra ranging from 400 to 4000 cm⁻¹ of 73 Halal and 78 non-Halal Chinese ham sausages were measured. Sample preparation involved finely grinding of samples and formation of KBr disks (under 10 MPa for 5 min). The influence of data preprocessing methods including smoothing, taking derivatives and standard normal variate (SNV) on partial least squares discriminant analysis (PLSDA) and least squares support vector machine (LS-SVM) was investigated. The results indicate removal of spectral background and baseline plays an important role in discrimination. Taking derivatives, SNV can improve classification accuracy and reduce the complexity of PLSDA. Possibly due to the loss of detailed high-frequency spectral information, smoothing degrades the model performance. For the best models, the sensitivity and specificity was 0.913 and 0.929 for PLSDA with SNV spectra, 0.957 and 0.929 for LS-SVM with second derivative spectra, respectively.