Effectiveness of two different at-line instruments for the assessment of cheese composition, major minerals and fatty acids content

The at-line performance of two different NIRS instruments to predict major and minor cheese nutritional traits was evaluated. For this purpose, 158 samples from dairy products were collected and analysed by reference methods. Spectra were acquired using a transmittance and a reflectance instrument. Predictive equations were developed on the whole dataset or dividing samples in groups. Samples clustering was performed using pairwise Mahalanobis distance and centroid linkage algorithm. Prediction models for protein, fat, saturated fatty acids and minerals showed good prediction performances (R2 > 0.80). Instrument configuration had a limited impact on prediction accuracy. Overall, clustering approach reduced prediction error but coefficient of determination also decreased. Prediction of minor compounds with models built from a large variety of cheeses could be useful for process control. Cluster approach is recommended for specific traits and cheese type, for the fine tuning of final product characteristics.