In-Line Near-Infrared Spectroscopy Gives Rapid and Precise Assessment of Product Quality and Reveals Unknown Sources of Variation-A Case Study from Commercial Cheese Production.

Quality testing in the food industry is usually performed by manual sampling and at/off-line laboratory analysis, which is labor intensive, time consuming, and may suffer from sampling bias. For many quality attributes such as fat, water and protein, in-line near-infrared spectroscopy (NIRS) is a viable alternative to grab sampling. The aim of this paper is to document some of the benefits of in-line measurements at the industrial scale, including higher precision of batch estimates and improved process understanding. Specifically, we show how the decomposition of continuous measurements in the frequency domain, using power spectral density (PSD), may give a useful view of the process and serve as a diagnostic tool. The results are based on a case regarding the large-scale production of Gouda-type cheese, where in-line NIRS was implemented to replace traditional laboratory measurements. In conclusion, the PSD of in-line NIR predictions revealed unknown sources of variation in the process that could not have been discovered using grab sampling. PSD also gave the dairy more reliable data on key quality attributes, and laid the foundation for future improvements.

Sensory Assessment of Fish and Chicken Protein Hydrolysates. Evaluation of NMR Metabolomics Profiling as a New Prediction Tool.

Nuclear magnetic resonance (NMR) metabolomics profiling was evaluated as a new tool in sensory assessment of protein hydrolysates. Hydrolysates were produced on the basis of different raw materials (cod, salmon, and chicken), enzymes (Food Pro PNL and Bromelain), and hydrolysis time (10 and 50 min). The influence of raw material and hydrolysis parameters on sensory attributes was determined by traditional descriptive sensory analysis and 1H NMR spectroscopy. The raw material had a major influence on the attribute intensity and metabolite variation, followed by enzyme and hydrolysis time. However, the formation of bitter taste was not affected by the raw material. Partial least-squares regression (PLSR) on 1H NMR and sensory data provided good models (Q2 = 0.55-0.89) for 11 of the 17 evaluated attributes, including bitterness. Significant metabolite-attribute associations were identified. The study confirms the potential prediction of the sensory properties of protein hydrolysates from cod, salmon, and chicken based on 1H NMR metabolomics profiling.