Chemical characterization by gas chromatography-mass spectrometry and inductively coupled plasma-optical emission spectroscopy of membrane permeates from an industrial dairy ingredient production used as process water.

Reusing reverse osmosis (RO) membrane permeate instead of potable water in the dairy industry is a very appealing tactic. However, to ensure safe use, the quality of reclaimed water must be guaranteed. To do this, qualitative and quantitative information about which compounds permeate the membranes must be established. In the present study, we provide a detailed characterization of ultrafiltration, RO, and RO polisher (ROP) permeate with regard to organic and inorganic compounds. Results indicate that smaller molecules and elements (such as phosphate, but mainly urea and boron) pass the membrane, and a small set of larger molecules (long-chain fatty acids, glycerol-phosphate, and glutamic acid) are found as well, though in minute concentrations (<0.2 µM). Growth experiments with 2 urease-positive microorganisms, isolated from RO permeate, showed that the nutrient content in the ROP permeate supports limited growth of 1 of the 2 isolates, indicating that the ROP permeate may not be guaranteed to be stable during protracted storage.

Monitoring Process Water Quality Using Near Infrared Spectroscopy and Partial Least Squares Regression with Prediction Uncertainty Estimation.

Reuse of process water in dairy ingredient production-and food processing in general-opens the possibility for sustainable water regimes. Membrane filtration processes are an attractive source of process water recovery since the technology is already utilized in the dairy industry and its use is expected to grow considerably. At Arla Foods Ingredients (AFI), permeate from a reverse osmosis polisher filtration unit is sought to be reused as process water, replacing the intake of potable water. However, as for all dairy and food producers, the process water quality must be monitored continuously to ensure food safety. In the present investigation we found urea to be the main organic compound, which potentially could represent a microbiological risk. Near infrared spectroscopy (NIRS) in combination with multivariate modeling has a long-standing reputation as a real-time measurement technology in quality assurance. Urea was quantified Using NIRS and partial least squares regression (PLS) in the concentration range 50-200 ppm (RMSEP = 12 ppm, R2 = 0.88) in laboratory settings with potential for on-line application. A drawback of using NIRS together with PLS is that uncertainty estimates are seldom reported but essential to establishing real-time risk assessment. In a multivariate regression setting, sample-specific prediction errors are needed, which complicates the uncertainty estimation. We give a straightforward strategy for implementing an already developed, but seldom used, method for estimating sample-specific prediction uncertainty. We also suggest an improvement. Comparing independent reference analyses with the sample-specific prediction error estimates showed that the method worked on industrial samples when the model was appropriate and unbiased, and was simple to implement.