Dry matter is an important process control parameter in the bioconversion application field. Acoustic chemometrics, as a Process Analytical Technology (PAT) modality for quantitative characterisation of dry matter in complex bioslurry systems (biogas fermentation), has not been successful despite several earlier dedicated attempts. A fullscale feasibility study based on standard addition experiments involving natural plant biomass was conducted using multivariate calibration (Partial Least Squares Regression, PLS-R) of acoustic signatures against dry matter content (total solids, TS). Prediction performance of the optimised process implementation was evaluated using independent test set validation, with estimates of accuracy (slope of predicted vs. reference values) and precision (squared correlation coefficient, r 2 ) of 0.94 and 0.97 respectively, with RMSEP of 0.32 % w/w (RMSEP rel = 3.86 %) in the range of 5.8 -10.8 % w/w dry matter. Based on these excellent prediction performance measures, it is concluded that acoustic chemometrics has come of age as a full grown PAT approach for on-line monitoring of dry matter (TS) in complex bioslurry, with a promising application potential in other biomass processing industries as well.

A hydrophilic interaction liquid chromatography-UV method was developed for the determination of melamine in milk powder. Sulfobetaine type zwitterionic hydrophilic interaction liquid chromatography stationary phase was used to achieve straightforward separation of melamine in milk powder without any sample derivatization or addition of ion-pair reagent. The sample preparation was simple and fast with the steps of acetonitrile/perchloric acid extraction-centrifugation-filtration. No SPE or other pre-concentration procedure was required. By using large volume sample injection and choosing low UV wavelength (210 nm), the LOD and LOQ were 0.005 and 0.015 microg/mL for melamine standards, and 0.02 and 0.06 microg/mL for the spiked milk extracts. The LOD and LOQ of the latter correspond to 0.95 and 2.2 microg/g melamine in the milk powder. The correlation coefficients for melamine standard, pre-spiked milk extracts and post-spiked milk extracts in the range of 0-0.5 microg/mL were 0.9978, 0.9976 and 0.9995, respectively. This newly developed method is sensitive and cost effective, therefore, suitable for screening of melamine in tainted milk products.

Enzymatic protein hydrolysis (EPH) is one of the industrial bioprocesses used to recover valuable constituents from food processing by-products. Extensive heterogeneity of byproducts from, for example, meat-processing is a major challenge in production of protein hydrolysates with stable and desirable quality attributes. Therefore, there is a need for process control tools for production of hydrolysates with defined qualities from such heterogeneous raw materials. In the present study, we are reporting a new feed-forward process control strategy for enzymatic protein hydrolysis of poultry by-products. Four different spectroscopic techniques, i.e., NIR imaging scanner, a miniature NIR (microNIR) instrument, fluorescence and Raman, were evaluated as tools for characterization of the raw material composition. Partial least squares (PLS) models for ash, protein and fat content were developed based on Raman, fluorescence and microNIR measurements, respectively. In an effort to establish feed-forward process control tools, we developed statistical models that enabled prediction of end-product characteristics, i.e., protein yield and average molecular weight of peptides (Mw), as a function of raw material quality and hydrolysis time. A multi-block sequential orthogonalised-PLS (SO-PLS) model, where spectra from one or more techniques and hydrolysis time were used as predictor variables, was fitted for the feed-forward prediction of product qualities. The best model was obtained for protein yield based on combined use of microNIR and fluorescence (R 2 = 0.88 and RMSE = 4.8). A Raman-based model gave a relatively moderate prediction model for Mw (R 2 = 0.56 and RMSE = 150). Such statistical models based on spectroscopic measurements of the raw material can be vital process control tools for EPH. To our knowledge, the present work is the first example of a spectroscopic feed-forward process control for an industrially relevant bioprocess.

Fish farmers consider the cost of fish feed pellets as one of the most expensive factors in fish cultivation. Proper control of the handling and conveying systems is necessary to avoid damage and disintegration of the cylindrically shaped fish feed pellets. Pneumatic conveying is widely used to transport large quantities of fish feed. Proneness of crushing the fish feed pellets caused by pellets interaction with the inner wall of the pipeline is a major concern to the manufacturer due to the associated economic loss; pellet damage increases exponentially with the conveying air velocity. On the other hand, too low conveying rates would lead to pipeline blockages and severe pipe vibration. In order to address the foregoing issues, it is necessary to optimize the conveying velocity of fish feed pellets during pneumatic transport. Application of an on-line monitoring technique based on non-invasive passive acoustic measurements and multivariate regression modeling (acoustic chemometrics) was investigated. A partial least squares regression (PLS-R) model was calibrated to predict pellet velocity from 19 m/s to 36 m/s in a pilot scale pneumatic conveying system. The PLS-R prediction model was validated based on independent experimental data (test set validation). The root mean square error of prediction (RMSEP), slope and r 2 of the prediction results were 0.64 m/s, 1.02 and 0.97 respectively. The prediction results obtained shows the applicability of acoustic chemometrics for real-time prediction of the velocities of fish feed pellets during pneumatic conveying.