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The main objective of this study was to design, build, and test a compact, multi-well, portable dry film FTIR system for industrial food and bioprocess applications. The system features dry film sampling on a circular rotating disc comprising 31 wells, a design that was chosen to simplify potential automation and robotic sample handling at a later stage. Calibration models for average molecular weight (AMW, 200 samples) and collagen content (68 samples) were developed from the measurements of industrially produced protein hydrolysate samples in a controlled laboratory environment. Similarly, calibration models for the prediction of lactate content in samples from cultivation media (59 samples) were also developed. The portable dry film FTIR system showed reliable model characteristics which were benchmarked with a benchtop FTIR system. Subsequently, the portable dry film FTIR system was deployed in a bioprocessing plant, and protein hydrolysate samples were measured at-line in an industrial environment. This industrial testing involved building a calibration model for predicting AMW using 60 protein hydrolysate samples measured at-line using the portable dry film FTIR system and subsequent model validation using a test set of 26 samples. The industrial calibration in terms of coefficient of determination (R2= 0.94), root mean square of cross-validation (RMSECV = 194 g mol−1), and root mean square of prediction (RMSEP = 162 g mol−1) demonstrated low prediction errors as compared to benchtop FTIR measurements, with no statistical difference between the calibration models of the two FTIR systems. This is to the authors' knowledge the first study for developing and employing a portable dry film FTIR system in the enzymatic protein hydrolysis industry for successful at-line measurements of protein hydrolysate samples. The study therefore suggests that the portable dry film FTIR instrument has huge potential for in/at-line applications in the food and bioprocessing industries.

The results presented here describe a comparative study on pork loins with drip loss varying from 0.25% w/w to 10.69% w/w. Thirty samples were measured using both near infrared (NIR) interactance and X-ray scattering. Partial least squares regression was used to build calibration models for each method. Results show that the correlation for the calibration model between NIR interactance and drip loss was R2= 0.47 (leaving out three outliers) while that for X-ray and drip loss was R2= 0.72 (leaving out three outliers).

A low-cost Fourier transform infrared (FTIR) instrument was developed where the traditional He–Ne reference laser was replaced by a low-cost linear encoder. An RMS sampling error of less than 20 nm was achieved by oversampling both the interferogram and the encoder signal and then resampling the interferogram using a correction table for the encoder. A gas calibration model was developed for the system, which was chosen to have a stroke length of 21 mm and, thereby, a resolution of 0.4  cm^−1 after apodization. The instrument was mounted on a vehicle and employed in an agricultural field test for measuring soil emissions, in particular nitrous oxide (N_2O). The concentration of N_2O was measured with a root mean squared error of 6 ppb. The results compared well with lab-based gas chromatography measurements.

A novel system for on-line measurement of fat content in inhomogeneous pork trimmings is presented. The system allows near infrared (NIR) energy to interact with the meat using non-contact optics while it is travelling in large plastic boxes on a conveyor belt. A comparison was made between the log of the inverse of the interactance NIR spectra [log(1/T)], standard normal variate (SNV) and extended multiplicative signal correction (EMSC) as techniques for the correction of physical light scattering due to colour and textural differences, height variation and temperature fluctuations, depending on whether the meat was warm-cut or cold-cut. EMSC gave the best prediction results; a robust partial least squares regression using two factors resulted in a root mean square error (RMSEP) of 1.9% on 20 kg batches of inhomogeneous meat trimmings. The model was fully tested twice in an on-line environment at a slaughter house and performed with a RMSEPof 3.4% for a fat range of 8–55% in the first industrial trial and 2.82% in the second industrial trial.

A novel system for on-line measurement of fat content in inhomogeneous pork trimmings is presented. The system allows near infrared (NIR) energy to interact with the meat using non-contact optics while it is travelling in large plastic boxes on a conveyor belt. A comparison was made between the log of the inverse of the interactance NIR spectra [log(1/T)], standard normal variate (SNV) and extended multiplicative signal correction (EMSC) as techniques for the correction of physical light scattering due to colour and textural differences, height variation and temperature fluctuations, depending on whether the meat was warm-cut or cold-cut. EMSC gave the best prediction results; a robust partial least squares regression using two factors resulted in a root mean square error (RMSEP) of 1.9% on 20 kg batches of inhomogeneous meat trimmings. The model was fully tested twice in an on-line environment at a slaughter house and performed with a RMSEP of 3.4% for a fat range of 8–55% in the first industrial trial and 2.82% in the second industrial trial.