Orthogonality constrained inverse regression to improve model selectivity and analyte predictions from vibrational spectroscopic measurements.

In analytical chemistry spectroscopy is attractive for high-throughput quantification, which often relies on inverse regression, like partial least squares regression. Due to a multivariate nature of spectroscopic measurements an analyte can be quantified in presence of interferences. However, if the model is not fully selective against interferences, analyte predictions may be biased. The degree of model selectivity against an interferent is defined by the inner relation between the regression vector and the pure interfering signal. If the regression vector is orthogonal to the signal, this inner relation equals zero and the model is fully selective. The degree of model selectivity largely depends on calibration data quality. Strong correlations may deteriorate calibration data resulting in poorly selective models. We show this using a fructose-maltose model system. Furthermore, we modify the NIPALS algorithm to improve model selectivity when calibration data are deteriorated. This modification is done by incorporating a projection matrix into the algorithm, which constrains regression vector estimation to the null-space of known interfering signals. This way known interfering signals are handled, while unknown signals are accounted for by latent variables. We test the modified algorithm and compare it to the conventional NIPALS algorithm using both simulated and industrial process data. The industrial process data consist of mid-infrared measurements obtained on mixtures of beta-lactoglobulin (analyte of interest), and alpha-lactalbumin and caseinoglycomacropeptide (interfering species). The root mean squared error of beta-lactoglobulin (% w/w) predictions of a test set was 0.92 and 0.33 when applying the conventional and the modified NIPALS algorithm, respectively. Our modification of the algorithm returns simpler models with improved selectivity and analyte predictions. This paper shows how known interfering signals may be utilized in a direct fashion, while benefitting from a latent variable approach. The modified algorithm can be viewed as a fusion between ordinary least squares regression and partial least squares regression and may be very useful when knowledge of some (but not all) interfering species is available.

Prediction of α-Lactalbumin and ß-Lactoglobulin Composition of Aqueous Whey Solutions Using Fourier Transform Mid-Infrared Spectroscopy and Near-Infrared Spectroscopy.

Characterization and quantification of individual whey proteins are of crucial importance to many industrial dairy processes. Labor intensive wet-chemical methods are still being used for this purpose, but a rapid quantification method for individual whey proteins is highly desired. This work investigate the utility of Fourier transform mid-infrared spectroscopy and Fourier transform near-infrared spectroscopy for rapid quantification of the two main whey proteins (ß-lactoglobulin and α-lactalbumin) in complex aqueous whey solutions simulating production process streams. MIR and NIR spectra obtained on whey samples with known and varying amounts of the proteins of interest and are used to develop partial least squares prediction models. Selection of informative wavelength regions allowed for prediction of ß-lactoglobulin and α-lactalbumin concentrations with very high precision and accuracy (root mean square error of cross-validation, or RMSECV, of 0.6% and R2 of 0.99 for NIR), demonstrating the potential of being implemented for rapid in-line monitoring of protein composition in industrial whey streams. Two-dimensional MIR-NIR correlation spectroscopy is used to identify the most informative parts of the NIR spectra in relation to protein secondary structure. In addition multivariate curve resolution is applied to the MIR data to resolve mixture spectra and to elucidate the spectral ranges that were most useful in distinguishing between the two whey proteins. The study concludes that NIR spectroscopy has potential for accurate in-line protein quantification and overall secondary protein structure quantification which open new possibilities for in-line industrial applications.

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.

Gas chromatography - mass spectrometry data processing made easy.

Evaluation of GC-MS data may be challenging due to the high complexity of data including overlapped, embedded, retention time shifted and low S/N ratio peaks. In this work, we demonstrate a new approach, PARAFAC2 based Deconvolution and Identification System (PARADISe), for processing raw GC-MS data. PARADISe is a computer platform independent freely available software incorporating a number of newly developed algorithms in a coherent framework. It offers a solution for analysts dealing with complex chromatographic data. It allows extraction of chemical/metabolite information directly from the raw data. Using PARADISe requires only few inputs from the analyst to process GC-MS data and subsequently converts raw netCDF data files into a compiled peak table. Furthermore, the method is generally robust towards minor variations in the input parameters. The method automatically performs peak identification based on deconvoluted mass spectra using integrated NIST search engine and generates an identification report. In this paper, we compare PARADISe with AMDIS and ChromaTOF in terms of peak quantification and show that PARADISe is more robust to user-defined settings and that these are easier (and much fewer) to set. PARADISe is based on non-proprietary scientifically evaluated approaches and we here show that PARADISe can handle more overlapping signals, lower signal-to-noise peaks and do so in a manner that requires only about an hours worth of work regardless of the number of samples. We also show that there are no non-detects in PARADISe, meaning that all compounds are detected in all samples.

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.