Iben Ellegaard Bechmann

Comparative Chemometric Analysis of Transverse Low-field 1H NMR Relaxation Data

1 Abstract Transverse relaxation data from low-field (23.2 MHz) 1H NMR was investigated by different data analytical approaches. The quantitative information in the relaxation data of 200 intact salmon samples with respect to overall water and fat content was evaluated by four different data analytical methods: (a) bi-exponential curve fitting followed by linear regression, (b) forward selection of variables followed by multiple linear regression (FS-MLR), (c) partial least squares regression (PLS) and (d) non-negative alternating least squares regression (NN-ALSR). The investigation demonstrates that the quantitative prediction performance is significantly enhanced (reduction of the prediction error from 14% to 34%) by the use of multivariate chemometric procedures such as PLS. While PLS is an extraordinarily robust and efficient algorithm its strictly orthogonal latent variables suffer from a difficult qualitative interpretation. NN-ALSR and FS-MLR which exhibit a quantitative performance comparable to PLS do not suffer from this problem, but are more unstable and ineffective data analytical techniques.

Determination of formaldehyde in frozen fish with formaldehyde dehydrogenase using a flow injection system with an incorporated gel-filtration chromatography column

Abstract A flow injection analysis (FIA) system for determination of formaldehyde in frozen fish products is described. The system provides a rapid and selective determination of formaldehyde in aqueous fish extracts by the combination of a deproteinization procedure and a stopped-flow enzymatic approach in a FIA system. The FIA system is furnished with a gel-filtration chromatography column for on-line removal of the proteins from the extract before the enzymatic analysis is performed. Compared with the standard methods for determination of formaldehyde in fish products the present method is much faster and less affected by interferences. The limit of detection for the proposed method is 2.5 mg/l of formaldehyde. The sampling frequency is about 10 determinations per hour.

Prediction of chemical, physical and sensory data from process parameters for frozen cod using multivariate analysis

Physical, chemical and sensory quality parameters were determined for 115 cod (Gadus morhua) samples stored under varying frozen storage conditions. Five different process parameters (period of frozen storage, frozen storage temperature, place of catch, season for catching and state of rigor) were varied systematically at two levels. The data obtained were evaluated using the multivariate methods, principal component analysis (PCA) and partial least squares (PLS) regression. The PCA models were used to identify which process parameters were actually most important for the quality of the frozen cod. PLS models that were able to predict the physical, chemical and sensory quality parameters from the process parameters of the frozen raw material were generated. The prediction abilities of the PLS models were good enough to give reasonable results even when the process parameters were characterised by ones and zeroes only. These results illustrate the application of multivariate analysis as an effective strategy for improving the quality of frozen fish products.

Generalized standard addition in flow-injection analysis with UV-visible photodiode array detection

Abstract A flow-injection analysis (FIA) system which implements the generalized standard addition method is constructed. The FIA system, which is a model system, is furnished with a photodiode-array spectrophotometer as a detector and is a two-line system: one line transports the undiluted sample, which is merged with the second line, into which the standard is injected and dispersed to obtain different amounts of added standard. The performance of the constructed flow system and the partial least-squares solution to the generalized standard addition model is investigated with indicator samples, and an outlier detection technique capable of detecting direct interferences is implemented. By introducing two chasing zones in the standard stream a two analyte calibration space will be spanned, and a generalized standard addition can be performed. Samples containing one analyte and two analytes as well as samples containing direct interferences are analysed to test the FIA system and the outlier detection method.

Second-order data by flow injection analysis with spectrophotometric diode-array detection and incorporated gel-filtration chromatographic column

A flow injection analysis (FIA) system furnished with a gel-filtration chromatographic column and with photodiode-array detection was used for the generation of second-order data. The system presented is a model system in which the analytes are blue dextran, potassium hexacyanoferrate(III) and heparin. It is shown that the rank of the involved sample data matrices corresponds to the number of chemical components present in the sample. The PARAFAC (parallel factor analysis) algorithm combined with multiple linear regression and tri-PLS (tri-linear partial least-squares regression), which allows unknown substances to be present in the sample, are implemented for FIA systems and it is illustrated how these three-way algorithms can handle spectral interferents. The prediction ability of the two methods for pure two-component samples and also the predictions ability in the presence of unknown interferents are satisfactory. However, the predictions obtained by tri-PLS are slightly better than those obtained using PARAFAC regression algorithm.