Nils Kristian Afseth

Raman Spectra of Biological Samples: A Study of Preprocessing Methods

In this study preprocessing of Raman spectra of different biological samples has been studied, and their effect on the ability to extract robust and quantitative information has been evaluated. Four data sets of Raman spectra were chosen in order to cover different aspects of biological Raman spectra, and the samples constituted salmon oils, juice samples, salmon meat, and mixtures of fat, protein, and water. A range of frequently used preprocessing methods, as well as combinations of different methods, was evaluated. Different aspects of regression results obtained from partial least squares regression (PLSR) were used as indicators for comparing the effect of different preprocessing methods. The results, as expected, suggest that baseline correction methods should be performed in advance of normalization methods. By performing total intensity normalization after adequate baseline correction, robust calibration models were obtained for all data sets. Combination methods like standard normal variate (SNV), multiplicative signal correction (MSC), and extended multiplicative signal correction (EMSC) in their basic form were not able to handle the baseline features present in several of the data sets, and these methods thus provide no additional benefits compared to the approach of baseline correction in advance of total intensity normalization. EMSC provides additional possibilities that require further investigation.

Raman and Near-Infrared Spectroscopy for Quantification of Fat Composition in a Complex Food Model System

Raman and near-infrared (NIR) spectroscopy have been evaluated for determining fatty acid composition and contents of main constituents in a complex food model system. A model system consisting of 70 different mixtures of protein, water, and oil blends was developed in order to create a rough chemical imitation of typical fish and meat samples, showing variation both in fatty acid composition and in contents of main constituents. The model samples as well as the pure oil mixtures were measured using Raman and NIR techniques. Partial least squares regression was utilized for prediction, and fatty acid features were expressed in terms of the iodine value and as contents of saturated, monounsaturated, and polyunsaturated fatty acids. Raman spectroscopy provided the best results for predicting iodine values of the model samples, giving validated estimation errors accounting for 2.8% of the total iodine value range. Both techniques provided good results for predicting the content of saturated, monounsaturated, and polyunsaturated fatty acids in the model samples, yielding validated estimation errors in the range of 2.4–6.1% of the total range of fatty acid content. Prediction results for determining fatty acid features of the pure oil mixtures were similar for the two techniques. NIR was clearly the best technique for modeling content of main constituents in the model samples.

Monitoring Protein Structural Changes and Hydration in Bovine Meat Tissue Due to Salt Substitutes by Fourier Transform Infrared (FTIR) Microspectroscopy

The objective of this study was to investigate the influence of NaCl and two salt substitutes, MgSO4 and KCl, in different concentrations (1.5, 6.0, and 9.0%) on meat proteins by using Fourier transform infrared (FTIR) microspectroscopy. Hydration properties and secondary structural properties of proteins were investigated by studying the amide I, amide II, and water regions (3500-3000 cm(-1)) in FTIR spectra. By applying multivariate analysis (PCA and PLSR), differences between samples according to salt concentration and salt type were found and correlated to spectral bands. The most distinctive differences related to salt type were obtained by using the water region. It was found that samples salted with MgSO4 exhibited hydration and subsequent denaturation of proteins at lower concentrations than those salted with NaCl. Samples salted with KCl brines showed less denaturation even at the 9.0% concentration. The FTIR results were further supported by water-binding capacity (WBC) measurements.

Noninvasive analysis of thin turbid layers using microscale spatially offset Raman spectroscopy

Here, we demonstrate, for the first time, the extension of applicability of recently developed microscale spatially offset Raman spectroscopy (SORS), micro-SORS, from the area of cultural heritage to a wider range of analytical problems involving thin, tens of micrometers thick diffusely scattering turbid layers. The method can be applied in situations where a high turbidity of layers prevents the deployment of conventional confocal Raman microscopy with its depth resolving capability. The method was applied successfully to detect noninvasively the presence of thin, highly turbid layers within polymers, wheat seeds, and paper. An invasive, cross sectional analysis confirmed the micro-SORS findings. Micro-SORS represents a new Raman imaging modality expanding the portfolio of noninvasive, chemically specific analytical tools.

Predicting the Fatty Acid Composition of Milk: A Comparison of Two Fourier Transform Infrared Sampling Techniques

In the present study a novel approach for Fourier transform infrared (FT-IR) characterization of the fatty acid composition of milk based on dried film measurements has been presented and compared to a standard FT-IR approach based on liquid milk measurements. Two hundred and sixty-two (262) milk samples were obtained from a feeding experiment, and the samples were measured with FT-IR as dried films as well as liquid samples. Calibrations against the most abundant fatty acids, CLA (i.e., 18:2cis-9, trans-11), 18:3cis-9, cis-12, cis-15, and summed fatty acid parameters were obtained for both approaches. The estimation errors obtained in the dried film calibrations were overall lower than the corresponding liquid sample calibrations. Similar and good calibrations (i.e., R2 ranges from 0.82 to 0.94 (liquid samples) and from 0.88 to 0.97 (dried films)) for short-chain fatty acids (6:0–14:0), 18:1cis-9, SAT, MUFA, and iodine value were obtained by both approaches. However, the dried film approach was the only approach for which feasible calibrations (i.e., R2 ranges from 0.78 to 0.93) were obtained for the major saturated fatty acids 16:0 and 18:0, the minor fatty acid features 4:0, CLA (i.e., 18:2cis-9, trans-11), PUFA, and the summed 18:1 trans isomers. For the dried film approach, logical spectral features were found to dominate the respective fatty acid calibration models. The preconcentration step of the dried film approach could be expected to account for a major part of the prediction improvements going from predictions in liquid milk to predictions in dried films. The dried film approach has a significant potential for use in high-throughput applications in industrial environments and might also serve as a valuable supplement for determination of genetic and breeding factors within research communities.

Fourier transform infrared spectroscopy for the prediction of fatty acid profiles in Mucor fungi grown in media with different carbon sources

Fungal production of polyunsaturated fatty acids (PUFAs) is a highly potential approach in biotechnology. Currently the main focus is directed towards screening of hundreds strains in order to select of few potential ones. Thus, a reliable method for screening a high number of strains within a short period of time is needed. Here, we present a novel method for screening of PUFA-producing fungi by high-throughput microcultivation and FTIR spectroscopy. In the study selected Mucor fungi were grown in media with different carbon sources and fatty acid profiles were predicted on the basis of the obtained spectral data. FTIR spectra were calibrated against fatty acid analysis by GC-FD. The calibration models were cross-validated and correlation coefficients (R2) from 0.71 to 0.78 with RMSECV (root mean squared error) from 2.86% to 6.96% (percentage of total fat) were obtained. The FTIR results show a strong correlation to the results obtained by GC analysis, where high total contents of unsaturated fatty acids (both PUFA and MUFA) were achieved for Mucor plumbeus VI02019 cultivated in canola, olive and sunflower oil and Mucor hiemalis VI01993 cultivated in canola and olive oil.

Determining quality of caviar from Caspian Sea based on Raman spectroscopy and using artificial neural networks

The purpose of this study was to evaluate the feasibility of Raman spectroscopy for predicting purity of caviars. The 93 wild caviar samples of three different types, namely; Beluga, Asetra and Sevruga were analysed by Raman spectroscopy in the range 1995 cm(-1) to 545 cm(-1). Also, 60 samples from combinations of every two types were examined. The chemical origin of the samples was identified by reference measurements on pure samples. Linear chemometric methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used for data visualisation and classification which permitted clear distinction between different caviars. Non-linear methods like Artificial Neural Networks (ANN) were used to classify caviar samples. Two different networks were tested in the classification: Probabilistic Neural Network with Radial-Basis Function (PNN) and Multilayer Feed Forward Networks with Back Propagation (BP-NN). In both cases, scores of principal components (PCs) were chosen as input nodes for the input layer in PC-ANN models in order to reduce the redundancy of data and time of training. Leave One Out (LOO) cross validation was applied in order to check the performance of the networks. Results of PCA indicated that, features like type and purity can be used to discriminate different caviar samples. These findings were also supported by LDA with efficiency between 83.77% and 100%. These results were confirmed with the results obtained by developed PC-ANN models, able to classify pure caviar samples with 93.55% and 71.00% accuracy in BP network and PNN, respectively. In comparison, LDA, PNN and BP-NN models for predicting caviar types have 90.3%, 73.1% and 91.4% accuracy. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R(2)) of 0.86, 0.83, 0.92 and 0.91 with root mean square error (RMSE) of validation of 0.32, 0.11, 0.03 and 0.09 for fatty acids of 16.0, 20.5, 22.6 and fat, respectively.

Towards on-line prediction of dry matter content in whole unpeeled potatoes using near-infrared spectroscopy

Prediction of dry matter content in whole potatoes is a desired capability in the processing industry. Accurate prediction of dry matter content may greatly reduce waste quantities and improve utilization of the raw material through sorting, hence also reducing the processing cost. The following study demonstrates the use of a low resolution, high speed NIR interactance instrument combined with partial least square regression for prediction of dry matter content in whole unpeeled potatoes. Three different measuring configurations were investigated: (1) off-line measurements with contact between the potato and the light collection tube; (2) off-line measurements without contact between the potato and the light collection tube; and (3) on-line measurements of the potatoes. The offline contact measurements gave a prediction performance of R(2)=0.89 and RMSECV=1.19. Similar prediction performance were obtained from the off-line non-contact measurements (R(2)=0.89, RMSECV=1.23). Significantly better (p=0.038) prediction performance (R(2)=0.92, RMSECV=1.06) was obtained with the on-line measuring configuration, thus showing the possibilities of using the instrument for on-line measurements. In addition it was shown that the dry matter distribution across the individual tuber could be predicted by the model obtained.

Characterizing salt substitution in beef meat processing by vibrational spectroscopy and sensory analysis

In this investigation, the effect of NaCl, KCl and MgSO4 on bovine meat was studied, where the salts were used in standard marinades in 5.5% concentration. The effect of salts on secondary structure of the myofibrillar proteins, protein-water interactions, WHC, and sensory properties of the meat was followed by carrying out FTIR and NIR measurements, cooking loss and sensory analysis. The information obtained by spectroscopic analysis was integrated by using CPCA. This revealed that MgSO4 increased ratio of α-helices and CO and NH groups (followed by FTIR) that are involved in H-bonding with surrounding water molecules (followed by NIR). This was also supported by increased WHC. Conversely, KCl reduced WHC of meat and was correlated to non-hydrogenated CO and NH groups. Furthermore, the sensory analysis confirmed that MgSO4 was acceptable only when the share of this salt in the mixture was one third.

FTIR imaging for structural analysis of frankfurter sausages subjected to salt reduction and salt substitution.

In this study, the effects of NaCl, KCl, and MgSO4 in various concentrations on structural and sensory properties of frankfurter sausages were investigated. FTIR was used to analyze the overall homogeneousness of the sausages by simultaneously following the distribution of main sausage ingredients, i.e., proteins, fats, and starch. A more homogeneous distribution of the main ingredients was observed with higher concentration of added salts, while it was most pronounced for the MgSO4 recipe. Furthermore, FTIR imaging was used in order to follow the distribution of protein secondary structure motifs throughout the sausage matrix. It was confirmed that KCl inhibited the partial denaturation of proteins, unlike that observed for MgSO4 recipes, where an additional increase in protein hydration was detected. These findings were unequivocally supported by WHC measurements. However, the sensory analysis clearly distinguished the sausages prepared with MgSO4 due to undesired sensory attributes, which underlines the necessity for using taste masking agents.