Chemical Imaging of Heterogeneous Muscle Foods Using Near-Infrared Hyperspectral Imaging in Transmission Mode.

Foods and biomaterials are, in general, heterogeneous and it is often a challenge to obtain spectral data which are representative for the chemical composition and distribution. This paper presents a setup for near-infrared (NIR) transmission imaging where the samples are completely trans-illuminated, probing the entire sample. The system measures falling samples at high speed and consists of an NIR imaging scanner covering the spectral range 760-1040 nm and a powerful line light source. The investigated samples were rather big: whole pork bellies of thickness up to 5 cm, salmon fillets with skin, and 3 cm thick model samples of ground pork meat. Partial least square regression models for fat were developed for ground pork and salmon fillet with high correlations (R = 0.98 and R = 0.95, respectively). The regression models were applied at pixel level in the hyperspectral transmission images and resulted in images of fat distribution where also deeply embedded fat clearly contributed to the result. The results suggest that it is possible to use transmission imaging for rapid, nondestructive, and representative sampling of very heterogeneous foods. The proposed system is suitable for industrial use.

A chromatographic approach for fluorescence reduction in liquid Raman analysis.

In this study a chromatographic approach for fluorescence reduction in liquid Raman analysis has been evaluated. The idea behind the approach is to apply a chromatographic separation step prior to Raman analysis in order to separate fluorescing compounds from other components of interest, thus facilitating better quantitative and qualitative analysis of the latter components. A real-time liquid-core Raman waveguide detector designed for chromatographic applications was used in the study, thus providing real-time chemical pretreatment of liquid samples for Raman analysis. Twenty aqueous mixtures of additives frequently found in beverages were analyzed, and for comparative purposes the mixtures were also analyzed in the Raman waveguide detector without chromatographic separation and with a conventional immersion probe. Both qualitatively and quantitatively satisfying results were obtained using the chromatographic Raman approach, and the technique provided possibilities for quantitative and qualitative assessments superior to the two other instrumental setups. The technique may provide additional benefits through sensitivity enhancements, and the approach is simple, inexpensive, and easy to implement in the average applied Raman laboratory. The analysis of various chemical systems and factors such as system stability over time need further evaluation in order to confirm the general applicability of the approach.

The potential of Raman spectroscopy for characterisation of the fatty acid unsaturation of salmon.

Raman spectroscopy has been evaluated for characterisation of the degree of fatty acid unsaturation (iodine value) of salmon (Salmo salar). The Norwegian Quality Cuts from 50 salmon samples were obtained, and the samples provided an iodine value range of 147.8-170.0 g I2/100 g fat, reflecting a normal variation of farmed salmon. Raman measurements were performed both on different spots of the intact salmon muscle, on ground salmon samples as well as on oil extracts, and partial least squares regression (PLSR) was utilised for calibration. The oil spectra provided better iodine value predictions than the other data sets, and a correlation coefficient of 0.87 with a root mean square error of cross-validation of 2.5 g I2/100 g fat was achieved using only one PLSR component. The ground samples provided comparable results, but at least two PLSR components were needed. Higher prediction errors were obtained from Raman spectra of intact salmon muscle, and this may partly be explained by sampling uncertainties in the relation between Raman measurements and reference analysis. All PLSR models obtained were based on chemically sound regression coefficients, and thus information regarding fatty acid unsaturation is readily available from Raman spectra even in systems with high contents of protein and water. The accuracy, the robustness and the low complexity of the PLSR models obtained suggest Raman spectroscopy as a promising method for rapid in-process control of the degree of unsaturation in salmon samples.

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.