Friday, January 9, 2015

Background Alginate is a linear polysaccharide extracted from brown seaweeds (Phaeophyceae), and is

Chemometric quality of alginate - an example from the real world
In collaboration with Danisco A / S has developed a rapid method based on chemometrics and infrared spectroscopy for the analysis of food ingredient alginate. The method reduces analysis time and optimizes the quality control of alginate. Read the original article here
The method described here are prepared in connection with an industrial PhD project conducted in cooperation caco3 between industrial PhD student Tina Salomonsen, Danisco A / S and the research group Quality and Technology at the Faculty of Life Sciences, University of Copenhagen . The new method can in under a minute predict alginate molecular caco3 composition [1], which are important for the functionality of the products where it is used.
Background Alginate is a linear polysaccharide extracted from brown seaweeds (Phaeophyceae), and is used primarily in the food industry to form thermally stable gels jam in bake-off products, and the restructuring of fruit, vegetables, fish and meat. The building blocks of alginatmolekylet is aL-guluronic (G) and bD-mannuronic acid (M) and the relationship between them is closely related to the functionality of the products. If, for example. like a very firm and hard gel, using an alginate with very G, whereas a softer and more elastic gel formed from an alginate with very M. M / G ratio varies in relation. the species of seaweed, harvest time and growth conditions. It is important for the manufacturer to know the M / G ratio in the finished products, in order to ensure that the functionality is in line with customers' specifications. M / G ratio measured traditionally using. Nuclear magnetic resonance (NMR) spectroscopy. The disadvantage of this method is that sample preparation is relatively caco3 long lasting. In order to obtain caco3 useful NMR spectra, it is necessary to break down the polysaccharides. They typically consist of about 1500 monomer units, which must be broken down to ranges of around 150 units. This process involves caco3 several steps and is therefore time consuming. Additionally, it requires expertise to operate an NMR instrument, so it will typically be located centrally in the company's development and not out of production. It was therefore examined whether the spectra obtained by infrared spectroscopy caco3 (rapid measurement) could be correlated with the M / G ratio determined caco3 by NMR (slow measurement) and thus build a predictive model that predicts the M / G ratio in the commercial alginatpulver from a spectrum of the powder.
Samples To develop a predictive model was composed a sample consisting of 100 samples of the type of alginate (sodium alginatpulver), which the company wants to use the model. The samples were chosen so as to be varied as much as possible in their M / G ratio (0.5 to 2.1). M / G ratio of all the samples was determined by NMR spectroscopy (reference method). The samples were divided into a calibration set and a test kit comprising, respectively. 75 and 25 samples. Figure 1 shows the M / G ratio of 75 calibration samples and 25 test samples in order of the M / G ratio determined using the. Reference method. There were also measured the Fourier transform caco3 infrared (FT-IR) spectra of all the samples on a Perkin Elmer Spectrum One FT-IR spectrometer caco3 equipped with an Attenuated Total Reflectance (ATR) unit with a single caco3 bounce diamond. The spectra were measured in the range of 650-4000 cm-1 1 cm-1 intervals. For data analysis, only the range of from 650 cm-1 to 1800 cm-1 (1150 variable) was used. Each sample was measured three times and the average spectrum was used in data analysis.
Exploratory caco3 data analysis Figure 2a shows the raw spectra of 75 calibration samples. The spectra are colored by M / G ratio, but visually it is difficult to get an overview of what parts of the spectrum that is related to M / G ratio. To get an overview of the variation in the data is performed first a Principal Component Analysis (PCA) of the raw spectra after centering (each measurement is less variable mean). PCA and centering is described previously in this column (Danish Chemistry 2, 2008). PCA score plot of the raw spectra are shown in Figure 2b and shows a principal component (PC1) and two (PC2) explains respectively. 92.5% and 4.3% of the variation in the data. It is also seen that the samples in the score plot, which is also colored by M / G ratio, are distributed along PC2. There is a gradient of the lower M / G ratio at the top of the plot to the highest M / G ratio at the bottom of the plot. It can therefore be concluded that the majority caco3 of the spectral variation (92.5%) are related to information other than the M / G ratio. This unknown variation is likely related to small differences in contact with the diamond in the ATR. It is therefore likely that the variation that is explained in the PC1, is non-chemical and advantageously can be removed using. Multiplicative Scatter / Signal Correction (MSC) (Danish Chemistry 1, 2009). This provides a simpler model that focuses exclusively on the chemical information. The MSC-treated spectra are shown in Figure 2c, and there is a clear effect of cpd

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