Sugar, fluorescence and SIMCA
Complex fluorescence spectra of a sugar sample was dissolved in water can tell where the sample is produced. The chemometric method to solve such a problem is Soft Independent Modeling of Class Analogy (SIMCA). Read the original food standards in india article here
In the 40 evaluated the quality of the sugar by illuminating the sample food standards in india with ultraviolet light. [1] The more bluish tint sample had in the ultraviolet light, the more texture was it. Texture is probably a strong word to use on today's sugar. Sugar is an extremely pure product: plain beet consists of more than 99.9% sucrose and small amounts of other components such as amino acids and phenolic food standards in india components. Some of these components are fluorescent, and the concentration of these is that by UV test determines whether the sample is considered as clean. In 1993 Professor Lars Munck idea of using modern fluorescence spectroscopy for quality analysis of sugar tests, and he decided to work with Danisco Sugar. Instead of only illuminating sugar test with a UV lamp and visually assess the success, he proposed to measure the exact fluorescenslandskaber for each sample and analyze these chemometric to find similarities, food standards in india differences and groupings. The aim was to develop food standards in india new online quality management systems for sugar. This is why the analysis with SIMCA, we now demonstrate.
Data We analyze a sample set of 57 samples from four different sugar factories food standards in india (Danish and German) supplied by Danisco. The set consists of 14 samples food standards in india from the factory H, 12 from the factory food standards in india I, 13 factory-K food standards in india and 13 factory-M; Additionally, there are five unknowns (U1-U5) samples in the set, which is classified in order to check the potential of the method. It is important that the samples selected to describe the factories are representative and comprehensive. Otherwise, future classifications mistakenly judge certain tests not to belong to groups. Sample preparation consists in dissolving 2.25 grams of sugar in 15.0 mL of deionized water. Fluorescence emission spectra recorded in solution at four different excitation wavelengths (230, 240, 290, 340 nm) with a LS50B instrument from Perkin-Elmer [1]. In this example, only the emission spectra upon excitation at 240 nm. They are seen in Figure 1, colored by factory belonging. In the scientific publication [2] utilized the whole scenery in the data analysis, but we will save for a later column. By inspection of Figure 1 shows the trends in raw data: samples from the factory H has high intensities, samples from factory K slightly lower, and samples from the factory I and M have overlapping spectral signatures. The unknown samples ranks with both high and low intensities with U3 as the sample with the highest intensity. It is not straightforward to classify the samples in relation to the four groups, but U3 could tentatively be tempted food standards in india to suggest that a sample from factory H.
Global PCA model in order to get a better view of data make a PCA on all samples incl. the unknown (centered data). Score plot in Figure 2 shows the component 1 vs. Component 3, as this combination groups factories best. From this plot shows that factory I stand out from the other three factories at a location with high negative component 3 score values, while factories M, K and H are separated food standards in india along component 1 samples U2 and U4 position themselves well in the I-group and it is a strong indication that these samples are from factory I. U1 and U5 are harder to place unique, while U3 is located close to the H group without being up close. Figure 2 shows only a small part of the variation, so definitive conclusions are difficult to draw without a more thorough analysis.
SIMCA first step in SIMCA is to make local PCA models for each group (centered data). For these groups, we arrive at the following models primarily using full cross-validation and inspection of the structure / noise in loadingplots: Factory H 2 components factory in 3 parts, one outlier removed Factory food standards in india K: 3 components food standards in india factory M: 3 components Three components may be a little too abundant for a data set with only 11 samples, but in this case it is clear that the third component is real, inter alia, estimated from the spectral shape of loadingvektoren. It would have been good to have a larger data set. We will now classify food standards in india the five unknown samples in relation to the four PCA models. food standards in india To this end, the five projected unknown samples into each of the four PCA models by the optimal number of components, and the models are inspected using Residualvarians vs. Hotellings T2 plot (Danish Chemistry, No. 3, 2008). In Figure 3a-d shows the four relevant plots. Factory H: Sample U5 seems to situate itself food standards in india in this group; no other samples belonging to the group. Factory I: Sample U2 and U4 must clearly be regarded as samples from the factory I. Factory K: Sample U5 positions itself as the only try of the group. Factory M: Sample U1 is a sample from the factory M; all other samples are far from M-model (U3 is far outside the indicated food standards in india part of the plot). The above be
Complex fluorescence spectra of a sugar sample was dissolved in water can tell where the sample is produced. The chemometric method to solve such a problem is Soft Independent Modeling of Class Analogy (SIMCA). Read the original food standards in india article here
In the 40 evaluated the quality of the sugar by illuminating the sample food standards in india with ultraviolet light. [1] The more bluish tint sample had in the ultraviolet light, the more texture was it. Texture is probably a strong word to use on today's sugar. Sugar is an extremely pure product: plain beet consists of more than 99.9% sucrose and small amounts of other components such as amino acids and phenolic food standards in india components. Some of these components are fluorescent, and the concentration of these is that by UV test determines whether the sample is considered as clean. In 1993 Professor Lars Munck idea of using modern fluorescence spectroscopy for quality analysis of sugar tests, and he decided to work with Danisco Sugar. Instead of only illuminating sugar test with a UV lamp and visually assess the success, he proposed to measure the exact fluorescenslandskaber for each sample and analyze these chemometric to find similarities, food standards in india differences and groupings. The aim was to develop food standards in india new online quality management systems for sugar. This is why the analysis with SIMCA, we now demonstrate.
Data We analyze a sample set of 57 samples from four different sugar factories food standards in india (Danish and German) supplied by Danisco. The set consists of 14 samples food standards in india from the factory H, 12 from the factory food standards in india I, 13 factory-K food standards in india and 13 factory-M; Additionally, there are five unknowns (U1-U5) samples in the set, which is classified in order to check the potential of the method. It is important that the samples selected to describe the factories are representative and comprehensive. Otherwise, future classifications mistakenly judge certain tests not to belong to groups. Sample preparation consists in dissolving 2.25 grams of sugar in 15.0 mL of deionized water. Fluorescence emission spectra recorded in solution at four different excitation wavelengths (230, 240, 290, 340 nm) with a LS50B instrument from Perkin-Elmer [1]. In this example, only the emission spectra upon excitation at 240 nm. They are seen in Figure 1, colored by factory belonging. In the scientific publication [2] utilized the whole scenery in the data analysis, but we will save for a later column. By inspection of Figure 1 shows the trends in raw data: samples from the factory H has high intensities, samples from factory K slightly lower, and samples from the factory I and M have overlapping spectral signatures. The unknown samples ranks with both high and low intensities with U3 as the sample with the highest intensity. It is not straightforward to classify the samples in relation to the four groups, but U3 could tentatively be tempted food standards in india to suggest that a sample from factory H.
Global PCA model in order to get a better view of data make a PCA on all samples incl. the unknown (centered data). Score plot in Figure 2 shows the component 1 vs. Component 3, as this combination groups factories best. From this plot shows that factory I stand out from the other three factories at a location with high negative component 3 score values, while factories M, K and H are separated food standards in india along component 1 samples U2 and U4 position themselves well in the I-group and it is a strong indication that these samples are from factory I. U1 and U5 are harder to place unique, while U3 is located close to the H group without being up close. Figure 2 shows only a small part of the variation, so definitive conclusions are difficult to draw without a more thorough analysis.
SIMCA first step in SIMCA is to make local PCA models for each group (centered data). For these groups, we arrive at the following models primarily using full cross-validation and inspection of the structure / noise in loadingplots: Factory H 2 components factory in 3 parts, one outlier removed Factory food standards in india K: 3 components food standards in india factory M: 3 components Three components may be a little too abundant for a data set with only 11 samples, but in this case it is clear that the third component is real, inter alia, estimated from the spectral shape of loadingvektoren. It would have been good to have a larger data set. We will now classify food standards in india the five unknown samples in relation to the four PCA models. food standards in india To this end, the five projected unknown samples into each of the four PCA models by the optimal number of components, and the models are inspected using Residualvarians vs. Hotellings T2 plot (Danish Chemistry, No. 3, 2008). In Figure 3a-d shows the four relevant plots. Factory H: Sample U5 seems to situate itself food standards in india in this group; no other samples belonging to the group. Factory I: Sample U2 and U4 must clearly be regarded as samples from the factory I. Factory K: Sample U5 positions itself as the only try of the group. Factory M: Sample U1 is a sample from the factory M; all other samples are far from M-model (U3 is far outside the indicated food standards in india part of the plot). The above be
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