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33 results for “Chemometrics,”
Exploring the potential of Near Infrared Hyperspectral Imaging and chemometrics to discriminate soil seed bank of two timber species central African : Erythrophleum suaveolens (Guill. & Perr.) Brenan, and Erythrophleum ivorense A. Chev.
<p>The data of this study are accessible by sending a request to the corresponding author at the email address: douhch382@gmail.com. <a href="https://doi.org/10.5281/zenodo.13908452" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13908452</a></p>
Classification and quantification of sucrose from sugar beetand sugarcane using optical spectroscopy and chemometrics
<p>Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380 nm. Spectral differences were observable in the ultraviolet–visible (UV–Vis) region from 200 to 600 nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV–Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky–Golay were most accept- able with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV–Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries’ agricultural policies.</p>
Salmon Creek Organic Geochemistry Chemometric Data
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Figure 2 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 2. Results of Cluster Analysis.
Figure 1 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 1. Rusałka Lake in Szczecin City, own elaboration, after Poleszczuk et al. (2012).
Figure 3 in Assessment of water quality using chemometric methods - a case study of Rusałka Lake, NW-Poland
Figure 3. Results of discriminant analysis.
Fig. 3 in Metabolic fingerprinting of Ganoderma spp. using UHPLC-ESI-QTOF-MS and its chemometric analysis
Fig. 3. Structures of five isomeric compounds with the molecular formula C30H42O7 and molecular mass 514.2931 found in the G44 sample.
Fig. 2 in Metabolic fingerprinting of Ganoderma spp. using UHPLC-ESI-QTOF-MS and its chemometric analysis
Fig. 2. General chemical structure of a typical triterpene showing the position of various side chains and list of side chains typically found in Ganoderma.
Fig. 1 in Metabolic fingerprinting of Ganoderma spp. using UHPLC-ESI-QTOF-MS and its chemometric analysis
Fig. 1. (a) Total ion chromatogram of G44 mushroom extract in ESI negative mode (b) Extracted ion chromatogram (EIC) of G44 mushroom extract in ESI negative mode.
Fig. 1 in Quantitative variations of usnic acid and selected elements in terricolous lichen Cladonia mitis Sandst., with respect to different environmental factors - A chemometric approach
Fig. 1. Relationship between usnic acid content in Cladonia mitis and the latitude of the collection sites (R = 0.547, p =0.019). The circles denote samples from open area, while squares denote samples from forest area.
Fig. 4 in Quantitative variations of usnic acid and selected elements in terricolous lichen Cladonia mitis Sandst., with respect to different environmental factors - A chemometric approach
Fig. 4. The projection of samples on the plane defined by the first two latent components of the PLS model. The circles denote samples from open area, while squares denote samples from forest area.
Fig. 2 in Quantitative variations of usnic acid and selected elements in terricolous lichen Cladonia mitis Sandst., with respect to different environmental factors - A chemometric approach
Fig. 2. Relationship between usnic acid content in Cladonia mitis and the altitude (in the range of 50 and 500 m above sea level) of the collection sites (n = 13). The circles denote samples from open area, while squares denote samples from forest area.
Fig. 3 in Quantitative variations of usnic acid and selected elements in terricolous lichen Cladonia mitis Sandst., with respect to different environmental factors - A chemometric approach
Fig. 3. The weights of the first two latent components of the partial least square model. Usnic acid and Pb concentrations are response parameters, all other parameters are predictors.
Fig. 6 in A chemometric assessment of essential oil variation of three Salvia species indigenous to South Africa
Fig. 6. Discriminant analysis plots derived from the aligned GC-MS data of essential oils from S. africana-lutea, S. lanceolata and S. chamelaeagnea. A) OPLS-DA scores plot (PC1 versus PC2) showing three major groups correlating to the three species, B) corresponding loadings plot indicating the marker compounds associated with each group.
Fig. 3. PCA scores scatter plots and HCA dendrograms for S in A chemometric assessment of essential oil variation of three Salvia species indigenous to South Africa
Fig. 3. PCA scores scatter plots and HCA dendrograms for S. africana-lutea (A and B), S. lanceolata (C and D) and S. chamelaeagnea (E and F) samples from various localities. The inserts are the PCA scores plots coloured according to the HCA dendrograms.
Fig. 4. A, C and E in A chemometric assessment of essential oil variation of three Salvia species indigenous to South Africa
Fig. 4. A, C and E − OPLS-DA scores scatter plots and B, D and F – S-plots for the three species, S. africana-lutea, S. lanceolata and S. chamelaeagnea, respectively.
Fig. 5. A in A chemometric assessment of essential oil variation of three Salvia species indigenous to South Africa
Fig. 5. A - PCA scores scatter plot and B - HCA dendrogram of the samples indicating three distinct clusters with S. africana-lutea, S. lanceolata and S. chamelaeagnea.
Fig. 2. 2D in A chemometric assessment of essential oil variation of three Salvia species indigenous to South Africa
Fig. 2. 2D-GC contour plots of A) S. africana-lutea, B) S. lanceolata and C) S. chamelaeagnea essential oils, with the different classes of compounds and some specific compounds indicated. The complete separation of β-caryophyllene and terpinen-4-ol achieved is evident. 1D column – Stabilwax, 30 m × 0.25 mm i.d. x 0.25 μm film thickness, 2D column – Rxi 5Sil MS, 1 m × 0.25 mm ID x 0.25 μm film thickness.
Fig. 1 in A chemometric assessment of essential oil variation of three Salvia species indigenous to South Africa
Fig. 1. Typical total ion GC-MS chromatograms of essential oils of A) S. africana-lutea, B) S. lanceolata with an enlarged insert of the coeluting peaks terpinen-4-ol and β-caryophyllene and C) S. chamelaeagnea, indicating a large degree of variation in the chemical constituents of the three species. Analysis was performed using a HPInnowax column, 250 μm i.d., 0.25 μm film thickness.
Data from: Chemical taphonomy and preservation modes of Jurassic spinicaudatans from Patagonia: a chemometric approach
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