Simultaneous Determination of Sugars by Multivariate Analysis Applied to Mid-Infrared Spectra of Biological Samples

1997 ◽  
Vol 51 (3) ◽  
pp. 369-375 ◽  
Author(s):  
Frédéric Cadet ◽  
Christine Robert ◽  
Bernard Offmann
Keyword(s):  
Spectral Data ◽  
Biological Samples ◽  
Reducing Sugars ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Mid Infrared ◽  
Total Sugars ◽  
Infrared Spectral ◽  
Ir Bands

We have investigated the use of principal component analysis (PCA) to describe and assess mid-infrared spectral data obtained from complex biological samples containing sucrose, fructose, and glucose. The correlation coefficients between spectral data and chemical values of each variable (sucrose, glucose, fructose, total sugars, and reducing sugars) showed that in each case, axes 1, 3, 4, and 5 had the highest values. These values also indicated which axes each variable was mostly correlated with. The results also showed that the samples were distributed according to their sucrose concentrations (or total sugars) along a concentration gradient in the projection plan formed between axes 1 and 3. No clear discrimination according to concentration was observed with other factorial maps. Prediction equations that linked sucrose, fructose, glucose, total sugar, and reducing sugars concentrations to the spectral data were established by regression on the principal component. Very high correlation coefficients values between the first 10 axes and the chemical values were obtained (between 0.9757 and 0.998). From such aqueous biological samples containing a ternary mixture of sucrose, fructose, and glucose, it was possible to (1) identify the characteristic IR bands of these different sugars (and their combination: reducing sugars/total sugars) and (2) to specifically measure their concentrations with a relatively good accuracy.

CHEMICAL COMPOSITION OF VARIOUS COMMERCIAL GRADES OF CANADIAN FLUE-CURED TOBACCO

1958 ◽  
Vol 38 (1) ◽  
pp. 73-80 ◽  
Author(s):  
J. M. Elliot ◽  
E. C. Birch
Keyword(s):  
Chemical Composition ◽  
Reducing Sugars ◽  
Petroleum Ether Extract ◽  
Correlation Coefficients ◽  
Protein Nitrogen ◽  
Laboratory Methods ◽  
Total Sugars ◽  
Total Alkaloids ◽  
Positive Correlations ◽  
Ethanol Extracts

A study was made of the chemical composition of 21 commercial grades of Canadian flue-cured tobacco, selected from a 50-acre crop of Hicks variety in 1955. Arbitrary prices were assigned to the various grades of tobacco. Correlation coefficients between the chemical values and the assigned grade prices were calculated. Ethanol extracts, total sugars, reducing sugars, and hygroscopicity gave significant positive correlations; total nitrogen, protein nitrogen, total alkaloids, nicotine, calcium, and magnesium gave negative correlations. These coefficients indicated that quality measured by these laboratory methods conformed with leaf-graded quality. Correlation coefficients were not significant between grade quality and petroleum ether extract, sucrose, starch, ash, silica, potassium, phosphorus, chlorine, sulphur, burn, or pH.

Varietal Differentiation of Grape Juice Based on the Analysis of Near- and Mid-infrared Spectral Data

2011 ◽  
Vol 5 (3) ◽  
pp. 381-387 ◽  
Author(s):  
Daniel Cozzolino ◽  
Wies Cynkar ◽  
Nevil Shah ◽  
Paul Smith
Keyword(s):  
Spectral Data ◽  
Grape Juice ◽  
Mid Infrared ◽  
Infrared Spectral ◽  
Varietal Differentiation

scholarly journals Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning

2020 ◽  
Vol 103 (10) ◽  
pp. 9355-9367
Author(s):  
S.J. Denholm ◽  
W. Brand ◽  
A.P. Mitchell ◽  
A.T. Wells ◽  
T. Krzyzelewski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dairy Cows ◽  
Spectral Data ◽  
Bovine Tuberculosis ◽  
Mid Infrared ◽  
Infrared Spectral

scholarly journals Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

2014 ◽  
Vol 38 (2) ◽  
pp. 372-385 ◽  
Author(s):  
Rodnei Rizzo ◽  
José A. M. Demattê ◽  
Fabrício da Silva Terra
Keyword(s):  
Spectral Data ◽  
Principal Components ◽  
Visual Analysis ◽  
Numerical Technique ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Soil Classification ◽  
Numerical Classification ◽  
Similarity Coefficients

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

scholarly journals Simultaneous Spectrophotometric Kinetic Determination of Four Flavor Enhancers in Foods with the Aid of Chemometrics

2011 ◽  
Vol 94 (4) ◽  
pp. 1210-1216 ◽  
Author(s):  
Yongnian Ni ◽  
Jinfeng Chen ◽  
Serge Kokot
Keyword(s):  
Spectral Data ◽  
Principal Component Regression ◽  
Principal Component ◽  
Food Samples ◽  
Hplc Method ◽  
Ethyl Vanillin ◽  
Sulfuric Acid Medium ◽  
Kinetic Spectrophotometric ◽  
Four Flavor

Abstract A sensitive kinetic spectrophotometric method was developed for the determination of four flavor enhancers—maltol, ethyl maltol, vanillin, and ethyl vanillin—in food samples. The method was based on the reduction of iron(III) by the four analytes in a sulfuric acid medium (0.012 mol/L), and the subsequent interaction of iron(II) with hexacyanoferrate(III) to form the strongly colored Prussian blue complex, which exhibited an absorption maximum at 800 nm. The optimized method had linear calibrations over the concentration ranges of 0.2–2.8 mg/L for maltol, ethyl maltol, and vanillin, as well as 0.2–1.8 mg/L for ethyl vanillin; the corresponding detection limits were 0.07, 0.07, 0.06, and 0.06 mg/L, respectively. Calibration models were constructed from the original and frst-derivative spectral data with the use of partial least-squares (PLS) and principal component regression chemometrics methods. Ultimately, the proposed analytical procedure was successively applied for the determination of the four compounds in commercial food samples with the use of a PLS calibration based on the frst-derivative spectral data. The results were comparable with those from a reference HPLC method.

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