Characterization of pH Variation in Lysed Blood by Near-Infrared Spectroscopy

1998 ◽  
Vol 52 (3) ◽  
pp. 393-399 ◽  
Author(s):  
M. Kathleen Alam ◽  
James E. Franke ◽  
Thomas M. Niemczyk ◽  
John D. Maynard ◽  
Mark R. Rohrscheib ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Primary Source ◽  
Principal Component ◽  
Imidazole Ring ◽  
Data Sets ◽  
Spectral Variation ◽  
Ph Variation ◽  
Exchangeable Proton ◽  
Ph Range

Near-infrared spectra (1300–2500 nm) collected from lysed blood solutions were shown to correlate with the pH of the solutions measured potentiometrically. Cross-validated partial least-squares (PLS) models were developed from these spectral data, which provided standard error of prediction (SEP) values below 0.05 pH units for a pH range of 1.0 (6.8–7.8). Experiments were designed to eliminate possible correlation between pH and other components in the blood in order to ensure that variations in the spectral data correlated to pH were due to hydrogen ion changes only. Further work was performed to discern the primary source of pH information in the lysed blood spectra by using spectra collected from plasma and histidine solutions. The blood, plasma, and histidine data sets were compared with the use of loading vectors from principal component analysis (PCA). These loading vectors show that variations in the spectra of the titrated amino acid histidine mimic those seen in lysed blood, but not those seen in plasma. These results suggest that histidine residues of hemoglobin are providing the spectral variation necessary for pH modeling in the lysed blood solutions. It is further shown that the observed pH-sensitive histidine bands do not arise from the exchangeable proton on the imidazole ring of histidine; rather they arise from the variation in the C–H bonds of the C2 and/or the C4 carbons of the imidazole ring as they are influenced by the titration of the nitrogen-bound proton of the imidazole ring.

scholarly journals Separation of Cultivars of Soybeans by Chemometric Methods Using Near Infrared Spectroscopy

2018 ◽  
Vol 10 (4) ◽  
pp. 351
Author(s):  
João S. Panero ◽  
Henrique E. B. da Silva ◽  
Pedro S. Panero ◽  
Oscar J. Smiderle ◽  
Francisco S. Panero ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Relevant Information ◽  
Whole Grains ◽  
Signal Correction ◽  
Different Types ◽  
Linear Fit

Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm-1), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm-1 to 4570.9 cm-1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.

The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

1988 ◽  
Vol 42 (7) ◽  
pp. 1273-1284 ◽  
Author(s):  
Tomas Isaksson ◽  
Tormod Næs
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Data Sets ◽  
Prediction Errors ◽  
Regression Methods ◽  
Multiplicative Scatter Correction ◽  
Linearity Improvement

Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7–68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.

scholarly journals Anatomical Properties and Near Infrared Spectra Characteristics of Four Shorea Species from Indonesia

2020 ◽  
Vol 27 (3) ◽  
pp. 247
Author(s):  
Danang Sudarwoko Adi ◽  
Sung-Wook Hwang ◽  
Dwi Ajias Pramasari ◽  
Yusup Amin ◽  
Hairi Cipta ◽  
...  
Keyword(s):  
Visual Inspection ◽  
Near Infrared ◽  
Principal Component ◽  
Spectral Variation ◽  
Classification Models ◽  
Color Information ◽  
Near Infrared Spectra ◽  
Shorea Species ◽  
Anatomical Properties ◽  
Resin Canals

This study investigated the anatomical properties and absorbance characteristics of NIR spectra of four Shorea species from Indonesia. Macroscopic section revealed that Balau has similarity with Heavy Red Meranti, whereas White Meranti was almost identical with Light Red Meranti. All of the woods have diffuse porous and axial resin canals in tangential lines at the microscopic level. Original NIR spectra of Shorea species showed different absorbance characteristic. Wood density was assumed to be one of the factors that affected to the absorbances. Principal component analysis (PCA) of second derivative NIR spectra at the wavenumber 8,000-4,000 cm-1 (full) and 6,200-5,600 cm-1 (specific) showed different orientation among the Principal Component (PC) number. PC1, which contained highest spectral variation, had two closed clusters (1) Balau and Heavy Red Meranti and (2) White and Light Red Meranti at full spectral range. In contrast, the results at specific range were (1) Balau and White Meranti and (2) Heavy and Light Red Meranti. Hierarchical clustering dendrogram using PCA data from two spectral regions resulted in two types of clustering, the 8,000-4,000 cm-1 was somehow related to ‘density’, while the 6,200-5,600 cm-1 was grouped in ‘color’ information from visual inspection.  From both spectral regions, k-nearest neighbour (k-NN) classification models revealed 100% accuracy in identification four Shorea species using NIR spectra.

Parameter Selection Based on Support Vector Machines for Tongue Spectral Identification Model

2014 ◽  
Vol 678 ◽  
pp. 242-251
Author(s):  
Wen Juan Yan ◽  
Guo Quan He ◽  
Shi Jian Huang ◽  
Lin Qin
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Kernel Functions ◽  
Support Vector ◽  
Data Sets ◽  
Spectral Identification ◽  
Kernel Parameter ◽  
Two Samples ◽  
Infrared Spectral ◽  
Identification Model

Support Vector Machine (SVM) method is suitable for machine learning. In order to detect pathological information from tongue diagnosis rapidly, noninvasively and objectively, a near infrared spectral identification model is proposed based on SVM. The tongue spectral data of healthy people and hepatitis patients were collected. Twenty two samples were obtained for individual groups, and for each group, fifteen samples were randomly selected and used as the training sets, while the other seven were taken as the prediction sets. For the data sets, The effects of the principal component number, kernel parameters, and kernel functions on the identification model were investigated respectively. The results showed that the penalty parameter c was always 0.25, not related to the values of the principal component number and kernel parameter g. The kernel parameter g decreased along with the increased number of principal components, and ultimately reached a relatively stable value. When the Radial Basis Function (RBF) was applied, the established model was the best, indicating that the SVM approach is feasible to classify and recognize tongue near infrared spectroscopy, as along as right parameters are selected. This can provide a novel tongue spectral analysis method to distinguish healthy individuals from hepatitis patients.

scholarly journals A note on Mahalanobis and related distance measures in WinISI and The Unscrambler

2019 ◽  
Vol 27 (4) ◽  
pp. 253-258 ◽  
Author(s):  
A Garrido-Varo ◽  
J Garcia-Olmo ◽  
T Fearn
Keyword(s):  
Spectral Data ◽  
Outlier Detection ◽  
Mahalanobis Distance ◽  
Near Infrared ◽  
Distance Measure ◽  
Principal Component ◽  
Distance Measures ◽  
Total Variance ◽  
Software Packages ◽  
Component Scores

In identifying spectral outliers in near infrared calibration it is common to use a distance measure that is related to Mahalanobis distance. However, different software packages tend to use different variants, which lead to a translation problem if more than one package is used. Here the relationships between squared Mahalanobis distance D2, the GH distance of WinISI, and the T2 and leverage (L) statistics of The Unscrambler are established as D2 = T2 ≈ L × n ≈ GH × k, where n and k are the numbers of samples and variables, respectively, in the set of spectral data used to establish the distance measure. The implications for setting thresholds for outlier detection are discussed. On the way to this result the principal component scores from WinISI and The Unscrambler are compared. Both packages scale the scores for a component to have variances proportional to the contribution of that component to total variance, but the WinISI scores, unlike those from The Unscrambler, do not have mean zero.

A New Mathematical Procedure for NIR Analysis: The Lattice Technique. Application to the Prediction of Sugar Content of Apples

1989 ◽  
Vol 43 (6) ◽  
pp. 1045-1049 ◽  
Author(s):  
P. Robert ◽  
D. Bertrand ◽  
M. Crochon ◽  
J. Sabino
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Sugar Content ◽  
Principal Component ◽  
Base System ◽  
Mathematical Procedure ◽  
Analytical Applications ◽  
Spectral Libraries ◽  
Calibration Equations

Analytical applications of near-infrared spectroscopy require the determination of calibration equations linking chemical and spectral values. Such equations are difficult to update by including new calibration specimens. A new procedure for prediction which was not based on multiple linear regression has been investigated. This procedure could be included in a data base system. The proposed method consists of three steps: compression of the spectral data by applying principal component analysis, creation of a predictive lattice, and projection of the spectra of unknown specimens on to the predictive lattice. This enables the prediction of chemical data that are not perfectly linked to spectral data by a linear relationship. The procedure has been applied to the prediction of the refractive index of apples. A predictive lattice was designed with the use of 45 specimens of calibration. A prediction with 43 verification specimens gave a standard error of 0.8%, which appeared sufficient for grading apples in quality classes. Further studies are required in order to include the proposed method in spectral libraries specializing in analytical applications.

Principal Component Analysis of Near-Infrared Spectra of Alteration Products in Calcareous Samples

1997 ◽  
Vol 51 (5) ◽  
pp. 700-706 ◽  
Author(s):  
M. Bacci ◽  
S. Porcinai ◽  
B. Radicati
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Nondestructive Method ◽  
Data Sets ◽  
Reaction Products ◽  
Good Discrimination ◽  
Near Infrared Spectra ◽  
Alteration Processes

Principal component analysis (PCA) of diffuse reflectance near-infrared (NIR) spectra has been used as a suitable methodology for discriminating areas involved in the sulfation process of calcareous stones. NIR spectra of standard mixtures containing CaSO4 · 2H2O, CaSO3 · 1/2H2O, and CaCO3 were recorded. For all data sets submitted to PCA, a good discrimination between the two reaction products, i.e., CaSO3 · 1/2H2O and CaSO4 · 2H2O, in the alteration process was obtained. The actual availability of fiber-optic spectrum analyzers working in the NIR region suggests that the proposed procedure can be used as a safe and nondestructive method for monitoring alteration processes in calcareous works of art.

scholarly journals Near infrared spectra features of cocoa pod husk used for feedstuff

2021 ◽  
Vol 922 (1) ◽  
pp. 012011
Author(s):  
Samadi ◽  
S Wajizah ◽  
Z Zulfahrizal
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Animal Feed ◽  
Raw Materials ◽  
Chemical Properties ◽  
Principal Component ◽  
Chemical Information ◽  
Crude Fibre ◽  
Aceh Province ◽  
Cocoa Pod Husk

Abstract This presented study aimed to study the near infrared spectroscopic features of cocoa pod husk samples used as raw materials for animal feedstuff. Spectral data of organic material samples contains chemical properties information that can be revealed through modelling, Thus, the study of this features is essential to assess and reveal buried respective information. Cocoa pod husk samples were obtained from several districts in Aceh Province, grinded and prepared as bulk samples. Diffuse reflectance spectral data for a total of 30 bulk cocoa pod husk samples were acquired and recorded in wavelength range from 1000 to 2500 nm. Spectral data were firstly projected onto principal component analysis to observe similarities among samples. Spectra correction, namely mean normalization was employed to enhance spectra features. The results showed that several chemical information related to cocoa properties can be revealed such as dry matter, crude protein, crude fibre, ether extract, nitrogen-free extract and ash content due to the second and third overtones pf combination bands O-H, C-O-H and N-H. Optimum wavelength for estimating cocoa pod husk attributes are in 1217, 1405-1474 nm, 1629 nm, 1906-1979 nm, and 2283 nm. Based on obtained study, it may conclude that several quality attributes of animal feed samples further can be determined by means of near infrared spectroscopy approach.

Automatic classification of tobacco leaves based on near infrared spectroscopy and nonnegative least squares

2018 ◽  
Vol 26 (2) ◽  
pp. 101-105 ◽  
Author(s):  
Zhang Jianqiang ◽  
Liu Weijuan ◽  
Zhang Huaihui ◽  
Hou Ying ◽  
Yang Panpan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Spectral Data ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Nonnegative Least Squares ◽  
Tobacco Leaves ◽  
Infrared Spectral

A nonnegative least squares classifier was proposed in this paper to classify near infrared spectral data. The method used near infrared spectral data of training samples to make up a data dictionary of the sparse representation. By adopting the nonnegative least squares sparse coding algorithm, the near infrared spectral data of test samples would be expressed via the sparsest linear combinations of the dictionary. The regression residual of the test sample of each class was computed, and finally it was assigned to the class with the minimum residual. The method was compared with the other classifying approaches, including the well-performing principal component analysis–linear discriminant analysis and principal component analysis–particle swarm optimization–support vector machine. Experimental results showed that the approach was faster and generally achieved a better prediction performance over compared methods. The method can accurately recognize different classes of tobacco leaves and it provides a new technology for quality evaluation of tobacco leaf in its purchasing activities.

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