scholarly journals Measurements of Urea and Glucose in Aqueous Solutions with Dual-Beam Near-Infrared Fourier Transform Spectroscopy

2002 ◽  
Vol 56 (12) ◽  
pp. 1593-1599 ◽  
Author(s):  
Peter Snoer Jensen ◽  
Jimmy Bak
Keyword(s):  
Aqueous Solutions ◽  
Near Infrared ◽  
Principal Component Regression ◽  
Principal Component ◽  
Prediction Errors ◽  
Single Beam ◽  
Dual Beam ◽  
Calibration Models ◽  
Ft Ir ◽  
Beam Technique

This study investigates the use of a dual-beam, optical null, FT-IR spectrometer to measure trace organic components in aqueous solutions in the combination band region 5000–4000 cm−1. The spectrometer may be used for both single- and dual-beam measurements, thereby facilitating comparison of these two modes of operation. The concentrations of aqueous solutions of urea and glucose in the ranges 0–40 mg/dL and 0–250 mg/dL, respectively, were determined by principal component regression using both modes. The dual-beam technique eliminated instrumental variations present in the single-beam measurements that must be taken into account when quantifying trace components from single-beam spectra. The data obtained with the dual-beam technique resulted in more stable calibration models based on principal component regression. These calibration models need fewer factors and yield lower prediction errors than those based on traditional single-beam data.

Nondestructive NIR and NIT Determination of Protein, Fat, and Water in Plastic-Wrapped, Homogenized Meat

1992 ◽  
Vol 46 (11) ◽  
pp. 1685-1694 ◽  
Author(s):  
Tomas Isaksson ◽  
Charles E. Miller ◽  
Tormod Næs
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Prediction Errors ◽  
Optimal Test ◽  
Meat Samples ◽  
Water Contents

In this work, the abilities of near-infrared diffuse reflectance (NIR) and transmittance (NIT) spectroscopy to noninvasively determine the protein, fat, and water contents of plastic-wrapped homogenized meat are evaluated. One hundred homogenized beef samples, ranging from 1 to 23% fat, wrapped in polyamide/polyethylene laminates, were used. Results of multivariate calibration and prediction for protein, fat, and water contents are presented. The optimal test set prediction errors (root mean square error of prediction, RMSEP), obtained with the use of the principal component regression method with NIR data, were 0.45, 0.29 and 0.50 weight % for protein, fat, and water, respectively, for plastic-wrapped meat (compared to 0.40, 0.28 and 0.45 wt % for unwrapped meat). The optimal prediction errors for the NIT method were 0.31, 0.52 and 0.42 wt % for protein, fat, and water, respectively, for plastic-wrapped meat samples (compared to 0.27, 0.38, and 0.37 wt % for unwrapped meat). We can conclude that the addition of the laminate only slightly reduced the abilities of the NIR and NIT method to predict protein, fat, and water contents in homogenized meat.

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.

Locally Weighted Regression in Diffuse Near-Infrared Transmittance Spectroscopy

1992 ◽  
Vol 46 (1) ◽  
pp. 34-43 ◽  
Author(s):  
Tormod Næs ◽  
Tomas Isaksson
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Principal Component ◽  
Distance Measures ◽  
Weighted Regression ◽  
Weight Functions ◽  
Prediction Errors ◽  
Least Squares Regression ◽  
Locally Weighted Regression ◽  
Infrared Transmittance

This paper presents an application of locally weighted regression (LWR) in diffuse near-infrared transmittance spectroscopy. The data are from beef and pork samples. The LWR method is based on the idea that a nonlinearity can be approximated by local linear equations. Different weight functions (for the samples) as well as different distance measures for “closeness” are tested. The LWR is compared to principal component regression and partial least-squares regression. The LWR with weighted principal components is shown to give the best results. The improvements with respect to linear regression are up to 15% of the prediction errors.

Examination of Criteria for Local Model Principal Component Regression

1997 ◽  
Vol 51 (12) ◽  
pp. 1814-1822 ◽  
Author(s):  
Gregory A. Bakken ◽  
Dixie R. Long ◽  
John H. Kalivas
Keyword(s):  
Near Infrared ◽  
External Validation ◽  
Principal Component Regression ◽  
Principal Component ◽  
Singular Values ◽  
Local Model ◽  
Prediction Errors ◽  
Top Down ◽  
Predictive Information ◽  
Factors Associated

In analytical chemistry, principal component regression (PCR) is widely used as a method for calibration and prediction. The motivation behind PCR is to select factors associated with predictive information and eliminate those associated with noise. The classical approach, referred to as top-down selection, chooses sequential factors based on singular value magnitudes, and the same factors are used for all future unknown samples; i.e., a global model is formed. The number of factors needed is often determined through cross-validation on the calibration samples or with an external validation set. Alternatively, a model developed specific to an unknown sample, i.e., a local model or sample-dependent model, could offer improved accuracy. The idea behind sample-dependent PCR is that factors associated with small singular values not included in a top-down PCR model can still contain relevant predictive information. This paper shows that local models generated by selecting factors on a sample-by-sample basis often reduce prediction errors compared with those for the global top-down model. However, evidence is also provided that supports the use of global top-down models. Several criteria are proposed and examined for selecting factors on a sample-dependent basis. Observations and conclusions presented are based on two near-infrared data sets.

Optimised Scaling (OS-2) Regression Applied to near Infrared Diffuse Spectroscopy Data from Food Products

1993 ◽  
Vol 1 (2) ◽  
pp. 85-97 ◽  
Author(s):  
Tomas Isaksson ◽  
Ziyi Wang ◽  
Bruce Kowalski
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component Regression ◽  
Predictive Ability ◽  
Principal Component ◽  
Calibration Method ◽  
Meat Sample ◽  
Data Sets ◽  
Regression Methods ◽  
Calibration Models

A recently presented calibration method, called optimised scaling (OS-2) was tested and compared to multiplicative scatter correction (MSC) and principal component regression (PCR). The predictive ability of these regression methods was tested on eight data sets consisting of diffuse near infrared (NIR) reflectance and transmittance continuous spectra of meat, sausages, soya bean and designed sample sets. Calibration was performed for constituents such as fat, protein, water, carbohydrate, temperature, lactate and glucose. A total of 21 calibration models were validated and compared. OS-2 gave good or promising prediction results for the major constituents with large variation, such as prediction of fat in two of the studied meat sample sets. OS-2 gave poorer prediction results of minor constituents compared to MSC or first derivatives of the data and PCR.

scholarly journals Transformasi Attenuated Total Reflectance Untuk Prediksi Vitamin C Pada Buah Mangga Arumanis (Mangifera indica)

2019 ◽  
Vol 4 (3) ◽  
pp. 75-84
Author(s):  
Muslem Muslem ◽  
Sri Purnama Sari ◽  
Agus Arip Munawar
Keyword(s):  
Principal Component Analysis ◽  
Vitamin C ◽  
Near Infrared ◽  
Mangifera Indica ◽  
Principal Component Regression ◽  
Principal Component ◽  
Attenuated Total Reflectance ◽  
Total Reflectance ◽  
Ft Ir ◽  
Pcr Method

Abstrak, Parameter yang digunakan dalam penilaian mutu buah mangga antara lain ukuran atau berat, kekerasan, tingkat ketuaan serta bebas dari cacat. Kekerasan pada buah mangga merupakan fungsi dari tingkat kematangan, sedangkan kematangan berhubungan dengan tingkat ketuaan yang dapat diduga melalui penampilan visual. Vitamin C merupakan vitamin yang larut dalam air dan esensial untuk biosintesis kolagen.pengukuran vitamin C pada buah mangga menggunkan metode tetrasi, dan penggunaan gelombang elektromaknetik seperti Near Infrared. Penelitian ini bertujuan untuk memprediksi kadar vitamin C dalam buah mangga menggunakan metode Spektrofotometri UV-Vis dan Iodimetri, serta membandingkan hasil dari kedua metode tersebut. Sampel yang diidentifikasi yaitu buah mangga yang sudah matang dengan menggunakan model transformasi Attenuated Total Reflectance dan menggunakan metode Principal Component Analysis (PCA) dan menggunakan metode Principal Component Regression  (PCR). Penelitian ini menggunakan buah mangga jenis Arumanis, yang berjumlah 30 sampel. Prediksi vitamin C dengan NIRS menggunakan alat FT-IR IPTEK T-1516. Pengolahan data menggunakan Unscramble software® X versi 10.5. Hasil penelitian menunjukkan prediksi vitamin C mangga dengan metode Principal Component Regression (PCR) menghasilkan sufficient performance dengan nilai RPD yang didapat yaitu 2,0083 (r) sebesar 0,8638 , (R2 ) sebesar 0,7463 dan (RMSEC) sebesar 5,1854 Transformation Of Attenuated Total Reflectance (ATR) Near Infrared for prediction of Vitamin C In Arumanis Mangoes (Mangifera Indica)Abstract. Parameters used in assessing the quality of mangoes are size or weight, hardness, age level and free from defects. Hardness in mangoes is a function of maturity level, while the maturity is related to the level of aging that can be predicted through visual appearance. Vitamin C is a water-soluble vitamin which is essential for collagen biosynthesis. The measurement of vitamin C in mangoes use tetration methods, and the using of electromagnetic waves such as Near Infrared. This study aims to predict vitamin C contains in mango fruit using the UV-Vis and Iodymetry Spectrophotometry method, and comparing the results of the two methods. The samples identified were mature mangoes using the attenuated total reflectance transformation model and using the Principal Component Analysis (PCA) method also using the Principal Component Regression (PCR) method. This study used Arumanis mangoes, which amounted to 30 samples. Prediction of vitamin C with NIRS using the FT-IR IPTEK T-1516. Data processing use the Unscramble software® X 10.5 version. The results showed that the prediction of vitamin C mango using the Principal Component Regression (PCR) method resulted in sufficient performance with the obtained RPD value of  2,0083, (r) of 0,8638, (R2) of 0,7463 and (RMSEC) of 5,1854.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Elise A. Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnostic Methods ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

Linear transformations of multivariate calibration models in near infrared spectroscopy: A comparative study

2017 ◽  
Vol 25 (4) ◽  
pp. 223-230 ◽  
Author(s):  
Joseph Dubrovkin
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Cetane Number ◽  
Total Protein Content ◽  
Prediction Errors ◽  
Calibration Data ◽  
Linear Transformations ◽  
Calibration Models

It was shown that linear transformations are suitable for use in multivariate calibration in near infrared spectroscopy as data compression tools. Partial Least Squares calibration models were built using spectral data transformed by expansion in the series of classical orthogonal polynomials, Fourier and wavelet harmonics. These models allowed effective prediction of the cetane number of diesel fuels, Brix and pol parameters of syrup in sugar production and fat and total protein content in milk. Depending on the compression ratio, prediction errors were no larger than 30% of corresponding errors obtained by the use of the non-transformed models. Although selection of the most suitable transformation depends on the calibration data and on the cross-validation method, in many cases Fourier transform gave satisfactory results.

scholarly journals Principal component articial neural network calibration models for the simultaneous spectrophotometric estimation of mefenamic acid and paracetamol in tablets

2006 ◽  
Vol 71 (11) ◽  
pp. 1207-1218
Author(s):  
Dondeti Satyanarayana ◽  
Kamarajan Kannan ◽  
Rajappan Manavalan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mefenamic Acid ◽  
Principal Component Regression ◽  
Principal Component ◽  
Simultaneous Estimation ◽  
Uv Spectrophotometry ◽  
Novel Approach ◽  
Marquardt Algorithm ◽  
Calibration Models

Simultaneous estimation of all drug components in a multicomponent analgesic dosage form with artificial neural networks calibration models using UV spectrophotometry is reported as a simple alternative to using separate models for each component. A novel approach for calibration using a compound spectral dataset derived from three spectra of each component is described. The spectra of mefenamic acid and paracetamol were recorded as several concentrations within their linear range and used to compute a calibration mixture between the wavelengths 220 to 340 nm. Neural networks trained by a Levenberg-Marquardt algorithm were used for building and optimizing the calibration models using MATALAB? Neural Network Toolbox and were compared with the principal component regression model. The calibration models were thoroughly evaluated at several concentration levels using 104 spectra obtained for 52 synthetic binary mixtures prepared using orthogonal designs. The optimized model showed sufficient robustness even when the calibration sets were constructed from a different set of pure spectra of the components. The simultaneous prediction of both components by a single neural network with the suggested calibration approach was successful. The model could accurately estimate the drugs, with satisfactory precision and accuracy, in tablet dosage with no interference from excipients as indicated by the results of a recovery study.

Identification of Electrolytes in Aqueous Solutions from Near-IR Spectra

1996 ◽  
Vol 50 (4) ◽  
pp. 444-448 ◽  
Author(s):  
Jie Lin ◽  
Jing Zhou ◽  
Chris W. Brown
Keyword(s):  
Aqueous Solutions ◽  
Ir Spectra ◽  
Principal Component Regression ◽  
Principal Component ◽  
Transition Metal Ions ◽  
Water Molecules ◽  
Spectral Library ◽  
Dipole Interactions ◽  
Near Ir ◽  
Solutions Of Electrolytes

Dissolution of electrolytes causes characteristic changes in the near-IR spectrum of water. These changes result from a decrease in the concentration of water; charge-dipole interactions between ions and water molecules; formation of hydrogen bonds between oxygen or nitrogen atoms in some ions and water molecules; production of H+ and OH− ions from dissociation and hydrolysis; absorptions due to OH, NH, and CH groups in some ions; and intrinsic colors of some transition metal ions. Changes in spectra were used for identification of electrolytes in aqueous solutions. Near-IR spectra of 71 solutions of single electrolytes were measured and used to develop a spectral library. This near-IR spectral library was processed with principal component regression (PCR) and used for the identification of single and multiple electrolytes in aqueous solutions with the use of their spectra. Most of the unknown electrolytes were identified correctly. For the others, very similar electrolytes were selected with one ion identified correctly. The near-IR spectral library of aqueous solutions of electrolytes can be used as a simple and fast approach for the identification of electrolytes.

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