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.

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.

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.

Piece-Wise Multiplicative Scatter Correction Applied to Near-Infrared Diffuse Transmittance Data from Meat Products

1993 ◽  
Vol 47 (6) ◽  
pp. 702-709 ◽  
Author(s):  
Tomas Isaksson ◽  
Bruce Kowalski
Keyword(s):  
Root Mean Square Error ◽  
Near Infrared ◽  
Scatter Correction ◽  
Meat Product ◽  
Meat Products ◽  
Correction Method ◽  
Prediction Errors ◽  
Mean Square ◽  
Multiplicative Scatter Correction ◽  
Further Development

This paper presents a nonlinear scatter correction method, called piece-wise multiplicative scatter correction (PMSC), that is a further development of the multiplicative scatter correction (MSC) method. Near-infrared diffuse transmittance (NIT) data from meat and meat product samples were used to test the predictive performances of the PMSC and the MSC methods. With the use of PMSC, the prediction errors, expressed as the root mean square error of prediction (RMSEP), were improved by up to 36% for protein, up to 55% for fat, and up to 37% for water, in comparison to uncorrected data. The corresponding improvements by using PMSC compared to MSC were up to 22%, 24%, and 31% for protein, fat, and water, respectively.

Multivariate Calibration and Chemometrics for near Infrared Spectroscopy: Which Method?

2000 ◽  
Vol 8 (4) ◽  
pp. 229-237 ◽  
Author(s):  
Pierre Dardenne ◽  
George Sinnaeve ◽  
Vincent Baeten
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Scatter Correction ◽  
Large Data ◽  
Data Sets ◽  
Agricultural Commodity ◽  
Regression Methods ◽  
Standard Normal Variate ◽  
Pre Treatment ◽  
Artificial Neural Network Ann

The four most important regression methods are evaluated on very large data sets: Multiple Linear Regression (MLR), Partial Least Squares (PLS), Artificial Neural Network (ANN) and a new concept called “LOCAL” (PLS with selection of a calibration sample subset of the closest neighbours for each sample to predict). The Standard Errors of Prediction ( SEPs) are statistically tested and the results show that the regression methods are almost equal and that the data matrices are more important than the fitting methods themselves. The types of pre-treatments (Multiplicative Scatter Correction, Detrend, Standard Normal Variate, derivative etc.) of the spectra are too numerous to be able to test all the combinations. For each test, the pre-treatment found as the best with the PLS method is fixed for the other ones. The second part of the paper emphasises the importance of the number of samples. If any agricultural commodity, and probably any kind of product measured by an NIR instrument, can be considered as a mixture of several constituents, the databases built by collecting actual samples bringing new information can reach hundreds, if not thousands, of samples.

Beyond Prediction: Extracting Relevant Information from near Infrared Spectra

1996 ◽  
Vol 4 (1) ◽  
pp. 75-84 ◽  
Author(s):  
P. Robert ◽  
M.-F. Devaux ◽  
D. Bertrand
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Relevant Information ◽  
Correlated Data ◽  
Spectral Interpretation ◽  
Highly Correlated ◽  
Prediction Techniques ◽  
Spectral Patterns

With the increase of near infrared (NIR) applications, numerous chemometric methods have been developed. Among the mathematical treatments available, principal comoponent analysis (PCA) is certainly the most well-known when considering highly correlated data. In the field of near infrared spectroscopy, it allows the study of spectra without deleting wavelengths and without making any preliminary assumptions on the data. One advantage of PCA lies in the graphical displays obtained and, more precisely, on the similarity maps and spectral patterns. While the maps reveal clusters of the samples, the spectral patterns make a spectral interpretation possible. The present paper reviews our contribution to the development and application of PCA to NIR spectroscopy. It shows that PCA is the core of various mathematical treatments such as principal component regression (PCR), factorial discriminant analysis (FDA) and canonical correlation analysis (CCA). One advantage of using PCA in the prediction techniques lies in the use of all the wavelengths in the predictive model. The extraction of relevant and comprehensive wavelengths can be guided by CCA which allows the description of the samples by taking both mid- and near infrared data into account. Besides a comprehensive presentation of the mathematical treatements, examples are given.

scholarly journals The Combined Optimization of Savitzky-Golay Smoothing and Multiplicative Scatter Correction for FT-NIR PLS Models

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Huazhou Chen ◽  
Qiqing Song ◽  
Guoqiang Tang ◽  
Quanxi Feng ◽  
Liang Lin
Keyword(s):  
Latent Variables ◽  
Near Infrared ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Spectroscopy Analysis ◽  
Multiplicative Scatter Correction ◽  
Pls Modeling ◽  
Smoothing Parameters ◽  
Better Than

The combined optimization of Savitzky-Golay (SG) smoothing and multiplicative scatter correction (MSC) were discussed based on the partial least squares (PLS) models in Fourier transform near-infrared (FT-NIR) spectroscopy analysis. A total of 5 cases of separately (or combined) using SG smoothing and MSC were designed and compared for optimization. For every case, the SG smoothing parameters were optimized with the number of PLS latent variables (F), with an expanded number of smoothing points. Taking the FT-NIR analysis of soil organic matter (SOM) as an example, the joint optimization of SG smoothing and MSC was achieved based on PLS modeling. The results showed that the optimal pretreatment was successively using SG smoothing and MSC, in which the SG smoothing parameters were 4th degree of polynomial, 2nd-order derivative, and 67 smoothing points, the best corresponding F, RMSEP, and RP were 7, 0.3982 (%), and 0.8862, respectively. This result was far better than those without any pretreatment. The combined optimization of SG smoothing and MSC could obviously improve the modeling result for NIR analysis of SOM. In addition, a new method for the classification of calibration and prediction was proposed by normalization principle. The optimizations were done on this basis of this classification.

Use of Near-Infrared Spectroscopy to Determine the Composition of High-Density/Low-Density Polyethylene Blend Films

1993 ◽  
Vol 47 (2) ◽  
pp. 222-228 ◽  
Author(s):  
Charles E. Miller
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
High Density ◽  
Low Density Polyethylene ◽  
Low Density ◽  
Blend Films ◽  
Polyethylene Blend

The ability of near-infrared (NIR) spectroscopy, combined with principal component regression (PCR), to nondestructively determine the blend ratio of high-density polyethylene (HDPE) and low-density polyethylene (LDPE) in extruded films is demonstrated. Results indicate that the NIR spectrum in the region 2100 to 2500 nm can be used to determine the HDPE mass percentage of 60–80- μm-thick film samples to within 2.5%, over a range of 0 to 100%. NIR spectral effects from scattering are important for the determination of the HDPE % for HDPE contents above 50%, and spectral effects from changes in the methyl group concentration and perhaps the PE crystallinity are important for the determination of the HDPE % for HDPE contents below 50%. In addition, a large variation between the spectra of replicate samples, probably caused by variations in the degree or direction of molecular orientation in the samples, was observed.

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 Characterizations of Polyethylene/Nylon Laminates by Near-Infrared Spectroscopy

1993 ◽  
Vol 47 (3) ◽  
pp. 346-356 ◽  
Author(s):  
Charles E. Miller ◽  
Svend A. Svendsen ◽  
Tormod Næ
Keyword(s):  
Layer Thickness ◽  
Food Packaging ◽  
Near Infrared ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Polyamide 6 ◽  
Chemical Modeling ◽  
Ethylene Vinyl Alcohol ◽  
Laminate Thickness

The use of near-infrared (NIR) spectroscopy for the rapid and nondestructive analysis of food packaging laminates containing polyethylene (PE), polyamide-6 (PA-6), and ethylene vinyl alcohol (EVOH) layers is demonstrated. The method of Pathlength Correction with Chemical Modeling (PLC-MC) is used to estimate the total laminate thickness, and Principal Component Regression (PCR), is used to estimate the thickness percentages of PE and EVOH in the laminates, from NIR reflective-transmission spectra in the region 1500–2500 nm. Results indicate that the NIR method can be used to determine the total laminate thickness within 2–4 μm, the PE layer thickness percentage within 0.7–1.8%, and the EVOH layer thickness within 0.7–0.8 μm. In addition, detailed observation of the PCR models indicates that the NIR method is also sensitive to the absorbed water content, the morphology of the polymers, and perhaps the amount of polyurethane adhesive in the laminates. The usefulness of PCR outlier detection, for identification and characterization of strange samples, and principal component rotation, for improvement of PCR model interpretability, is also demonstrated.

scholarly journals Estimation of Glucosinolates and Anthocyanins in Kale Leaves Grown in a Plant Factory Using Spectral Reflectance

Horticulturae ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 56
Author(s):  
Milon Chowdhury ◽  
Viet-Duc Ngo ◽  
Md Nafiul Islam ◽  
Mohammod Ali ◽  
Sumaiya Islam ◽  
...  
Keyword(s):  
Spectral Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Principal Component Regression ◽  
Principal Component ◽  
Least Squares Regression ◽  
Regression Methods ◽  
Freeze Dried ◽  
Plant Factory ◽  
Functional Components

The spectral reflectance technique for the quantification of the functional components was applied in different studies for different crops, but related research on kale leaves is limited. This study was conducted to estimate the glucosinolate and anthocyanin components of kale leaves cultivated in a plant factory based on diffuse reflectance spectroscopy through regression methods. Kale was grown in a plant factory under different treatments. After specific periods of transplantation, leaf samples were collected, and reflectance spectra were measured immediately from nine different points on each leaf. The same leaf samples were freeze-dried and stored for analysis of the functional components. Regression procedures, such as principal component regression (PCR), partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR), were applied to relate the functional components with the spectral data. In the laboratory analysis, progoitrin and glucobrassicin, as well as cyanidin and malvidin, were found to be dominating components in glucosinolates and anthocyanins, respectively. From the overall analysis, the SMLR model showed better performance, and the identified wavelengths for estimating the glucosinolates and anthocyanins were in the early near-infrared (NIR) region. Specifically, reflectance at 742, 761, 787, 796, 805, 833, 855, 932, 947, and 1000 nm showed a strong correlation.

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