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.

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.

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.

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.

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.

scholarly journals Comparison of Field and Laboratory Wet Soil Spectra in the Vis-NIR Range for Soil Organic Carbon Prediction in the Absence of Laboratory Dry Measurements

2020 ◽  
Vol 12 (18) ◽  
pp. 3082
Author(s):  
James Kobina Mensah Biney ◽  
Luboš Borůvka ◽  
Prince Chapman Agyeman ◽  
Karel Němeček ◽  
Aleš Klement
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Properties ◽  
Near Infrared ◽  
Rapid Determination ◽  
Principal Component Regression ◽  
Principal Component ◽  
Support Vector ◽  
Least Squares Regression ◽  
Orthogonal Signal Correction

Spectroscopy has demonstrated the ability to predict specific soil properties. Consequently, it is a promising avenue to complement the traditional methods that are costly and time-consuming. In the visible-near infrared (Vis-NIR) region, spectroscopy has been widely used for the rapid determination of organic components, especially soil organic carbon (SOC) using laboratory dry (lab-dry) measurement. However, steps such as collecting, grinding, sieving and soil drying at ambient (room) temperature and humidity for several days, which is a vital process, make the lab-dry preparation a bit slow compared to the field or laboratory wet (lab-wet) measurement. The use of soil spectra measured directly in the field or on a wet sample remains challenging due to uncontrolled soil moisture variations and other environmental conditions. However, for direct and timely prediction and mapping of soil properties, especially SOC, the field or lab-wet measurement could be an option in place of the lab-dry measurement. This study focuses on comparison of field and naturally acquired laboratory measurement of wet samples in Visible (VIS), Near-Infrared (NIR) and Vis-NIR range using several pretreatment approaches including orthogonal signal correction (OSC). The comparison was concluded with the development of validation models for SOC prediction based on partial least squares regression (PLSR) and support vector machine (SVMR). Nonetheless, for the OSC implementation, we use principal component regression (PCR) together with PLSR as SVMR is not appropriate under OSC. For SOC prediction, the field measurement was better in the VIS range with R2CV = 0.47 and RMSEPcv = 0.24, while in Vis-NIR range the lab-wet measurement was better with R2CV = 0.44 and RMSEPcv = 0.25, both using the SVMR algorithm. However, the prediction accuracy improves with the introduction of OSC on both samples. The highest prediction was obtained with the lab-wet dataset (using PLSR) in the NIR and Vis-NIR range with R2CV = 0.54/0.55 and RMSEPcv = 0.24. This result indicates that the field and, in particular, lab-wet measurements, which are not commonly used, can also be useful for SOC prediction, just as the lab-dry method, with some adjustments.

Application of visible and near-infrared spectroscopy for evaluation of ewes milk with different feeds

2019 ◽  
Vol 59 (6) ◽  
pp. 1190 ◽  
Author(s):  
A. Bahri ◽  
S. Nawar ◽  
H. Selmi ◽  
M. Amraoui ◽  
H. Rouissi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Random Forest ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Principal Component ◽  
Least Squares Regression ◽  
Random Forest Regression ◽  
Milk Samples

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.

scholarly journals The Determination of Parkinson’s Drugs in Human Urine by Applying Chemometric Methods

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Guzide Pekcan Ertokus
Keyword(s):  
Urine Sample ◽  
Human Urine ◽  
Principal Component Regression ◽  
Principal Component ◽  
Healthy Person ◽  
Drug Analysis ◽  
Uv Spectrophotometry ◽  
Least Squares Regression ◽  
Chemometric Methods

The spectrophotometric-chemometric analysis of levodopa and carbidopa that are used for Parkinson’s disease was analyzed without any prior reservation. Parkinson’s drugs in the urine sample of a healthy person (never used drugs in his life) were analyzed at the same time spectrophotometrically. The chemometric methods used were partial least squares regression (PLS) and principal component regression (PCR). PLS and PCR were successfully applied as chemometric determination of levodopa and carbidopa in human urine samples. A concentration set including binary mixtures of levodopa and carbidopa in 15 different combinations was randomly prepared in acetate buffer (pH 3.5).). UV spectrophotometry is a relatively inexpensive, reliable, and less time-consuming method. Minitab program was used for absorbance and concentration values. The normalization values for each active substance were good (r2>0.9997). Additionally, experimental data were validated statistically. The results of the analyses of the results revealed high recoveries and low standard deviations. Hence, the results encouraged us to apply the method to drug analysis. The proposed methods are highly sensitive and precise, and therefore they were implemented for the determination of the active substances in the urine sample of a healthy person in triumph.

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