Global or Local? A Choice for NIR Calibrations in Analyses of Forage Quality

1994 ◽  
Vol 2 (3) ◽  
pp. 163-175 ◽  
Author(s):  
G. Sinnaeve ◽  
P. Dardenne ◽  
R. Agneessens
Keyword(s):  
Scatter Correction ◽  
Forage Quality ◽  
Calibration Method ◽  
Local Regression ◽  
Global Calibration ◽  
Grass Hay ◽  
Data Points ◽  
Tropical Forages ◽  
Pre Treatment ◽  
Whole Plants

This paper investigates the effect of spectral data pre-treatment by using scatter correction techniques, detrending and derivatives on the standard error of NIR predictive models. It is shown that no particular spectral pre-treatment or no single derivative works best for the three constituents (protein, cellulose, organic matter digestibility) of the three forage databases which we investigated (grass-hay, tropical forages, maize whole plants). The best analytical results are obtained with SNVD, MSC or WMSC treatments. The best results are obtained with a first or second derivative with a segment and a gap of five data points. Local Regression was investigated for the prediction of forage quality. The standard errors of prediction were compared with those obtained with the best global calibration. Trial and error is the only way to fix the number of samples in the subset and the number of terms to retain in the model. Compared to the results for the traditional universal calibration method, the gain in SEP for protein, cellulose and digestibility in grass-hay, tropical forages or maize ranges between 5 and 11%.

Near-Infrared Reflectance Spectroscopy: Different Strategies for Local Calibrations in Analyses of Forage Quality

1993 ◽  
Vol 47 (4) ◽  
pp. 463-469 ◽  
Author(s):  
Are Halvor Aastveit ◽  
Petter Marum
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Calibration Method ◽  
Neutral Detergent Fiber ◽  
Optimum Number ◽  
Local Regression ◽  
Near Infrared Reflectance ◽  
Data Set ◽  
Positive Effects

This paper deals with the problem of how to utilize a large calibration set with 10 different analytes in order to make the best predictions possible on a routine basis. Ten different strategies of using the data set were studied with the use of numbers of principal components ranging from 4 to 12. We found positive effects of scatter correction for most of the analytes. On average, the local regression methods were superior to the others. The optimum number of samples for local regression seems to be between 50 and 100. The largest reduction in root mean square error of prediction (RMSEP), in comparison to results for the traditional method, was found on scatter-corrected spectra and a proposed local calibration with 50 calibration samples. The gain in RMSEP for neutral detergent fiber (NDF), acid detergent fiber (ADF), and crude fiber was about 25% and for protein and in vitro digestible dry matter digestibility (IVDMD) about 10%, compared to results for the traditional universal calibration method.

scholarly journals Global Calibration of Multi-Cameras Based on Refractive Projection and Ray Tracing

2017 ◽  
Author(s):  
Mingchi Feng ◽  
Xiang Jia ◽  
Jingshu Wang ◽  
Song Feng ◽  
Taixiong Zheng
Keyword(s):  
Ray Tracing ◽  
Real Data ◽  
Calibration Method ◽  
Calibration Error ◽  
Multiple Cameras ◽  
Projection Model ◽  
Vision Measurement ◽  
3D Computer Vision ◽  
Global Calibration ◽  
Calibration Methods

Multi-cameras system is widely applied in 3D computer vision especially when multiple cameras are distributed on both sides of the measured object. The calibration methods of multi-cameras system are critical to the accuracy of vision measurement and the key is to find an appropriate calibration target. In this paper, a high-precision camera calibration method for multi-cameras system based on transparent glass checkerboard and ray tracing is described, which is used to calibrate multiple cameras distributed on both sides of the glass checkerboard. Firstly, the intrinsic parameters of each camera is obtained by Zhang’s calibration method. Then, multiple cameras capture several images from the front and back of the glass checkerboard with different orientations, and all images contain distinct grid corners. As the cameras on one side are not affected by the refraction of glass checkerboard, extrinsic parameters can be directly calculated. However, the cameras on another side are influenced by the refraction of glass checkerboard, and the direct use of projection model will produce calibration error. A multi-cameras calibration method using refractive projection model and ray tracing is developed to eliminate this error. Furthermore, both synthetic and real data are employed to validate the proposed approach. The experimental results of refractive calibration show that the error of the 3D reconstruction is smaller than 0.2 mm, the relative errors of both rotation and translation are less than 0.014%, and the mean and standard deviation of reprojection error of 4-cameras system are 0.00007 and 0.4543 pixel. The proposed method is flexible, high accurate, and simple to carry out.

Research on global calibration method for multi-camera visual measurement system

2016 ◽  
Author(s):  
Dongzhao Huang ◽  
Qiancheng Zhao ◽  
Yun Ou ◽  
Tianlong Yang
Keyword(s):  
Measurement System ◽  
Calibration Method ◽  
Visual Measurement ◽  
Global Calibration

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.

Discrimination between Glutinous and Non-Glutinous Rice by Vibrational Spectroscopy. II: Effects of Spectral Pre-Treatment on the Classification of the Two Types of Rice

1998 ◽  
Vol 6 (1) ◽  
pp. 251-258 ◽  
Author(s):  
Yoshisato Ootake ◽  
Serge Kokot
Keyword(s):  
Vibrational Spectroscopy ◽  
Amylose Content ◽  
Moving Average ◽  
Scatter Correction ◽  
Glutinous Rice ◽  
Rice Samples ◽  
Pre Treatment ◽  
Spectral Sampling ◽  
Ft Raman

To measure the amylose content of rice, discrimination between glutinous and non-glutinous rice by vibrational spectroscopy was performed. It was previously demonstrated that classification of raw spectra by the SIMCA method showed the FT-Raman technique provided the best discrimination of classes. In this paper, the effects of spectral pre-treatments such as differentiation and multiplicative scatter correction (MSC) on classification of the rice samples are presented. For FT-NIR DRIFT measurements, differences in classification following different pre-treatments were relatively small but the best classification was obtained with the 2nd derivative pre-treatment, although precision was generally poor. With the PAS spectral sampling method, the classification was better after MSC pre-treatment of raw spectra than either after conversion to the 1st derivative or 2nd derivative. For FT-Raman, the best result was obtained with the MSC pre-treatment. The different effects of pre-treatment on the classification of spectra of the two types of rice probably reflect the information contained in the width of the absorbance bands, because the spectra were collected at 4 cm−1 step with 8 cm−1 resolution. Since the differentiation procedure follows the moving average, very small bands would be eliminated in the process, and would not contribute to the classification analysis. It was noted that effects of fluorescence in the FT-Raman spectra were not removed, even after the MSC pre-treatment.

scholarly journals Global Calibration Method of a Camera Using the Constraint of Line Features and 3D World Points

2016 ◽  
Vol 16 (4) ◽  
pp. 190-196 ◽  
Author(s):  
Guan Xu ◽  
Xinyuan Zhang ◽  
Xiaotao Li ◽  
Jian Su ◽  
Zhaobing Hao
Keyword(s):  
Calibration Method ◽  
Original Method ◽  
Transformation Matrix ◽  
Mean Values ◽  
Global Calibration ◽  
The Stability ◽  
Stability And Accuracy ◽  
Value Decomposition ◽  
Projective Lines ◽  
Mean Square Errors

Abstract We present a reliable calibration method using the constraint of 2D projective lines and 3D world points to elaborate the accuracy of the camera calibration. Based on the relationship between the 3D points and the projective plane, the constraint equations of the transformation matrix are generated from the 3D points and 2D projective lines. The transformation matrix is solved by the singular value decomposition. The proposed method is compared with the point-based calibration to verify the measurement validity. The mean values of the root-mean-square errors using the proposed method are 7.69×10−4, 6.98×10−4, 2.29×10−4, and 1.09×10−3 while the ones of the original method are 8.10×10−4, 1.29×10−2, 2.58×10−2, and 8.12×10−3. Moreover, the average logarithmic errors of the calibration method are evaluated and compared with the former method in different Gaussian noises and projective lines. The variances of the average errors using the proposed method are 1.70×10−5, 1.39×10−4, 1.13×10−4, and 4.06×10−4, which indicates the stability and accuracy of the method.

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