scholarly journals Calibration Transfer Based on Affine Invariance for NIR without Transfer Standards

Molecules ◽  
2019 ◽  
Vol 24 (9) ◽  
pp. 1802 ◽  
Author(s):  
Yuhui Zhao ◽  
Ziheng Zhao ◽  
Peng Shan ◽  
Silong Peng ◽  
Jinlong Yu ◽  
...  
Keyword(s):  
Affine Transformation ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Regression Coefficients ◽  
Rank Test ◽  
Affine Invariance ◽  
Standard Samples ◽  
Calibration Transfer ◽  
Practical Applications ◽  
Predicted Values

Calibration transfer is an important field for near-infrared (NIR) spectroscopy in practical applications. However, most transfer methods are constructed with standard samples, which are expensive and difficult to obtain. Taking this problem into account, this paper proposes a calibration transfer method based on affine invariance without transfer standards (CTAI). Our method can be utilized to adjust the difference between two instruments by affine transformation. CTAI firstly establishes a partial least squares (PLS) model of the master instrument to obtain score matrices and predicted values of the two instruments, and then the regression coefficients between each of the score vectors and predicted values are computed for the master instrument and the slave instrument, respectively. Next, angles and biases are calculated between the regression coefficients of the master instrument and the corresponding regression coefficients of the slave instrument, respectively. Finally, by introducing affine transformation, new samples are predicted based on the obtained angles and biases. A comparative study between CTAI and the other five methods was conducted, and the performances of these algorithms were tested with two NIR spectral datasets. The obtained experimental results show clearly that, in general CTAI is more robust and can also achieve the best Root Mean Square Error of test sets (RMSEPs). In addition, the results of statistical difference with the Wilcoxon signed rank test show that CTAI is generally better than the others, and at least statistically the same.

scholarly journals PLS Subspace-Based Calibration Transfer for Near-Infrared Spectroscopy Quantitative Analysis

Molecules ◽  
2019 ◽  
Vol 24 (7) ◽  
pp. 1289 ◽  
Author(s):  
Yuhui Zhao ◽  
Jinlong Yu ◽  
Peng Shan ◽  
Ziheng Zhao ◽  
Xueying Jiang ◽  
...  
Keyword(s):  
Least Squares ◽  
Near Infrared ◽  
Least Squares Method ◽  
Ordinary Least Squares ◽  
Rank Test ◽  
Transfer Model ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Standard Samples ◽  
Calibration Transfer

In order to enable the calibration model to be effectively transferred among multiple instruments and correct the differences between the spectra measured by different instruments, a new feature transfer model based on partial least squares regression (PLS) subspace (PLSCT) is proposed in this paper. Firstly, the PLS model of the master instrument is built, meanwhile a PLS subspace is constructed by the feature vectors. Then the master spectra and the slave spectra are projected into the PLS subspace, and the features of the spectra are also extracted at the same time. In the subspace, the pseudo predicted feature of the slave spectra is transferred by the ordinary least squares method so that it matches the predicted feature of the master spectra. Finally, a feature transfer relationship model is constructed through the feature transfer of the PLS subspace. This PLS-based subspace transfer provides an efficient method for performing calibration transfer with only a small number of standard samples. The performance of the PLSCT was compared and assessed with slope and bias correction (SBC), piecewise direct standardization (PDS), calibration transfer method based on canonical correlation analysis (CCACT), generalized least squares (GLSW), multiplicative signal correction (MSC) methods in three real datasets, statistically tested by the Wilcoxon signed rank test. The obtained experimental results indicate that PLSCT method based on the PLS subspace is more stable and can acquire more accurate prediction results.

Modified linear model correction: A calibration transfer method without standard samples

NIR news ◽  
2018 ◽  
Vol 29 (8) ◽  
pp. 24-27 ◽  
Author(s):  
Jin Zhang ◽  
Xiaoyu Cui ◽  
Wensheng Cai ◽  
Xueguang Shao
Keyword(s):  
Linear Model ◽  
Near Infrared ◽  
High Efficiency ◽  
Correction Method ◽  
Standard Samples ◽  
Calibration Transfer ◽  
Practical Applications ◽  
Model Correction ◽  
Transfer Method ◽  
Model Similarity

Calibration transfer without standard samples is essential for practical applications of near infrared spectroscopy because, sometimes, it is difficult or even impossible to obtain the standard samples for measuring their spectra on the secondary instrument. In this work, a modified linear model correction method is proposed for improving the transfer accuracy and computational efficiency. The constraint of linear model correction was replaced by a robust convex equation to restrict the model similarity in the optimization. The near infrared dataset of pharmaceutical tablet measured with different instruments are used to test the performance of the method. The result shows that a modified linear model correction achieves a high efficiency of the transfer while the computational complexity can be considerably reduced. The method may provide a robust way in practical application.

Calibration transfer based on the weight matrix (CTWM) of PLS for near infrared (NIR) spectral analysis

2018 ◽  
Vol 10 (18) ◽  
pp. 2169-2179 ◽  
Author(s):  
Feiyu Zhang ◽  
Ruoqiu Zhang ◽  
Jiong Ge ◽  
Wanchao Chen ◽  
Wuye Yang ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Weight Matrix ◽  
Calibration Model ◽  
Calibration Transfer ◽  
Practical Applications

Calibration transfer is of great necessity for practical applications of near infrared (NIR) spectroscopy, since the original calibration model would become invalid when spectra are measured on different instruments or under different detection conditions.

Transfer of calibration model between near-infrared spectrometers for hematocrit measurement of grazing cattle

NIR news ◽  
2017 ◽  
Vol 28 (7) ◽  
pp. 16-21
Author(s):  
Xuan Luo ◽  
Akifumi Ikehata ◽  
Kunio Sashida ◽  
Shanji Piao ◽  
Tsutomu Okura ◽  
...  
Keyword(s):  
Near Infrared ◽  
Optimal Solution ◽  
Nir Spectroscopy ◽  
Final Diagnosis ◽  
Rapid Diagnosis ◽  
Calibration Model ◽  
Calibration Transfer ◽  
Current Time ◽  
Grazing Cattle ◽  
Robust Model

A major concern for the practical use of NIR spectroscopy is calibration transfer. In this study, different ways of calibration transfer were tried and compared to seek the optimal solution for our developed portable NIR spectrometers, which are designed for rapid diagnosis of bovine anemia due to parasites and are believed to be promising to replace the current time-consuming centrifugation way of measuring Hematocrit value (%) for final diagnosis. Our results show the importance of a robust model during the process of calibration transfer. It is risky to transfer a model which is not robust enough by using standardization algorithm.

Estimation of Wood Stiffness and Strength Properties of Hybrid Larch by Near-Infrared Spectroscopy

2007 ◽  
Vol 61 (8) ◽  
pp. 882-888 ◽  
Author(s):  
Takaaki Fujimoto ◽  
Hiroyuki Yamamoto ◽  
Satoru Tsuchikawa
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Strength Properties ◽  
Density Variation ◽  
Modulus Of Rupture ◽  
Cellulose Content ◽  
Hybrid Larch ◽  
Absorption Bands ◽  
Wood Stiffness ◽  
Predicted Values

This work was undertaken to investigate the feasibility of near-infrared (NIR) spectroscopy for estimating wood mechanical properties, i.e., modulus of elasticity (MOE) and modulus of rupture (MOR) in bending tests. Two sample sets having large and limited density variation were prepared to examine the effects of wood density on estimation of MOE and MOR by the NIR technique. Partial least squares (PLS) analysis was employed and it was found that the relationships between laboratory-measured and NIR-predicted values were good in the case of sample sets having large density variation. MOE could be estimated even when density variation in the sample set was limited. It was concluded that absorption bands due to the OH group in the semi-crystalline or crystalline regions of cellulose strongly influenced the calibrations for bending stiffness of hybrid larch. This was also suggested from the result that both α-cellulose content and cellulose crystallinity showed moderate positive correlation to wood stiffness.

Scalable calibration transfer without standards via dynamic time warping for near-infrared spectroscopy

2019 ◽  
Vol 11 (35) ◽  
pp. 4481-4493 ◽  
Author(s):  
Congming Zou ◽  
Huimin Zhu ◽  
Junru Shen ◽  
Yue He ◽  
Jiaen Su ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Dynamic Time Warping ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Time Warping ◽  
Calibration Transfer ◽  
Transfer Method ◽  
Dynamic Time

A standard-free calibration transfer method has been developed for NIR spectroscopy based on variable penalty dynamic time warping.

The Use of near Infrared Spectroscopy to Quantify Lignite-Derived Carbon in Humus-Lignite Mixtures

2007 ◽  
Vol 15 (3) ◽  
pp. 195-200 ◽  
Author(s):  
Marcin Chodak ◽  
Maria Niklińska ◽  
Friedrich Beese
Keyword(s):  
Near Infrared ◽  
Regression Coefficient ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Mine Soils ◽  
Organic Horizons ◽  
Predicted Values ◽  
Calibration Equations ◽  
Mixed Samples ◽  
Forest Humus

Assessment of the percentage of lignite-derived C (lign-C%) in mine soils may be achieved only by using time-consuming and expensive methods. The objectives of this study were (1) to compare near infrared (NIR) spectra of forest humus and lignite and (2) to test whether NIR spectroscopy may assess lign-C% in artificial mixtures of humus and lignite. The experiment consisted of three trials (T1, T2 and T3). In T1 the mixed samples ( n = 75) were produced from one humus sample and one lignite sample, in T2 (n = 74) from 74 different humus samples and one lignite sample and in T3 (n = 74) from 74 different humus samples and 15 lignite samples. In each trial, 35 samples were used to develop calibration equations and the remaining samples were used for validation. The humus and the lignite samples used to produce the mixed samples were analysed for C, H, N and S and their NIR spectra were recorded. The lignite samples contained more C, H and S and less N than the humus samples. Principal component analysis revealed significant differences between NIR spectra of the humus and the lignite samples. The prediction of lign-C% in T1 [regression coefficient (b) of linear regression (measured against predicted values) = 0.99, correlation coefficient ( r2) = 1.00, standard error of prediction (SEP) = 1.2%] and T2 ( b = 0.99, r2 = 0.99, SEP = 1.9%) was very good and in T3 satisfactory ( b = 0.83, r2 = 0.92, SEP = 4.0% ). The calibration equations of T2 predicted lign-C% satisfactorily and also in the validation samples of T3 (b = 0.88, r2 = 0.93, SEP = 4.0% ). The results indicate the ability of NIR spectroscopy to predict lign-C% in the mixed humus and lignite samples and suggest usefulness of NIR spectroscopy for the assessment of the percentage of lignite-derived C in the organic horizons of mine soils.

scholarly journals Near-Infrared Spectroscopy as an Analytical Process Technology for the On-Line Quantification of Water Precipitation Processes during Danhong Injection

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xuesong Liu ◽  
Chunyan Wu ◽  
Shu Geng ◽  
Ye Jin ◽  
Lianjun Luan ◽  
...  
Keyword(s):  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Process Technology ◽  
Danhong Injection ◽  
Fiber Optic Probes ◽  
Quantitative Monitoring ◽  
On Line ◽  
Predicted Values

This paper used near-infrared (NIR) spectroscopy for the on-line quantitative monitoring of water precipitation during Danhong injection. For these NIR measurements, two fiber optic probes designed to transmit NIR radiation through a 2 mm flow cell were used to collect spectra in real-time. Partial least squares regression (PLSR) was developed as the preferred chemometrics quantitative analysis of the critical intermediate qualities: the danshensu (DSS, (R)-3, 4-dihydroxyphenyllactic acid), protocatechuic aldehyde (PA), rosmarinic acid (RA), and salvianolic acid B (SAB) concentrations. Optimized PLSR models were successfully built and used for on-line detecting of the concentrations of DSS, PA, RA, and SAB of water precipitation during Danhong injection. Besides, the information of DSS, PA, RA, and SAB concentrations would be instantly fed back to site technical personnel for control and adjustment timely. The verification experiments determined that the predicted values agreed with the actual homologic value.

Calibration Transfer for Near-Infrared (NIR) Spectroscopy Based on Neighborhood Preserving Embedding

2020 ◽  
pp. 1-19
Author(s):  
Lijuan Chen ◽  
Dawei Liu ◽  
Jiheng Zhou ◽  
Jun Bin ◽  
Zhen Li
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Calibration Transfer

Real-time and field monitoring of the key parameters in industrial trough composting process using a handheld near infrared spectrometer

2020 ◽  
Vol 28 (5-6) ◽  
pp. 334-343
Author(s):  
Yuanping Huang ◽  
Xiaoxi Sun ◽  
Keke Liao ◽  
Lujia Han ◽  
Zengling Yang
Keyword(s):  
Real Time ◽  
Near Infrared ◽  
Process Analysis ◽  
Organic Fertilizer ◽  
Nir Spectroscopy ◽  
Field Monitoring ◽  
Organic Fertilizers ◽  
Total Carbon ◽  
Composting Process ◽  
Predicted Values

Producing organic fertilizer by aerobic composting is an effective way to solve the livestock manure pollution problem and to achieve economic utilization of the valuable resource. To control the composting process effectively and ensure the quality of such organic fertilizers, it is necessary to quantify the key parameters and provide timely feedback of their changes during the composting process. In the industrial field, the traditional laboratory analysis is being transferred into process analysis. This study explored the application of real-time and field monitoring of the key parameters in the industrial trough composting process using handheld near infrared (NIR) spectroscopy and evaluated its ability to accurately predict these changes. The results showed that the handheld NIR could accurately detect moisture content (MC), total nitrogen (TN), total carbon (TC), the carbon/nitrogen (C/N) ratio, organic matter (OM) and electrical conductivity (EC) during the trough composting process, with excellent predictions for MC, good predictions for TN and OM, approximate predictions for TC, C/N ratio and EC. Changes in NIR-predicted values and measured values were consistent as the composting process progressed. The handheld NIR sensor shows good potential for real-time and field monitoring of the composting process and organic fertilizer quality assurance.

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