Near-Infrared Spectroscopic Measurement of Organic Matter in Soil

2012 ◽  
Vol 499 ◽  
pp. 414-418
Author(s):  
Tao Pan ◽  
Zhen Tao Wu ◽  
Jie Mei Chen
Keyword(s):  
Organic Matter ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Modeling Prediction ◽  
Validation Set ◽  
Selection Of

Near-infrared (NIR) spectroscopy was successfully applied to chemical free and rapid determination of the organic matter in soil, and moving window partial least square (MWPLS) combining with Savitzky-Golay (SG) smoothing was used to the selection of NIR waveband. Thirty-five samples were randomly selected from all 97 collected soil samples as the validation set. The remaining 62 samples were divided into similar modeling calibration set (37 samples) and modeling prediction set (25 samples) based on partial least square cross-validation predictive bias (PLSPB). The selected waveband was 1896 nm to 2138 nm; the SG smoothing parameters and PLS factor OD, DP, NSP and F were 2, 6, 71 and 15, respectively; the modeling effect M-SEP and M-RPwere 0.219% and 0.944, respectively; the validating effect V-SEP and V-RPwere 0.243% and 0.878, respectively. The result provided a reliable NIR model and valuable references for designing specialized NIR instruments.

scholarly journals   Near infrared spectroscopy for deoxynivalenol content estimation in intact wheat grain

2012 ◽  
Vol 58 (No. 4) ◽  
pp. 196-203 ◽  
Author(s):  
V. Dvořáček ◽  
A. Prohasková ◽  
J. Chrpová ◽  
L. Štočková
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Resistance Breeding ◽  
Partial Least Square ◽  
Least Square ◽  
Prediction Errors ◽  
Wheat Grain ◽  
Wide Range

Non-invasive determination of deoxynivalenol (DON) still presents a challenging problem. Therefore, the present study was aimed at a rapid determination of DON in whole wheat grain by means of FT-NIR spectroscopy, with a wide range of concentrations for potential applications in breeding programs and common systems of quality management using partial least square calibration (PLS) and discriminant analysis technique (DA). Using a set of artificially infected wheat samples with a known content of DON, four PLS models with different concentration range were created. The broadest model predicting DON in the concentration range of 0&ndash;90 mg/kg possessed the highest correlation coefficients of calibration and cross validation (0.94 and 0.88); but also possessed the highest prediction errors (SEP = 6.23 mg/kg). Thus the subsequent combination of DA as the wide range predictive model and the low-range PLS model was used. This technique gave more precise results in the samples with lower DON concentrations &ndash; below 30 mg/kg (SEP = 2.35 mg/kg), when compared to the most wide-range PLS model (SEP = 5.95 mg/kg).<br />Such technique enables to estimate DON content in collections of artificially infected wheat plants in Fusarium resistance breeding experiments. &nbsp;

scholarly journals Rapid Determination of β-Glucan Content of Hulled and Naked Oats Using near Infrared Spectroscopy Combined with Chemometrics

Foods ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 43
Author(s):  
Maninder Meenu ◽  
Yaqian Zhang ◽  
Uma Kamboj ◽  
Shifeng Zhao ◽  
Lixia Cao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Wavelength Region ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Naked Oats ◽  
Non Destructive ◽  
Selection Of

The quantification of β-glucan in oats is of immense importance for plant breeders and food scientists to develop plant varieties and food products with a high quantity of β-glucan. However, the chemical analysis of β-glucan is time consuming, destructive, and laborious. In this study, near-infrared (NIR) spectroscopy in conjunction with Chemometrics was employed for rapid and non-destructive prediction of β-glucan content in oats. The interval Partial Least Square (iPLS) along with correlation matrix plots were employed to analyze the NIR spectrum from 700–1300 nm, 1300–1900 nm, and 1900–2500 nm for the selection of important wavelengths for the prediction of β-glucan. The NIR spectral data were pre-treated using Savitzky Golay smoothening and normalization before employing partial least square regression (PLSR) analysis. The PLSR models were established based on the selection of wavelengths from PLS loading plots that present a high correlation with β-glucan content. It was observed that wavelength region 700–1300 nm is sufficient for the satisfactory prediction of β-glucan of hulled and naked oats with R2c of 0.789 and 0.677, respectively, and RMSE < 0.229.

scholarly journals Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Mohd Yusop Nurida ◽  
Dolmat Norfadilah ◽  
Mohd Rozaiddin Siti Aishah ◽  
Chan Zhe Phak ◽  
Syafiqa M. Saleh
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Infrared Spectrometry ◽  
Calibration Model ◽  
Co2 Absorption ◽  
Processing Unit

The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.

Application of Near Infrared Spectroscopy in the Analysis of Printing Color Detection

2012 ◽  
Vol 262 ◽  
pp. 59-64
Author(s):  
Hong Wei Lu ◽  
Hong He ◽  
Jun Ji ◽  
Guo Qiang Liu ◽  
Ying Hu
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Color Parameters ◽  
Detection Model ◽  
Color Detection ◽  
Validation Set

For the fast and exact detection of printing color, we combine the near infrared (NIR) spectroscopy technique with partial least square (PLS) to build the detection model of printing color. Applying the 144 samples of spectral curve which obtained by the near infrared spectroscopy randomly separated into calibration set and validation set, and base on the 120 calibration set data to establish the prediction model of printing color by PLS, then this model is employed for predicting the color of the 24 validation set. The RMSEC of the 24 blocks’ color parameters L*, a*, b*, E are 0.73, 2.26, 3.03 and 1.09 respectively; The RMSEP are 0.97, 2.77, 3.57 and 1.34 respectively. Those results tell that the NIR spectrum and blocks’ color parameters L*, a*, b*, E could accurately establish a quantitative regression model, applying such model also can accurately predict unknown samples, and the results approximate to the original reference data. The use of near infrared spectroscopy to detect the printed matter nondestructively is feasible, and lays the foundation for the further analysis and establishment of printing chroma model.

scholarly journals Mechanical properties of wood materials using near-infrared spectroscopy based on correlation local embedding and partial least-squares

2019 ◽  
Vol 31 (3) ◽  
pp. 1053-1060 ◽  
Author(s):  
Lei Yu ◽  
Yuliang Liang ◽  
Yizhuo Zhang ◽  
Jun Cao
Keyword(s):  
Mechanical Properties ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Tangential Surface ◽  
Prediction Coefficient ◽  
Validation Set ◽  
Mongolian Oak

Abstract This study used near-infrared (NIR) spectroscopy to predict mechanical properties of wood. NIR spectra were collected in wavelengths 900–1700 nm, and spectra averaged by radial and tangential surface spectra were used to establish a partial least square (PLS) model based on correlation local embedding (CLE). Mongolian oak (Quercus mongolica Fisch. ex Ledeb.) was used to test the effectiveness of the model. The cross-validation method was used to verify the robustness of the CLE–PLS model. Ninety samples were tested as the calibration set and forty-five as the validation set. The results show that the prediction coefficient of determination ($$R_{p}^{2}$$Rp2) is 0.80 for MOR, and 0.78 for MOE. The ratio of performance to deviation is 2.23 for MOR and 2.15 for MOE.

Application of near Infrared Spectroscopy for the Quantification of Quality Parameters in Selected Vegetables and Essential Oil Plants

1998 ◽  
Vol 6 (A) ◽  
pp. A125-A130 ◽  
Author(s):  
H. Schulz ◽  
H.-H. Drews ◽  
R. Quilitzsch ◽  
H. Krüger
Keyword(s):  
Essential Oil ◽  
Oil Content ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Partial Least Square ◽  
Least Square ◽  
Total Carotenoids ◽  
Good Calibration

The use of near-infrared (NIR) spectroscopy for the prediction of the essential oil content and composition in various umbelliferae genotypes was investigated. Furthermore an NIR method was developed for the quantification of total carotenoids and sugars present in different carrot varieties. Applying partial least square algorithm very good calibration statistics ( SECV, R2) were obtained for the prediction of the essential oil content in fennel (0.47, 0.83), caraway (0.29, 0.93), dill (0.30, 0.96) and coriander (0.29, 0.93). Satisfactory calibration results were received for the NIR determination of total carotenoids (1.54, 0.80) and of saccharose(0.74, 0.76) in carrots. The performed study demonstrates that NIR can be used to rapidly and accurately predict secondary metabolites such as carotenoids, anethole, fenchone, estragole, limonene and carvone occurring in vegetables and in fruits of various essential oil plants.

Reagentless near-Infrared Determination of Cholesterol in Undiluted Human Serum Using Interval Partial Least Square

2011 ◽  
Vol 345 ◽  
pp. 128-133
Author(s):  
Hong Zhi Gao ◽  
Qi Peng Lu ◽  
Fu Rong Huang
Keyword(s):  
Human Serum ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Prediction Ability ◽  
Sample Set ◽  
Cholesterol Measurement

In order to determination of cholesterol in human serum with no reagent using near-infrared (NIR) spectroscopy. Interval partial least square (iPLS) was proposed as an effective variable selection approach for multivariate calibration. For this purpose, an independent sample set was employed to evaluate the prediction ability of the resulting model. The spectrum was split into different interval. Then, the informative region of cholesterol (1688-1760nm), in which the PLS model has a low RMSEP with 0.241mmol/L and a high R with 0.975, is selected with 23 intervals. The results indicate that, the informative region of cholesterol can be obtained by iPLS and applied to design the simpler reagentless NIR instruments for inexpensive cholesterol measurement in future.

Quality Determination of Glass/Epoxy Prepreg by NIR Spectroscopy

2010 ◽  
Vol 152-153 ◽  
pp. 77-80
Author(s):  
Wei Li ◽  
Wei Jia Gao ◽  
Ping Chen ◽  
Bao Lei Sun
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Volatile Content ◽  
Calibration Models ◽  
Significant Difference ◽  
Nondestructive Quality ◽  
The Mean

A near-infrared spectroscopy (NIR) technique has been applied for rapid and nondestructive quality determination of glass/epoxy prepreg. Abundant information related with resin and volatile was observed in the NIR spectra of the prepreg cloth. The partial least square (PLS) regression was used to develop the calibration models by utilizing several spectral pretreatments combined with different spectra ranges. Some unknown samples were analyzed by the NIR method. The mean absolute predicted errors were 0.32% and 0.214% for the resin content and the volatile content respectively. The results of the paired t-test revealed that there was no significant difference between the NIR method and standard method. The NIR method can be used to predict the resin and volatile content simultaneously within 30s. The study indicates that the NIR method is sufficiently for quality determination of glass/epoxy prepreg cloth.

Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machines Modeling based on Near Infrared Spectra

2020 ◽  
Vol 16 ◽  
Author(s):  
Linqi Liu ◽  
JInhua Luo ◽  
Chenxi Zhao ◽  
Bingxue Zhang ◽  
Wei Fan ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Rapid Determination ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Aloe Emodin ◽  
Relative Differences

BACKGROUND: Measuring medicinal compounds to evaluate their quality and efficacy has been recognized as a useful approach in treatment. Rhubarb anthraquinones compounds (mainly including aloe-emodin, rhein, emodin, chrysophanol and physcion) are its main effective components as purgating drug. In the current Chinese Pharmacopoeia, the total anthraquinones content is designated as its quantitative quality and control index while the content of each compound has not been specified. METHODS: On the basis of forty rhubarb samples, the correlation models between the near infrared spectra and UPLC analysis data were constructed using support vector machine (SVM) and partial least square (PLS) methods according to Kennard and Stone algorithm for dividing the calibration/prediction datasets. Good models mean they have high correlation coefficients (R2) and low root mean squared error of prediction (RMSEP) values. RESULTS: The models constructed by SVM have much better performance than those by PLS methods. The SVM models have high R2 of 0.8951, 0.9738, 0.9849, 0.9779, 0.9411 and 0.9862 that correspond to aloe-emodin, rhein, emodin, chrysophanol, physcion and total anthraquinones contents, respectively. The corresponding RMSEPs are 0.3592, 0.4182, 0.4508, 0.7121, 0.8365 and 1.7910, respectively. 75% of the predicted results have relative differences being lower than 10%. As for rhein and total anthraquinones, all of the predicted results have relative differences being lower than 10%. CONCLUSION: The nonlinear models constructed by SVM showed good performances with predicted values close to the experimental values. This can perform the rapid determination of the main medicinal ingredients in rhubarb medicinal materials.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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