scholarly journals The Evaluation of the Applicability of Fourier Transform Near-Infrared (FT-NIR) Spectroscopy in the Measurement of Analytical Parameters in Must and Wine

2017 ◽  
Vol 22 (2) ◽  
Author(s):  
M. Manley ◽  
A. van Zyl ◽  
E.E.H. Wolf
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Nir Spectroscopy

Rapid Determination of Heartwood Extractives in Larix sp. by Means of Fourier Transform near Infrared Spectroscopy

2002 ◽  
Vol 10 (3) ◽  
pp. 203-214 ◽  
Author(s):  
N. Gierlinger ◽  
M. Schwanninger ◽  
B. Hinterstoisser ◽  
R. Wimmer
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Solid Wood ◽  
Hot Water ◽  
Extractive Content ◽  
Fast Method ◽  
Wood Powder ◽  
Test Set

The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy to rapidly determine extractive and phenolic content in heartwood of larch trees ( Larix decidua MILL., L. leptolepis (LAMB.) CARR. and the hybrid L. x eurolepis) was investigated. FT-NIR spectra were collected from wood powder and solid wood using a fibre-optic probe. Partial Least Squares (PLS) regression analyses were carried out describing relationships between the data sets of wet laboratory chemical data and the FT-NIR spectra. Besides cross and test set validation the established models were subjected to a further evaluation step by means of additional wood samples with unknown extractive content. Extractive and phenol contents of these additional samples were predicted and outliers detected through Mahalanobis distance calculations. Models based on the whole spectral range and without data pre-processing performed well in cross-validation and test set validation, but failed in the evaluation test, which is based on spectral outlier detection. But selection of data pre-processing methods and manual as well as automatic restriction of wavenumber ranges considerably improved the model predictability. High coefficients of determination ( R2) and low root mean square errors of cross-validation ( RMSECV) were obtained for hot water extractives ( R2 = 0.96, RMSECV = 0.86%, range = 4.9–20.4%), acetone extractives ( R2 = 0.86, RMSECV = 0.32%, range = 0.8–3.6%) and phenolic substances ( R2 = 0.98, RMSECV = 0.21%, range = 0.7–4.9%) from wood powder. The models derived from wood powder spectra were more precise than those obtained from solid wood strips. Overall, NIR spectroscopy has proven to be an easy to facilitate, reliable, accurate and fast method for non-destructive wood extractive determination.

scholarly journals A New Plant Indicator (Artemisia lavandulaefolia DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Lu Xu ◽  
Qiong Shi ◽  
Bang-Cheng Tang ◽  
Shunping Xie
Keyword(s):  
Support Vector Machine ◽  
Fourier Transform ◽  
Least Squares ◽  
Near Infrared ◽  
Mercury Level ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Classification Model ◽  
Support Vector ◽  
Reference Analysis

A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0–3 kilometers, 7.52–88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0–0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.

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.

Classification of Chinese Soybean Paste by Fourier Transform Near-Infrared (FT-NIR) Spectroscopy and Different Supervised Pattern Recognition

2011 ◽  
Vol 5 (4) ◽  
pp. 928-934 ◽  
Author(s):  
Hui Jiang ◽  
Guohai Liu ◽  
Xiahong Xiao ◽  
Shuang Yu ◽  
Congli Mei ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Fourier Transform ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soybean Paste

scholarly journals Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform-Near Infrared Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2309
Author(s):  
Qiang Liu ◽  
Shaoxia Chen ◽  
Dandan Zhou ◽  
Chao Ding ◽  
Jiahong Wang ◽  
...  
Keyword(s):  
Weight Loss ◽  
Fourier Transform ◽  
Chinese Cabbage ◽  
Loss Rate ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Prediction Performance ◽  
Surface Color ◽  
Weight Loss Rate ◽  
Plsr Model

A nondestructive optical method is described for the quality assessment of mini-Chinese cabbage with nanopackaging during its storage, using Fourier transform-near infrared (FT-NIR) spectroscopy. The sample quality attributes measured included weight loss rate, surface color index, vitamin C content, and firmness. The level of freshness of the mini-Chinese cabbage during storage was divided into three categories. Partial least squares regression (PLSR) and the least squares support vector machine were applied to spectral datasets in order to develop prediction models for each quality attribute. For a comparative analysis of performance, the five preprocessing methods applied were standard normal variable (SNV), first derivative (lst), second derivative (2nd), multiplicative scattering correction (MSC), and auto scale. The SNV-PLSR model exhibited the best prediction performance for weight loss rate (Rp2 = 0.96, RMSEP = 1.432%). The 1st-PLSR model showed the best prediction performance for L* value (Rp2 = 0.89, RMSEP = 3.25 mg/100 g), but also the lowest accuracy for firmness (Rp2 = 0.60, RMSEP = 2.453). The best classification model was able to predict freshness levels with 88.8% accuracy in mini-Chinese cabbage by supported vector classification (SVC). This study illustrates that the spectral profile obtained by FT-NIR spectroscopy could potentially be implemented for integral assessments of the internal and external quality attributes of mini-Chinese cabbage with nanopacking during storage.

Rapid prediction of natural durability of larch heartwood using Fourier transform near-infrared spectroscopy

2003 ◽  
Vol 33 (9) ◽  
pp. 1727-1736 ◽  
Author(s):  
Notburga Gierlinger ◽  
Dominique Jacques ◽  
Manfred Schwanninger ◽  
Rupert Wimmer ◽  
Barbara Hinterstoisser ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Wood Decay ◽  
Nir Spectroscopy ◽  
Larix Kaempferi ◽  
Natural Durability ◽  
Fast Method ◽  
European Standard ◽  
Decay Tests ◽  
Mean Square Errors

The feasibility of Fourier transform near-infrared (FT-NIR) spectroscopy for rapidly determining the natural durability of the heartwood of larch trees (Larix decidua Mill. and Larix kaempferi (Lamb.) Carrière) was investigated. FT-NIR spectra were collected from solid wood with a fibre-optical probe. Basidiomycetes tests using Coniophora puteana and Poria placenta were carried out on larch heartwood (European standard EN 113), with pine sapwood (Pinus sylvestris L.) used as a reference. The relative resistance to decay (x value) was calculated, and durability classes were estimated according to European standard EN 350-1. Partial least squares regressions between the data sets of wood decay tests (x values) and the FT-NIR spectra were calculated. It was found that multiplicative scatter correction considerably improved the model predictability. High coefficients of correlation (r) and low root mean square errors of prediction (RMSEP) were obtained for cross validation based on wood decay tests with P. placenta (r = 0.92, RMSEP = 0.077, range 0.27-1.13) and C. puteana (r = 0.97, RMSEP = 0.078, range 0.07-1.58). Overall, NIR spectroscopy has proven to be an accurate and fast method for the nondestructive determination of natural durability, which might be highly relevant for intensive tree breeding programs and for efforts to optimize wood utilization.

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