Fast Evaluation of Pectin Content in Ramie Using NIR Techniques

2011 ◽  
Vol 287-290 ◽  
pp. 2689-2692
Author(s):  
Wei Jiang ◽  
Guang Ting Han ◽  
Yuan Ming Zhang ◽  
Jian Hua Chen
Keyword(s):  
Near Infrared ◽  
Tunable Filter ◽  
Partial Least Square ◽  
Least Square ◽  
National Standard ◽  
Ramie Fiber ◽  
Calibration Model ◽  
Calibration Data ◽  
Fast Evaluation ◽  
Pectin Content

Near-infrared (NIR) prediction model of ramie pectin content was established in this research. Wet chemical analysis method which was based on Chinese national standard was conducted for getting calibration data, and NIR data of 60 ramie samples were collected using acousto-optic tunable filter (AOTF) near-infrared spectrometer. The NIR model of pectin of ramie were derived by partial least square (PLS) regression. Prediction of chemical composition of independent ramie samples showed that R/SEP values of pectin is 17.87. Such NIR calibration model can be utilized by ramie fiber manufacturers and breeding workers, in order to better manage the degumming process and evaluate the quality of ramie varieties.

Hyperspectral Imaging for Predicting Soluble Solid Content of Starfruit

2015 ◽  
Vol 73 (1) ◽  
Author(s):  
Feri Candra ◽  
Syed Abd. Rahman Abu Bakar
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Solid Content ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Soluble Solid Content ◽  
Calibration Model ◽  
Soluble Solid

Hyperspectral imaging technology is a powerful tool for non-destructive quality assessment of fruits. The objective of this research was to develop novel calibration model based on hyperspectral imaging to estimate soluble solid content (SSC) of starfruits. A hyperspectral imaging system, which consists of a near infrared  camera, a spectrograph V10, a halogen lighting and a conveyor belt system, was used in this study to acquire hyperspectral  images of the samples in visible and near infrared (500-1000 nm) regions. Partial least square (PLS) was used to build the model and to find the optimal wavelength. Two different masks were applied for obtaining the spectral data. The optimal wavelengths were evaluated using multi linear regression (MLR). The coefficient of determination (R2) for validation using the model with first mask (M1) and second mask (M2) were 0.82 and 0.80, respectively.

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.

scholarly journals Nondestructive Prediction of Isoflavones and Oligosaccharides in Intact Soybean Seed Using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) Spectroscopic Techniques

Foods ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 232
Author(s):  
Hanim Z. Amanah ◽  
Salma Sultana Tunny ◽  
Rudiati Evi Masithoh ◽  
Myoung-Gun Choung ◽  
Kyung-Hwan Kim ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Prediction Models ◽  
Soybean Seed ◽  
Fourier Transform Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Calibration Model ◽  
Spectroscopic Techniques ◽  
Ft Ir

The demand for rapid and nondestructive methods to determine chemical components in food and agricultural products is proliferating due to being beneficial for screening food quality. This research investigates the feasibility of Fourier transform near-infrared (FT-NIR) and Fourier transform infrared spectroscopy (FT-IR) to predict total as well as an individual type of isoflavones and oligosaccharides using intact soybean samples. A partial least square regression method was performed to develop models based on the spectral data of 310 soybean samples, which were synchronized to the reference values evaluated using a conventional assay. Furthermore, the obtained models were tested using soybean varieties not initially involved in the model construction. As a result, the best prediction models of FT-NIR were allowed to predict total isoflavones and oligosaccharides using intact seeds with acceptable performance (R2p: 0.80 and 0.72), which were slightly better than the model obtained based on FT-IR data (R2p: 0.73 and 0.70). The results also demonstrate the possibility of using FT-NIR to predict individual types of evaluated components, denoted by acceptable performance values of prediction model (R2p) of over 0.70. In addition, the result of the testing model proved the model’s performance by obtaining a similar R2 and error to the calibration model.

scholarly journals Near-Infrared Spectroscopy for Measuring Urea in Hemodialysis Fluids

2001 ◽  
Vol 47 (7) ◽  
pp. 1279-1286 ◽  
Author(s):  
Christopher V Eddy ◽  
Mark A Arnold
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Spectral Range ◽  
Near Infrared ◽  
Factor Model ◽  
Partial Least Square ◽  
Least Square ◽  
Calibration Model ◽  
Calibration Models ◽  
Sensitivity And Selectivity

Abstract Background: Near-infrared spectroscopy is proposed as a method for providing real-time urea concentrations during hemodialysis treatments. The feasibility of such noninvasive urea measurements is evaluated in undiluted dialysate fluid. Methods: Near-infrared spectra were collected from calibration solutions of urea prepared in dialysate fluid. Spectra were collected over three distinct spectral regions, and partial least-squares calibration models were optimized and compared for each. Selectivity for urea was demonstrated with two-component samples composed of urea and glucose in the dialysate matrix. The clinical significance of this approach was assessed by measuring urea in real hemodialysate samples. Results: Urea absorptions within the combination and short-wavelength, near-infrared spectral regions provided sufficient spectral information for sound calibration models in the dialysate matrix. The combination spectral region had SEs of calibration (SEC) and prediction (SEP) of 0.38 mmol/L and 0.26 mmol/L, respectively, over the 4720–4600 cm−1 spectral range with 5 partial least-square factors. A second calibration model was established over the combination region from a series of solutions prepared with independently variable concentrations of urea and glucose. The best calibration model for urea in the presence of variable glucose concentrations had a SEC of 0.6 mmol/L and a SEP of 0.4 mmol/L for a 5-factor model over the 4600–4350 cm−1 spectral range. There was no significant decrease in SEP when the 4720–4600 cm−1 calibration model was used to measure urea in real samples collected during actual hemodialysis. Conclusions: Urea can be determined with sufficient sensitivity and selectivity for clinical measurements within the matrix of the hemodialysis fluid.

scholarly journals Near Infrared (NIR) Spectroscopy to Predict Physical Properties of Acacia mangium at Three Different Age Classes

2017 ◽  
Vol 4 (1) ◽  
pp. 7-12
Author(s):  
Lina Karlinasari ◽  
Merry Sabed
Keyword(s):  
Moisture Content ◽  
Physical Properties ◽  
Wood Density ◽  
Near Infrared ◽  
Acacia Mangium ◽  
Nir Spectroscopy ◽  
Solid Wood ◽  
Partial Least Square ◽  
Least Square ◽  
Calibration Model

Near Infrared (NIR) spectroscopy has been used to predict several properties of wood. This is one of the nondestructive testing (NDT) methods providing fast and reliable wood characterization analysis which can be applied in various manufacture industry, included forest sector, in control and process monitoring task. Moisture content and wood density are important properties related to strength properties. The aim of this study was to evaluate NIR technique in obtaining calibration models for determining moisture content and wood density of Acacia mangium in the age of 5, 6, 7 years-old. Spectra were measured in both solid and ground wood samples. Laboratory testing of physical properties were determined by volumetric and gravimetric methods. The laboratory values were correlated with the NIR spectra using multivariate analysis statistic of Partial Least Square (PLS). The calibration-validation model of this relationship was evaluated by using the coefficient of determination (R2), root means square error of calibration (RMSEC) and cross-validation (RMSECV) values. Generally, a better accuracy was obtained by using calibration model of ground wood compared to that of solid wood samples. At age of 7 years-old, the R2 allowed the use of NIR spectra of solid samples to develop calibration and validation model, especially for wood density. Based on ratio of performance to deviation (RPD) and RMSE, ground samples demonstrated a higher value of RPD, RMSEC, and RMSECV compared to solid wood for all properties.

scholarly journals Evaluation of the moisture content of tapioca starch using near-infrared spectroscopy

2015 ◽  
Vol 08 (02) ◽  
pp. 1550014 ◽  
Author(s):  
Kittisak Phetpan ◽  
Panmanas Sirisomboon
Keyword(s):  
Moisture Content ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Calibration Model ◽  
Drying Process ◽  
Data Set ◽  
Prediction Ability ◽  
Tapioca Starch

The purpose of this study was to develop a calibration model to evaluate the moisture content of tapioca starch using the near-infrared (NIR) spectral data in conjunction with partial least square (PLS) regression. The prediction ability was assessed using a separate prediction data set. Three groups of tapioca starch samples were used in this study: tapioca starch cake, dried tapioca starch and combined tapioca starch. The optimum model obtained from the baseline-offset spectra of dried tapioca starch samples at the outlet of the factory drying process provided a coefficient of determination (R2), standard error of prediction (SEP), bias and residual prediction deviation (RPD) of 0.974, 0.16%, -0.092% and 7.4, respectively. The NIR spectroscopy protocol developed in this study could be a rapid method for evaluation of the moisture content of the tapioca starch in factory laboratories. It indicated the possibility of real-time online monitoring and control of the tapioca starch cake feeder in the drying process. In addition, it was determined that there was a stronger influence of the NIR absorption of both water and starch on the prediction of moisture content of the model.

scholarly journals Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods

Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 335
Author(s):  
Ning Ai ◽  
Yibo Jiang ◽  
Sainab Omar ◽  
Jiawei Wang ◽  
Luyue Xia ◽  
...  
Keyword(s):  
Near Infrared ◽  
Lignin Content ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sargassum Horneri ◽  
Calibration Model ◽  
Selection Methods ◽  
Calibration Models ◽  
Variables Selection

Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.

Determination of resin and moisture content in melamine-formaldehyde paper using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 311-323 ◽  
Author(s):  
Ana Henriques ◽  
Miguel Gonçalves ◽  
Nádia Paiva ◽  
João Ferra ◽  
Jorge Martins ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Resin Content ◽  
Partial Least Square ◽  
Least Square ◽  
Statistical Criterion ◽  
Calibration Model ◽  
Melamine Formaldehyde

This paper describes the use of near infrared spectroscopy as a tool for the determination of moisture and resin content on papers impregnated with melamine-formaldehyde resins for high-pressure laminate production. The papers had different colours and grammages. The near infrared analysis range comprised wavelengths between 12,000 cm−1 and 4000 cm−1. Several multivariate calibration procedures and pre-processing techniques were tested for selection of the best spectral interval, including interval partial least-square, forward interval partial least-square and synergy interval partial least-square. The performance of calibration models was evaluated computing the root mean-squared error of cross-validations and the coefficient of determination (R2). An external validation procedure was done using different decorative papers (red, pearl, yellow, violet and pale green). The performances of the best models were compared using the statistical criterion root mean square error of prediction. It was shown that the developed models can be applied in the determination of resin content independently of the grammage and colour of the papers. However, regarding the volatile content, the models seemed to be affected by external factors, such as the presence of dyes and pigments, and were only applicable to papers having spectra similar to those used in the calibration model.

Quantitative Evaluation of Glycyrrhizic Acid That Affects the Product Quality of Kakkonto Extract, a Traditional Herbal Medicine, by a Chemometric near Infrared Spectroscopic Method

2009 ◽  
Vol 17 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Yoshifumi Mohri ◽  
Yukoh Sakata ◽  
Makoto Otsuka
Keyword(s):  
Acid Content ◽  
Glycyrrhizic Acid ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Wave Number ◽  
Coefficient Of Determination ◽  
Calibration Model

The purpose of this study was to construct a calibration model for the prediction of glycyrrhizic acid content in Kakkonto extracts using near infrared (NIR) spectroscopy. The NIR spectra of the Kakkonto extracts were obtained using a Fourier transform NIR spectrometer in transmission mode and chemometric analysis was performed using partial least-square (PLS) regression. The calibration model was constructed by the selection of wave number regions and by the first derivative pre-treatment of NIR spectra. The glycyrrhizic acid content could be predicted using a calibration model comprising three principal components (PCs) obtained by the PLS method. The calibration model was theoretically analysed by investigating the standard error of prediction values, the loading vectors of each PC and the regression vector. The relationship between the actual and predicted glycyrrhizic acid contents in the Kakkonto extract exhibited a straight line with a coefficient of determination of 0.966 (calibration) and 0.945 (validation), respectively. The predicted glycyrrhizic acid content in the Kakkonto extract was within the 95% predictive intervals.

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.

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