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.

scholarly journals Different spectrophotometric and TLC-densitometric methods for determination of olmesartan medoxomil and hydrochlorothiazide and their degradation products

2018 ◽  
Vol 9 (4) ◽  
pp. 400-407 ◽  
Author(s):  
Selvia Maged Adly ◽  
Maha Mohamed Abdelrahman ◽  
Nada Sayed Abdelwahab ◽  
Nourudin Wageh Ali
Keyword(s):  
Multivariate Calibration ◽  
Degradation Products ◽  
External Validation ◽  
Olmesartan Medoxomil ◽  
Partial Least Square ◽  
Least Square ◽  
Pharmaceutical Dosage Form ◽  
Calibration Models ◽  
Significant Difference

In this work, multivariate calibration models and TLC-densitometric methods have been developed and validated for quantitative determination of olmesartan medoxomil (OLM) and hydrochlorothiazide (HCZ) in presence of their degradation products, olmesartan (OL) and salamide (SAL), respectively. In the first method, multivariate calibration models including principal component regression (PCR) and partial least square (PLS) were applied. The wavelength range 210-343 nm was used and data was auto-scaled and mean centered as pre-processing steps for PCR and PLS models, respectively. These models were tested by application to external validation set with mean percentage recoveries 99.78, 100.01, 100.41 and 100.46% for OLM, HCZ, OL and SAL, respectively, for PLS model and also, 100.22, 100.40, 102.25 and 100.13% for them, respectively, for PCR model. The second method is TLC-densitometry at which the chromatographic separation was carried out using silica gel 60F254 TLC plates and the developing system consisted of a mixture of ethyl acetate:chloroform:methanol: formic acid:tri-ethylamine (60:40:4:4:1, by volume) with UV-scanning at 254 nm. The developed methods were successfully applied for determination of OLM and HCZ in their pharmaceutical dosage form. Also, statistical comparison was made between the developed methods and the reported method using student’s-t test and F-test and results showed that there was no significant difference between them concerning both accuracy and precision.

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   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;

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.

Non-Contact Quality Monitoring of Laid Fabric Carbon/Epoxy Resin Prepreg Using near Infrared Spectroscopy

2007 ◽  
Vol 15 (5) ◽  
pp. 299-305 ◽  
Author(s):  
Bo Jiang ◽  
Yu Dong Huang ◽  
Wei Li ◽  
Li Liu
Keyword(s):  
Epoxy Resin ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Processing Parameters ◽  
Resin Content ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Volatile Content ◽  
Resin Solution

Near infrared (NIR) spectroscopy has been applied for non-contact monitoring of the quality of laid fabric carbon/epoxy resin prepreg. Partial least square regression was used to develop the models with 94 samples in the calibration set and 30 samples in the prediction set, respectively. The NIR spectroscopy method was able to predict the resin content and the volatile content with root mean square error of prediction of 0.85% and 0.22%, respectively. The developed models of resin content and volatile content were put into process control software so that during manufacture the laid fabric carbon/epoxy resin prepreg could be monitored and the processing parameters could be adjusted according to the results given by the NIR analysis. The prepreg could be analysed once within 30 s without sample destruction. The change of the concentration of epoxy resin solution and the distance of nip rollers could be used to control the resin content and an increase or decrease in the concentration of the solvent and the production speed could control the volatile content. This study indicates that NIR analysis is sufficiently accurate and effective for quality control in laid fabric carbon/epoxy resin prepreg.

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.

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.

scholarly journals Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2195
Author(s):  
Lucas de Paula Corrêdo ◽  
Leonardo Felipe Maldaner ◽  
Helizani Couto Bazame ◽  
José Paulo Molin
Keyword(s):  
Sample Preparation ◽  
Prediction Models ◽  
Spectral Response ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Partial Least Square ◽  
Least Square ◽  
Sample Type ◽  
Sampling Strategies ◽  
Significant Difference

Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

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