Fast Determination of Dioscin in Dioscorea zingiberensis C.H.Wright by PLS-NIR Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 2079-2084 ◽  
Author(s):  
Cai Xia Xie ◽  
Rui Liu ◽  
Hai Yan Gong ◽  
Jing Wei Lei ◽  
Xiao Yan Duan ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Calibration Model ◽  
Mean Square ◽  
Dioscorea Zingiberensis

A rapid determination method was builded about content of dioscin in Dioscorea zingiberensis C. H. Wright by near-infrared spectroscopy (NIRS) technology. The calibration model were builded through comparison of the content of dioscin in Dioscorea zingiberensis C. H. Wright and the near infrared spectroscopy of Dioscorea zingiberensis C. H. Wright with partial least squares. The internal correlation coefficient of cross-validation (R2) was 0.99208, root-mean-square error of cross-validation (RMSECV) was 0.0104, and external root mean square prediction deviation RMSEP was 0.0105, and the predictive value of the aversge relative error was 4.12%.

Quantitative Analysis of Forsythiae fructus by Near Infrared Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 1967-1971
Author(s):  
Yan Bai ◽  
Xiao Yan Duan ◽  
Hai Yan Gong ◽  
Cai Xia Xie ◽  
Zhi Hong Chen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Ethanol Extract ◽  
Mean Square ◽  
Validation Set

In this paper, the content of forsythoside A and ethanol-extract were rapidly determinated by near-infrared reflectance spectroscopy (NIRS). 85 samples of Forsythiae Fructus harvested in Luoyang from July to September in 2012 were divided into a calibration set (75 samples) and a validation set (10 samples). In combination with the partical least square (PLS), the quantitative calibration models of forsythoside A and ethanol-extract were established. The correlation coefficient of cross-validation (R2) was 0.98247 and 0.97214 for forsythoside A and ethanol-extract, the root-mean-square error of calibration (RMSEC) was 0.184 and 0.570, the root-mean-square error of cross-validation (RMSECV) was 0.81736 and 0.36656. The validation set were used to evaluate the performance of the models, the root-mean-square error of prediction (RMSEP) was 0.221 and 0.518. The results indicated that it was feasible to determine the content of forsythoside A and ethanol-extract in Forsythiae Fructus by near-infrared spectroscopy.

Rapid Determination of Docosahexaenoic Acid in Powdered Oil by Near-Infrared Spectroscopy

2010 ◽  
Vol 16 (2) ◽  
pp. 187-193 ◽  
Author(s):  
Yang Meiyan ◽  
Li Jing ◽  
Nie Shaoping ◽  
Hu Jielun ◽  
Yu Qiang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Docosahexaenoic Acid ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Mean Square ◽  
Validation Set

Near-infrared spectroscopy (NIRS) was used as a rapid and nondestructive method to determine the content of docosahexaenoic acid (DHA) in powdered oil samples. A total of 82 samples were scanned in the diffuse reflectance mode by Nicolet 5700 FTIR spectrometer and the reference values for DHA was measured by gas chromatography. Calibration equations were developed using partial least-squares regression (PLS) with internal cross-validation. Samples were split in two sets, one set used as calibration (n = 66) whereas the remaining samples (n=16) were used as validation set. Two mathematical treatments (first and second derivative), none (log(1/R)) and standard normal variate as scatter corrections and Savitzky—Golay smoothing were explored. To decide upon the number of PLS factors included in the PLS model, the model with the lowest root mean square error of cross-validation (RMSECV=0.44) for the validation set is chosen. The correlation coefficient (r) between the predicted and the reference results which used as an evaluation parameter for the models is 0.968. The root mean square error of prediction of the final model is 0.59. The results reported in this article demonstrate that FT-NIR measurements can serve as a rapid method to determine DHA in powdered oil.

scholarly journals Detection of deltamethrin in cabbages using visible shortwave near-infrared spectroscopy

Food Research ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 273-280
Author(s):  
C.D.M. Ishkandar ◽  
N.M. Nawi ◽  
R. Janius ◽  
N. Mazlan ◽  
T.T. Lin
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Spectral Data ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Calibration Model ◽  
Mean Square

Pesticides have long been used in the cabbage industry to control pest infestation. This study investigated the potential application of low-cost and portable visible shortwave near-infrared spectroscopy for the detection of deltamethrin residue in cabbages. A total of sixty organic cabbage samples were used. The sample was divided into four batches, three batches were sprayed with deltamethrin pesticide whereas the remaining batch was not sprayed (control sample). The first three batches of the cabbages were sprayed with the pesticide at three different concentrations, namely low, medium and high with the values of 0.08, 0.11 and 0.14% volume/volume (v/v), respectively. Spectral data of the cabbage samples were collected using visible shortwave near-infrared (VSNIR) spectrometer with wavelengths range between 200 and 1100 nm. Gas chromatography-electron capture detector (GC-ECD) was used to determine the concentration of deltamethrin residues in the cabbages. Partial least square (PLS) regression method was adopted to investigate the relationship between the spectral data and deltamethrin concentration values. The calibration model produced the values of coefficient of determination (R2 ) and the root mean square error of calibration (RMSEC) of 0.98 and 0.02, respectively. For the prediction model, the values of R2 and the root mean square error of prediction (RMSEP) were 0.94 and 0.04, respectively. These results demonstrated that the proposed spectroscopic measurement is a promising technique for the detection of pesticide at different concentrations in cabbage samples.

Analysis of sorghum content in corn–sorghum flour bioethanol feedstock by near infrared spectroscopy

2020 ◽  
Vol 28 (5-6) ◽  
pp. 267-274
Author(s):  
KHS Peiris ◽  
SR Bean ◽  
M Tilley ◽  
SVK Jagadish
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
The United States ◽  
Calibration Model ◽  
Mean Square ◽  
Sorghum Flour

In the sorghum-growing regions of the United States, some bioethanol plants use mixtures of corn and sorghum grains as feedstocks depending on price and availability. For regulatory purposes and for optimizing the ethanol manufacturing process, knowledge of the grain composition of the milled feedstock is important. Thus, a near infrared spectroscopy method was developed to determine the content of sorghum in corn–sorghum flour mixtures. Commercial corn and sorghum grain samples were obtained from a bioethanol plant over an 18-month period and across two crop seasons. An array of corn–sorghum flour mixtures having 0–100% sorghum was prepared and scanned using a near infrared spectrometer in the 950–1650 nm wavelength range. A partial least squares regression model was developed to estimate sorghum content in flour mixtures. A calibration model with R2 of 0.99 and a root mean square error of cross validation of 3.91% predicted the sorghum content of an independent set of flour mixtures with r2 = 0.97, root mean square error of prediction = 5.25% and bias = −0.49%. Fourier-transform infrared spectroscopy was utilized to examine spectral differences in corn and sorghum flours. Differences in absorptions were observed at 2930, 2860, 1710, 1150, 1078, and 988 cm−1 suggesting that C–H antisymmetric and symmetric, C=O and C–O stretch vibrations of corn and sorghum flours differ. The regression coefficients of the near infrared model had major peaks around overtone and combination bands of C–H stretch and bending vibrations at 1165, 1220, and 1350 nm. Therefore, the above results confirmed that sorghum content in corn sorghum flour mixtures can be determined using near infrared spectroscopy.

Near infrared spectroscopy as an alternative method for rapid evaluation of toluene swell of natural rubber latex and its products

2018 ◽  
Vol 26 (3) ◽  
pp. 159-168 ◽  
Author(s):  
Chin Hock Lim ◽  
Panmanas Sirisomboon
Keyword(s):  
Thin Film ◽  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Thick Film ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Mean Square

Toluene swell or equilibrium swelling is universally used by rubber factories to measure the degree of crosslink of their compounded or prevulcanized latices at different stages of production. To apply near infrared spectroscopy for rapid and accurate quality control, spectral acquisition of prevulcanized latex, thin film and thick film was performed using a Fourier transform near infrared spectrometer in diffuse reflection mode across the wavenumber range of 12,500–3600 cm−1. For prevulcanized latex an effective model was developed using partial least squares regression with preprocessing (first derivative + straight line subtraction method). The coefficient of determination (r2), root mean square error of cross validation and bias of the validation set were 0.71, 3.93% and −0.005%, respectively. For the thin film model the r2, root mean square error of cross validation and bias were 0.65, 4.01% and −0.028%, respectively. Whereas for the thick film model the r2, root mean square error of cross validation and bias were 0.70, 4.00% and −0.006%, respectively. Three models including prevulcanized latex, thin film and thick film were validated by 23 unknown samples, providing standard error of prediction and bias of 5.357 and 2.494, 4.565 and 1.001 and 3.641 and −0.961%, respectively, for prevulcanized latex, thin film and thick film. The model developed for the thick film spectra gave the best results.

Rapid Determination of Paeoniflorin and Moisture in Xiaoyao Pills (Concentrated) by Near-Infrared Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 1972-1977
Author(s):  
Yan Bai ◽  
Hai Yan Gong ◽  
Xiao Qing Li ◽  
Cai Xia Xie ◽  
Xiao Yan Duan ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Least Square ◽  
Calibration Model ◽  
Mean Square

The objective of the present research was to establish a rapid analytical method for paeoniflorin and moisture in Xiaoyao Pills (condensed) by near-infrared spectroscopy. The near-infrared spectral data of 97 samples was collected by Nicolet 6700 NIR spectrograph,and the reference value of index component content were obtained by HPLC and oven-drying method. Then the multivariate calibration model of paeoniflorin and moisture were established by patrical least square (PLS) and predicting the content of unknow samples. The results showed that the correlation coefficients (R2) of the quantitative calibration model for paeoniflorin and moisture were 0.99774,0.95352, the root-mean-square error of calibration (RMSEC) were 0.00489,0.132,the root-mean-square error of prediction (RMSEP) were 0.00827,0.177. The results indicated that NIRS can provide a simple and accurate way for the fast determination of index component in large numbers of Xiaoyao Pills (concentrated).

Near infrared spectroscopy as an alternative method for rapid determination of the solidification point of 2,4,6-trinitrotoluene in production

2019 ◽  
Vol 27 (4) ◽  
pp. 286-292
Author(s):  
Chongchong She ◽  
Min Li ◽  
Yunhui Hou ◽  
Lizhen Chen ◽  
Jianlong Wang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Quality Parameter ◽  
Solidification Point ◽  
Fast Analysis ◽  
Online Application

The solidification point is a key quality parameter for 2,4,6-trinitrotoluene (TNT). The traditional solidification point measurement method of TNT is complicated, dangerous, not environmentally friendly and time-consuming. Near infrared spectroscopy (NIR) analysis technology has been applied successfully in the chemical, petroleum, food, and agriculture sectors owing to its characteristics of fast analysis, no damage to the sample and online application. The purpose of this study was to study near infrared spectroscopy combined with chemometric methods to develop a fast and accurate quantitative analysis method for the solidification point of TNT. The model constructed using PLS regression was successful in predicting the solidification point of TNT ([Formula: see text] = 0.999, RMSECV = 0.19, RPDCa = 33.5, [Formula: see text] = 0.19, [Formula: see text] = 0.999). Principal component analysis shows that the model could identify samples from different reactors. The results clearly demonstrate that the solidification point can be measured in a short time by NIR spectroscopy without any pretreatment for the sample and skilled laboratory personnel.

scholarly journals The Kinetic Model of the Peel Brittleness of Stored Cucumis Melons Based on Visible/Near-Infrared Spectroscopy

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Defang Xu ◽  
Huamin Zhao ◽  
Shujuan Zhang ◽  
Chengji Li ◽  
Fei Zhao
Keyword(s):  
Infrared Spectroscopy ◽  
Kinetic Model ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Storage Time ◽  
Near Infrared ◽  
Mean Square ◽  
Zero Order Reaction ◽  
Variation Tendency

A kinetic model based on visible/near-infrared spectroscopy of the peel brittleness of “Xintian-125” Cucumis melons, the research object, stored under room temperature, was established in order to realize real-time monitoring of the peel brittleness of Cucumis melons and for prediction of storage time. The NIR and peel brittleness of melons stored for 1, 4, and 7 days were collected and measured. SG was confirmed to be the best pretreatment by comparing the PLS models established with 4 pretreatment methods, and the differences of the prediction set determination coefficient and root-mean-square were 0.818 and 23.755, respectively. CARS and SPA were adopted to extract the feature wavelengths and establish the peel brittleness of PLS prediction model. The model’s prediction accuracy was 0.919, and the prediction root-mean-square was 25.413, indicating that NIR is able to realize the prediction of the peel brittleness of Cucumis melons. As a result, a NIR-based peel brittleness kinetic model was created. The P value of the regression model was less than 0.001, and the model’s correlation coefficient was 0.8503, showing that the model is of extreme significance and high precision. The zero-order reaction equation was overfitted according to the variation tendency of the average peel brittleness of stored melons. The model’s correlation coefficient was 0.981, the standard error was 4.624, and the linear relation between the stored period and NIR was established based on it. The research proves that the NIR-based technology is able to realize quick and loss-free inspection of melons’ peel brittleness and prediction of the stored period.

Fast Determination of Active Components in Fructus Corni by Near Infrared Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 2054-2058
Author(s):  
Hai Yan Gong ◽  
Ya Nan Hu ◽  
Cai Xia Xie ◽  
Yong Xia Cui ◽  
Yan Bai
Keyword(s):  
Quality Control ◽  
Infrared Spectroscopy ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Calibration Model ◽  
Mean Square ◽  
Active Components ◽  
Near Infrared Reflectance ◽  
Pharmaceutical Industries

Today, near-infrared (NIR) has been proved to be a powerful analytical tool. It has been applied widely in agricultural, petrochemical, textile and pharmaceutical industries. In this paper, near-infrared spectroscopy (NIRS) combined with partical least square (PLS) was used as a qualitative tool to rapidly determinate two active components in Fructus Corni. The PLS calibration model of NIR Spectroscopy, the correlation coefficients (R2) of Loganin and Morroniside were 0.95895 and 0.98450, the root-mean-square error of cross-validation (RMSECV), the Correction of deviation, the prediction mean square error was 0.0344,0.109;0.0625, 0.2641 and 0.0948, 0.233. The result shows that, the near-infrared Reflectance Spectroscopy could be used to determinate the content of Loganin and Morroniside, and meanwhile as a simple and rapid new method for the quality assessment of Fructus Corni. In addition, the NIRS has a unique advantage in the quality control of traditional Chinese Medicine (TCM), such as rapid, accurate, nondestructive and no pollution. It is expected to be further uses in the quality control of TCM. It is can achieve the requirement of rapid detection of large quantities of Fructus Corni.

Rapid determination of oil content in dried-ground oil palm mesocarp and kernel using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 338-347 ◽  
Author(s):  
Sudarno ◽  
Divo D Silalahi ◽  
Tauvik Risman ◽  
Baiq L Widyastuti ◽  
F Davrieux ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Oil Palm ◽  
Standard Error ◽  
Oil Content ◽  
Near Infrared ◽  
Cross Validation ◽  
Calibration Model ◽  
Reliable Technique

Near infrared spectroscopy calibrations for rapid oil content determination of dried-ground oil palm mesocarp and kernel were developed. Samples were analyzed, one set using the Soxhlet extraction method for reference analysis and the other set scanned by near infrared spectroscopy instrument for calibration. Successful calibrations were obtained with good accuracy and precision for mesocarp and kernel, based on statistical models. Math treatment and scatter correction had significant effects on the fitting of the calibration model. The best obtained calibration models were demonstrated by multiple correlation coefficient (R2), standard error of calibration, standard error of cross validation, coefficient of determination in cross validation (1-VR) and relative predictive deviation of calibration, which respectively were 0.997, 1.21%, 1.23%, 0.997 and 17.89 for mesocarp and 0.952, 0.47%, 0.53%, 0.94 and 4.00 for kernel. The correlations between reference and predicted values for samples in the validation sets were in agreement with high linearity, high ratio performance to deviation of prediction (≥4.00) and low standard error of prediction samples for both samples. The results demonstrated that near infrared spectroscopy can be used as an alternative and reliable technique to estimate the mesocarp and kernel oil contents in dry matter basis accurately and rapidly.

Sign in / Sign up

Export Citation Format

Share Document