Determination of Loganin in Qiju Dihuang Pills by Near-Infrared Spectroscopy Technique

2013 ◽  
Vol 807-809 ◽  
pp. 1978-1983 ◽  
Author(s):  
Cai Xia Xie ◽  
Hai Yan Gong ◽  
Jian Ying Liu ◽  
Jing Wei Lei ◽  
Xiao Yan Duan ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Partial Least Square ◽  
Calibration Model ◽  
Mean Square

To establish a rapid analytical method for Loganin in Qiju Dihuang Pills (condensed) by Near-infrared Diffuse Reflectance Technique. Collecting NIR spectra by NIR Diffuse Reflectance Spectroscopy, the partial least square calibration model was built. The correlation coefficients (R2) and the root-mean-square error of cross-validation (RMSECV) were 0.99764 and 0.09340, respectively. In the external validation,coefficients of determination (r2) between NIRS and HPLC values was 0.97348,the root-mean-square error of prediction (RMSEP) was 0.08491. The results showed that the method was rapid, accurate, and could be applied to the fast determination of Loganin in Qiju Dihuang Pills (condensed).

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.

The Actoside Content Analysis in Radix Rehmanniae Using Near-Infrared Technology

2014 ◽  
Vol 912-914 ◽  
pp. 374-377
Author(s):  
Hai Yan Gong ◽  
Cai Xia Xie ◽  
Yong Xia Cui ◽  
Zhi Hong Chen ◽  
Yan Bai ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Mean Square ◽  
Calibration Models ◽  
Average Recovery Rate

In this paper, near-infrared diffuse reflectance spectroscopy was used to analysis the acteoside content of Radix Rehmanniae. The quantitative calibration models of acteoside content were established by the partial least square (PLS). The results showed that the correlation coefficient of calibration (R2) was 0.98102, the root-mean-square error of calibration (RMSEC) was 0.00464, the root-mean-square error of prediction (RMSEP) was 0.00163, and the average recovery rate was 104.837%. So the recovery of prediction set was available. The results indicated that it was available to determine the content of actoside in Radix Rehmanniae by near-infrared spectroscopy.

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).

scholarly journals Attenuated Total Reflectance-FTIR Spectra Combined with Multivariate Calibration and Discrimination Analysis for Analysis of Patchouli Oil Adulteration

2019 ◽  
Vol 20 (1) ◽  
pp. 1
Author(s):  
Zaki Fahmi ◽  
Mudasir Mudasir ◽  
Abdul Rohman
Keyword(s):  
Root Mean Square Error ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Multivariate Calibration ◽  
Ftir Spectra ◽  
Partial Least Square ◽  
Least Square ◽  
Calibration Model ◽  
Mean Square

The adulteration of high priced oils such as patchouli oil with lower price ones is motivated to gain the economical profits. The aim of this study was to use FTIR spectroscopy combined with chemometrics for the authentication of patchouli oil (PaO) in the mixtures with Castor Oil (CO) and Palm Oil (PO). The FTIR spectra of PaO and various vegetable oils were scanned at mid infrared region (4000–650 cm–1), and were subjected to principal component analysis (PCA). Quantitative analysis of PaO adulterated with CO and PO were carried out with multivariate calibration of Partial Least Square (PLS) regression. Based on PCA, PaO has the close similarity to CO and PO. From the optimization results, FTIR normal spectra in the combined wavenumbers of 1200–1000 and 3100–2900 cm–1 were chosen to quantify PaO in PO with coefficient of determination (R2) value of 0.9856 and root mean square error of calibration (RMSEC) of 4.57% in calibration model. In addition, R2 and root mean square error of prediction (RMSEP) values of 0.9984 and 1.79% were obtained during validation, respectively. The normal spectra in the wavenumbers region of 1200–1000 cm–1 were preferred to quantify PaO in CO with R2 value of 0.9816 and RMSEC of 6.89% in calibration, while in validation model, the R2 value of 0.9974 and RMSEP of 2.57% were obtained. Discriminant analysis was also successfully used for classification of PaO and PaO adulterated with PO and CO without misclassification observed. The combination of FTIR spectroscopy and chemometrics provided an appropriate model for authentication study of PaO adulterated with PO and CO.

Nondestructive Method for Analysis of the Soybean Quality

2012 ◽  
Vol 8 (4) ◽  
Author(s):  
Yan Dong ◽  
Shi You Qu
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Crude Protein ◽  
Near Infrared ◽  
Nondestructive Method ◽  
Partial Least Square ◽  
Least Square ◽  
Mean Square ◽  
Pre Treatment

Abstract Fourier transform near infrared (NIR) spectra combined with chemometric methods was proposed to the analysis of the crude protein and fat contents in whole-kernel soybean. The calibration models were established by partial least square. After optimizing spectral pre-treatment, the determination coefficient (R2) of the crude protein and fat were 0.971, 0.970, and root mean square error of calibration (RMSEC) were 0.610, 0.365，respectively. For the prediction samples of the crude protein and fat, root mean square error of prediction (RMSEP) were 0.766, 0.420, respectively. The analytical results showed that NIR spectra had significant potential as a rapid and nondestructive method for the crude protein and fat contents in soybean.

scholarly journals Near Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1942
Author(s):  
Kamaranga H. S. Peiris ◽  
Xiaorong Wu ◽  
Scott R. Bean ◽  
Mayra Perez-Fajardo ◽  
Chad Hayes ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Grain Sorghum ◽  
Calibration Model ◽  
Mean Square ◽  
Starch Properties ◽  
End Uses

Starch, mainly composed of amylose and amylopectin, is the major nutrient in grain sorghum. Amylose and amylopectin composition affects the starch properties of sorghum flour which in turn determine the suitability of sorghum grains for various end uses. Partial least squares regression models on near infrared (NIR) spectra were developed to estimate starch and amylose contents in intact grain sorghum samples. Sorghum starch calibration model with a coefficient of determination (R2) = 0.87, root mean square error of cross validation (RMSECV) = 1.57% and slope = 0.89 predicted the starch content of validation set with R2 = 0.76, root mean square error of prediction (RMSEP) = 2.13%, slope = 0.93 and bias = 0.20%. Amylose calibration model with R2 = 0.84, RMSECV = 2.96% and slope = 0.86 predicted the amylose content in validation samples with R2 = 0.76, RMSEP = 2.60%, slope = 0.98 and bias = −0.44%. Final starch and amylose cross validated calibration models were constructed combining respective calibration and validation sets and used to predict starch and amylose contents in 1337 grain samples from two diverse sorghum populations. Protein and moisture contents of the samples were determined using previously tested NIR spectroscopy models. The distribution of starch and protein contents in the samples of low amylose (<5%) and normal amylose (>15%) and the overall relationship between starch and protein contents of the sorghum populations were investigated. Percent starch and protein were negatively correlated, low amylose lines tended to have lower starch and higher protein contents than lines with high amylose. The results showed that NIR spectroscopy of whole grain can be used as a high throughput pre-screening method to identify sorghum germplasm with specific starch quality traits to develop hybrids for various end uses.

A Comparative Approach to Screen the Capability of Raman and Infrared (Mid- and Near-) Spectroscopy for Quantification of Low-Active Pharmaceutical Ingredient Content Solid Dosage Forms: The Case of Alprazolam

2020 ◽  
Vol 74 (6) ◽  
pp. 661-673 ◽  
Author(s):  
Liljana Makraduli ◽  
Petre Makreski ◽  
Katerina Goracinova ◽  
Stefan Stefov ◽  
Maja Anevska ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Active Pharmaceutical Ingredient ◽  
Partial Least Square ◽  
Calibration Model ◽  
Mean Square ◽  
Powder Blends ◽  
Statistical Indicators

Content uniformity is a critical attribute for potent and low-dosage formulations of active pharmaceutical ingredient (API) that, in addition to the formulation parameters, plays pivotal role during pharmaceutical development and production. However, when API content is low, implementing a vibrational spectroscopic analytical tool to monitor the content and blend uniformity remains a challenging task. The aim of this study was to showcase the potentials of mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopy for quantitative analysis of alprazolam (ALZ) in a low-content powder blends with lactose, which is used as a common diluent for tablets produced by direct compression. The offered approach might be further scaled up and exploited for potential application in the process analytical technology (PAT). Partial least square and orthogonal PLS (OPLS) methodologies were employed to build the calibration models from raw and processed spectral data (standard normal variate, first and second derivatives). The models were further compared regarding their main statistical indicators: correlation coefficients, predictivity, root mean square error of estimation (RMSEE), and root mean square error of cross-validation (RMSEEcv). All statistical models presented high regression and predictivity coefficients. The RMSEEcv for the optimal models was 1.118, 0.08, and 0.059% for MIR, NIR, and Raman spectroscopy, respectively. The scarce information content extracted from the ALZ NIR spectra and the major band overlapping with those from lactose monohydrate was the main culprit of poor accuracy in the NIR model, whereas the subsampling instrumental setup (resulting in a non-representative spectral acquisition of the sample) was regarded as a main limitation for the MIR-based calibration model. The OPLS models of the Raman spectra of the powder blends manifested favorable statistical indicators for the accuracy of the calibration model, probably due to the distinctive ALZ Raman pattern resulting in the largest number of predictive spectral points that were used for the mathematical modeling. Furthermore, the Raman scattering calibration model was optimized in narrower scanning range (1700–700 cm−1) and its prediction power was evaluated (root mean square error of prediction, RMSEP = 0.03%). Thus, the Raman spectroscopy presented the most favorable statistical indicators in this comparative study and therefore should be further considered as a PAT for the quantitative determination of ALZ in low-content powder blends.

Quantitative Model for Diosgenin in Dioscorea zingiberensis C.H.Wright by NIRS Combined with TQ Software

2013 ◽  
Vol 807-809 ◽  
pp. 2085-2091 ◽  
Author(s):  
Yan Bai ◽  
Hai Yan Gong ◽  
Chun Fang Zuo ◽  
Jing Wei Lei ◽  
Xiao Yan Duan ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Quantitative Model ◽  
Calibration Model ◽  
Mean Square ◽  
Correction Model ◽  
Dioscorea Zingiberensis

To determine the Diosgenin in Dioscorea zingiberensis C.H.Wright by near-infraed spectroscopy (NIRS) combined with TQ software. The near-infrared sprectra and HPLC values of the Diosgenin in Dioscorea zingiberensis C.H.Wright from different areas were collected, and the quantitative calibration model was established with TQ software. And then the prediction samples were anylized by the model. The correlation coefficients (R2), the root-mean-square error of calibration (RMSEC) and the root-mean-square error of cross-validation (RMSECV) of the quantitative calibration model for diosgenin were 0.96459, 0.0999 and 0.30041 respectively; the correlation coefficients of prediction (r2) and the root-mean-square error of prediction (RMSEP) were 0.9634 and 0.128. The method is fast, convenient, non-polluted and accurate. The correction model could be used to predict the diosgenin in Dioscorea zingiberensis C.H.Wright.

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.

scholarly journals Carotenoid determination in tomato juice using near infrared spectroscopy

2015 ◽  
Vol 29 (3) ◽  
pp. 275-282 ◽  
Author(s):  
Konrád Deák ◽  
Tamás Szigedi ◽  
Zoltán Pék ◽  
Piotr Baranowski ◽  
Lajos Helyes
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Cross Validation ◽  
Diode Array ◽  
Mean Square ◽  
Least Squares Regression ◽  
Carotene Content

AbstractA rapid non-destructive method for profiling tomato carotenoids was developed using NIR spectrometry. One hundred and twenty tomato samples were produced at the Experimental Farm of Szent István University in Gödöllő (Hungary). The sample preparation was based on homogenization. The mixed samples were scanned with a diode array Perten DA7200 NIR Analyzer (950-1650 nm) and analyzed by high performance liquid chromatography. The calibration was based on partial least squares regression with cross-validation. The performance of the final model was evaluated according to root mean square error of cross-validation. The results indicate that the main carotenoid components were accurately predicted. The correlation between the NIR measurement and the β-carotene content of tomatoes was adequately high [R2CV = 0.89; root mean square error of cross-validation = 0.174 μg g−1]. The NIR method was also performed for the determination of the all-trans lycopene content (R2CV = 0.75; root mean square error of cross-validation = 6.88 μg g−1). It can be concluded that the diode array NIR spectrometer has the potential to be used for the determination of the main carotenoids of tomatoes.

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