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 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.

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.

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).

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 Feasibility Study on Use of Near Infrared Spectroscopy for Rapid and Non-Destructive Determination of Gossypol Content in Intact Cottonseeds

2020 ◽  
Author(s):  
Cheng Li ◽  
Bangsong Su ◽  
Tianlun Zhao ◽  
Cong Li ◽  
Jinhong Chen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Gossypol Content ◽  
Mean Square ◽  
Standard Normal Variate ◽  
Standard Normal

Abstract Background Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for integrated utilization of cottonseed products. It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in breeding program, so it is of great importance to predict the gossypol content in cottonseeds rapidly and non-destructively to substitute the traditional analytical method. Results Gossypol content in cottonseeds was investigated by near-infrared spectroscopy (NIRS) and High-performance liquid chromatography (HPLC). Partial least squares regression, combined with spectral pretreatment methods including Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, and first derivate, were tested for optimizing the calibration models. NIRS technique was efficient in predicting gossypol content in intact cottonseeds, as revealed by the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP), coefficient for determination of prediction (Rp2), and residual predictive deviation (RPD) values for all models, being 0.05–0.07, 0.04–0.06, 0.82–0.92, and 2.3–3.4, respectively. The optimized model pretreated by Savitzky-Golay smoothing + standard normal variate + first derivate resulted in good determination of gossypol content in intact cottonseeds. Conclusions Near infrared spectroscopy coupled with different spectral pretreatments and PLS regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds, rapidly and non-destructively. It could be used as an alternative method to substitute for traditional one to determine the gossypol content in intact cottonseeds.

Quantification of Silymarin in Silybi mariani fructus: Challenging the Analytical Performance of Benchtop vs. Handheld NIR Spectrometers on Whole Seeds

Planta Medica ◽  
2021 ◽  
Author(s):  
Sophia Mayr ◽  
Simon Strasser ◽  
Christian G. Kirchler ◽  
Florian Meischl ◽  
Stefan Stuppner ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Analytical Performance ◽  
Milk Thistle ◽  
Mean Square

AbstractThe content of the flavonolignan mixture silymarin and its individual components (silichristin, silidianin, silibinin A, silibinin B, isosilibinin A, and isosilibinin B) in whole and milled milk thistle seeds (Silybi mariani fructus) was analyzed with near-infrared spectroscopy. The analytical performance of one benchtop and two handheld near-infrared spectrometers was compared. Reference analysis was performed with HPLC following a Soxhlet extraction (European Pharmacopoeia) and a more resource-efficient ultrasonic extraction. The reliability of near-infrared spectral analysis determined through partial least squares regression models constructed independently for the spectral datasets obtained by the three spectrometers was as follows. The benchtop device NIRFlex N-500 performed the best both for milled and whole seeds with a root mean square error of CV between 0.01 and 0.17%. The handheld spectrometer MicroNIR 2200 as well as the microPHAZIR provided a similar performance (root mean square error of CV between 0.01 and 0.18% and between 0.01 and 0.23%, respectively). We carried out quantum chemical simulation of near-infrared spectra of silichristin, silidianin, silibinin, and isosilibinin for interpretation of the results of spectral analysis. This provided understanding of the absorption regions meaningful for the calibration. Further, it helped to better separate how the chemical and physical properties of the samples affect the analysis. While the study demonstrated that milling of samples slightly improves the performance, it was deemed to be critical only for the analysis carried out with the microPHAZIR. This study evidenced that rapid and nondestructive quantification of silymarin and individual flavonolignans is possible with miniaturized near-infrared spectroscopy in whole milk thistle seeds.

Evaluation of acetic acid and ethanol concentration in a rice vinegar internal venturi injector bioreactor using Fourier transform near infrared spectroscopy

2019 ◽  
Vol 27 (6) ◽  
pp. 416-423
Author(s):  
Thitima Phanomsophon ◽  
Panmanas Sirisomboon ◽  
Ravipat Lapcharoensuk ◽  
Bim Shrestha ◽  
Warawut Krusong
Keyword(s):  
Acetic Acid ◽  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Ethanol Concentration ◽  
Mean Square ◽  
Rice Vinegar

In the process of fermenting rice vinegar, the concentration of acetic acid and ethanol concentration must be measured for monitoring of the total concentration. Near infrared spectroscopy has been used to rapidly monitor the concentration of acetic acid and ethanol concentration daily during 10 cycles of the fermentation process. The model was developed using partial least squares regression. For predicting concentration of acetic acid with near infrared spectroscopy, the coefficient of determination ( R2), root mean square error of calibration, root mean square error of cross validation, ratio of standard error of validation to standard deviation, and bias was 0.96, 2.30 g L−1, 2.44 g L−1, 1.11 g L−1, and 5.56, respectively. For ethanol concentration, the value of R2, root mean square error of calibration, root mean square error of cross validation, bias and ratio of prediction to deviation were predicted to be 0.94, 3.15 g L−1, 2.73 g L−1, −0.40 g L−1, and 4.04, respectively. However, both models provided fair performance when tested with an external set of samples, indicating that the models could be applied for rough screening.

scholarly journals Feasibility study on the use of near-infrared spectroscopy for rapid and nondestructive determination of gossypol content in intact cottonseeds

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Cheng LI ◽  
Bangsong SU ◽  
Tianlun ZHAO ◽  
Cong LI ◽  
Jinhong CHEN ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Gossypol Content ◽  
Mean Square ◽  
Standard Normal Variate ◽  
Standard Normal

Abstract Background Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for the integrated utilization of cottonseed products. It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in the breeding program, so it is of great importance to predict the gossypol content in cottonseeds rapidly and nondestructively to substitute the traditional analytical method. Results Gossypol content in cottonseeds was investigated by near-infrared spectroscopy (NIRS) and high-performance liquid chromatography (HPLC). Partial least squares regression, combined with spectral pretreatment methods including Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, and first derivate were tested for optimizing the calibration models. NIRS technique was efficient in predicting gossypol content in intact cottonseeds, as revealed by the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP), coefficient for determination of prediction (Rp2), and residual predictive deviation (RPD) values for all models, being 0.05∼0.07, 0.04∼0.06, 0.82∼0.92, and 2.3∼3.4, respectively. The optimized model pretreated by Savitzky-Golay smoothing + standard normal variate + first derivate resulted in a good determination of gossypol content in intact cottonseeds. Conclusions Near-infrared spectroscopy coupled with different spectral pretreatments and partial least squares (PLS) regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds, rapidly and nondestructively. It could be used as an alternative method to substitute for traditional one to determine the gossypol content in intact cottonseeds.

scholarly journals Feasibility study on use of near infrared spectroscopy for rapid and non-destructive determination of gossypol content in intact cottonseeds

2021 ◽  
Author(s):  
Cheng Li ◽  
Bangsong Su ◽  
Tianlun Zhao ◽  
Cong Li ◽  
Jinhong Chen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Gossypol Content ◽  
Mean Square ◽  
Standard Normal Variate ◽  
Standard Normal

Abstract Background: Gossypol found in cottonseeds is toxic to human beings and monogastric animals and is a primary parameter for the integrated utilization of cottonseed products. It is usually determined by the techniques relied on complex pretreatment procedures and the samples after determination cannot be used in breeding program, so it is of great importance to predict the gossypol content in cottonseeds rapidly and non-destructively to substitute the traditional analytical method.Results: Gossypol content in cottonseeds was investigated by near-infrared spectroscopy (NIRS) and High-performance liquid chromatography (HPLC). Partial least squares regression, combined with spectral pretreatment methods including Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, and first derivate, were tested for optimizing the calibration models. NIRS technique was efficient in predicting gossypol content in intact cottonseeds, as revealed by the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP), coefficient for determination of prediction (Rp2), and residual predictive deviation (RPD) values for all models, being 0.05-0.07, 0.04-0.06, 0.82-0.92, and 2.3-3.4, respectively. The optimized model pretreated by Savitzky-Golay smoothing + standard normal variate + first derivate resulted in good determination of gossypol content in intact cottonseeds. Conclusions: Near infrared spectroscopy coupled with different spectral pretreatments and PLS regression has exhibited the feasibility in predicting gossypol content in intact cottonseeds, rapidly and non-destructively. It could be used as an alternative method to substitute for traditional one to determine the gossypol content in intact cottonseeds.

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