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.

Determination of Magnesium Oxide Content in Mineral Medicine Talcum Using Near-Infrared Spectroscopy Integrated with Support Vector Machine

2017 ◽  
Vol 71 (11) ◽  
pp. 2427-2436 ◽  
Author(s):  
Mi Lei ◽  
Long Chen ◽  
Bisheng Huang ◽  
Keli Chen
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Mean Square ◽  
Characteristic Spectrum

In this research paper, a fast, quantitative, analytical model for magnesium oxide (MgO) content in medicinal mineral talcum was explored based on near-infrared (NIR) spectroscopy. MgO content in each sample was determined by ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy, and then a variety of processing methods of spectra data were compared to establish a good NIR spectroscopy model. To start, 50 batches of talcum samples were categorized into training set and test set using the Kennard–Stone (K-S) algorithm. In a partial least squares regression (PLSR) model, both leave-one-out cross-validation (LOOCV) and training set validation (TSV) were used to screen spectrum preprocessing methods from multiplicative scatter correction (MSC), and finally the standard normal variate transformation (SNV) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLSR method, and the characteristic spectrum ranges were determined as 11995–10664, 7991–6661, and 4326–3999 cm−1, with four optimal ranks. In the support vector machine (SVM) model, the radical basis function (RBF) kernel function was used. Moreover, the full spectrum data of samples pretreated with SNV, the characteristic spectrum data screened using synergy interval partial least squares (SiPLS), and the scoring data of the first four ranks obtained by a partial least squares (PLS) dimension reduction of characteristic spectrum were taken as input variables of SVM, and the MgO content reference values of various sample were taken as output values. In addition, the SVM model internal parameters were optimized using the grid optimization method (GRID), particle swarm optimization (PSO), and genetic algorithm (GA) so that the optimal C and g-values were determined and the validation model was established. By comprehensively comparing the validation effects of different models, it can be concluded that the scoring data of the first four ranks obtained by PLS dimension reduction of characteristic spectrum were taken as input variables of SVM, and the PLS-SVM regression model established using GRID was the optimal NIR spectroscopy quantitative model of talc. This PLS-SVM regression model (rank = 4) measured that the MgO content of talcum was in the range of 17.42–33.22%, with root mean square error of cross validation (RMSECV) of 2.2127%, root mean square error of calibration (RMSEC) of 0.6057%, and root mean square error of prediction (RMSEP) of 1.2901%. This model showed high accuracy and strong prediction capacity, which can be used for rapid prediction of MgO content in talcum.

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.

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 Quantitative Analysis ofPanax ginsengby FT-NIR Spectroscopy

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Xin-fang Xu ◽  
Li-xing Nie ◽  
Li-li Pan ◽  
Bian Hao ◽  
Shao-xiong Yuan ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Multivariate Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Mean Square ◽  
Least Squares Regression ◽  
Regression Methods

Near-infrared spectroscopy (NIRS), a rapid and efficient tool, was used to determine the total amount of nine ginsenosides inPanax ginseng. In the study, the regression models were established using multivariate regression methods with the results from conventional chemical analytical methods as reference values. The multivariate regression methods, partial least squares regression (PLSR) and principal component regression (PCR), were discussed and the PLSR was more suitable. Multiplicative scatter correction (MSC), second derivative, and Savitzky-Golay smoothing were utilized together for the spectral preprocessing. When evaluating the final model, factors such as correlation coefficient (R2) and the root mean square error of prediction (RMSEP) were considered. The final optimal results of PLSR model showed that root mean square error of prediction (RMSEP) and correlation coefficients (R2) in the calibration set were 0.159 and 0.963, respectively. The results demonstrated that the NIRS as a new method can be applied to the quality control ofGinseng Radix et Rhizoma.

scholarly journals Rapid and Simultaneous Measurement of Praeruptorin A, Praeruptorin B, Praeruptorin E, and Moisture Contents in Peucedani Radix Using Near-Infrared Spectroscopy and Chemometrics

2020 ◽  
Vol 103 (2) ◽  
pp. 504-512
Author(s):  
Yijuan Hu ◽  
Hongjian Zhang ◽  
Weiqing Liang ◽  
Pan Xu ◽  
Kelang Lou ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Mean Square ◽  
Determination Coefficient ◽  
Moisture Contents ◽  
Calibration Models

Abstract Background: Peucedani Radix is a popular traditional Chinese medicine herb with a long history in China. Praeruptorin A (PA), praeruptorin B (PB), and praeruptorin E (PE) are usually taken as important quality indexes of Peucedani Radix. Objective: To establish a rapid method for simultaneous determination of PA, PB, PE, and moisture contents in Peucedani Radix using near-infrared (NIR) spectroscopy and chemometrics. Methods: One hundred twenty Peucedani Radix samples were analyzed with HPLC as a reference method. The NIR spectral scanning range was from 12000 cm−1 to 4000 cm−1. Partial least squares (PLS) regression algorithm was used to establish calibration models. Three variable selection methods were investigated, including variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and Monte Carlo uninformative variable elimination (MCUVE). The performances of the established models were evaluated by root-mean-square error (RMSEC) and determination coefficient (Rc2) of calibration set, root-mean-square error (RMSEP) and determination coefficient (Rp2) of prediction set, and residual predictive deviation (RPD). Results: A clear ranking of the performance of the calibration models could be as follows: CARS-PLS &gt; MCUVE-PLS &gt; VIP-PLS &gt; Full-PLS. For CARS-PLS, Rp2, RMSEP, and RPD of the prediction set are as follows: 0.9204, 0.0860%, and 3.5850 for PA; 0.8011, 0.0431%, and 2.0868 for PB; 0.8043, 0.0367%, and 2.1569 for PE; and 0.9249, 0.3350%, and 3.6551 for moisture, respectively. Conclusions: The NIR spectroscopy combined with CARS-PLS calibration models could be used for rapid and accurate determination of PA, PB, PE, and moisture contents in Peucedani Radix samples.

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).

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.

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