scholarly journals CHEMOMETRIC ASSISTED QUANTITATIVE DETERMINATION OF VITEXIN IN ETHANOLIC EXTRACT OF BINAHONG (ANREDERA CORDIFOLIA (TEN.) STEENIS) LEAVES

2020 ◽  
pp. 69-75
Author(s):  
OCTAVIANUS BUDI SANTOSA ◽  
MICHAEL RAHARJA GANI ◽  
SRI HARTATI YULIANI
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Multivariate Calibration ◽  
Data Sets ◽  
Calibration Data ◽  
Mean Square ◽  
Calibration Models ◽  
Pcr Method

Objective: The objective of this study was to develop a UV spectroscopy method in combination with multivariate analysis for determining vitexin in binahong (Anredera cordifolia (Ten.) Steenis) leaves extract. Methods: The partial least square (PLS) regression and the principal component regression (PCR) was performed in this study to evaluate several statistical performances such as coefficient of determination (R2), root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and relative error of prediction (REP). Cross-validation in this study was performed using leave one out technique. Results: The R2 values of calibration data sets resulted from PLS ​​and PCR method were 0.9675 and 0.9648, respectively. The low values of RMSEC and RMSECV both for PLS ​​and PCR method indicated the minimum error of the calibration models. The R2 values of validation data sets resulted from PLS ​​and PCR method were 0.9778 and 0.9820, respectively. The low values of RMSEP both for PLS ​​and PCR method indicated the minimum error of prediction generated from the calibration data sets. Multivariate calibration techniques were applied to determine the content of vitexin in binahong leaves extract. Predicted values from the multivariate calibration models were compared to the actual values determined from a validated HPLC method. It was found that PLS models resulted in the lowest REP values compared to the PCR models. Conclusion: The chemometrics technique can be applied as an alternative method for determining vitexin levels in the ethanol solution of binahong leaves extract.

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.

Multivariate analysis of three chemometric algorithms on rapid prediction of some important quality parameters of crude shea butter using Fourier transform-near infrared spectroscopy

2019 ◽  
Vol 27 (3) ◽  
pp. 220-231
Author(s):  
Emmanuel Amomba Seweh ◽  
Zou Xiaobo ◽  
Feng Tao ◽  
Shi Jiachen ◽  
Haroon Elrasheid Tahir ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Mean Square ◽  
Saponification Value ◽  
Peroxide Values

A comparative study of three chemometric algorithms combined with NIR spectroscopy with the aim of determining the best performing algorithm for quantitative prediction of iodine value, saponification value, free fatty acids content, and peroxide values of unrefined shea butter. Multivariate calibrations were developed for each parameter using supervised partial least squares, interval partial least squares, and genetic-algorithm partial least square regression methods to establish a linear relationship between standard reference and the Fourier transformed-near infrared predicted. Results showed that genetic-algorithm partial least square models were superior in predicting iodine value and saponification value while partial least squares was excellent in predicting free fatty acids content and peroxide values. The nine-factor genetic-algorithm partial least square iodine value calibration model for predicting iodine value yielded excellent ( R2 cal = 0.97), ( R2 val = 0.97), low (root mean square error of cross-validation = 0.26), low (root mean square error of Prediction = 0.23), and (ratio of performance to deviation = 6.41); for saponification value, the nine-factor genetic-algorithm partial least square saponification value calibration model had excellent R2 cal (0.97), R2 val (0.99); low root mean square error of cross-validation (0.73), low root mean square error of Prediction (0.53), and (ratio of performance to deviation = 8.27); while for free fatty acids, the 11-factor partial least square free fatty acids produced very high R2 cal (0.97) and R2 val (0.97) with very low root mean square error of cross-validation (0.03), low root mean square error of Prediction (0.04) and (ratio of performance to deviation = 5.30) and finally for peroxide values, the 11-factor partial least square peroxide values calibration model obtained excellent R2 cal (0.96) and R2val (0.98) with low root mean square error of cross-validation (0.05), low root mean square error of Prediction (0.04), and (ratio of performance to deviation = 5.86). The built models were accurate and robust and can be reliably applied in developing a handheld quality detection device for screening, quality control checks, and prediction of shea butter quality on-site.

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 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.

scholarly journals Development of calibration models for rapid determination of moisture content in rubber sheets using portable near-infrared spectrometers

2020 ◽  
Vol 13 (02) ◽  
pp. 2050009
Author(s):  
Amorndej Puttipipatkajorn ◽  
Amornrit Puttipipatkajorn
Keyword(s):  
Moisture Content ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Least Square ◽  
Mean Square ◽  
Ann Model ◽  
Rubber Sheet ◽  
Calibration Models

Rubber sheets are one of the primary products of natural rubber and are the main raw material in various rubber industries. The quality of a rubber sheet can be visually examined by holding it against clear light to inspect for any specks and impurities inside, but its moisture content is difficult to evaluate based on a visual inspection and this might lead to unfair trading. Herein, we developed a rapid, robust and nondestructive near-infrared spectroscopy (NIRS)-based method for moisture content determination in rubber sheets. A set of 300 rubber sheets were divided into a calibration (200 samples) and prediction groups (100 samples). The calibration set was used to develop NIRS calibration equation using different calibration models, Partial Least Square Regression (PLSR), Least Square Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN). Among the models investigated, the ANN model with the first derivative of spectral preprocessing presented the best prediction with a coefficient of determination ([Formula: see text] of 0.993, root mean square error of calibration (RMSEC) of 0.126% and root mean square error of prediction (RMSEP) of 0.179%. The results indicated that the proposed NIRS-ANN model will be able to reduce human error and provide a highly accurate estimate of the moisture content in a rubber sheet compared to traditional wet chemistry estimation methods according to AOAC standards.

Assessment of Pareto Calibration, Stability, and Wavelength Selection

2003 ◽  
Vol 57 (3) ◽  
pp. 309-316 ◽  
Author(s):  
Kelly J. Anderson ◽  
John H. Kalivas
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Principal Component Regression ◽  
Principal Component ◽  
Data Sets ◽  
Mean Square ◽  
Wavelength Selection ◽  
Pareto Model ◽  
Improve Analysis

Recent work has shown that ridge regression (RR) is Pareto to partial least squares (PLS) and principal component regression (PCR) when the variance indicator Euclidian norm of the regression coefficients, ‖p̂‖, is plotted against the bias indicator root mean square error of calibration (RMSEC). Simplex optimization demonstrates that RR is Pareto for several other spectral data sets when ‖p̂‖ is used with RMSEC and the root mean square error of evaluation (RMSEE) as optimization criteria. From this investigation, it was observed that while RR is Pareto optimal, PLS and PCR harmonious models are near equivalent to harmonious RR models. Additionally, it was found that RR is Pareto robust, i.e., models formed at one temperature were then used to predict samples at another temperature. Wavelength selection is commonly performed to improve analysis results such that bias indicators RMSEC, RMSEE, root mean square error of validation, or root mean square error of cross-validation decrease using a subset of wavelengths. Just as critical to an analysis of selected wavelengths is an assessment of variance. Using wavelengths deemed optimal in a previous study, this paper reports on the variance/bias tradeoff. An approach that forms the Pareto model with a Pareto wavelength subset is suggested.

scholarly journals MONITORING OXIDATIVE LEVELS OF FRYING OILS USING FTIR SPECTROSCOPY AND MULTIVARIATE CALIBRATION

2018 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Sulistyo Prabowo ◽  
Muflihah . ◽  
Abdul Rohman
Keyword(s):  
Root Mean Square Error ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Iodine Value ◽  
Peroxide Value ◽  
Multivariate Calibration ◽  
Acid Value ◽  
Mean Square ◽  
Frying Oils

Objective: To develop a rapid reliable technique based on Fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy in combination with multivariate calibrations for prediction of frying oil quality, namely acid value (AV), iodine value (IV) and peroxide value (PV).Methods: FTIR spectra were directly obtained and subjected to optimization and spectral treatments including a selection of wavenumbers region and spectral derivatization. The condition selected was based on its capability to provide the highest coefficient of determination (R2) and the lowest root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) for the relationship between actual values of AV, IV, and PV as determined using standard titrimetric methods and predicted values as determined by FTIR spectroscopy aided with multivariate calibrations.Results: Using optimized condition, FTIR spectroscopy combined with multivariate calibrations could be successfully used for prediction of AV, and PV. Acid value (AV) could be determined using the first derivative spectra at wavenumbers of 1524-658 cm-1. The R2of 0.973 (in calibration model) and 0.932 (in prediction model) with low RMSEC and RMSEP values was obtained. Iodine value (IV) was best predicted using principle component regression (PCR) with normal FTIR spectra at the combined wavenumbers region of 3076-2783 and 1811-656 cm-1. PCR using normal spectra at combined wavenumbers region of 3076-2783and 1811-656 cm-1 was also selected for prediction of PV. Conclusion: FTIR spectroscopy in combination with multivariate calibration has been successfully used for prediction of acid value, iodine value and peroxide value in frying oils. The developed method could be an alternative technique for analysis of these values to perform quality assurance of frying oils.

scholarly journals NIR assignment of isopsoralen by 2D-COS technology and model application in Yunkang Oral Liquid

2015 ◽  
Vol 08 (06) ◽  
pp. 1550023 ◽  
Author(s):  
Yanling Pei ◽  
Zhisheng Wu ◽  
Xinyuan Shi ◽  
Xiaoning Pan ◽  
Yanfang Peng ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Oral Liquid ◽  
Spectral Assignment

Near infrared (NIR) assignment of Isopsoralen was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. Yunkang Oral Liquid was applied to study Isopsoralen, the characteristic bands by spectral assignment as well as the bands by interval partial least squares (iPLS) and synergy interval partial least squares (siPLS) were used to establish partial least squares (PLS) model. The coefficient of determination in calibration [Formula: see text] were 0.9987, 0.9970 and 0.9982. The coefficient of determination in cross validation [Formula: see text] were 0.9985, 0.9921 and 0.9982. The coefficient of determination in prediction [Formula: see text] were 0.9987, 0.9955 and 0.9988. The root mean square error of calibration (RMSEC) were 0.27, 0.40 and 0.31 ppm. The root mean square error of cross validation (RMSECV) were 0.30, 0.67 and 0.32 ppm. The root mean square error of prediction (RMSEP) were 0.23, 0.43 and 0.22 ppm. The residual predictive deviation (RPD) were 31.00, 16.58 and 32.41. It turned out that the characteristic bands by spectral assignment had the same results with the chemometrics methods in PLS model. It provided guidance for NIR spectral assignment of chemical compositions in Chinese Materia Medica (CMM).

scholarly journals A novel prediction algorithm for multivariate data sets

2021 ◽  
Vol 4 (2) ◽  
pp. 225-240
Author(s):  
Pinki Sagar ◽  
◽  
Prinima Gupta ◽  
Rohit Tanwar ◽  
◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Multivariate Data ◽  
Prediction Algorithm ◽  
Series Data ◽  
Data Sets ◽  
Mean Square ◽  
Data Set ◽  
Independent Variables

Regression analysis is a statistical technique that is most commonly used for forecasting. Data sets are becoming very large due to continuous transactions in today's high-paced world. The data is difficult to manage and interpret. All the independent variables can’t be considered for the prediction because it costs high for maintenance of the data set. A novel algorithm for prediction has been implemented in this paper. Its emphasis is on extraction of efficient independent variables from various variables of the data set. The selection of variables is based on Mean Square Errors (MSE) as well as on the coefficient of determination r2p, after that the final prediction equation for the algorithm is framed on the basis of deviation of actual mean. This is a statistical based prediction algorithm which is used to evaluate the prediction based on four parameters: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and residuals. This algorithm has been implemented for a multivariate data set with low maintenance costs, preprocessing costs, lower root mean square error and residuals. For one dimensional, two-dimensional, frequent stream data, time series data and continuous data, the proposed prediction algorithm can also be used. The impact of this algorithm is to enhance the accuracy rate of forecasting and minimized average error rate.

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