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

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.

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Authentication of Patin Fish Oil (Pangasius micronemus) using FTIR Spectroscopy Combined with Chemometrics

Indonesian Journal of Chemometrics and Pharmaceutical Analysis ◽

10.22146/ijcpa.487 ◽

2020 ◽

pp. 23

Author(s):

Anggita Rosiana Putri ◽

Abdul Rohman ◽

Sugeng Riyanto ◽

Widiastuti Setyaningsih

Keyword(s):

Root Mean Square Error ◽

Fish Oil ◽

Ftir Spectroscopy ◽

Mean Square Error ◽

Root Mean Square ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

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APPLICATION OF FTIR SPECTRA COMBINED WITH CHEMOMETRICS FOR ANALYSIS OF CANDLENUT OIL ADULTERATION

International Journal of Applied Pharmaceutics ◽

10.22159/ijap.2018v10i5.28372 ◽

2018 ◽

Vol 10 (5) ◽

pp. 54

Author(s):

Fitri Yuliani ◽

Sugeng Riyanto ◽

Abdul Rohman

Keyword(s):

Root Mean Square Error ◽

Ftir Spectroscopy ◽

Mean Square Error ◽

Root Mean Square ◽

Corn Oil ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square

Objective: The aim of this study was to use FTIR spectroscopy in combination with chemometrics techniques for quantification and classification of candlenut oil (CnO) from oil adulterants, namely sunflower oil (SFO), soybean oil (SyO), and corn oil (CO).Methods: The spectra of all samples were scanned using Fourier Transform Infrared (FTIR) Spectrophotometer using attenuated total reflectance (ATR) as sampling technique at mid infrared region (4000-650 cm-1). Multivariate calibrations of principle component regression (PCR) and partial least square regression (PLSR) were used for quantitative models to predict the levels of CnO in the binary mixtures with SFO, SyO, and CO.Results: The results showed that CnO in SFO was best quantified using PCR at wavenumbers region of 3100-2800 cm-1. Quantitative analysis of CnO in SyO was carried out using PLSR with normal spectra mode using combined wavenumbers of 1765-1625 and 839-663 cm-1, while CnO in CO was analyzed quantitatively using normal spectra at wavenumbers of 970-857 cm-1. The coefficient of determination (R2) obtained were>0.99 with low values of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). The results of discriminant analysis revealed that authentic CnO can be discriminated from CnO adulterated with SFO, SyO and CO using selected wavenumbers.Conclusion: FTIR spectroscopy combined with chemometrics could be used as rapid and reliable method for authentication of candlenut oil (CnO) adulterated with other oils.

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Analysis of candlenut oil as oil adulterant in three functional oils of soybean oil, sunflower oil and grapeseed oil in quaternary mixture systems using FTIR spectroscopy and chemometrics

Food Research ◽

10.26656/fr.2017.5(2).443 ◽

2021 ◽

Vol 5 (2) ◽

pp. 248-253

Author(s):

A.B. Riyanta ◽

S. Riyanto ◽

E. Lukitaningsih ◽

A. Rohman

Keyword(s):

Root Mean Square Error ◽

Soybean Oil ◽

Ftir Spectroscopy ◽

Mean Square Error ◽

Root Mean Square ◽

Sunflower Oil ◽

Ftir Spectra ◽

Mean Square ◽

Grapeseed Oil ◽

Quaternary Mixture

Soybean oil (SBO), sunflower oil (SFO) and grapeseed oil (GPO) contain high levels of unsaturated fats that are good for health and have proximity to candlenut oil. Candlenut oil (CNO) has a lower price and easier to get oil from that seeds than other seed oils, so it is used as adulteration for gains. Therefore, authentication is required to ensure the purity of oils by proper analysis. This research was aimed to highlight the FTIR spectroscopy application with multivariate calibration is a potential analysis for scanning the quaternary mixture of CNO, SBO, SFO and GPO. CNO quantification was performed using multivariate calibrations of principle component (PCR) regression and partial least (PLS) square to predict the model from the optimization FTIR spectra regions. The highest R2 and the lowest values of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were used as the basis for selection of multivariate calibrations created using several wavenumbers region of FTIR spectra. Wavenumbers regions of 4000-650 cm-1 from the second derivative FTIR-ATR spectra using PLS was used for quantitative analysis of CNO in quaternary mixture with SBO, SFO and GPO with R2 calibration = 0.9942 and 0.0239% for RMSEC value and 0.0495%. So, it can be concluded the use of FTIR spectra combination with PLS is accurate to detect quaternary mixtures of CNO, SBO, SFO and GPO with the highest R2 values and the lowest RMSEC and RMSEP values.

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The employment of FTIR spectroscopy and chemometrics for authentication of pumpkin seed oil from sesame oil

Food Research ◽

10.26656/fr.2017.4(1).198 ◽

2019 ◽

Vol 4 (1) ◽

pp. 42-48 ◽

Cited By ~ 3

Author(s):

Irnawati ◽

S. Riyanto ◽

S. Martono ◽

A. Rohman

Keyword(s):

Discriminant Analysis ◽

Root Mean Square Error ◽

Ftir Spectroscopy ◽

Mean Square Error ◽

Root Mean Square ◽

Sesame Oil ◽

Ftir Spectra ◽

Mean Square ◽

Pumpkin Seed ◽

Principle Component

The adulteration practice of high price oils such as pumpkin seed oils (PSO) with lower ones could be motivated by economic gains. The objective of this study was to apply FTIR spectroscopy in combination with chemometrics of multivariate calibrations and discriminant analysis for the authentication of PSO. A total of fifteen oils were scanned using FTIR spectrophotometer at mid-infrared regions (4000-650 cm-1 ) and subjected to principal component analysis (PCA) using absorbance values at whole mid-IR regions to know oil having a close similarity to PSO in terms of FTIR spectra. Two multivariate calibrations namely principle component regression (PCR) and partial least square regression (PLSR) along with FTIR spectra modes (normal, derivative-1, and derivative2) were optimized to get the best prediction models. In addition, discriminant analysis (DA) was used for classification of PSO and PSO adulterated with oil adulterant. The results showed that among 15 oils, sesame oil (SeO) had the closer score plot in terms of the first principle component and second principle components with that of PSO. Based on the statistical parameters selected (higher R2 and lowest errors), FTIR spectra in derivative -1 mode at wavenumbers of 1800-663 cm-1 were selected for quantification of PSO in SeO with coefficient of determination (R) values of 0.9998 and 0.9994 in calibration and validation models, respectively. The values of root mean square error of calibration (RMSEC) and root mean square error of prediction obtained were 0.003% and 0.006%, respectively. DA using 10 principle components could clearly discriminate PSO and PSO adulterated with SeO with accuracy levels of 100%. FTIR spectroscopy in combination with chemometrics could be an effective means to detect the adulteration of PSO with SeO.

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

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033519830061 ◽

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.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.807-809.1978 ◽

2013 ◽

Vol 807-809 ◽

pp. 1978-1983 ◽

Cited By ~ 1

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).

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

Food Research ◽

10.26656/fr.2017.5(3).742 ◽

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.

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Prediction Is The Determination Of The Levels Of Total Flavonoid Extract Of 70% Ethanol On The Leaves Of Rose Guava (Syzigium Jambos (L.) Alston) By Using A Combination Of FTIR And Chemometrics Plsr (Partial Least Square Regression)

10.31219/osf.io/6j2d5 ◽

2019 ◽

Author(s):

Nur Tsalits Fahman Mughni

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Partial Least Square ◽

Least Square ◽

Total Flavonoid ◽

Partial Least Square Regression ◽

Mean Square ◽

Syzygium Jambos ◽

Least Square Regression

Rose Guava (Syzygium jambos (L.) Alston) is known to have flavonoid compounds. Where flavonoids are natural product compounds that have uses as a treatment. An alternative method used to determine the prediction of total flavonoid levels is a combination of FTIR and Chemometrics (Partial least square regression) through the parameter RMSEC value (Root mean square error of calibration), RMSECV (Root mean square error of validation), PRESS (Predicted residual error sum of squares) and R2. The results of the combination of FTIR and CEMOMETRICS (Partial least square regression) on the prediction of total flavonoid levels can provide a good model with calibration obtained R2 value0.9999, RMSEC 0.0000637 and the results of vaid obtained PRESS value0.19225, R2 0.978 and RMSECV 0.041 . Based on the literature, the model can be said to be good if the RMSEC and RMSECV values are smaller than R2.

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Nondestructive Method for Analysis of the Soybean Quality

International Journal of Food Engineering ◽

10.1515/1556-3758.2511 ◽

2012 ◽

Vol 8 (4) ◽

Cited By ~ 5

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.

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Chemometric Analysis of UV-Visible Spectral Fingerprints for the Discrimination and Quantification of Clinical Anthracycline Drug Preparation Used in Oncology

BioMed Research International ◽

10.1155/2021/5580102 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Aimen El Orche ◽

Casimir Adade Adade ◽

Hafid Mefetah ◽

Amine Cheikh ◽

Khalid Karrouchi ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Drug Preparation ◽

Mean Square ◽

Chemotherapeutic Drugs ◽

Uv Visible

In clinical treatment, the analytical quality assessment of the delivery of chemotherapeutic preparations is required to guarantee the patient’s safety regarding the dose and most importantly the appropriate anticancer drug. On its own, the development of rapid analytical methods allowing both qualitative and quantitative control of the formulation of prepared solutions could significantly enhance the hospital’s workflow, reducing costs, and potentially providing optimal patient care. UV-visible spectroscopy is a nondestructive, fast, and economical technique for molecular characterization of samples. A discrimination and quantification study of three chemotherapeutic drugs doxorubicin, daunorubicin, and epirubicin was conducted, using clinically relevant concentration ranges prepared in 0.9% NaCl solutions. The application of the partial least square discriminant analysis PLS-DA method on the UV-visible spectral data shows a perfect discrimination of the three drugs with a sensitivity and specificity of 100%. The use of partial least square regression PLS shows high quantification performance of these molecules in solution represented by the low value of root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSCECV) on the one hand and the high value of R -square on the other hand. This study demonstrated the viability of UV-visible fingerprinting (routine approach) coupled with chemometric tools for the classification and quantification of chemotherapeutic drugs during clinical preparation.

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