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 ◽  
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.

scholarly journals Analysis of sunflower oil in ternary mixture with grapeseed oil and candlenut oil in the ternary mixture system using FTIR spectroscopy and chemometrics

Food Research ◽  
2020 ◽  
Vol 4 (5) ◽  
pp. 1726-1731
Author(s):  
A.B. Riyanta ◽  
S. Riyanto ◽  
E. Lukitaningsih ◽  
A. Rohman
Keyword(s):  
Root Mean Square Error ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Sunflower Oil ◽  
Ternary Mixture ◽  
Ternary Mixtures ◽  
High Price ◽  
Mean Square ◽  
Grapeseed Oil

Candlenut oil (CDNO) has a high price in the market so that it can be adulterated with one or more oils, therefore, the authentication of CDNO is very important. This study was aimed to develop a Fourier transform infrared (FTIR) spectroscopy combined with chemometrics to analyze sunflower oil (SFO) in ternary mixture with grapeseed oil (GPO) and CDNO. SFO, GPO, CDNO and its ternary mixture were prepared and scanned randomly using FTIR spectrophotometer in the mid-infrared region of 4000-650 cm-1 , 8 cm-1 resolution and thirty-two scans. SFO quantification was performed using two multivariate calibrations of principle component regression (PCR) and partial least square (PLS). PLS using the second derivative FTIR–ATR spectra at wavenumbers regions of 3100-2750 cm-1 was used for quantitative analysis of SFO in ternary mixtures with GPO and CDNO, with correlation coefficient (R) values obtained for the relationship between actual values and FTIR predicted values of SFO of 0.9999 and 0.8744 in calibration and validation models, respectively. The errors in calibration and prediction models, expressed by the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP), were low, i.e. 0.233% and 7.90%, respectively. The use of FTIR spectroscopy method combined with multivariate calibration techniques is an appropriate method for detecting the adulteration of candlenut oil from GPO and SFO.

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

Food Research ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 42-48 ◽  
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.

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

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%.

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 QUANTITATIVE ANALYSISOF STEVIOSIDE AND REBAUDIOSIDE A IN STEVIAREBAUDIANA LEAVES USING INFRARED SPECTROSCOPY AND MULTIVARIATE CALIBRATION

2019 ◽  
Vol 11 (1) ◽  
pp. 38 ◽  
Author(s):  
Yohannes Martono ◽  
Abdul Rohman
Keyword(s):  
Multivariate Analysis ◽  
Quantitative Analysis ◽  
Root Mean Square Error ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Rebaudioside A ◽  
Pls Regression ◽  
Mean Square ◽  
Wavenumber Region

Objective: The objective of this research was to develop Fourier transform infrared (FTIR) spectroscopy in combination with multivariate analysis of partial least square (PLS) regression for quantitative analysis of stevioside and rebaudioside A in S. rebaudiana leaves extract.Methods: Stevia rebaudiana leaves with various ages were obtained from several high hills in Central Java, Indonesia. The extract samples were scanned using FTIR spectrophotometer in wavenumbers region of 4000–650 cm-1. PLS calibration model was established by plotting the actual value of stevioside and rebaudioside A as determined by high-performance liquid chromatography (HPLC) and FTIR predicted value. The performance of PLS regression was evaluated using coefficient determination (R2), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP).Results: PLS regression for stevioside determination was successfully established using the combined wavenumber region of 671–1450 and 3279-3301 cm-1. PLS regression revealed R2of 0.9952with RMSEC value of0.84%. Meanwhile, rebaudioside A was determined at wavenumber region of 921–1508 cm-1using normal spectra. PLS model revealed R2 and RMSEC of 0.9911 and 0.70%, respectively.Conclusion: FTIR spectroscopy in combination with multivariate analysis of PLS regression could be used as an alternative method for quantitative analysis ofstevioside and rebaudioside A in S. rebaudiana leaves.

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

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.

Discrimination of Gentiana rigescens from Different Origins by Fourier Transform Infrared Spectroscopy Combined with Chemometric Methods

2015 ◽  
Vol 98 (1) ◽  
pp. 22-26 ◽  
Author(s):  
Yanli Zhao ◽  
Ji Zhang ◽  
Hang Jin ◽  
Jinyu Zhang ◽  
Tao Shen ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Principal Components ◽  
Prediction Accuracy ◽  
Mean Square ◽  
Chemometric Methods ◽  
Contribution Rate ◽  
Gentiana Rigescens

Abstract Gentiana rigescens (“DianLongdan” in Chinese) medicinal plant is usually used for its activities of liver protection, cholagogic, anti-inflammatory, anti-fungal, anti-hyperthyroidism, anti-hypertension, hyperglycemia, and relieving spasm and pain. In this study, methods for thediscrimination of different geographical origins of G. rigescens by FTIR spectroscopy in hyphenation with chemometric methods were developed. Different pretreatments including standard normalvariate, multiplicative scatter correction, first orsecond derivative, Savitzky-Golay filter, and Norrisderivative filter were applied on the spectra to optimize the calibrations. According to spectrum SD, spectrum ranges (3559–2709 and 2026–756 cm–1) were selected, and principalcomponent analysis-Mahalanobis distance (PCA-MD) model was built [the cumulative contribution rate of the first 10 principal components, determination coefficient (R2), root-mean-square error of calibration (RMSEC), and root-mean-square error of prediction (RMSEP), and prediction accuracy were 96.4%,98.6%, 0.5031, 0.7758, and 96.23%, respectively]. The spectral regions (3791–3442, 3043–2765, and 2013–646 cm–1) wereselected by using the variable importance in projection, and partial least squares discriminant analysis(PLS-DA) model was built (the cumulative contribution rate of the first 10 principal components, R2, RMSEC, RMSEP, and prediction accuracy were 91.3%, 92.0%, 0.1171, 0.1806, and 100%, respectively). This research showed that FTIR spectroscopy in combination with chemometrics methods (PCA-MD and PLS-DA) was suitable for the discrimination of different geographical origins of G. rigescens. Furthermore, it was found that PLS-DA provided better results than PCA-MD.

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

scholarly journals Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1020
Author(s):  
Yanqi Dong ◽  
Guangpeng Fan ◽  
Zhiwu Zhou ◽  
Jincheng Liu ◽  
Yongguo Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Reference Value ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Structure ◽  
Mean Square ◽  
Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

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