FTIR Spectroscopy Combined with Partial Least Square for Analysis of Red Fruit Oil in Ternary Mixture System

Rohman, Abdul; Setyaningrum, Dwi Larasati; Riyanto, Sugeng

doi:10.1155/2014/785914

Cited by 24 publications

(16 citation statements)

References 18 publications

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“…is technique, integrated with statistical approach particularly partial least squares (PLS), has demonstrated promising sensitivity for adulterant analysis [12,13,14]. FTIR coupled with PLS has been used for detection of adulterants in edible oils including avocado oil, sunflower oil, and palm oil with a detection limit as low as 2-3% [15,16,17].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lard in Palm Olein Oil Using Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Partial Least Squares Regression (PLSR) Based on Fourier-Transform Infrared (FTIR)

Sim

Chai

Kimura

2018

Journal of Chemistry

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Fourier-transform infrared (FTIR) offers the advantages of rapid analysis with minimal sample preparation. FTIR in combination with multivariate approach, particularly partial least squares regression (PLSR), has been widely used for adulterant analysis. Limited study has been done to compare PLSR with other regression strategies. In this paper, we apply simple linear regression (SLR), multiple linear regression (MLR), and PLSR for prediction of lard in palm olein oil. Pure palm olein oil was adulterated with lard at different concentrations and subjected to analysis with FTIR. The marker bands distinguishing lard and palm olein oil were determined using Fisher’s weights. The marker regions were then subjected to regression analysis with the models verified based on 100 training/test sets. The prediction performance was measured based on the percentage root mean square error (%RMSE). The absorption bands at 3006 cm−1, 2852 cm−1, 1117 cm−1, 1236 cm−1, and 1159 cm−1 were identified as the marker bands. The bands at 3006 and 1117 cm−1 were found with satisfactory predictive ability, with PLSR demonstrating better prediction yielding %RMSE of 16.03 and 13.26%, respectively.

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Section: Introductionmentioning

confidence: 99%

Prediction of Lard in Palm Olein Oil Using Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Partial Least Squares Regression (PLSR) Based on Fourier-Transform Infrared (FTIR)

Sim

Chai

Kimura

2018

Journal of Chemistry

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“…Fig. 3 shows the first derivative treatment to the spectra at 1.200-1.000 cm -1 , this treatment removes baseline shifts and improves the accuracy of quantification [34].…”

Section: The Wavenumbers Marked With Bold Was Preferred For Predictionmentioning

confidence: 99%

Infrared Spectroscopy and Multivariate Calibration for the Rapid Quantification of Free Fatty Acid Content in Pangasius Hypopthalmus Oil

et al. 2018

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Objective: The objective of this study was to evaluate the capability of fourier transform infrared (FTIR) spectroscopy in combination with multivariate calibration for prediction of free fatty acids (FFA) in Pangasius hypopthalmus (P. hypopthalmus) oil.Methods: FFA content in P. hypopthalmus oil was determined by attenuated total reflectance-FTIR spectroscopy. P. hypopthalmus oil derived from Pangasius’s meat (MP), and Pangasius’s liver and fat (LFP) were subjected to heat treatments. Determination of FFA content in P. hypopthalmus oil’s was performed by gas chromatography-flame ionization detector.Results: Oleic acid was found to be the main fatty acid component in P. hypopthalmus oil. FTIR spectra of P. hypopthalmus oil has 3 main peaks, C-H bonds of cis-form of fatty acid showed the stretching vibration, symmetric and asymmetric vibrations of the C-H2 and C-H3 aliphatic group and vibrations of the carbonyl (C=O) ester derived from the oil triacylglycerols. Principal component regression (PCR) model showed a better performance than the partial least square (PLS) model. PCR at wavenumbers of 1200-1000 cm-1 with first derivative treatment was chosen for FFA prediction, which resulted in a coefficient of determination (R2) value of 0.9417, root means square error of calibration (RMSEC) of 0.725%, and root mean square error of prediction (RMSEP) value of 2.40%, respectively.Conclusion: FTIR spectroscopy combined with PCR can be used as an alternative method for analysis of fatty acid contents.

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“…Similar structures of both compounds make similar interaction with near infrared radiation. However, there are no exactly same spectra resulted in vibrational spectroscopy since each compound in the sample would interact uniquely with near infrared radiation [19]. The optimum PLS model established exploited single wavenumber region of 4000-6500 cm -1 although previous research applied different wavenumber region of 4035-6934 cm -1 [15] to the simultaneous determination of stevioside and rebaudioside A. PLS regression not only sensitive to a particular region but also a number of components, N, used to calculate PLS parameters.…”

Section: Simultaneous Calibration Of Rebaudioside a Stevioside And mentioning

confidence: 99%

Determination of Stevioside and Rebaudioside A in <i>Stevia rebaudiana</i> Bertoni Leaves Using near Infrared Spectroscopy and Multivariate Data Analysis

2018

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Rebaudioside A and stevioside are abundant steviol glycoside contained in Stevia rebaudiana leaves. These components are widely used as a natural sweetener. The objective of this study was to develop rapid determination method of stevioside, and rebaudioside A in S. rebaudiana leaves using near infrared trans-reflectance spectroscopy (NIRS) combined with multivariate analysis. The reference method used was HPLC. A prediction model was developed using partial least square (PLS) regression. Calibration parameters were calculated based on a calibration set of various stevioside, rebaudioside A from 23 samples. Performance of PLS model was assessed in term of optimum determination coefficient (R 2 ), and minimum root mean square error of cross-validation (RMSEV). Validation of PLS model was performed using cross-validation and leave one out calibration of PLS component. Rebaudioside A has well PLS model in wavenumber region of 4100-5100 cm -1 , and stevioside determination using difference wavenumber region of 47605016 cm -1 . PLS model for total (sum of stevioside and rebaudioside A content) was exploited in wavenumber region of 45684928 cm -1 . NIRS in combination with multivariate data analysis of PLSR can be applied as a rapid method for determining rebaudioside A and the total amount of steviol glycosides in S. rebaudiana leaves. ABSTRAKRebaudiosida A dan steviosida adalah senyawa steviol glikosida dominan yang terkandung dalam daun Stevia rebaudiana. Senyawa tersebut banyak digunakan sebagai pemanis alami. Tujuan penelitian ini adalah mengembangkan metode penetapan kadar setiosida dan rebaudiosida A dalam daun S. rebaudiana menggunakan spektroskopi Near Infrared (NIRS) trans-reflektan yang dikombinasi dengan analisis multivariat. Sebagai metode referens adalah HPLC. Model prediksi yang dikembangkan menggunakan regresi Partial Least Square (PLS). Parameter kalibrasi dihitung berdasarkan sejumlah 23 sampel kalibrasi set yang bervariasi kandungan steviosida dan rebaudiosida A-nya. Model PLS yang optimal ditentukan berdasarkan koefisien determinasi (R 2 ) dan root mean square error of cross-validation (RMSEV) yang minimum. Validasi model menggunakan model cross validation dan leave one out dari komponen PLS. Model PLS rebaudiosida A dikembangkan pada daerah bilangan gelombang 4100-5100 cm -1 dan steviosida pada daerah bilangan gelombang yang lain yaitu 47605016 cm -1 . Model PLS untuk jumlah steviol glikosida dominan dibangun pada daerah bilangan gelombang 45684928 cm -1 . Metode spektroskopi NIR yang dikombinasi dengan analisis multivariat dapat digunakan sebagai metode penetapan kadar steviosida dan rebaudiosida A serta jumlah totalnya dalam daun S. rebaudiana secara cepat.

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FTIR Spectroscopy Combined with Partial Least Square for Analysis of Red Fruit Oil in Ternary Mixture System

Cited by 24 publications

References 18 publications

Prediction of Lard in Palm Olein Oil Using Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Partial Least Squares Regression (PLSR) Based on Fourier-Transform Infrared (FTIR)

Prediction of Lard in Palm Olein Oil Using Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Partial Least Squares Regression (PLSR) Based on Fourier-Transform Infrared (FTIR)

Infrared Spectroscopy and Multivariate Calibration for the Rapid Quantification of Free Fatty Acid Content in Pangasius Hypopthalmus Oil

Determination of Stevioside and Rebaudioside A in <i>Stevia rebaudiana</i> Bertoni Leaves Using near Infrared Spectroscopy and Multivariate Data Analysis

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