scholarly journals Authentication analysis of milkfish fish oil using the combination of FTIR spectroscopy and chemometrics

Food Research ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 272-278
Author(s):  
M. Mustafidah ◽  
I. Irnawati ◽  
E. Lukitaningsih ◽  
A. Rohman
Keyword(s):  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Palm Oil ◽  
Essential Fatty Acids ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
High Price ◽  
Predicted Values ◽  
Mean Square Errors

Milkfish (Chanos chanos) is one of the highest-produced fish in Indonesia. Milkfish fish oil (MFO), extracted from milkfish, contains essential fatty acids and has a high price in the market, therefore MFO can be a target of adulteration. The objective of this study was to perform an authentication analysis of MFO from palm oil using FTIR spectroscopy and chemometrics. MFO was extracted from the head and flesh of milkfish and the obtained MFO was mixed with palm oil (PO) in binary mixtures to get the adulteration model of MFO with PO. All samples were scanned using ATR-FTIR at wavenumbers of 4000-650 cm-1 . Classification between authentic MFO and adulterated MFO with PO was carried out using discriminant analysis. The quantitative analysis of MFO adulterated with PO was carried out with PLSR (Partial Least Square regression) using first derivative spectra with wavenumbers of 3100-670 cm-1 . The R2 values for the correlation between the actual and predicted values in the calibration and validation of MFO were 0.9999 and 0.9994 (MFO extracted from the head) and 0.9997 and 0.9992 (MFO extracted from flesh), respectively. In addition, RMSE (root mean square errors) values of the calibration and prediction of milkfish head oil were 0.0032% and 0.0106%, while MFO extracted from the flesh was 0.0080% and 0.0123%. It can be concluded that FTIR spectroscopy combined with PLSR could be reliable for the prediction of MFO in a binary mixture with palm oil.

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 The employment of FTIR spectroscopy and chemometrics for authentication of essential oil of Curcuma mangga from candle nut oil

Food Research ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 515-521 ◽  
Author(s):  
M. Khudzaifi ◽  
S.S. Retno ◽  
Abdul Rohman
Keyword(s):  
Discriminant Analysis ◽  
Essential Oil ◽  
Ftir Spectroscopy ◽  
Prediction Models ◽  
Multivariate Calibration ◽  
Ftir Spectra ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
High Price

The adulteration of high price oil such as essential oil of Curcuma mangga Val. (EOCM) with lower price oil is common to get economical profit. This study was to investigate the authentication of EOCM toward candlenut oil (CNO) using FTIR spectroscopy combined with multivariate calibration and discriminant analysis. The selection of CNO as adulterant oil model was due to its close similarity to EOCM in terms of FTIR spectra. Besides, EOCM has similar color with CNO, therefore, CNO is potential adulterant toward EOCM. Two multivariate calibrations of partial least square regression (PLSR) and principle component regression (PCR) along with FTIR spectra (normal versus derivatization) were optimized to get prediction models for quantification. The results showed that the combination of PLSR and normal FTIR spectra at optimized wavenumbers of 1614-1068 cm-1 was capable of predicting the levels of EOCM adulterated with CNO. Discriminant analysis was also success to differentiate the classification of EOCM and EOCM adulterated with CNO with accuracy levels of 100%. Using FTIR spectroscopy for oil authentication is rapid, simple without any chemicals, solvents, and sample preparation so that this technique is considered as a green analytical method.

scholarly journals AUTHENTICATION OF PATIN (PANGASIUS MICRONEMUS) FISH OIL ADULTERATED WITH PALM OIL USING FTIR SPECTROSCOPY COMBINED WITH CHEMOMETRICS

2019 ◽  
pp. 195-199
Author(s):  
Anggita Rosiana Putri ◽  
Abdul Rohman ◽  
SUGENG RIYANTO
Keyword(s):  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Palm Oil ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Reliable Technique ◽  
Accuracy Level

Objective: The goal of this research was to perform authentication of patin (Pangasius micronemus) fish oil (PFO) adulterated with palm oil (PO) using FTIR spectroscopy combined with chemometrics method. Methods: Patin fish oil (PFO) and PFO adulterated with palm oil (PO) were measured using FTIR instrument at wavenumbers region of 4000–650 cm-1. The chemometrics methods, namely multivariate calibration of partial least square (PLS) and principal component regression (PCR) were used to make calibration and validation models during quantification. Discriminant analysis (DA) was used to make grouping pure PFO and PFO adulterated with PO. Results: The results showed that PLS and PCR could be used to quantify PO as adulterant in PFO, either in calibration or validation models. FTIR spectroscopy combined with multivariate calibration offered accurate and precise method for quantitative analysis with R2 value of >0.999 and low RMSEC and RMSEP. DA was capable of grouping PFO and PFO adulterated with PO with an accuracy level of 100%. Conclusion: FTIR spectroscopy combined with chemometrics could be reliable technique for quantification and discrimination of PFO and PFO adulterated with PO.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

Identification and Quantification of Metamizole in Traditional Herbal Medicines using Spectroscopy FTIR-ATR combined with Chemometrics

2021 ◽  
pp. 4413-4419
Author(s):  
Dharmastuti Cahya Fatmarahmi ◽  
Ratna Asmah Susidarti ◽  
Respati Tri Swasono ◽  
Abdul Rohman
Keyword(s):  
Discriminant Analysis ◽  
Herbal Medicine ◽  
Ftir Spectroscopy ◽  
Herbal Medicines ◽  
Principal Component ◽  
Attenuated Total Reflection ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Precision Data

The study aims to develop an effective, efficient, and reliable method using Fourier Transform Infrared (FTIR) spectroscopy with Attenuated Total Reflection (ATR) combined with chemometric for identifying the synthetic drug in Indonesian herbal medicine known as Jamu. Jamu powders, Metamizole, and the binary mixture of Jamu and Metamizole were measured using FTIR-ATR at the mid-infrared region (4000-650 cm-1). The obtained spectra profiles were further analyzed by Principal Component Analysis, Partial Least Square Regression, Principal Component Regression, and Discriminant Analysis. Jamu Pegel Linu (JPL), Jamu Encok (JE), Jamu Sakit Pinggang (JSP), Metamizole (M), and adulterated Jamu by Metamizole were discriminated well on PCA score plot. PLSR and PCR showed the accuracy and precision data to quantify JPL, JE, and JSP, and each adulterated by M with R2 value > 0,995 and low value of RMSEC and RMSEP. Discriminant Analysis (DA) was successfully grouping Jamu and Metamizole without any misclassification. A combination of FTIR spectroscopy and chemometrics offered useful tools for detecting Metamizole in traditional herbal medicine.

scholarly journals Potential of FTIR-Spectroscopy for Drugs Screening against Helicobacter pylori

Antibiotics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 897
Author(s):  
Pedro Sousa Sampaio ◽  
Cecília R. C. Calado
Keyword(s):  
Helicobacter Pylori ◽  
Ftir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
New Drugs ◽  
Partial Least Square Regression ◽  
Dilution Method ◽  
Agar Dilution Method ◽  
Molecular Fingerprint ◽  
H Pylori

Helicobacter pylori colonizes the human stomach of half of the world’s population. The infection if not treated, persists through life, leading to chronic gastric inflammation, that may progress to severe diseases as peptic ulcer, gastric adenocarcinoma, and mucosa-associated lymphoid tissue lymphoma. The first line of treatment, based on 7 to 21 days of two antibiotics associated with a proton pump inhibitor, is, however, already failing most due to patient non-compliance that leads to antibiotic resistance. It is, therefore, urgent to screen for new and more efficient antimicrobials against this bacterium. In this work, Fourier Transform Infrared (FTIR) spectroscopy was evaluated to screen new drugs against H. pylori, in rapid (between 1 to 6 h), and high-throughput mode and based on a microliter volume processes in relation to the agar dilution method. The reference H. pylori strains 26,695 and J99, were evaluated against a peptide-based antimicrobial and the clinical antibiotic clarithromycin, respectively. After optimization of the assay conditions, as the composition of the incubation mixture, the time of incubation, and spectral pre-processing, it was possible to reproducibly observe the effect of the drug on the bacterial molecular fingerprint as pointed by the spectra principal component analysis. The spectra, obtained from both reference strains, after its incubation with drugs concentrations lower than the MIC, presented peak ratios statistically different (p < 0.05) in relation to the bacteria incubated with drugs concentrations equal or higher to the MIC. It was possible to develop a partial least square regression model, enabling to predict from spectra of both bacteria strains, the drug concentration on the assay, with a high correlation coefficient between predicted and experimental data (0.91) and root square error of 40% of the minimum inhibitory concentration. All this points to the high potential of the technique for drug screening against this fastidious growth bacterium.

scholarly journals COMPARATIVE STUDY OF FATTY ACID PROFILES IN PATIN (PANGASIUS MICRONEMUS) AND GABUS (CHANNA STRIATA) FISH OIL AND ITS AUTHENTICATION USING FTIR SPECTROSCOPY COMBINED WITH CHEMOMETRICS

2019 ◽  
pp. 55-60
Author(s):  
ANGGITA ROSIANA PUTRI ◽  
ABDUL ROHMAN ◽  
SUGENG RIYANTO
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Principal Component Regression ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Channa Striata

Objective: The aims of this research were to analyse the fatty acids contained in Patin (Pangasius micronemus) and Gabus (Channa striata) fish oils also its authentication using FTIR spectroscopy combined with chemometrics. Methods: Patin fish oil (PFO) was extracted from patin flesh using the maceration method with petroleum benzene as the solvent, while gabus fish oil (GFO) was purchased from the market in Yogyakarta. The analysis of fatty acid was done using gas chromatography–flame ionization detector (GC-FID). The authentication was performed using FTIR spectrophotometer and chemometrics methods. Principal component analysis (PCA) was used to determine the proximity of oils based on the characteristic similarity. The quantification of adulterated PFO was performed using multivariate calibrations, partial least square (PLS) and principal component regression (PCR). The classification between authentic oils and those adulterated used discriminant analysis (DA). Results: The level of saturated and polyunsaturated fatty acids in PFO is higher than in GFO. The PLS and PCR methods using the second derivative spectra at wavenumbers of 666–3050 cm-1 offered the highest values of coefficient of determination (R2) and lowest root means the square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). Conclusion: The PCA method was successfully used to determine the proximity of oils. Among oils studied, PFO has a similarity fatty acid composition with GFO. The DA method was able to screen pure PFO from adulterated PFO without any misclassification reported. FTIR spectroscopy in combined with chemometrics can be used for authentication and quantification.

scholarly journals Mapping the Variability of Soil Texture-Based on Vis-NIR Proximal Sensing

2018 ◽  
Vol 2 (1) ◽  
pp. 108-116 ◽  
Author(s):  
Sari Virgawati ◽  
Muhjidin Mawardi ◽  
Lilik Sutiarso ◽  
Sakae Shibusawa ◽  
Hendrik Segah ◽  
...  
Keyword(s):  
Soil Texture ◽  
Interpolation Method ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Spatial And Temporal Variability ◽  
Proximal Sensing ◽  
Distance Weighted ◽  
Predicted Values

Soil texture is one of the soil properties influencing most physical, chemical, and biological soil processes.  Information on soil texture is important to support the agronomic decisions for farm management. The problem is how to provide reliable, fast and inexpensive information of soil texture in numerous soil samples and repeated measurement. The objective of this research was to generate the soil texture map based on laboratory Vis-NIR (Visible - Near Infra-Red) spectroscopy and inverse distance weighted (IDW) interpolation method. An ASD Fieldspec 3 with a spectral range from 350 nm to 2500 nm was used to measure the soil reflectance. Pipette method was used to measure the silt, clay and sand fractions. The partial least square regression (PLSR) was performed to establish the prediction model of soil texture. The predicted values were mapped and showing the information of spatial and temporal variability of soil texture. Keywords: Vis-NIR, spectroscopy, soil texture, PLSR, IDW

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