INTRODUCTION:

Jamu or Indonesian traditional herbal medicine is one of Indonesian heritage that has been consumed almost all of the Indonesian people, especially in rural areas. Almost 5000 species of Indonesian herbal medicine have been observed to their pharmacological activities¹. Some of pharmacological activities that have been observed are analgesic and antiinflammation. According to Indonesia Ministry of Health regulation No. 007 2012 and The National Agency for Drugs and Food Control declare that Jamu should not contain chemical drugs including natural isolate product, chemical substances, and ethanol (<1%)². But, the number report of adulteration Jamu with drug synthetic is increasing. Ubijk The addition of chemicals into traditional herbal medicines is used to ensure the consumer about the instant effect of herbal medicine. Nevertheless, consuming adulteration of Jamu with chemical substance will be very harmful to consumers if it is continuously consumed over a long period and uncontrolled dosage³.

Metamizole is commonly found in herbal medicine for pain killer^4,5. The side effects of those chemical substances are hypersensitive reaction, indigestion, and hypertension^6,7. Analysis methods that had been developed to identify those chemical substances on herbal products are thin layer chromatography (TLC) and high performance liquid chromatography (HPLC)^8–10. However, these chromatographic methods have low precision due to inadequate chromatographic resolution and tailing peaks¹¹. Meanwhile, FTIR spectroscopy could identify, qualify, and quantify the samples without any pretreatment sample requirement, and the use of toxic reagents probably limited^12–15. The advantages of FTIR spectroscopy are rapid, simple, cost-effective technique and capable to analyze analytes as a whole^16,17.

The result of FTIR spectroscopy is spectra profile contained multivariate data further analyzed using chemometrics. This combination method has been widely used to determine adulterant in herbal medicine. Multivariate analysis usually used to interpret IR spectra are principal component analysis (PCA), Partial Least Square Regression (PLSR), Principal Component Regression (PCR), and Discriminant Analysis (DA)^18–20. A combination of FTIR Spectroscopy and chemometrics have been widely applied for authentication herbal medicine from adulterant^17,21,22. PCA and DA were used to qualitative analysis, while PLSR and PCR were used to quantitative analysis. This study aimed to develop a rapid, simple, non-destructive, efficient, reliable, and cost-effective analytical method to analyze synthetic drugs in the herbal product by using FTIR spectroscopy combined with multivariate analysis, namely PCA, PLSR, PCR, and DA.

MATERIAL AND METHODS:

Chemical and Reagents:

Three different samples of powder traditional herbal medicine for reducing pain were purchased from the local herbal industry in Central Java. Their name and ingredients details are tabulated (Table 1). The synthetic drug used was Metamizole, given by PT. Phapros Tbk.

FTIR Spectroscopy Measurement:

The FTIR spectra of samples were scanned using FTR-ATR Nicolet iS10 equipped with Deuterated Tri-Glycine Sulfate (DTGS) as the element detector and controlled with the operating Omnic software. The samples' measurement was recorded in the region 4000-650 cm^-1 with 32 scans/min and resolution of 8 cm^-1 at controlled room temperature (25^oC). Collecting background has to do to reduce the effect of the reference spectrum of the air. ATR crystal has to be cleaned before and after the analysis sample with acetone p.a to reduce noise. The spectra were recorded as absorbance mode at each data point in triplicate.

Principal Component Analysis (PCA) of Indonesian Traditional Herbal:

Three different samples herbal Jamu claimed as helping relieve pain, Metamizole as anti-inflammation drug synthetic, and the binary mixture of Jamu samples and Metamizole were recorded using FTIR spectrophotometer triplicate. The used variables were absorbance values at specific wavenumbers region.

Quantitative Analysis of Jamu adulterated with Metamizole:

Jamu powders and adulterated Jamu powders by Metamizole as many as eleven samples were prepared accurately, with the concentration range 0-100% (wt/wt) for calibration and validation analysis. All samples were scanned and recorded as absorbance using FTIR spectrophotometer.

Discriminant Analysis:

The set data training of pure Jamu samples and adulterated Jamu was prepared and recorded using the FTIR spectrophotometer. The 100% Jamu samples were appointed as “pure” Jamu samples and adulterated Jamu by Metamizole were appointed as “fake”.

Chemometric Analysis:

Chemometric analysis including multivariate calibration such as PLSR and PCR also discriminant analysis (DA) was performed using TQ Analyst software version 9 (Thermo Fisher Scientific, Inc.). The principal component analysis was carried out using software Minitab version 18 (Minitab Inc., USA).

RESULT:

FTIR Spectra Analysis:

In Indonesia, traditional herbal medicine called Jamu in powders form is easily adulterated with other chemical substances with unknown dosage. Physically, it was hard to distinguish the synthetic drug in Jamu powder. Meanwhile, FTIR spectroscopy can differentiate Jamu and drug synthetic based on their functional group. The FTIR Spectrum of Jamu Pegal Linu (JPL), Jamu Encok (JE), and Jamu Sakit Pinggang (JSP) presented a similar pattern (Fig. 1A). The showed peaks corresponding to each functional group and responsible for the absorption of infrared radiation. Molecular structure and vibration of functional groups are obtained information from spectra FTIR at the certain wavenumber²³. For interpretation of FTIR spectra Jamu samples, the absorption band at 3288 cm^-1 belonged to O-H functional group, while the influential intensity band at 1018 corresponded to the C-O functional group. The vibration of the C=O functional group caused the appeared band at 1613 cm^-1. The weak band at 2923 cm^-1 was interpreted to -CH3. The appeared signal peak at 1238 cm^-1 and 1318 cm^-1 corresponded to =CH2 and C=C functional groups, subsequently²⁴. The FTIR-ATR spectrum of Jamu samples and Metamizole (M) exhibited variations in pattern spectrum, intensities, and peaks position (Fig. 1B).

Table 2: Statistical parameters of selected model PLS and PCR for quantitative analysis of Jamu in binary mixture with Metamizole.

Sample	Model	Wavenumber and Spectra	Equation		R²		RMSEC (%wt/wt)	RMSEP (%wt/wt)
Sample	Model	Wavenumber and Spectra	Calibration	Validation	Calibration	Validation	RMSEC (%wt/wt)	RMSEP (%wt/wt)
JPL	PLSR	1076-722 and 1521-1069 cm^-1 2^nd derivative	y=0.9994x+ 0.0295	y=1.0261x-2.582	0.9995	0.9953	0.724	2.62
JE	PLSR	2934-1069 cm^-1 2^ndderivative	y=0.9997x+ 0.0136	y=0.9658x+4.0132	0.9997	0.9957	0.563	3.24
JSP	PCR	1763-1081 cm^-1 2^nd derivative	y=0.9984x+ 0.1245	y=1.0065x+0.1918	0.9964	0.9965	1.93	1.95

Figure 3. The PLS regression analysis between actual value and calculated value of JPL in binary mixture with metamizole on calibration and validation model at combined wavenumber region of 1076-722 dan 1521-1069 cm^-1using second derivative (A) and the Coomans plot of jamu pegel linu (JPL) and adulterated JPL with metamizole: (o) JPL and (r) JPL adulterated with metamizole (B).

Figure 4. The PLS regression analysis between actual value and calculated value of JE in binary mixture with metamizole on calibration and validation model at combined wavenumber region of 2934-1069 cm^-1using second derivative (A) and the coomans plot of jamu encok (JE) and adulterated JE with metamizole: (o) JE and (r) JE adulterated with metamizole (B).

Figure 5. The Principal Component Regression (PCR) analysis between actual value and calculated value of JSP in binary mixture with metamizole on calibration and validation model at combined wavenumber region of 2934-1069 cm^-1using second derivative (A) and the Coomans plot of jamu encok (JSP) and adulterated JPL with metamizole: (o) JSP and (r) JSP adulterated with metamizole (B).

Discriminant Analysis:

Discriminant analysis (DA) is a supervised pattern recognition technique²⁹. This analysis was used to classify Jamu and Jamu adulterated with Metamizole. Fig. 3B, 4B, and 5B exhibit the Coomans plot describing the classification of JPL, JE, and JSP and adulterated each Jamu with M at concentration 10-100% wt/wt of M. DA model is capable of classification the powders with an accuracy level of 100%, meaning there are no samples were mistakenly classified into the wrong group. The data suggested that this method is compelling in analyzing synthetic drugs in herbal products such as Jamu.

DISCUSSION:

The lack of analysis method to identify unallowed drugs synthetic in herbal product urged us to find a rapid, reliable, and effective method in determining adulterated compounds in herbal product. FTIR spectrophotometry performs spectrum containing full information such as functional groups, wavenumber, type of functional groups vibrations, and sample absorbance according to Lambert-Beer law³⁰. In this research, Attenuated Total Reflection (ATR) was used as a sampling technique meaning the assessed samples were straightforwardly positioned into crystal ATR without any solvent used, hence this method included in green analytical chemistry¹⁷. PCA is an unsupervised pattern recognition for making a classification based on the similarity of the principal components (PCs) score and widely used for Exploratory Data Analysis (EDA). Before building the calibration method for quantification, EDA is needed to be done in order that outliers recognize patterns can be detected, and the correlation between variables and classes can be evaluated³¹. PCA reduces the correlated variables through the projection method to become PCs without convert primary data variability²⁴. In this study, we combined spectroscopy FTIR-ATR and multivariate analysis to identify and quantify adulterated Indonesian traditional herbal known as Jamu by Metamizole. One of the problems using spectroscopy FTIR-ATR as method analysis is the overlapping spectrum, but this matter could be overcome by employ PCA as an analysis multivariate. The score plot showed excellent performance to distinguish the samples.

Calibration multivariate, for example, principal component regression (PCR) and partial least square regression (PLSR), could be applied to quantitative analysis. A combination of FTIR-ATR spectra and calibration multivariate for quantification could be a powerful method. The signal peaks at specific wavenumbers known as absorbance values would be applied as independent variables on PCR and PLSR. The selected region of wavenumber depended on the differences between peak intensities among the samples²⁴. The derivative spectra were also applied to be compared to their performance with normal spectra. Spectra derivatization could become a solution to overlapping peaks, but it would make the decrease of sensitivity²⁸. The regression curve of PLSR and PCR analysis compared the actual value and the predicted value of samples. The statistical result, as shown in Table 2. presented the chosen condition according to the value of RMSEC, RMSEP, and R². The precise data were performed by the lower value of RMSEC and RMSEP, while the high value of R² showed the accurate models (<0.99)²⁸. The developed models for quantitative analysis using the calibration model could be used to predict the Metamizole in a binary mixture with Jamu.

Discriminant Analysis (DA) could predict the pure sample and the adulterated sample of unknown samples by using Mahalanobis distance units of absorbance to compute the distance from each group, then Cooman’s plot was created after calculated the absorbance value³². The data for discriminant analysis were also used for quantitative analysis. This discriminant analysis method performed an accurate result to categorize pure and fake samples.

The developed method of identifying unallowed synthetic drugs in Indonesian traditional herbal medicine using FTIR spectroscopy coupled with the chemometrics approach, successfully performed good results. According to the principal components, the PCA score plot could give good classification on Jamu samples, Metamizole, and adulterated Jamu. The quantitative analysis technique using FTIR spectroscopy in combination with PLSR and PCR was valid due to its low RMSEC and RMSEP value and its high R² as the result of acceptable accuracy and precision. The discriminant analysis gave success technique for grouping pure Jamu samples and adulterated Jamu samples.

ACKNOWLEDGMENT:

The author acknowledges to Ministry of Research, Technology and Higher Education of the Republic Indonesia for fund support through scheme Master Education Leading to Doctoral Program for Excellent Graduate (PMDSU) with contract number of 6304/UN1/DITLIT/DIT-LIT/LT/2019 awarded to Prof. Dr. Ratna Asmah Susidarti, MS., Apt. The author also thanks to PT. Phapros Tbk for giving the drugs.

CONFLICT OF INTEREST:

All of the authors confirmed that we disclose all of conflicts of interest in this study.

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Received on 22.07.2020 Modified on 24.10.2020

Research J. Pharm. and Tech. 2021; 14(8):4413-4419.

DOI: 10.52711/0974-360X.2021.00766

No.	Sample name	Ingredients
1.	Jamu Pegel Linu (JPL)	Melaleucae fructus, Retrofracti fructus, Zingiberis aromaticae Rhizoma, Languatis Rhizoma, Curcumae Rhizoma, Baeckae folium, Kaempferiae rhizome, Zingiberis rhizome, Blumeae folium, Phyllanthi herba, Cyperi rhizome, Menthae arvensitis herba, Foeniculli fructus, Alyxiae cortex, Usneae thallus, Dioscoreae tubera.
2.	Jamu Encol (JE)	Melaleucae fructus, Retrofracti fructus, Zingiberis Rhizoma, Orthosiphonis folium, Zingiberis aromaticae Rhizoma, Languatis Rhizoma, Piperis nigiri fructus, Cyperi rhizome, Blumeae folium, Rhei radix, Messuae flos, Alyxiae cortex, Ocimi, bassilici folium, Achyranti folium, Stevia rebaudiana folium.
3.	Jamu Sakit Pinggang (JSP)	Orthosiphonis folium, Imperatae rhizome, Simploci cortex, Plantagonis folium, Phyllanti herba, Blumeae folium, Melaleucae fructus, Sericocalysis folium, Coriandri fructus, Zingiberis aromaticae Rhizoma, Jasminum Quinquenervium folium, Cubebae fructus, Cassia fistula semen, Alyxiae cortex, Rhei radix, Stevia rebaudiana folium.