Evaluasi Kualitas Minyak Nilam (Pogostemon Cablin Benth.) dengan Near Infrared Spektroskopi

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EVALUATION OF THE QUALITY OF PATCHOULI AROMATIC OIL (Pogostemon Cablin Benth.) BY NEAR

INFRARED SPECTROSCOPY

DIEGO MAURICIO CANO REINOSO

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY BOGOR

2018

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DECLARATION

I hereby declare that a thesis entitled Evaluation of the Quality of Patchouli Aromatic Oil (Pogostemon Cablin Benth.) by Near Infrared Spectroscopy is based on my original work and produced through the guidance of my academic advisors and that neither the whole work nor part of it has been submitted for another degree at this or any other University, where other sources of information have been used, they have been acknowledged in text as well as in the references.

I hereby transfer the copyright from my paper to Bogor Agricultural University.

Bogor, April 2018 Diego Mauricio Cano Reinoso

F152168211

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RINGKASAN

DIEGO MAURICIO CANO REINOSO. Evaluasi Kualitas Minyak Nilam (Pogostemon Cablin Benth.) dengan Near Infrared Spectroscopy. Dibimbing oleh SUTRISNO, Y ARIS PURWANTO, I WAYAN BUDIASTRA.

Indonesia adalah negara yang kaya akan spesies tanaman aromatik dengan sekitar 40 jenis yang dihasilkan. Tanaman aromatik ini mudah ditanam, dikelola dan dipanen, serta mudah diproses dan distribusi membuat mereka diminati oleh petani kecil dan masyarakat pedesaan. Tanaman aromatik biasanya digunakan sebagai minyak atsiri dalam parfum, obat-obatan, dan makanan, dimana industri parfum merupakan salah satu konsumen terbesar. Sedangkan untuk makanan, kebanyakan produsen umumnya membeli dan menggabungkannya dengan bahan- bahan lain untuk menciptakan produk turunan, seperti coklat, permen dan minuman.

Nilam (Pogostemon cablin Benth.) adalah tanaman keluarga Lamiaceae yang terkenal dengan khasiat obat dan aromatiknya. Nilam dibudidayakan terutama untuk minyak atsiri dan dalam bisnis internasional dan di negara-negara dengan produksi parfum, Nilam yang berasal dari Indonesia memiliki kualitas terbaik.

Meski begitu, terlepas dari reputasinya dalam industri, minyak nilam mengalami ketidakberesan karena sumber produknya. Biasanya situasi ini terkait dengan masalah dalam pengolahan produk, sehingga perlu dikaji kualitasnya berhubungan dengan komposisi minyak nilam.

Penentuan sifat kimiawi minyak aromatik umumnya dilakukan dengan menggunakan metode kimiawi yang bersifat destruktif, memakan waktu, dan biaya tinggi. Oleh karena itu, metode alternatif untuk penentuan cepat diperlukan, dan salah satunya menggunakan Near Infrared Spectroscopy (NIRS).

Tujuan dari penelitian ini adalah untuk mengevaluasi kualitas minyak nilam dengan NIRS, menentukan karakteristik spektra minyaknya dan mengklasifikasikannya berdasarkan asal-usulnya, serta dan mengembangkan model kalibrasi NIRS untuk memprediksi komposisi kimia utamanya. Dalam penelitian ini dipilih 84 sampel minyak nilam dari tujuh tempat yang berbeda di seluruh Indonesia; Konawe, Kolaka dan Masamba dari pulau Sulawesi, Bogor, dan Garut dari Jawa Barat dan Aceh dan Jambi dari Sumatera. Secara total sekitar 50 ml per sampel diterima dan setiap menyelesai produksi.

Data spektral minyak nilam diolah dengan beberapa data pretreatment dan kemudian Principal Component Analysis (PCA) dan Partial Least Square (PLS) dilakukan. Análisis dengan diskriminan komponen utama dikembangkan untuk mengklasifikasikan minyak nilam berdasarkan asalnya. PCA digunakan untuk mengelompokkan berbagai jenis minyak nilam berdasarkan komponen utama data spektra olahan. Analisis diskriminan hasil analisis kualitatif dengan PCA dibuat dan diterapkan untuk klasifikasi tujuh jenis minyak nilam. Data masukan adalah hasil PC1, PC2, dan PC3.

Dalam kasus PLS, spektrum transflektansi yang dihasilkan untuk korelasi linier antara nilai serapan NIR dan data kimia digunakan. Data dipisahkan dalam dua model berdasarkan sifat fisik dan kimia (Bogor, Garut, Konawe dan Kolaka ke model pertama dan Aceh, Jambi dan Masamba kedua). Senyawa kimia utama

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minyak nilam (Nilam Alkohol - C15H26O) diukur dengan spektrometri massa kromatografi cair (LC-MS) sebagai metode referensi.

Hasil penelitian menunjukkan bahwa adalah mengevaluasi kualitas minyak nilam dengan teknologi NIR. PCA diizinkan untuk mengklasifikasikan sampel minyak berdasarkan asalnya denga memiliki akurasi tinggi menggunakan spektroskopi NIR. Analisis diskriminan menunjukkan bahwa untuk spektrum, nilai prediksi keseluruhan adalah yang tertinggi, denga nilai probabilitas klasifikasi 100% untuk semuanya.

Dalam kasus PLS, kalibrasi model untuk memprediksi komposisi kimia utama dari minyak nilam telah dibuat. Kalibrasi terbaik untuk model pertama adalah yang memiliki normalisasi rata-rata sebagai data pretreatment, dalam kasus model kedua adalah yang memiliki perlakuan awal turunan pertama dan kedua.

Memang, hasil ini sangat terkait dengan sifat fisik dan kimia dari spektrum yang ada pada setiap model. Secara numerik kedua model pada umumnya mendapat koefisien korelasi tinggi r> 0,90 dan koefisien variasi CV rendah <2,98%, sangat bisa digunakan untuk mengetahui komposisi kimia minyak nilam.

Kata kunci: Patchouli Alcohol, PCA, PLS, Minyak, Non-destruktif, Kualitas.

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SUMMARY

DIEGO MAURICIO CANO REINOSO. Evaluation of the Quality of Patchouli Aromatic oil (Pogostemon Cablin Benth.) by Near Infrared Spectroscopy.

Supervised by SUTRISNO, Y ARIS PURWANTO, I WAYAN BUDIASTRA.

Indonesia is a rich country in aromatic plant species with around 40 kinds produced. They are easy to plant, to maintain and harvesting, easy to process and transport, making them preferable for small farmers and remote communities.

Aromatic plants normally are used as an essential oil in fragrance, medicine and culinary, where the perfume industry is one of the largest consumers. As for culinary, many food manufacturers usually purchased those and combine it with other substances and materials to create delicious products, such as chocolate, candy and beverages.

Patchouli (Pogostemon cablin Benth.) is a plant from Lamiaceae family that is well known for its medicinal and aromatic properties. Patchouli is cultivated mainly for its essential oil and in international business and in countries with perfume productions, it is considered that the Patchouli coming from Indonesia has the best quality. Nevertheless, despite of its reputation in the industry, continually it suffers irregularities due to the sources of its products. Normally this situation is related with problems in the processing of the product, making necessary the study of its quality by the chemical composition of oil.

The determination of the chemical properties of aromatic oil is commonly carried out using a chemical methods which are destructive, time consuming, and high cost. Therefore, alternative methods for rapid determination are required, and one of them is Near Infrared Spectroscopy (NIRS).

The objective of this study was to evaluate the quality of patchouli aromatic oil by NIRS, determining its spectra characteristics and classifying it based on its origin and to develop a NIRS calibration model for predicting its main chemical composition. In this study were selected 84 oil samples of Patchouli aromatic plant from seven different places around Indonesia; Konawe, Kolaka and Masamba from Sulawesi island, Bogor, and Garut from West Java, while Aceh and Jambi from Sumatra. In total it was around 50 ml per samples in recipient based on every field production.

The spectral data of patchouli oil was processed by several data pretreatments and then Principal Component Analysis (PCA) and Partial Least Square (PLS) was carried out. Discriminant analysis of the principal component was developed to classify patchouli oil based on its origin. PCA was used to cluster the different kinds of patchouli oil based on principal components of processed spectra data. Discriminant analysis of the result of qualitative analysis by PCA was constructed and applied for classification of the seven kinds of patchouli oil. The input data were the results of PC1, PC2, and PC3.

In the case of the PLS, the resulting transflectance spectra for a linear correlation between the NIR uptake value and the chemical data was used. The data were separated in two models based on its physical and chemical properties (Bogor, Garut, Konawe and Kolaka to the first model and Aceh, Jambi and Masamba to the

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second one). The main chemical compound of patchouli oil (Patchouli Alcohol - C15H26O) was measured by a liquid chromatography-mass spectrometry (LC-MS) as a reference method.

The results showed that it was possible to evaluate the quality of the patchouli oil by NIR technology. PCA permitted to classify the oil samples based on its origin which a high accuracy using NIR spectroscopy. The discriminant analysis suggested that for the spectra the overall prediction values were the highest, which a probability of classification of 100% for all of them.

In the case of the PLS, a calibration a model for predicting the main chemial composition of the patchouli oil was created. The best calibration for the first model was the one with normalization mean center as a data pretreatment, in the case of the second model was the one with 2^nd derivative pretreatment. Indeed, this results were highly related with the physical and chemical properties of the spectra existing in every model. In numeric terms both models in general got high correlation coefficient r>0.90 and low coefficient of variation CV<2.98%, which could be used to determine the chemical composition of the patchouli oil.

Keywords: Patchouli Alcohol, PCA, PLS, Patchouli Oil, Non-destructive, Quality

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©Copyright of IPB, year 2018 Copyright reserved

No part or all this thesis may be expected without inclusion or mentioning the sources, excerption only for research and education use, writing for scientific paper, reporting, critical writing or reviewing of a problem, excerption does inflict a financial loss in the proper of Bogor Agricultural University

No part or all of this thesis may be transmitted and reproduced in any form without a written permission from Bogor Agricultural University

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EVALUATION OF THE QUALITY OF PATCHOULI AROMATIC OIL (POGOSTEMON CABLIN BENTH.) BY NEAR

INFRARED SPECTROSCOPY

DIEGO MAURICIO CANO REINOSO

A Thesis submitted to Graduate School

In Partial Fulfillment in the Requirements for the degree of Master of Science In

The Study Program of Postharvest Technology

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

2018

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External Examiner for Thesis Final Examination Dr Ir Mohammad Solahudin

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Thesis Title: Evaluation of the Quality of Patchouli Aromatic Oil (Pogostemon Cablin Benth.) by Near Infrared Spectroscopy

Name : Diego Mauricio Cano Reinoso NIM : F152168211

Approved by Advisory Committe

Prof Dr Ir Sutrisno, M.Agr Chairman

Known by

Head of the Study Program of Postharvest technology

Dr Ir Usman Ahmad, M.Agr

On Dean of Graduate School

Prof Dr Ir Anas M Fauzi, M.Eng.

Prof Dr Ir Y. Aris Purwanto, M.Sc Member

Dr Ir I Wayan Budiastra, M.Agr Member

Examination Date: 03 April 2018 Graduate Date:

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ACKNOWLEDGEMENTS

All the thanks and love for the gifts and accomplishments achieved in completing this degree goes to My mother, she teach me that the love of a woman and specially the love of a mother for her sons is capable of anything in the world, creating miracles in the difficult moments and scarifying everything with compassion. Special thanks are extended to the government of the Republic of Indonesia for giving me this opportunity to further my studies in Indonesia under the Developing Countries Partnership Scholarship and for funding these projects.

I would not be here today without the support, encouragement, and value system of hard work, self-confidence, and honesty my three advisors in my pursuit of my college degrees: Prof Dr Ir Sutrisno, M.Agr, Prof Dr Ir Y. Aris Purwanto, M.Sc and Dr Ir I Wayan Budiastra, M.Agr, really thank you very much for their guidance, caring and patience. I wish to express my sincere thanks to FATETA staff at large for student friendly environment they showed during my studies.

I would like to pay a tribute to International Students Forum of IPB, KNB community in Indonesia. I would like to special thanks to Mutasem(Palestine), Gilness (Tanzania), Amal (Egypt), Gabriello (Madagascar), Hamed (Sudan), Nawawee (Thailand) and my special thanks to our President of International students Waheed from Pakistan for his help, guidance and his precious knowledge that he have shared during my studies.

Last but not least, I would also like bow in honor of professionalism displayed by IPB administration staff that was involved in welcoming and induction of international students in 2015/2016 and also throughout of my academic years. Mam. Feilda, Mam. Fatima, Mr. Soleh, Mam. Anna and Mr.

Rashid, thanks for your love, guidance, smile and laughter made me settle in Indonesia.

Bogor, April 2018 Diego Mauricio Cano Reinoso

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LIST OF TABLE

1 Main components of Patchouli Essential Oil by INS... 6

2 Chemical characteristic of the patchouli oil ... 17

3 Classification of each sample for transflectance spectra based on its origin ... 24

4 Percentage of discrimination between each sample for the transflectance spectra. ... 25

5 Classification of each sample for absorbance spectra based on its origin ... 25

6 Percentage of discrimination between each sample for the absorbance spectra ... 25

7 Result of the calibration and validation of the models by PLS analysis ... 27

LIST OF FIGURES

1 General process of Patchouli aromatic plant chain production ... 3

2 Example of the basic way of working of NIRS ... 7

3 Technical compression of the data by PLS ... 10

4 Technical compression of the data by PCA ... 11

5 Example of working way of transflectance method ... 14

6 General NIRS process of the research in laboratory ... 15

7 Transflectance spectra of different types of patchouli oil tested ... 18

8 Oil samples from used in laboratory in their respective recipients ... 19

9 General representation of the valley related to electromagnetic wave ... 20

10 Absorbance spectra showing the behaviour of the wavelength selected for the PCA ... 21

11 Cluster score graphic of the principal component analysis of the transflectance spectra ... 21

12 Residual plot of the PCA of the samples for the transflectance spectra ... 22

13 Cluster score graphic of the principal component analysis of the absorbance spectra ... 23

14 Residual plot of the PCA of the samples for the absorbance spectra ... 23

15 Absorbance spectra of the first model (Konawe, Kolaka, Bogor and Garut) showing the behavior of the wavelength ... 26

16 Absorbance spectra of the second model (Masamba, Jambi) showing the behavior of the wavelength ... 26

17 Plot of the concentration (%) of the data prediction and reference of the Patchouli Alcohol for the second model ... 28

18 Plot of the concentration (%) of the data prediction and reference of the Patchouli Alcohol for the first model ... 29

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LIST OF APPENDIX

1 Residual plot of the second model during the PCA for the

precalibration of the model ... 36 2 Residual plot of the first model during the PCA for the

precalibration of the model ... 36 3 Chemical % of the mail patchouli oils components (LC-MS Results) ... 37 4 Sketch of the Indonesian map with the origin zones of Patchouli ... 37

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1

1 INTRODUCTION

Background

Indonesia is a rich country in aromatic plant species with around 40 kinds produced. They are easy to plant, to maintain and harvesting, easy to process and transport, making them preferable for small farmers and remote communities.

Aromatic plants normally are used as an essential oil in fragrance, medicine and culinary, where the perfume industry is one of the largest consumers. As for culinary, many food manufacturers usually purchased those and combine it with other substances and materials to create delicious products, such as chocolate, candy and beverages.

Patchouli (Pogostemon cablin Benth.) is a plant from Lamiaceae family that is well known for its medicinal and aromatic properties. Patchouli is cultivated mainly for its essential oil and especially it is notable how its extracted oil is internationally important and valuable, principally for the aromatherapy, perfumery, cosmetics, incense stick production and food flavoring industries (Ramya 2013).

Indonesia shares around 80% of all the production of Patchouli plant in the world market, meanwhile the other 20% comes from countries like Malaysia, Philippine, China, India and Brazil (ICTA 2009). In Indonesia, patchouli is cultivated since 100 years ago and the biggest areas with the best production are Sumatra, Bengkulu, Lampung and east of Jawa (ICTA 2009).

In international business and in countries with perfume industry, it is considered that the Patchouli coming from Indonesia has the best quality.

Nevertheless, despite of its reputation, continually it suffers irregularities due to the sources of its productions (ICTA 2009). Normally this situation is related with problems in the harvest and postharvest of the product, making necessary the study of its quality by the chemical composition of the plant. This composition can be revealed using destructive methods like gas chromatography (Cserhátiet et al. 2005;

Daferera et al. 2002; Nikoli´cet et al. 2014) and gas chromatography-sniffing (Chin and Marriott 2015; Cserháti et al. 2005).

Silva-Filho et al. (2016), in a study about the evaluation of the effect of Pogostemon cablin essential oil (PEO) on leukocyte behavior in the inflammatory response, applied gas chromatography for showing the predominance chemical compounds of the patchouli plant. In the same way, Yahya & Yunus (2013) in an experience about the influence of sample preparation and extraction time on chemical composition of steam distillation derived patchouli oil, carried out gas chromatography for detecting the influence of the sample and time extraction in the chemical composition of patchouli oil in Malaysia.

The problem with destructive methods like gas chromatography is that they are time consuming and complex. In view of this current issue, it is necessary to develop an appropriate tool to assess the quality of the patchouli production. In this context, vibrational spectroscopic methods such as NIRS (Near Infrared Spectroscopic) together with chemometrics treatments can be successfully

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2

introduced as a rapid analysis for a non-destructive determination of metabolites occurring in the patchouli plant.

According to that, Lee et al (2014) developed a process of characterization of caffeine and nine individual catechins in the leaves of green tea (Camellia sinensis L) by near-infrared reflectance spectroscopy. They proved that NIRS could be applied for the rapid determination of the contents of caffeine, and total catechins in tea leaves for breeding programs that develop high-quality tea plants.

Parallel that, (Li et al (2013) determined the dry matter content of tea by near and middle infrared spectroscopy coupled with wavelet-based data mining algorithms. They explored the potential of near and middle infrared spectroscopy application in fast determination of dry matter content (DMC) of tea through the whole process from fresh tea leaf, semi-manufactured tea and finished tea. Despite in the matter of Patchouli aromatic plant still there is no well documentation about application of NIRS, This tool has a huge potential to be developed in this field as the rapid methodology needed for determining the quality of the products coming from plant.

Formulation of the Problem

Patchouli aromatic oil production continually suffer irregularities. This situation is related with problems in the processing of the product. In view of the current issue associated with the lack of quality present in the production of the Patchouli oil, it is necessary enforces the requirement of a rapid and accurate analytical method for the correct value estimation of its quality however, the current analytical tools, like gas chromatography and gas chromatography-sniffing are time consuming and complex. Therefore, attempts have been made to find alternative other methods. In this context, vibrational spectroscopic technology such as NIRS (Near Infrared Spectroscopic) together with chemometrics treatments can be successfully introduced as a rapid analysis for the determination of chemical compound occurring in this aromatic oil. To evaluate the quality the patchouli oil qualitative and quantitative by NIRS, permits to know properties that define in thoroughly the presentation of the Patchouli products.

Research Objective

The general objective of this study is to evaluate the quality of patchouli aromatic oil by NIRS. As a specific objectives:

1. To determine its spectra characteristics and classifying it based on its origin.

2. To develop a NIRS calibration model for predicting its main chemical composition.

Benefits of the Research

The development of NIR spectroscopy method for the evaluation of the quality of Patchouli Aromatic Oil (Pogostemon Cablin Benth.) expected to provide the following benefits:

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3 1. This research can provide benefits to the development of science, especially in the field of non-destructive measurements.

2. This research may help to determine the patchouli plant origin based on its oil constitution and the chemical composition accurately for the industry.

3. This research may help to determine when there is an adulteration in the quality of the products coming from patchouli plant.

2 LITERATURE REVIEW

Patchouli Plant and Chemical Oil Composition

Patchouli (Pogostemon cablin Benth.) is a perennial, fragrant herb that grows to 2-3 ft high. It develops successfully up to an altitude of 800 - 1000 m above the mean sea level. It prefers a warm and humid climate. The crop can be cultivated under a heavy and evenly distributed rainfall, ranging from 150 - 300 cm per year (Ramya et al. 2013). Patchouli requires deep, well drained, fertile, slightly acidic, humus and nutrients. It flourishes best in loose deep loamy soils, rich in organic matter, which makes a loose friable texture. The PH value of the soil should have a range from 5.5 to 7.5 for a good growing. In addition, it thrives well in coastal region having 80% - 90% of relative humidity and temperature between 20 - 35℃ (Ramya et al. 2013).

Patchouli is cultivated mainly for its essential oil, which is found it in the leaves. The dry leaves of patchouli on steam distillation yield have an essential oil, which is called ‘oil of patchouli’. The leaves harvested and dried in shade have oil content in the range of 2.5% – 3.5% (Vijyakumar 2004). The world production of patchouli oil is around 800 tons per year. Java produces about 2/3 of the quantity followed by China and Malaysia (Ramya et al. 2013; ICTA 2009). Figure 1 explains the process of patchouli aromatic oil chain production, starting from the raw material as a plant or its leaf, followed by the steaming stage until the final acquisition of the aromatic oil.

Figure 1Scheme of the general process of patchouli aromatic oil chain production.

(SENA 2009).

Patchouli Plant Pretreatment of the raw material

Drying of the raw material

Size reduction Steam

Extraction Condensation

Separation of the

Oil Packaging and Storage

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4

The chemical formula of Patchouli Oil is C15H26O. The main secondary metabolites present in patchouli are essential oil (up to 5%) and tanitos (1.7%). The essential oil consists mainly of Sesquiterpenes (patchoulene, alpha- guaiene, alpha- bulnesene and alcohol of patchouli (Yougen et al. 2013).

Yougen et al (2013) In a study about the comparison of the essential oil compositions between Pogostemon Cablin and Agatache Rugosa found that the main components of the patchouli essential oil are Seychellene, Bulnesene,- guaiene, Patchouli Alcohol, -longifolene, Thujopsene and β-patchoulene. It was an experiment carry out on stems extractions of tree chinese varieties of Pogostemon cablin Benth. (Nanxiang, Paixiang, Zhaoxiang).

In the same way, Nguyên Xuân et al (2011) develop and investigation about the chemical composition of patchouli oil from Vietnam. The essential oil of Pogostemon cablin (Blanco) Benth cultivated in Vietnam was analyzed by capillary gas chromatography and mass spectrometry. They found that Patchouli alcohol accounted about 32-38% of the patchouli oil. Ten more compounds were identified where  -bulnesene and -guaiene were the main components.

Consequently, Swamy & Sinniah (2015) elaborated a review for having comprehensive knowledge on the phytochemistry and pharmacological activities of essential oil and different plant extracts of patchouli based on the available scientific literature. The pointed out that until now more than 140 compounds, including terpenoids, phytosterols, flavonoids, organic acids, lignins, alkaloids, glycosides, alcohols, aldehydes have been isolated and identified from patchouli. The main phytochemical compounds are patchouli alcohol, α-patchoulene, β-patchoulene, α- bulnesene, seychellene, norpatchoulenol, pogostone, eugenol and pogostol.

(Donelian et al. 2009; Bauer et al. 1997; Ramya et al. 2013; Akhila et al. 1984;

Akhila et al. 1988).

The biological activities of patchouli oil are strongly associated with the chemical constituents such as pogostone, patchoulol, α and β-patchoulene.

Previous studies have stated that pogostone as one of the major chemical constituent of patchouli oil largely responsible for the intense aromatic odor and more recently this compound has been demonstrated to exert many pharmaceutical activities (Yi et al. 2012; Li et al. 2012; Li et al. 2014).

The chemical composition of patchouli oil varies among samples collected from different geographic locations. Li et al. (2004) revealed the significant effect of different habitats, collection periods, and processing methods on the volatile oil yield and its main constituents. Similarly, the oil yield is also influenced by different collection times. It is reported that contents of volatile oil obtained from leaves harvested from June to August and cultivated in Hainan, China were 0.8%, 0.7%

and 0.6%, respectively, while the patchouli alcohol content was highest in the month of June (Luo et al. 2002). Moreover, P. cablin was differentiated into 2- chemotypes namely, pogostone type and patchoulol type on the basis of chemical differences in their volatile oil composition (Luo et al. 2003).

Gas chromatography (GC), Gas chromatography/mass spectroscopy (GC/MS) and nuclear magnetic resonance (NMR) were used for studying the chemical composition of patchouli oil from Vietnam. The major compounds identified were α, β and δ-patchoulene, β-elemene, β-caryophyllene, α and δ- guaiene, seychellene, α-bulnesene, δ-cardinene, pogostol and patchouli alcohol.

The presence of 32%–37% patchouli alcohol content was found to be more odor

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5 intensive component of the essential oil (Dung et al. 1989). However, in Philippines, it was predicted that a distinct aroma is due to the occurrence of germacrene-B, a new sesquiterpene identified as the major component of the patchouli oil (Hasegawa et al. 1992).

The essential oil from P. cablin plants collected from China (Gaoyao County, Guangdong Province) and its volatile chemical compositions were analyzed by GC/MS. The study revealed the presence of pogostone (30.99% in stems, 21.31% in leaves), patchouli alcohol (10.26% in stems, 37.53% in leaves), trans-caryophyllene (4.92% in stems, 6.75% in leaves), α-guaiene (2.27% in stems, 6.18% in leaves) and seychellene (1.56% in stems, 1.99% in leaves) as the main constituents (Luo et al. 1999). Similarly, GC/MS analysis of essential oil extracted from both leaves and stems of Patchouli plants collected from the Leizhou County of China revealed sesquiterpenes such as patchouli alcohol, α-guaiene, δ-guaiene, α-patchoulene, seychellene, aciphyllene and trans-caryophyllene (Feng et al.

1999). Guan et al. (1998) have identified nine sesquiterpene compounds, namely patchouli alcohol, pogostone, frieddelin, epifriedelinol, pachypodol, retusine, oleanolic acid, β-sitosterol and daucosterol on the basis of spectral data.

A GC and GC/MS study of Indonesian patchouli oil indicated the presence of the following compounds; α-pinene, δ-patchoulene, β-pinene, aciphyllene, limonene, δ-guaiene, δ-elemene, 7-epi-α-selinene, α-copaene, norpatchoulenol, α- patchoulene, 1-10-epoxy-11-bulnesene, β-elemene, caryophyllene oxide, cycloseychellene, nortetrapatchoulol, β-caryophyllene, patchouli alcohol, α- guaiene, patchoulenone, seychellene, 9-oxopatchoulol, α-humulene, pogostol, α- patchoulene, isopatchoulenone, γ-gurjunene, and germacrene D (Bure et al. 2004).

Furthermore, Gokulakrishnan et al (2013) carried out an experience about the study of Pupicidal and repellent activities of Pogostemon cablin essential oil chemical compounds against medically important human vector mosquitoes. They applied gas chromatography for determining what fractions of the Pogostmons cablin plant are the most active during their repellent activities and how is the interaction of those with the mosquitoes. In the same way, Murugan & Mallavarapu (2013) in their study focus in  - Bisabolol as the main constituent of the essential oil of Pogostemon speciosus, make a gas chromatography process for the chemical characterization of  - bisabolol quantitatively and qualitatively.

Briefly, the general quality standard of an essential oil is established by the legislation of each country. Generally, the specifications for a given essential oils are often very similar by different agencies. The main differences are about the end- use relation that could have the essence.

Summarizing, in the Table 1 is exposed the chemical composition of a commercial patch of patchouli essential oil in Indonesia. It shows the percentages in which the main components have to be found it.

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Table 1 Table of main components of Patchouli Essential Oil by INS (Indonesia National Standard) and the indication of its chemical formulation (ICTA 2009).

Standard Limit

INS (%) Chemical Components

% minimum

% maximum

1 α-Copan 0 1 0.84

2 β-Patchoulene 2 3 2.49

3 β-Caryophyllene 3 5 4.04

4 α-Guaiene 13 18 16.49

5 Seychellene 3 7 4.28

6 α-Patchoulene 5 9 7.84

7 α-Bulnesene 15 19 17.09

8 Pogostol 1 5 2.25

9

Patchoulol (Patchouli Alcohol)

C15H26O ₂₃ ₃₅ _29.78

Near Infrared Spectroscopy

Near infrared Spectroscopy (NIRS) is a technology that tests the inner and superficial characteristics of agricultural products non-destructively. All food and food ingredients derived from plants and animals are composed of elements that contain groups of atoms absorbed in the near infrared region, among which are C-H, O-H, N-H, and other groups (Williams & Norris 1990).

Once emitted, the radiation of NIR is absorbed by all the organic materials and key information named before, CH (such as organic materials petroleum derivatives), OH (such as moisture content, carbohydrates, and fats), CN, and NH (in the constitution of proteins and amino acids) which are the basis of all the bonding chemical bonds of organic materials. Such information can be viewed from the response of NIR spectra generated in the way of reflection or transflectance.

Near-infra-red light works in the material that has a smaller energy and penetrates only about a millimeter inside the surface depending on the composition of the material.

When the wave NIR light / radiation is applied to the product there are three common interactions (Williams & Norris 1990):

Reflectance Light reflectance (R) Transmittance Transmission / Forward light (T)

Absorbance NIR light absorption (A)

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7

Applying the law of conservation of energy:

𝐼 = 𝑅 + 𝑇 + 𝐴 (1)

Where,

I: The amount of radiation that is fired into the product R: reactant or the amount of reflected radiation

T: transmittance or transmitted radiation A: Absorbance or radiation absorbed

Figure 2 Figure about the example of the basic way of working principle of NIRS (Williams & Norris 1990).

Figure 2 exposed the internal process which is carry inside the NIR machine when this is working. Basically, in there is possible to see basic way of working of NIRS according to the theory named beforehand. Traditionally NIR methods have been used either direct transmission or diffuse reflectance geometries. These techniques are applicable for samples with low light scattering and low optical density, samples where the optical path length can be adjusted to minimize the sample’s optical density, where the composition of the sample’s surface is the same as its interior, or where the skin is sufficiently thin as to pose little interference in the signal. Dull et al (1989) were able to obtain satisfactory results using NIR transmission in their work on intact potato, also Lammertyn et al (2000) was able to obtain satisfactory results using NIR reflectance techniques in their work on whole fruit.

A NIR spectrum is between 700-2500 nm (Dryden 2003). Near infrared radiation reflected from the sample can be used to predict the characteristics of the sample by data obtained that are included in the calculation of calibration.

Values expressed as log (1/R) or (1/T), which gives a high value on high levels of absorbance. There is a linear relationship between log (1/R) and log (1/T) and concentration of the absorbed component. Variations in sample particle size and temperature influence the distribution of infrared radiation as it passes through the sample. Large particles could not spread as much infrared radiation the small particles.

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8

Previous Studies Applying NIRS

Sujitra et al (2016) in their investigation on Prediction of 2-acetyl-1- pyrroline content in grains of Thai Jasmine rice based on planting condition, plant growth and yield component data using chemometric and applying partial least square as a mathematical treatment. The aim of this research was to simultaneously investigate the effects of nitrogen (N) fertilizer and salinity (NaCl) treatment on aromatic quality of Oryza sativa L. ssp. indica cv. Pathumthani 1 rice grains, based on PLS coefficients and variable influence on projection (VIP) values, N, Na, the shoot dry weight and the number of tillers per plant that were identified to have a strong influence on the prediction of the 2-acetyl-1-pyrroline content.

Similar to that, Branislava et al (2017) used principal component analysis mathematical treatment in their investigation about experimental and chemometric study of antioxidant capacity of basil (Ocimum basilicum) extracts. The aim of the work was to test the antioxidant activity of basil (Ocimum basilicum L) extracts obtained by extraction with water (in presence and absence of light), methanol (95%), ethanol (30, 40, 50, 60, and 96%), chloroform, dichloromethane and hexane.

The chemometric analysis showed good correlation between the yield and total phenolic composition, and between the flavonoid content and antioxidant activity, predicting thus, basil extract quality.

Most NIRS research has used diffraction type of instruments. More recently, Fourier transform NIR (FT-NIR) technique has been applied for optimization of green tea steaming process conditions (Ono et al. 2011). The constructed prediction models by metabolic fingerprinting of fresh green tea leaves using Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares (PLS) regression analysis to objectively optimize of the steaming process conditions in green tea manufacture. In addition to this Wu et al (2013) applied near infrared spectroscopy for the rapid determination of antioxidant activity of bamboo leaf extract. The study was carried out to evaluate the feasibility of using near infrared (NIR) spectroscopy for determining three antioxidant activity index of the extract of bamboo leaves using four different linear and nonlinear regressions tools.

Moreover, Li et al (2015) determinate tea polyphenols (TP) content by infrared spectroscopy coupled with iPLS (Interval partial least squares) and random frog techniques.

They investigated the potential of infrared spectroscopy for fast determination of tea polyphenols (TP) of 14 cultivars of tea trees based on data mining technique. The TP determination models were respectively developed for large leaf cultivars, middle leaf cultivars and all the cultivars getting feasibility of infrared spectra for measurement of the TP content of tea.

Olsoy et al (2016) elaborated a nutritional analysis of sagebrush by near- infrared spectroscopy using reflectance. They evaluated the capacity of near-infrared spectroscopy by reflectance (NIRS) to measuring and monitoring the dietary quality of sagebrush. Sagebrush (Artemisia spp.) habitat in the intermountain west is one of the most endangered ecosystems in North America due, in part, to fire, climate change, and anthropogenic disturbances. However, restoration efforts rarely consider the dietary quality of sagebrush that is conserved or restored. The results indicate that NIRS may offer a rapid, noninvasive, diagnostic tool for assessing dietary quality of sagebrush.

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9 As another experience, Chen et al (2006) carried out a feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties demonstrating that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.

NIR spectroscopy has shown good results for estimation of oil content in groundnuts and free fatty acids content in sunflower seeds. Misra et al (2000) predicted the oil content of groundnuts using NIR transflectance measurements with a coefficient of determination of 0.87. Moschner & Biskupek-Korell (2006) reported good results on prediction of the free fatty acids content of ground high- oleic sunflower seeds using NIR reflectance, with a R2 of 0.94. Although transflectance measurement may provide more spectral information about the interior properties of fruit as light travels longer path in the fruit, it is not preferred for applications as this sensing mode requires a more powerful light source and a sensitive detector (Lu & Ariana 2002).

In the field of essential oil, Dupuy et al. (2014) elaborated a quantitative analysis of Lavender (Lavandula angustifolia) Essential Oil using multiblock data from Infrared Spectroscopy. Near-infrared and mid-infrared spectroscopies were currently used to analyze natural compounds. During the last ten years various multiblocks methods were developed such as Concatenated PLS, Hierarchical-PLS (H-PLS), and MultiBlock-PLS (MB-PLS). These three algorithms were used to analyze 55 lavender (Lavandula angustifolia) essential oil samples. The results obtained were compared to the ones obtained respectively in NIR and MIR ranges.

The accuracies of the models depend on the spectroscopic technique, pretreatment and the PLS methods. The results showed that the choice of the factor numbers used to build the multiblock models was the most important parameter for the H-PLS and MB-PLS methods.

Furthermore, (Cayuela & Garcia 2017) applied NIRS to Sort olive oil based on α-tocopherol and total tocopherol content. In this study, they assessed models based on partial least squares (PLS) and discriminant analysis (PLS-DA), using near infrared. Estimating the α-tocopherol and total tocopherols contents by using the PLS models were suitable according to the predicting exercises, which gave residual predictive deviations 2.37 and 2.01. Sorting test of olive oil in two classes by α-tocopherol with the PLS model provided 99.9% success. The PLS-DA assessment for the same purpose gave coefficients of predictive specificity and sensitivity for the high α-tocopherol class 0.96 and 0.84, respectively. The data proves the feasibility of estimating the olive oil α-tocopherol or total tocopherols contents by using NIRS. Besides, these techniques can be helpful rapid methods in the industry for sorting olive oils according to their vitamin E content.

Finally, Kuriakose & Joe (2013) studied the feasibility of using near infrared spectroscopy to detect and quantify an adulterant in high quality sandalwood oil.

The study focuses on the application of NIRS to detect sample authenticity and quantify economic adulteration of sandalwood oils. Several data pretreatments were investigated for calibration and prediction using partial least square regression (PLSR). The optimum number of PLS components was obtained according to the lowest root mean square error of calibration (RMSEC = 0.00009% v/v). The lowest

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10

root mean square error of prediction (RMSEP = 0.00016% v/v) in the test set and the highest coefficient of determination (R2 = 0.99989) were used as the evaluation tools for the best model.

Pretreatment of NIRS Data

The spectra obtained from the NIR reflectance/transflectance measurement could be transformed into absorbance spectra. Furthermore, the pretreatment data for further analysis using PLS or PCA. Treatment of spectral data is done before calibration and validation, to eliminate disturbances that occur during the capture spectra. Pretreatment data to be given in this study are smoothing, normalization mean center, 1^st and 2^nd derivative Savitzky-Golay. According to William (1990), 1^st and 2^nd derivative Savitzky Golay is the method most commonly used. This method is a combination of Savitzky Golay smoothing method and second derivatives.

The Smoothing and normalization function choose carefully does not remove the existing spectrum of information and reducing the noise. The second derivative serves to separate the peaks experienced overlapping and eliminate baseline shifts. The application of the first and second-derivative algorithm to the raw spectra (log 1/R or 1/T) induced a clear separation between peaks, which overlapped in the raw spectra (Lee & Choung inside Lee et al. 2014).

Calibration and Validation

Calibration and validation is performed commonly on the data NIR absorbance, where to get the data transformation is needed into reflectance/transflectance (log 1 / R, log 1/T). Calibration is building a model that connects the response spectra of each sample with each wavelength to know the concentration of a chemical laboratory analysis, Andasuryani (2014). Model calibration can be developed using algorithms PLS (Partial Least Square) or PCA (Principal component Analysis).

= +

Figure 3 Figure of the technical compression of data by PLS (Martens & Naes inside William & Norris 1990).

In the Figure 3 is explained the PLS goal, which is to build a linear model between the matrix and the concentration C spectra matrix A, C = AB. It is comparable to the MLR (multiple linear regression) method and PCA (principal component analysis), PLS produces weight matrix W to A therefore S = AW, column W is the weight vector for a column that produces a matrix score S. The

A S F E

C

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11 weights are calculated in such a way it maximizes the covariance between the matrix C and the matrix spectrum concentration A. C can then be decomposed as C

= SF, with F kwon as the matrix for C. After F is calculated, C = AB, and B = WF, the PLS model can be built (Chen & Wang inside Andasuryani 2014).

According to (Daszykowski et al. 2007; Kumar et al. 2014) PLS is a supervised method which is based on the relation between signal intensity (spectrum) and the characteristics of the sample. Interference and overlapping information can be overcome by using a powerful multicomponent analysis such as PLS. The algorithm is based on the ability to mathematically correlate spectral data for a property.

The PCR (principal component regression) method is a quantitative regression algorithm that is directly used for linear data. This method is based on a factor model and uses information from all wavelengths to predict the sample composition. PCR uses a data reduction approach to reduce the number of many variables into new variables with fewer numbers. The amount of content in the sample can be predicted from the new variables. The PCR method combines principal component analysis (PCA), from the spectrum with multiple linear regression (MLR) in modeling the quantitative model for complex samples (Chen

& Wang inside Andasuryani 2014).

(Cozzolino et al. 2011; Kumar et al. 2014) named PCA as an unsupervised modeling method that allows for exploratory data analysis; it extracts information from data set and removes noise; it reduces the number of dimensions; and it allows for classification of samples by investigating similarities and differences between the samples. The principal component analysis projects into a smaller number of latent variables called principal components (PC). Each principal component explains part of the total information contained in the original data and the first PC is the one that contains the most information, followed in descending order in terms of information by PC2 and PC3 and so on. Plotting two PCs relatively to each other thus allows for interpretation of some groups, thanks to the similarities or differences between samples.

= +

Figure 4 Figure of the technical compression of data by PCA (Martens & Naes inside William & Norris 1990).

The Figure 4 is a featuring technique compresses data using PCA methods.

It Outlines the original spectrum of data matrix (A) to be a major component or eigen matrix spectrum of A (F) that expressed the relationship by a matrix score (A

= SF). Matrix S correlated linearly with the concentration matrix C. Furthermore, by regressing the matrix C from the matrix S, it is a model MLR (multiple linear regression); C = SB then B can be resolved, namely B = (S^(T) S)^(-1) *S^(T) * C.

A S F E

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12

The sample matrix C of unknown concentration can be obtained if A, B and F and S (obtainable from S=AF^T) are known (Chen & Wang 2001).

Statistical Parameters Used to Evaluate the Development of NIR Calibration Model

The average deviation between the reference value (xn) and the predicted value (yn) of V-Set. Williams & Norris (1990):

(2) The standard error of calibration set (SEC). It is the standard deviation of

the differences between the reference value (xn) and the predicted value (yn) of C- Set:

(3) The standard error of validation set (SEP). It is the standard deviation of the

differences between the reference value (xn) and the predicted value (yn) of V-Set:

(4) Coefficient of correlation (r) between the reference value (xn) and the predicted value (yn):

(5)

The model is considered more useful when r-value approaches 1, whereby r value is larger than 0.90, considered as high correlation.

Coefficient of determination (R2) between the reference value (xn) and the predicted value (yn):

(6) Coefficient of variation (CV)

CV in C-Set: (7) (8)

CV in V-Set:

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13 A very reliable calibration could be achieved when the value of CV in C-Set was lower than 5% and the value of CV in V-Set was lower than 10%.

3 METHODOLOGY

Place and Time

Research conducted from June 2017 until December 2017 at the Laboratory of Food and Agricultural Products Process Engineering (Laboratory (TPPHP)), Department of Mechanical and Biosystem Engineering, Faculty of Agricultural Engineering and Technology, Integrated Science and Technology Laboratory, IPB (Bogor Agricultural University).

Materials

In this study were selected 84 oil samples of patchouli aromatic plant from seven different places around Indonesia; Konawe, Kolaka and Masamba from Sulawesi island, Bogor, and Garut from west java and Aceh and Jambi from Sumatra. In total it was around 50 ml per samples in recipient based on every field production.

Oil as a raw material gives a good respond for the data acquisition in the experiment and following processing of those by the chemometrics treatment inside the NIRS process (Yougen et al. (2013) & Nguyên Xuân et al. (2011)). The idea also is following the recommendations of the Indonesia National Standard (INS) for the manipulation and study of the samples.

As well, there was used FT NIR SPECTROMETER (Fourier Transform Type) NIRFlex N-500 machine, which provides reliable analytical results for quality control, research and development in the pharmaceutical industry, chemical, food, drink and feed. NIRFlex N-500 offers various modules sample measurement and accessories for ultimate performance.

Collection Procedures and Data Processing

Measuring Procedure of NIR in Laboratory

The methodology applied for taking the spectrum of the samples is known as a transflectance (Cayuela & Garcia 2017), being this innovative procedure.

Figure 5 evidences the arrangement elaborated for the application of this method.

The device was adapted to measuring liquid samples taking advantage of its characteristics. According to this, the signal processed was in function of the transmittance and reflectance of the spectrum before the respective data analysis.

In this picture is exposed the image based on the real time in laboratory together with a scheme as an explanation for the transflectance method.

The oil was arrange in a small bottle of 3 cm of height with 1.5 cm of diameter. In there, the gun was introduced for measuring three time according to the theory explained. The NIR radiation coming from the optical fiber (gun) goes through the liquid sample, it reflects in a white background surface making contact

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14

with the sensor. Then, it backs through the oil sample, heading to the spectrometer detector.

Figure 5 Example of working way of transflectance method. A: Gun with the NIR signal and spectrometer detector, B: Oil sample, C: White background, F: Reflectance Sensor.

This methodology has not been well documented because normally the NIRS investigation results focus more on the analysis and calibration of the model and only special name arrangements in the machine if those are required.

Patchouli Spectra Acquisition

The transflectance of samples was measured by scanning the samples 3 times at 3 different points, taking the data in there and setting the gun inside the recipient of the oil during the process. After that, the spectra was transformed to absorbance for the subsequent analysis. The wavelength interval used was 1000- 2500 nm.

Chemical Analysis

The main chemical compound of patchouli oil (Patchouli-Alcohol - C15H26O) was measured by a liquid chromatography-mass spectrometry (LC-MS) method in every sample studied (Gokulakrishnan et al. 2013; Murugan &

Mallavarapu 2013). The oil samples suffer a process of separating mixture

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15 individual components where each component in a sample was identified (qualitatively) and measured (quantitatively). The results were assorted based on the percentage of concentration of the patchouli alcohol per sample.

NIRS Working Methodology Process in Laboratory

No No

Yes

Figure 6 Figure of the scheme of the general NIRS process of the research in laboratory.

In the Figure 6 is explained the normal process applied in laboratory when the NIR technology is used. For this investigation, it was carried out similar to the briefly way exposed in the graphic making clear that for the PCA analysis the reference data are not necessary for the respective calibration of a model in opposite to PLS.

To insert the samples in the recipient necessary. To use 84 samples. Scanning 3, λ = 1000-2500 nm

Patchouli Aromatic Oil

(2/3) of the samples

To scan in the length NIR

wave

To analyze chemical composition

with laboratory

methods Pretreatment of

NIR Data;

Derivative, Normalization,

Smoothing

Chemical Data

Calibration (PCA, PLS)

R ~ 1, Standard

Error `0

(1/3) of the samples

To scan in the length NIR

wave

To analyze chemical composition

with laboratory

methods Pretreatment of

NIR Data;

Derivative, Normalization,

Smoothing

Chemical Data

Validation

Standard Error, CV

ok

End

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Spectra Processing

For the PCA, the transflectance and absorbance spectra were processed with several spectra data pretreatments such a Smoothing Savitzky-Golay (Savitzky and Golay 1964) and first and second derivatives (Polynomial order of 2, 3 smoothing points, left and right sight point of 1) (Lee et al. 2014). Due to the liquid characteristics of the samples, there were not uneven surface effects, multiplication effects like scattering, particle size and multicolinearity changes, which can cause large variations in the reflectance spectrum.

Principal Component Analysis (PCA) was used to cluster the different kinds of patchouli oil based on principal components of processed spectra data (Cozzolino et al. 2011; Kumar et al. 2014). Discriminant analysis of the result of qualitative analysis by PCA was constructed and applied for classification of the seven kinds of patchouli oil. The input data were the results of PC1, PC2, and PC3.

In the case of the PLS analysis, the resulting transflectance spectra for a linear correlation between the NIR uptake value and the chemical data was used.

Due to the physical and chemical characteristics of the spectra and also because of the method elaborated in the investigation, it was not transformed to transflectance (Dupuy et al. (2014); Kuriakose & Joe (2013).

Patchouli oil contains terpenes in combination with alcohols, aldehyde and esters which interact with each other and provide a unique smell of patchouli oil.

Patchouli alcohol is a major component of the basic and essential oils to determine smell of patchouli essential oil. In addition, components such as β-patchoulene and α-patchoulene are also components that provide the characteristics and physical properties of essential oil (Ketaren 1985).

Yahya & Yunus (2013) explained that the quality of the essential patchouli oil increases with respect to increase the extraction time. Nevertheless, upon careful examination, it was noticed that some components decreased with increasing extraction time, some other components increased with increasing extraction time, while there were also some components were not affected by the prolonged extraction time.

The decrease in the percentage of some chemicals was due to the side reaction occurring between the carbon double bond and oxygen or hydroxyl ion and transformed the chemical species into another form of chemical, which resulted in the increase of some other components with same number of carbon, as mentioned above (Hu et al. 2005). The longer the extraction time, the higher composition of Patchouli Alcohol was obtained. The best quality of patchouli oil was found at ten hours in which 47% of patchouli alcohol was obtained from grinded sample during steam distillation (Yahya & Yunus 2013).

Consequently, in this investigation it was divided the calibration models for the PLS analysis in two groups, one for the samples of Konawe, Kolaka, Bogor and Garut and the other one for Jambi, Masamba and Aceh samples. The criteria for this process was based on the chemical and physical properties of the spectra obtained based on the information exposed above.

The chemical composition of the samples oil manipulated and physical properties like the odor and color were highly influenced by the extraction time, the extraction method and the harvest period of the patchouli plant together with the manipulation of the oil before arriving to laboratory. All this conditions gave an

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17 NIR spectra differenced in two groups, especially on the peaks were the literature establish the aromatic properties. Those groups were evidenced in the two models decided to build in here.

The statistical parameters used for testing the performance of the built calibration model were correlation coefficient (r), root mean square error prediction (RMSEP) (Equation 9), coefficient of variation (CV) (Equation 10) and standard deviation ratio with RMSEP (RPD) (Equation 11). A good calibration model has a near-zero RMSEP value Low CV and high r and RPD values.

𝑅_𝑀𝑆𝐸 = √¹

𝑛∑^𝑛_𝑖=1(𝑌_{𝑁𝐼𝑅𝑆}− 𝑌)² (9)

𝐶_𝑣 = (^𝑅^{𝑀𝑆𝐸𝑃}

𝑌̅ ) × 100% (10)

𝑅_𝑃𝐷 = ^𝑆^𝐷𝑝

𝑅𝑀𝑆𝐸𝑃 (11)

Good predictive models have r close to 1, the consistency value is in the range of 80-110% and the SEC and SEP with a value close to 0 (Nircal 5.5 manual 2013). In addition, a good prediction model has an RPD value greater than 2 (Lebot et al, 2009).

4 RESULTS AND DISCUSSION

Chemical Analysis Results and the Original Spectra of Patchouli Oil

Some differences are exposed by results of the chemical analysis (Table 2), which indicates the valor of major chemical component (Patchouli-Alcohol - C15H26O among the patchouli oil types). It is important to notice that there are other chemical components like Seychellene and α-Guaiene which are mainly in the patchouli oil. The idea of showing this analysis is to expose a descriptive different between the seven kinds of oil and its behavior based on its constitution.

Table 2 Chemical characteristic of the patchouli oil based on the main chemical component (Patchouli-Alcohol C15H26O)

Patchouli Oil Patchouli Alcohol C15H26O Samples

% of the component

(Average) Standard Deviation

Jambi 33.46 0.79

Kolaka 27.18 0.95

Aceh 29.49 1.04

Konawe 29.81 0.71

Masamba 28.75 0.93

Garut 31.79 1.06

Bogor 24.18 0.18

Range 24 – 34% 0.81

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The patchouli alcohol content in every type of oil has a significant difference. Jambi has the highest content (33.46%) followed by Garut (31, 79%), and konawe (29,81%) and the lowest are Bogor together with kolaka, which is 24.18% and 27.18 % respectively. (ICTA (2009); Yahya & Yunus (2013)) exposed that the common value of the patchouli alcohol chemical content is between 24 % and 34% also, that the different in the chemical composition is influence by the extract method of the oil in the field and the quality of the seed cultivated, which explain why some oil from the same area could have different percentage of the chemical composition.

Figure 7 Oil samples from used in laboratory in their respective recipients. From left to right: Aceh, Jambi, Masamba, Bogor, Kolaka,Konawe and Garut.

Figure 7 evidence all the information uphold before. In there is notice that there are samples brighter than others also the aroma was highly related with the color. In laboratory was determined that between darker the color of the samples, stronger odor it spread compare with the brighter ones. It meant, samples from Garut and Masamba could have higher percentage of chemical compounds like patchouli alcohol, α and δ- guaiene, seychellene, which produce this physical particularity (Hasegawa et al. 1992). Moreover, It is pointed out that for example, the period of the harvest, time and method of extraction affect the final chemical and physical constitution of the oil (Luo et al. 2002); Due to the sample were receive directly in laboratory, such kind on information is unknown however, by this exposition is possible to establish that those parameters were different from every oil production place.

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Figure 8 Transflectance spectra of different types of patchouli oil tested. Axis X:

Wavelength (nm), Axis Y: Transflectance. 1: Aceh, 2: Bogor, 3: Garut, 4:

Jambi, 5: Kolaka, 6: Konawe and 7: Masamba.

In the Figure 8 is possible to see that almost all transflectance spectra are similar however, by the analysis of the chemical results there is evidence of the different which could exist in the chemical and physical properties. In order to that, the PCA takes more relevant because it is an analytic way to determine the different according to the spectra obtained.

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Figure 9 General representation of the valley related to electromagnetic wave and its responses to the molecular bonds(Cen & He 2007).

Figure 9 shows the transflectance spectra as an example of the different patchouli oil with several peaks and valleys in a specific wavelength for each origin place. Also, the valley existed related to electromagnetic wave and its responses to the molecular bonds of O-H, C-H, C-O, and C-C in the Figure 9. The result is subjected to vibrational energy, both of stretch and bent vibration (Cen & He 2007).

Based on Burns and Ciurzak (2008), if it is analyzed the spectra graphic of the patchouli oil, every peak and valley that exist in there have a possible meaning.

The wavelength of 1680 and 2230 nm are reference values, 1940 nm to water, 2270 nm to lignin, 2336 nm to cellulose, 2180 nm to protein, 2100 nm to carbohydrate and finally 2310 nm to oil in general for the combination region of the vibration throughout the spectrum. In the case of the oil, the literature exposed that it is possible to find its main characteristics in the combination region and overtone region of NIR spectrum (Kuriakoe & Joe 2013; Dupuy et al. 2014).

Chemometrics of the Samples

Clustering Analysis Using PCA of Spectra Data Pretreatment

The principal components were emphasized in the spectra signal that was centered on the wavelength of 1780-1680 nm, which allegedly shows the aromatic characteristics of patchouli oil and its components according to Burns and Ciurzak (2008). For example, the Patchouli alcohol, another chemical compounds like Pogostone, Seychellene and α-Bulnesene are considered the main compounds of the patchouli oil because they give the basic aromatic properties to the oil (Yougen et al 2013; Nguyên Xuân et al. 1989) and those can be found in the wavelength selected. Moreover, the reason behind the selection of this wavelength range was related to the best clustering obtained as well. After narrowing the wavelength several times, it was in this range where a correct grouping of data was determined.