Open AccessAplikasi Teknologi Near Infrared Reflectance Spectroscopy Dengan Metode Partial Least Square Untuk Prediksi Kadar Patchouli Alkohol Minyak Nilam
Author(s) -
Sry Afrita Fitia,
Rita Khathir,
Zulfahrizal Zulfahrizal
Publication year - 2021
Publication title -
jurnal ilmiah mahasiswa pertanian
Language(s) - English
Resource type - Journals
eISSN - 2615-2878
pISSN - 2614-6053
DOI - 10.17969/jimfp.v6i4.18127
Subject(s) - patchouli , chemistry , alcohol , food science , mathematics , essential oil , organic chemistry
Abstrak. Minyak nilam merupakan salah satu jenis dari minyak atsiri yang dapat digunakan sebagai bahan baku dalam industri parfum, kosmetik serta untuk pengobatan. Minyak nilam mengandung patchouli alkohol yaitu penyusun utama yang digunakan sebagai indikator untuk mengetahui kualitas dari minyak nilam. Tujuan dari penelitian ini yaitu untuk mengkaji kemampuan teknologi near infrared reflectance spectroscopy (NIRS) dengan metode partial least square (PLS) dalam memprediksi kadar patchouli alkohol pada minyak nilam. Hasil penelitian menunjukkan bahwa PLS mampu memprediksi kadar patchouli alkohol dengan menghasilkan model yang tergolong good model performance. Peningkatan performa kinerja PLS terbaik diperoleh pada penggunaan pretreatment standart normal variate dengan nilai RPD 2,83 dengan karakteristik model nilai koefisien korelasi (r) sebesar 0,93, nilai koefisien determinasi (R2) 0,86 dan nilai error (RMSEC) sebesar 4,11.The Technology Application Near Infrared Reflectance Spectroscopy With Partial Least Square Method (PLS) To Prediction Patchouli Alcohol Content In Patchouli OilAbstract. Patchouli oil is one type of essential oil that is used as a raw material in the perfume, cosmetic and medical industry. Patchouli oil is categorized in with the main component of essential oil patchouli alcohol used as an indicator to determine the quality of patchouli oil. The purpose of this research is to assessing technological capabilities of near infrared reflectance spectroscopy (NIRS) with method partial least square (PLS) to predict patchouli alcohol content in patchouli oil. The results showed that PLS be able to predict patchouli alcohol levels by producing a model that is classified as good model performance. The best improved performance in the pretreatment PLS performance was by using standard normal variate as pretreatment with RPD value about 2,83 correlation coefficient (r) 0.93, the coefficient of determination (R2) at 0.86, and (RMSEC) about 4.11