Prediksi Tanin dan Total Padatan Tidak Terlarut Buah Kesemek (Diospyros kaki L.) Menggunakan Spektroskopi NIR
Abstract
Abstract
Determination of tannin and non-soluble solid content of persimmon are usually carried out by a chemical method, these methods are destructive, time-consuming and can not be applied to the development of online grading. The objective of this study was to develop rapid prediction method of tannin and non-soluble solid content of persimmon non-destructively using NIR Spectroscopy. NIR spectra were measured by NIRFlex N-500 fiber optic solid with the wavelength of 1000-2500 nm. For the reference method, tannin and non-soluble solid content were measured using conventional method. Some pre-processing methods were applied, and the results were calibrated to chemical data using principal component regression (PCR) and partial least square (PLS). The best model for prediction of non-soluble solid content was multiplicative scatter correction (MSC) pre-processing and PLS with a correlation coefficient (r), standard error prediction (SEP) and the ratio of standard deviation to SEP (RPD) of 0.83, 1.48% and 1.59 respectively. The best model for predicting tannin was first derivative Savitzky-Golay (dg1) and PLS with r, SEP and RPD of 0.72, 0.14% and 1.06 respectively. PLS method was better than PCR in predicting non-soluble solid content and tannin of persimmon.
Abstrak
Penentuan tanin dan total padatan tidak terlarut buah kesemek biasa dilakukan dengan metode kimia, metode ini bersifat destruktif, memakan waktu dan tidak dapat diterapkan untuk pengembangan grading secara on-line. Tujuan penelitian ini adalah untuk memprediksi secara cepat tanin dan padatan tidak terlarut buah kesemek secara non destruktif menggunakan Spektroskopi NIR. Spektrum NIR diukur dengan NIRFlex N-500 fiber optic solid pada panjang gelombang 1000-2500 nm, Untuk metode referensi, kandungan tannin dan total padatan tidak terlarut diukur dengan menggunakan metode konvensional. Beberapa metode pra-pengolahan data NIR diterapkan, dan hasilnya dikalibrasi dengan data kimia menggunakan metode principal component regression (PCR) dan partial least square (PLS). Model terbaik untuk memprediksi non-soluble solid content adalah menggunakan pra-pengolahan multiplicative scatter correction (MSC) dan PLS dengan r, SEP dan RPD masing - masing 0.83, 1.48%, dan 1.59. Model terbaik untuk memprediksi tanin diperoleh dengan menggunakan turunan pertama Savitzky-Golay (dg1) dan metode PLS dengan r, SEP dan RPD masing - masing 0.72, 0.14% dan 1.06. Metode PLS menghasilkan model kalibrasi lebih baik daripada PCR dalam memprediksi tanin dan non-soluble solid content buah kesemek.
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