Prediksi Kadar Air Gabah Menggunakan Near Infrared Reflectance Spectroscopy dengan Metode Principal Component Regression (Pre-treatment Multiplicative Scatter Correction, Second Derivative dan De-trending) - Marvika Sari; Indera Sakti Nasution; Zulfahrizal Zulfahrizal (Fakultas Pertanian, Universitas Syiah Kuala, 2019)
Abstrak. Penelitian ini bertujuan untuk membangun model pendugaan kandungan kadar air pada gabah menggunakan Near Infrared Reflectance Spectroscopy (NIRS) dengan metode Principal Component Regression (PCR) sebagai metode regresi serta membandingkan antara pre-treatment Multiplicative Scatter Correction (MSC), Second Derivative (D2) dan De-trending sebagai metode koreksi. Penelitian ini dilakukan pada gabah kering simpan varietas Ciherang yang didapatkan di daerah Blang Bintang, Aceh Besar. Perlakuan yang diberikan pada sampel yaitu tanpa perendaman dan perendaman (10, 20 dan 30 menit). Pengujian kadar air di laboratorium menggunakan metode thermogravimetri dan akuisisi spektrum kadar air gabah menggunakan self developed FT-IR IPTEK T-1516. Pengolahan data menggunakan Unscramble software® X version 10.5. Hasil penelitian yang telah dilakukan yaitu spektrum kadar air gabah yang telah diberikan pre-treatment menunjukkan adanya perubahan yang baik dimana spektrum tampak lebih tipis dan noise pada spektrum berkurang. Panjang gelombang optimum dapat dilihat melalui grafik loading plot dimana kandungan kadar air dengan struktur senyawa kimia H-O-H dapat dideteksi pada panjang gelombang 1869 – 2015 nm dan 1411 – 1493 nm. Model prediksi terbaik didapatkan dengan penggabungan antara PCR dan metode koreksi de-trending dengan nilai RPD sebesar 2,508, koefisien korelasi (r) sebesar 0,912, koefisien determinasi (R2) sebesar 0,832 dan RMSEC sebesar 0,883. Prediction of Grain Moisture Content Using Near Infrared Reflectance Spectroscopy With Principal Component Regression Method (Pretreatment MSC, Second Derivative dan De-trending)Abstract. This study aims are to build a model for estimating water content in grain using Near Infrared Reflectance Spectroscopy (NIRS) with Principal Component Regression (PCR) as a regression method and comparing between pre-treatment Multiplicative Scatter Correction (MSC), Second Derivative (D2) and De-trending as a correction method. This research was carried out on Ciherang variety dry grain which was obtained in Blang Bintang, Aceh Besar. The treatment given to the sample is without soaking and soaking (10, 20 and 30 minutes). Testing the water content in the laboratory using thermogravimetric method and the acquisition of grain moisture content using the self-developed FT-IR IPTEK T-1516. Data processing using Unscramble software® X version 10.5. The results of the research that has been carried out show that spectrum of grain moisture content that has been given pre-treatment shows a good change in the spectrum which appears thinner and the noise in the spectrum is reduced. The optimum wavelength can be seen through the loading plot graph where the water content with the structure of the chemical compound H-O-H can be detected at a wavelength of 1869 - 2015 nm and 1411 - 1493 nm. The best prediction models in this study obtained by PCR and de-trending correction method with RPD value of 1.83, correlation coefficient (r) of 0.827, determination coefficient (R2) of 0.683 and RMSEC of 1.303.