Electronic Theses and Dissertation

Ekoenzim merupakan hasil fermentasi limbah organik yang berpotensi dimanfaatkan sebagai pupuk organik cair karena mengandung unsur hara makro, salah satunya kalium (k). penentuan kandungan kalium umumnya dilakukan melalui analisis laboratorium yang memerlukan waktu, biaya, serta penggunaan bahan kimia. penelitian ini bertujuan memprediksi kandungan kalium pada ekoenzim menggunakan teknologi fourier transform near-infrared spectroscopy (ft-nirs) serta menentukan metode kalibrasi dan pretreatment spektrum terbaik. sebanyak 35 sampel ekoenzim dibuat dari berbagai jenis limbah buah dan sayuran yang difermentasi menggunakan perbandingan limbah organik, molase, dan air sebesar 3:1:10 selama 90 hari. kandungan kalium dianalisis menggunakan atomic absorption spectrophotometry (aas) sebagai data referensi, sedangkan data spektrum diperoleh menggunakan instrumen bruker mpa ft-nirs. spektrum dikoreksi menggunakan pretreatment raw, derivative 1 (d1), de-trending (dt), standard normal variate (snv), dan mean normalization (mn), kemudian dimodelkan menggunakan principal component regression (pcr) dan partial least squares (pls). hasil penelitian menunjukkan bahwa model terbaik diperoleh menggunakan pls dengan pretreatment derivative 1 yang menghasilkan nilai koefisien korelasi (r) sebesar 0,9877, koefisien determinasi (r²) sebesar 0,9756, rmsec sebesar 0,0111, dan rpd sebesar 6,5045. nilai tersebut menunjukkan bahwa ft-nirs mampu memprediksi kandungan kalium ekoenzim dengan tingkat akurasi yang sangat baik. teknologi ft-nirs berpotensi menjadi metode analisis yang cepat, non-destruktif, dan ramah lingkungan untuk evaluasi kualitas ekoenzim.

Eco-enzyme is a fermentation product of organic waste with potential application as a liquid organic fertilizer due to its macronutrient content, particularly potassium (K). Conventional potassium determination generally relies on laboratory analysis, which is time-consuming, costly, and requires chemical reagents. This study aimed to predict the potassium content of eco-enzyme using Fourier Transform Near-Infrared Spectroscopy (FT-NIRS) and to determine the best spectral pretreatment and calibration model. Thirty-five eco-enzyme samples were produced from various fruit and vegetable wastes fermented with molasses and water at a ratio of 3:1:10 for 90 days. Potassium content was determined using Atomic Absorption Spectrophotometry (AAS) as reference data, while spectral data were collected using a Bruker MPA FT-NIRS instrument. Spectral pretreatments included Raw, First Derivative (D1), De-trending (DT), Standard Normal Variate (SNV), and Mean Normalization (MN). Calibration models were developed using Principal Component Regression (PCR) and Partial Least Squares (PLS). The best prediction performance was obtained using the PLS model with First Derivative pretreatment, producing a correlation coefficient (r) of 0.9877, coefficient of determination (R²) of 0.9756, RMSEC of 0.0111, and RPD of 6.5045. These results indicate that FT-NIRS provides highly accurate prediction of potassium content in eco-enzyme. Therefore, FT-NIRS has strong potential as a rapid, non-destructive, and environmentally friendly analytical method for eco-enzyme quality evaluation.