PENGGUNAAN UV-VIS SPECTROSCOPY DAN METODE SIMCA UNTUK IDENTIFIKASI MADU LEBAH HUTAN (Apis dorsata) BERDASARKAN SUMBER NEKTAR

RIZKI FIRMANSYAH, 1514071016 (2019) PENGGUNAAN UV-VIS SPECTROSCOPY DAN METODE SIMCA UNTUK IDENTIFIKASI MADU LEBAH HUTAN (Apis dorsata) BERDASARKAN SUMBER NEKTAR. FAKULTAS PERTANIAN , UNIVERSITAS LAMPUNG.

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Abstrak

Nilai jual madu hutan ditentukan oleh kemurnian pada madu, akan tetapi banyak terjadi proses pencampuran pada madu hutan murni, pencampuran pada madu bukan hanya penambahan bahan-bahan lain seperti air, sukrosa, fruktosa maupun zat warna, tetapi juga dapat berupa pencampuran dengan jenis madu lain. Penelitian identifikasi madu hutan Apis dorsata asal Indonesia menggunakan UV-Vis Spectroscopy dan metode SIMCA belum pernah dilakukan. Penelitian ini bertujuan untuk mengidentifikasi tiga jenis madu hutan Apis dorsata berdasarkan sumber nektar dengan menggunakan UV-Vis Spectroscopy dan metode soft independent modelling of class analogy (SIMCA). Jumlah sampel yang digunakan sebanyak 100 sampel madu uniflora durian (MUD), 100 sampel madu multiflora (MM), dan 100 sampel madu uniflora akasia (MUA). Sampel madu dipanaskan terlebih dahulu dengan menggunakan waterbatch pada suhu 60 ℃ selama 30 menit, kemudian 1 ml sampel madu diencerkan dengan aquades sejumlah 20 ml dan diaduk selama 10 menit menggunakan magnetic stirrer. Selanjutnya 2 ml sampel hasil pengenceran dimasukkan ke dalam kuvet dan diambil data spektranya sebanyak 2 kali pengulangan dengan menggunakan UV-Vis Spectrometer (UV-Vis Genesys 10s, Thermo Scientific, USA) pada panjang gelombang 190 – 1100 nm. Kemudian data spektra yang diperoleh dianalisis menggunakan metode PCA dan SIMCA menggunakan software The Unscrambler versi 9.2. Hasil klasifikasi menunjukkan bahwa PCA dan SIMCA mampu mengidentifikasi MUD, MM dan MUA. Hasil analisis PCA terbaik diperoleh melalui proses perbaikan spektra, dengan menggunakan metode perbaikan spektra kombinasi multiplicative scatter correction (MSC) dan moving average 9 segmen, pada panjang gelombang 190 – 1100 nm (panjang gelombang penuh). Pada pengembangan model spektra kombinasi MSC dan moving average 9 segmen menghasilkan nilai PC1 sebesar 86% dan PC2 sebesar 12%. Sedangkan untuk klasifikasi model SIMCA MUD dengan model SIMCA MM diperoleh nilai akurasi, nilai sensitivitas, dan nilai spesifisitas sebesar 100% dengan nilai false alarm rate 0%. Pada model SIMCA MUD dengan model SIMCA MUA diperoleh nilai akurasi, nilai sensitivitas, dan nilai spesifisitas sebesar 100% dengan nilai false alarm rate 0%. Pada klasifikasi model SIMCA MM dengan model SIMCA MUA diperoleh nilai akurasi, nilai sensitivitas, dan nilai spesifisitas sebesar 100% dengan nilai false alarm rate 0%. Berdasarkan analisis kurva ROC seluruh klasifikasi yang dibangun dapat dinyatakan sebagai excellent classification. Kata kunci : Madu, UV-Vis Spectrometer, Principal Component Analysis (PCA), Soft Independent Modelling of Class Analogy (SIMCA), Receiver Operating Characteristic (ROC). The value of forest honey is determined by the purity of honey, however there is a lot of mixing in pure honey, mixing in forest honey is not only the addition of other ingredients such as water, sucrose, fructose or dyes, but also in the form of mixing with other types of honey. Research on identification of Apis dorsata forest honey from Indonesia using UV-Vis Spectroscopy and SIMCA methods has never been done. The purpose of this research to identify three types of Apis dorsata forest honey based on nectar sources using UV-Vis Spectroscopy and SIMCA method. The number of samples used were 100 samples of durian uniflora honey (MUD), 100 samples of multiflora honey (MM), and 100 samples of acacia uniflora honey (MUA). The honey sample is preheated using a waterbatch at 60 ℃ for 30 minutes, then 1 ml of the honey sample is diluted with 20 ml of distilled water and stirred for 10 minutes using a magnetic stirrer. Furthermore, 2 ml of the sample that has been diluted, put in a cuvette and the spectral data taken 2 times with the use of UV-Vis Spectrometers (UV-Vis Genesys 10s, Thermo Scientific, USA) at a wavelength of 190-1100 nm with the amount. Then the spectra data obtained were analyzed using the PCA and SIMCA methods using The Unscrambler software version 9.2. The result of absorbance data is processed with the unscrambler version 9.2 software. The classification results show that PCA and SIMCA are able to identify MUD, MM and MUA. The best PCA analysis results are obtained through a spectral repair process, using a combination of multiplicative scatter correction (MSC) and 9 segment moving average spectra correction methods, at a wavelength of 190-1100 nm (full wavelength). In the development of MSC and 9 segment moving average spectra models, PC1 values of 86% and PC2 of 12%. As for the classification of the SIMCA MUD model with the SIMCA MM model, the accuracy value, sensitivity value, and specificity value are 100% with a false alarm rate of 0%. In the SIMCA MUD model with the SIMCA MUA model, values of accuracy, sensitivity and specificity values of 100% were obtained with a false alarm rate of 0%. In the classification of the SIMCA MM model with the SIMCA MUA model, values of accuracy, sensitivity and specificity values of 100% were obtained with a false alarm rate of 0%. Based on ROC curve analysis, all classifications built can be stated as excellent classification. Keywords : Honey, UV-Vis Spectrometer, Principal Component Analysis (PCA), Soft Independent Modelling of Class Analogy (SIMCA), Receiver Operating Characteristic (ROC).

Tipe Karya Ilmiah: Skripsi
Subyek: 600 Teknologi
600 Teknologi > 630 Pertanian
Program Studi: Fakultas Pertanian > Prodi Teknik Pertanian
Depositing User: 2018080725 . Digilib
Date Deposited: 18 Oct 2019 03:54
Last Modified: 18 Oct 2019 03:54
URI: http://digilib.unila.ac.id/id/eprint/59393

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