Judul: Pengaruh tahap-tahap proses ekstraksi terhadap kandungan aflatoksin dalam pembuatan minyak kacang tanah kasar
Abstrak: Minyak kacang tanah adalah salah satu minyak nabati yang mempunyai banyak kegunaan. Dalam bidang pangan, minyak ini digunakan sebagai bahan baku pembuatan mayonais, "salad dressing", shortening atau sebagai minyak goreng. Untuk mengekstrak minyak dari kacang tanah dapat dilakukan tiga cara yaitu rendering, pengepresan dan ekstraksi dengan pelarut. Minyak kacang tanah seringkali dibuat dari kacang tanah yang tercemar aflatoksin. Aflatoksin merupakan toksin yang ditakuti karena dapat menimbulkan kanker hati dan ginjal. Tercatat beberapa galur Aspergillus flavus dan Aspergillus parasiticus mampu memproduksi toksin ini. Pada penelitian ini dilakukan pengukuran perubahan kandungan aflatoksin selama tahap-tahap proses tiga cara ekstraksi minyak kacang tanah kasar yaitu rendering basah, pengepresan hidraulik dan ekstraksi dengan pelarut. Bahan baku pembuatan minyak adalah kacang tanah varietas Gajah yang telah dikontaminasi Aspergillus flavus pada kadar air bahan 20% dan diinkubasi pada suhu 30°C selama 9 hari. Analisis aflatoksin sampel dibandingkan dengan standar aflatoksin B1, B2, G1 dan G2 yang dianalisis dengan HPLC, masing-masing mempunyai waktu retensi 5, 6.5, 9.5 dan 11 menit. Tahap-tahap proses ekstraksi minyak kacang tanah kasar dengan metode rendering basah dapat menurunkan kandungan aflatoksin B1, B2, G1 G2 dan total, sebesar 27.55%, 12.58%, 18.15%, 8.34% dan 22.84%. rata-rata Dengan menggunakan metode pengepresan hidraulik sampai 50.52%, 58.35%, 55.58%, 53.48% dan 52.85%, sedangkan dengan metode ekstraksi pelarut didapat rata-rata persentase penurunan sebesar 29.3%, 19.21%, 21.32%, 15.86% dan 24.07%.
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Judul: Populasi Aspergillus flavus dan Kandungan Aflatoksin B1 pada Biji Kacang Tanah Mentah dan Produk Olahannya di Kecamatan Bogor Tengah, Kotamadya Bogor
Abstrak: This study was a survey to get information on the population of A. flavus and aflatoxin B1 content of raw peanut kernels collected randomly from retailers in two traditional markets (Pasar Anyar and Pasar Bogor) and five processed peanut products collected from retailers in Kecamatan Bogor Tengah, Municipality of Bogor. Physical quality of peanut kernels was only determined in raw peanut kernel samples. A total of 155 samples were collected. They consisted of 26 samples of raw peanut kernels and 129 samples of processed peanut products. As much as 14 and 12 samples of raw peanut kernels were collected from Pasar Anyar and Pasar Bogor, respectively. Processed peanut product samples consisted of roasted peanuts with skin pod (33 samples), flour-coated peanuts (33), siomay sauce (18), pecel/gado-gado sauce (33), and sate sauce (12). The highest percentage of intact kernels (70.6%) and the lowest percentage of damaged kernels (17.1%) were found in raw peanut kernels collected from Pasar Anyar. The highest percentage of shriveled kernels (12.7%) was found in raw peanut kernels collected from Pasar Bogor. Population of A. flavus in raw peanut kernels was much higher than that of in processed peanut products. Population of A. flavus in raw peanut kernels, roasted peanuts with skin pod, flour-coated peanuts, siomay sauce, pecel/gado-gado sauce, and sate sauce were 4 865.8, 0.3, 0.1, 0.3, 13.2, and 0.4 cfu/g (w. b.), respectively. The aflatoxin B1 content of raw peanut kernels was 43.2 ppb. The highest aflatoxin B1 content of processed peanut products (43.2 ppb) was found in roasted peanuts with skin pod, followed by flour-coated peanuts (34.3), sate sauce (23.2), pecel/gado-gado sauce (17.1), and siomay sauce (4.4).
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Judul: The addition of a noun word graph dictionary module in bogordelftconstruct
Abstrak: Knowledge Graph is a new method in Natural Language Processing that is used to describe human language and displaying it into a graph form. BogorDelftConStruct is a tool to analyze Indonesian text, developed by Deni Romadoni (2009). The tool has limited feature, opening many opportunities to add some other features. The goal of this research is to develop a noun word graph dictionary module and add it in BogorDelftConStruct. The patterns of the noun word-graph are based the research of Saleh (2009). In this research, of the 20 patterns in Saleh (2009) only 13 patterns are used, since some of word graph patterns which have the same affix and also have the different meaning. As many as 144 nouns were used in scenario testing, with 1 error was generated (99,33% accuracy).
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