Judul: Penentuan daya jerap dan pencirian parafin dalam perlakuan penjerapan minyak jelantah
Abstrak: Minyak goreng merupakan salah satu kebutuhan pokok rumah tangga di Indonesia, yang komponen utamanya berupa trigliserida dan mengandung asam lemak tidak jenuh. banyak Cara penyiapan makanan dengan metode menggoreng adalah sangat populer di negara kita. Pada proses menggoreng minyak goreng berfungsi sebagai medium penghantar panas, penambah rasa gurih, penambah nilai gizi dan kalori makanan serta turut memperbaiki tekstur makanan (Winarno 1997). Pada umumnya metode menggoreng bahan pangan ada dua macam yaitu sistem gangsa (pan frying) dan sistem menggoreng biasa (deep frying). Dalam proses gangsa bahan yang digoreng tidak sampai terendam dalam minyak. Sedangkan proses deep frying, bahan pangan yang digoreng terendam dalam minyak (Ketaren 1986). Penyiapan makanan dengan metode deep frying akan memberikan minyak sisa (minyak jelantah) hasil penggorengan yang cukup banyak. Biasanya minyak ini tidak dibuang tetapi digunakan kembali untuk proses penggorengan berikutnya sebagai usaha untuk menghemat. Proses ini dilakukan sampai beberapa kali bahkan kadang-kadang sampai minyak habis. Akibatnya minyak mengalami pemanasan berulang-ulang.
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Judul: Kajian Proses Degumming dan Netralisasi Pada Pemurnian Minyak Goreng Bekas
Abstrak: Minyak goreng merupakan medium penghantar panas dalam pengolahan bahan pangan. Secara industri. bahan pangan digoreng dengan menggunakan sistem deep frying sehingga minyak dibutuhkan dalarn jumlah besar. Hal ini menyebabkan biaya produksi menjadi tinggi. Untuk menekan biaya produksi tersebut, minyak digunakan secara berulang-ulang sehingga minyak menjadi rusak. Minyak yang rusak apabila dikonsumsi dapat membahayakan kesehatan.
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Judul: Quantitative Trait Loci Mapping for Trait in Categorical Scale
Abstrak: Genes or regions on chromosome underlying a quantitative trait are called Quantitative Trait Loci (QTL). Characterizing genes controlling quantitative trait on their position in chromosome and their effect on trait is through a process called QTL mapping. In estimating the QTL position and its effect, QTL mapping basically utilize the association between QTL and DNA markers. However, many important traits are obtained in categorical scale, such as resistance from certain disease. From a theoretical point of view, QTL mapping method assuming continuous trait could not be applied to categorical trait. This research was focusing on the assessment of the performance of Maximum Likelihood (ML) and Regression (REG) approach employed in QTL mapping as well as the performance of Lander and Botstein (LB) and Piepho method in determining critical value in testing the existence of QTL for binary and ordinal trait by means of simulation study. The simulation study to evaluate the performance of ML and REG approach was conducted by taking into account several factors that may affecting the performance of both approaches. The factors are: (1) marker density; (2) QTL effect; (3) sample size; (4) shape of phenotypic distribution; (5) number of categories; and (6) number of QTL. Moreover, the simulation study for evaluating LB and Piepho method in determining critical value was conducted by generating distribution of the test statistic under null hypothesis. From simulation study, it was obtained that LB and Piepho method showing similar performance in determining critical value in testing the existence of QTL for binary and ordinal trait. The simulation study also indicating that both methods could be used in determining critical value in QTL mapping analysis for binary trait as well as for ordinal trait if the REG approach is used but not if ML approach is used due to their poor performance. In assessing the performance of ML and REG approach in QTL mapping analysis for binary trait, the two approaches showing comparable performance; whereas for ordinal trait REG approach showing poor performance compared with ML approach in estimating thresholds. As a result, in QTL mapping analysis, ML and REG approach could be used when dealing with binary trait, whereas ML approach is suggested when dealing with ordinal trait.
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