Judul: Kajian efisiensi energi proses penyulingan dan sifat fisik hasil penyulingan minyak serai dapur menggunakan tungku sekam dan heating mantel
Abstrak: Telah dilakukan penyulingan minyak atsiri dari bahan serai dapur menggunakan dua pemanas yaitu tungku sekam dan heating mantel. Pada perbandingan bahan (serai dapur : air = 0,125 Kg : 0,5 liter) penyulingan dengan pemanas tungku sekam membutuhkan energi 6022,5 Kkal, lama penyulingan 3660 sekon, biaya Rp 912,5 dengan rata-rata rendemen minyak yang dihasilkan 0,0635 %. Penyulingan dengan jumlah bahan sama menggunakan heating mantel membutuhkan energi 832,143 Kkal, lama penyulingan 6990 sekon, biaya Rp 608,6 dan menghasilkan rendemen 0,114 %. Debit destilat dipengaruhi oleh kalor yang dihasilkan oleh tungku sekam dan heating mantel. Kualitas minyak serai dapur yang dihasilkan dengan pemanas tungku sekam lebih baik dibandingkan heating mantel dilihat dari segi warna minyak serai dan indeks bias minyak. Minyak serai yang dihasilkan dengan pemanas tungku sekam berwarna coklat muda dan nilai indeks biasnya berada pada rentang nilai 1,4830-1,4890.
Keyword: Water distillation, Cymbapogon spp., Gramiae, Lemon grass oil

Judul: Kajian Termal Tungku Sekam pada Penerapan Penyulingan Minyak Atsiri Daun Cengkeh
Abstrak: Minyak atsiri daun cengkeh dihasilkan dengan menggunakan pemanas tungku sekam. Warna minyak atsiri daun cengkeh yang dihasilkan berwarna kuning - coklat tua sesuai dengan SNI 06-2387- 2006 dan nilai indeks bias minyak daun cengkeh yang dihasilkan adalah 1,5325 sampai 1,5341 masih berada pada rentang nilai indeks bias yang sesuai SNI 06-2387-2006 yaitu 1,528 sampai 1,535. Rendemen minyak daun cengkeh yang paling besar 2,7692 % dan yang paling kecil 1,0769 %. Debit yang dihasilkan dari penyulingan paling besar yaitu dengan debit rata-rata 0,038 ml/s, dan yang paling kecil yaitu 0,028 ml/s. Energi yang digunakan tungku sekam selama proses penyulingan paling besar adalah 7920 kcal dan yang terkecil 6765 kcal.
Keyword: Husk, clove oils, bias index, Energy

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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