Ekstrak Bawang Putih Bubuk Dengan Menggunakan Proses Spray Drying

Bawang putih banyak digunakan sebagai bumbu utama pada berbagai masakan karena aromanya yang khas. Aroma khas tersebut karena adanya komponen aktif (Allicin) pada bawang putih. Allicin juga berguna untuk daya anti kolesterol yang dapat mencegah penyakit jantung, tekanan darah tinggi dan lain sebagainya. Komponen Allicin bersifat volatil sehingga bila penanganannya salah maka dapat menyebabkan kerusakan. Untuk mengawetkan bawang putih yaitu dengan cara pengeringan. Salah satu proses yang dapat digunakan adalah spray drying karena proses ini membutuhkan waktu yang singkat. Proses spray drying adalah proses pengeringan dengan cara menyemprotkan fase cair menjadi butiran-butiran kecil kemudian mengontakkannya dengan udara panas sehingga menjadi bubuk. Umpan yang akan dikeringkan dapat berupa larutan ataupun suspensi dengan viskositas tertentu. Penelitian ini dilakukan percobaan pembuatan ekstrak bawang putih bubuk dengan variasi perbandingan massa bawang putih dengan pelarut air tertentu yang dimulai dari perbandingan 1:1, variasi konsentrasi maltodekstrin 0%, 10%, 20%, 30%, 40% dan 50%, serta variasi suhu udara masuk 160 oC, 170 oC, 180 oC dan 190 oC. Hal yang diamati adalah pengaruh konsentrasi maltodekstrin dan suhu udara masuk terhadap karakteristik ekstrak bawang putih bubuk yang dihasilkan. Karakteristik bubuk yang dianalisa meliputi kadar air, bulk density, wettability, solubility dan organoleptik. Dari hasil analisa diketahui bahwa dengan meningkatnya suhu udara inlet menyebabkan terjadinya penurunan kadar air. Begitu juga dengan meningkatnya suhu udara masuk menyebabkan terjadinya peningkatan bulk density, wettability dan solubility

Distribution of cofitness values for four experimentally characterized transcription factors in <i>E</i>. <i>coli</i>.

<p>Each panel shows the cofitness of the transcription factor with all other 3,788 genes in the <i>E</i>. <i>coli</i> genome with fitness data (x axis). The y axis is random.</p

Validating regulatory predictions from diverse bacteria with mutant fitness data

<div><p>Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium’s growth across many conditions, to test regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomics predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.</p></div

Distribution of cofitness and cofitness ranks in experimentally-identified TF-target pairs in <i>E</i>. <i>coli</i>.

<p>(A) The distribution of cofitness values for activating TF-target pairs, repressing TF-target pairs, and shuffled pairs. The distributions were smoothed using gaussian kernel density estimates. (B) The distribution of cofitness ranks for activating TF-target pairs, repressing TF-target pairs, and shuffled pairs. On the right side, ranks for anti-cofitness are shown with negative numbers, so that the most anti-cofit pair has <i>x = -1</i>. The proportion of pairs within each window of 10 cofitness ranks is plotted.</p

Examples of fitness patterns of experimentally-identified TF-target pairs in <i>E</i>. <i>coli</i>.

<p>(A) Heatmap showing the fitness values of the activator <i>dcuR</i> and its targets across 162 experiments. Gene fitness is a log₂ ratio that describes the change in abundance of strains with a transposon inside the gene over the course of pooled incubation [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178258#pone.0178258.ref015" target="_blank">15</a>]. Positive fitness suggests that the gene is detrimental to growth in the condition, while negative fitness suggests that the gene is important for growth. The labels above and below the heatmap describe the growth conditions. (B) Scatter plot of the fitness values of <i>dcuR</i> and <i>dctA</i>. Lines show <i>x = 0</i>, <i>y = 0</i>, and <i>x = y</i>. (C) Distribution of cofitness values for <i>dcuR</i> and <i>treR</i>. Each panel shows the cofitness of the transcription factor with 3,788 genes in the <i>E</i>. <i>coli</i> genome that have fitness data (x axis). The <i>y</i> axis is random. (D) Heatmap showing the fitness values of the <i>treR</i> repressor and its targets across 162 experiments.</p

Fitness pattern of <i>malR</i> in <i>Shewanella loihica</i>.

<p>(A) Heatmap showing fitness values of <i>malR</i> and its targets across 160 experiments. The labels above and below the heatmap are conditions. (B) Distribution of <i>malR</i>’s cofitness with all other 3,008 genes in the <i>Shewanella loihica</i> genome with fitness data (x axis). The y axis is random.</p

A Comparison of the Costs and Benefits of Bacterial Gene Expression

<div><p>To study how a bacterium allocates its resources, we compared the costs and benefits of most (86%) of the proteins in <i>Escherichia coli</i> K-12 during growth in minimal glucose medium. The cost or investment in each protein was estimated from ribosomal profiling data, and the benefit of each protein was measured by assaying a library of transposon mutants. We found that proteins that are important for fitness are usually highly expressed, and 95% of these proteins are expressed at above 13 parts per million (ppm). Conversely, proteins that do not measurably benefit the host (with a benefit of less than 5% per generation) tend to be weakly expressed, with a median expression of 13 ppm. In aggregate, genes with no detectable benefit account for 31% of protein production, or about 22% if we correct for genetic redundancy. Although some of the apparently unnecessary expression could have subtle benefits in minimal glucose medium, the majority of the burden is due to genes that are important in other conditions. We propose that at least 13% of the cell’s protein is “on standby” in case conditions change.</p></div

Summary of statistically validated RegPrecise predictions.

<p>If multiple ortholog groups with the same TF name were validated, the number of validated ortholog groups is noted. An ortholog group is a group of orthologous TFs from closely related organisms with a conserved motif.</p

Consistency of gene fitness values in minimal glucose medium.

<p>(A) Consistency between replicates at 12 generations. Fitness values less than −3 are shown at −3 so as to focus on the more subtle fitness defects that are more susceptible to noise. 123 genes have fitness under −3 in both replicates. (B) Consistency across time. Genes with significant phenotypes (of either sign) are subdivided into those with weak expression (under 2 ppm of monomers) or above. Fitness values less than −3 are shown at −3. (C) Consistency within each gene. Fitness values at 12 generations were computed separately for the first and second half of each gene that had sufficient coverage. (D) shows the same data as (C), but only for fitness values above −3. In all panels, lines show <i>x</i> = 0, <i>y</i> = 0, and <i>x</i> = <i>y</i>.</p

The 20 most highly-expressed genes, by fraction of amino acids, that have no measurable impact on growth.

<p>The 20 most highly-expressed genes, by fraction of amino acids, that have no measurable impact on growth.</p