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Register a new userطرائق استخلاص وتحليل الدهون في الأغذية
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الهيئة العامة للبحوث الزراعية محاضرة
Publication date
2020
fields
Agrarian Economy
and research's language is
العربية
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طرائق استخلاص وتحليل الدهون في الأغذية تعريف الليبيدات أهمية الليبيدات مكونات الليبيدات في الأغذية الليبيدات وصحة الانسان تحليل الليبيدات استخلاص الليبيدات بالمذيبات الحلمهة الحمضية استخلاص الليبيدات بالمذيبات طريقة سوكسلت لتقدير الدهون في الشيبس استخلاص الزيوت العطرية بطريقة سوكسليت اعداد المهندسة عبير أبو شعر
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This paper describes a study of the influence of extraction system
(centrifugation and pressure system) on the chemical composition and sensory
quality of Dan virgin olive oils produced in Syria. Analysis of the effect of the
extraction system on
the values of analytical determination revealed
statistically significant differences (P≤0.05) in a few parameters only, mainly in
antioxidant content and oxidative stability. The results appear to confirm the
influence of the extraction system on the quality of Dan virgin olive oils.
Domain divergence plays a significant role in estimating the performance of a model in new domains. While there is a significant literature on divergence measures, researchers find it hard to choose an appropriate divergence for a given NLP applicati
on. We address this shortcoming by both surveying the literature and through an empirical study. We develop a taxonomy of divergence measures consisting of three classes --- Information-theoretic, Geometric, and Higher-order measures and identify the relationships between them. Further, to understand the common use-cases of these measures, we recognise three novel applications -- 1) Data Selection, 2) Learning Representation, and 3) Decisions in the Wild -- and use it to organise our literature. From this, we identify that Information-theoretic measures are prevalent for 1) and 3), and Higher-order measures are more common for 2). To further help researchers choose appropriate measures to predict drop in performance -- an important aspect of Decisions in the Wild, we perform correlation analysis spanning 130 domain adaptation scenarios, 3 varied NLP tasks and 12 divergence measures identified from our survey. To calculate these divergences, we consider the current contextual word representations (CWR) and contrast with the older distributed representations. We find that traditional measures over word distributions still serve as strong baselines, while higher-order measures with CWR are effective.
This study was carried out to investigate of various
stabilizers on the physicochemical properties of
low fat yogurt and to determine.
This study was conducted in order to prolong the period of validity of
yoghurt by adding probiotics consisted of Lactobacillus acidophilus LA-5 and
Bifidobacterium animalis subsp lactis (Bb12) to milk allocated to yoghurt
industry and studied the
microbial, chemical and sensory characteristics of
yoghurt during storage periods for 0, 7،,14, 21, 28, 35 and 42 days at 4 ± 1 °C
and 10 ± 1 °C. The results of microbial characteristics showed that the vital
force of the first starter lasted for 7 days in the control samples while lasted for
35 days for the sample containing Lactobacillus acidophilus LA-5 or
Bifidobacterium animalis subsp lactis (Bb12) when stored at 4 ± 1°C. Adding
probiotics did not affect the taste, smell and pH of the yoghurt without change
in the property values during storage periods. It was also found that the shelf –
life of control samples can be prolonged and consumed safely up to 7 days of
storage while it was prolonged for 35 days in yoghurt samples containing
probiotics.
Undirected neural sequence models have achieved performance competitive with the state-of-the-art directed sequence models that generate monotonically from left to right in machine translation tasks. In this work, we train a policy that learns the ge
neration order for a pre-trained, undirected translation model via reinforcement learning. We show that the translations decoded by our learned orders achieve higher BLEU scores than the outputs decoded from left to right or decoded by the learned order from Mansimov et al. (2019) on the WMT'14 German-English translation task. On examples with a maximum source and target length of 30 from De-En and WMT'16 English-Romanian tasks, our learned order outperforms all heuristic generation orders on three out of four language pairs. We next carefully analyze the learned order patterns via qualitative and quantitative analysis. We show that our policy generally follows an outer-to-inner order, predicting the left-most and right-most positions first, and then moving toward the middle while skipping less important words at the beginning. Furthermore, the policy usually predicts positions for a single syntactic constituent structure in consecutive steps. We believe our findings could provide more insights on the mechanism of undirected generation models and encourage further research in this direction.
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