• Journal of Appropriate Technology
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• v.6 no.2
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• pp.88-93
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• 2020

Kaanaeli Agri Business intends to increase Vanilla production by establishing A DEMO Plot at Ngyeku Village and Conducting Seminars/workshops to Farmer's groups, mainly on Sustainable Vanilla husbandry and Fish farming practices with affordable and customized methods involving: • Proper land use demarcation at household levels • Soil fertility management • Bio-intensive agriculture practices (Organic farming) • Environmental conservation and • To address Market issues, to medium and smallholder farmers of Meru district, Arusha-Tanzania.

• Journal of Plant Biotechnology
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• v.8 no.1
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• pp.37-41
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• 2006

In vitro mass multiplication of Vanilla planifolia was investigated using node as explant. Multiple shoots were developed in MS medium supplemented with $2.0mgl^{-1}$ 6-benzylaminopurine and $1.0mgl^{-1}$ $\alpha$-naphthalene acetic acid. Multiple shoots were maintained for 6-T weeks with regular subculturing at the end of $3^{rd}$ week onto fresh medium. The maximum number of shoots at the rate of 12.8 per node segment was achieved over a period of four weeks. The elongated shoots were separated from the shoot clusters and were transferred onto half strength MS medium supplemented with indole-3-acetic acid ($1.0mgl^{-1}$) over a period of 28 days for induction of roots. The development of roots was observed on $7^{th}$ day of incubation. The in vitro raised plantlets were transferred to poly-cups, covered with polyethylene sheets and maintained under shade net for 25 days for hardening. Finally these plants were transferred to field and recorded that 85 % of tissue cultured plants were survived. From the present study, a simple and efficient micropropagation protocol was developed for Vanilla planifolia using single node segments as explants.

• Food Science of Animal Resources
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• v.35 no.5
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• pp.630-637
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• 2015

In the dairy industry, natural plant-based powders are widely used to develop flavor and functionality. However, most of these ingredients are water-insoluble; therefore, emulsification is essential. In this study, the efficacy of high pressure homogenization (HPH) on natural plant (chocolate or vanilla)-based model emulsions was investigated. The particle size, electrical conductivity, Brix, pH, and color were analyzed after HPH. HPH significantly decreased the particle size of chocolate-based emulsions as a function of elevated pressures (20-100 MPa). HPH decreased the mean particle size of chocolate-based emulsions from 29.01 μm to 5.12 μm, and that of vanilla-based emulsions from 4.18 μm to 2.44 μm. Electrical conductivity increased as a function of the elevated pressures after HPH, for both chocolate- and vanilla-based model emulsions. HPH at 100 MPa increased the electrical conductivity of chocolate-based model emulsions from 0.570 S/m to 0.680 S/m, and that of vanilla-based model emulsions from 0.573 S/m to 0.601 S/m. Increased electrical conductivity would be attributed to colloidal phase modification and dispersion of oil globules. Brix of both chocolate- and vanilla-based model emulsions gradually increased as a function of the HPH pressure. Thus, HPH increased the solubility of plant-based powders by decreasing the particle size. This study demonstrated the potential use of HPH for enhancing the emulsification process and stability of the natural plant powders for applications with dairy products.

• Culinary science and hospitality research
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• v.22 no.7
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• pp.36-46
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• 2016

The study examined the kinds of sweeteners(xylitol, sorbitol, acesulfame K, aspartame, stevioside) instead of sugar for vanilla sauce to satisfy customers' health needs. According to the results, the sauce with sugar had lowest salinity, highest sugar content, and sauce with aspartame had lowest sugar content. pH were highest in sauce with sorbitol, with aspartame were lowest. L-value, a-value was the highest in sauce with aspartame, while b-value was the lowest. The moisture content was the lowest, while viscosity was the highest in the sauce with xylitol. In a sensory evaluation, texture and overall preference was the highest in vanilla sauces with aspartame which have proper color intensity and strong flavor in mouth feel. The result indicated that health-oriented consumers and satisfy the health of modern people's needs when using the vanilla sauce with aspartame instead of sugar. It is also advised to vanilla sauce with aspartame for preventing adult disease and using the diets for patient.

• Communications of the Korean Mathematical Society
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• v.39 no.2
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• pp.535-546
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• 2024

We consider chooser options written on various underlying assets other than vanilla call and put options. Specifically, we deal with (i) the chooser option written on the power call and put options, and (ii) the chooser option written on the exchange options. We provide explicit formulas for the prices of these chooser options whose underlying assets are either power options or exchange options, rather than the vanilla call and put options.

• Journal of Radiation Industry
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• v.6 no.2
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• pp.111-117
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• 2012

This study was conducted to improve organoleptic quality of gamma-irradiated Tarakjuk at 10 kGy with cheeses (Camembert, Cheddar, Gouda, and Edam) and flavors (Nurungji, cream, and vanilla-cream). Overall acceptability of gamma-irradiated Tarakjuk added each camembert cheese and vanilla-cream flavor was the highest among the all samples. In the effect of sterilization method on the quality of Tarakjuk, the autoclaved samples added with Edam cheese, Gouda cheese, and vanilla-cream flavor, respectively, were showed higher score on the overall acceptability than the irradiated samples, meanwhile the irradiated samples were superior in the other samples. In case of added mixture of Edam cheese and vanilla-cream flavor, irradiated Tarakjuk with mixture was showed the most of high score on overall acceptability. In conclusion, addition of mixture of cheese and flavor to Tarakjuk may help to improve organoleptic quality of sterilized Tarakjuk by gammairradiation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• Analytical Science and Technology
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• v.24 no.6
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• pp.421-428
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• 2011

In this study, headspace disk type monolithic material sorptive extraction (HS-MMSE) was developed, validated and applied to the analysis of volatile aroma compounds from vanilla perfume by gas chromatography -mass spectrometry (GC/MS). HS-MMSE uses monolithic material (MonoTrap) based on silica bonded with octadecyl silane (ODS) and activated carbon as a sorbent. Aroma compounds was adsorbed onto the MonoTrap in headspace and extracted by only 100 ${\mu}L$ of solvent. Total 12 volatile compounds from vanilla perfume were successfully analyzed using HS-MMSE. The influence of extractive parameters was investigated and optimized, using benzyl acetate, linalyl acetate, vanillin, ethyl vanillin as target compounds. Under the optimum condition, the limit of detection (S/N = 3) and the limit of quantification (S/N = 10) of proposed method for the target compounds were obtained within the range of 8.35~13.76 ng and 27.82~45.88 ng, respectively. The method showed good linearity with correlation coefficient more than 0.9888, satisfactory recovery and reproducibility. These results showed that HS-MMSE using disk type MonoTrap is a new promising technique for the analysis of volatile aroma compounds from vanilla perfume.

• Journal of Life Science
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• v.31 no.11
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• pp.1046-1055
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• 2021

Vanillin is the primary flavor and fragrance compound of natural vanilla and is extensively used in the food, beverage, perfumery, pharmaceutical industries, and other applications. Vanillin can be produced by chemical synthesis, extraction from vanilla plants, microbial bioconversion of natural precursors to vanillin, and direct fermentation using glucose. Currently, most commercially available vanillin is produced by extraction from cured vanilla pods and by chemical synthesis using guaiacol and glyoxylic acid as starting raw materials. Due to environmental issues, health complaints, preference for natural sources, and the limited supply and soaring price of natural vanilla, biotechnology-based vanillin production is regarded as a promising alternative. As many microorganisms that are able to metabolize several natural precursors, including ferulic acid, eugenol, isoeugenol, and lignin, and accumulate vanillin, have been screened and evaluated, myriad strategies and efforts have been employed for the development of commercially viable production technology. This review outlines the recent advances in the biotechnological production of natural vanillin with the use of these natural precursors. Moreover, it highlights the recent engineering approaches for the production of natural vanillin from renewable carbon sources based on the de novo biosynthetic pathway of vanillin from glucose, together with appropriate solution strategies to overcome the challenges posed to increase production titers.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.