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1 – 10 of over 4000Babatunde Sunday Ogunsina, Motunrayo Funke Olumakaiye, Chiemela Eyinnaya Chinma and Opeyemi Paul Akomolafe
This paper aims to investigate the effect of hydrothermal treatment by conventional, microwave and pressure cooking on the cooking properties, proximate composition and…
Abstract
Purpose
This paper aims to investigate the effect of hydrothermal treatment by conventional, microwave and pressure cooking on the cooking properties, proximate composition and organoleptic characteristics of dehulled Moringa oleifera seeds.
Design/methodology/approach
Samples of Moringa oleifera seeds were subjected to cooking for the minimum time by each of the methods under study. Cooking properties, proximate composition and organoleptic characteristics were determined following standard analytical procedures.
Findings
The results showed that the average cooking time were 25, 30 and 40 mins for conventionally, pressure- and microwave-cooked moringa kernels, respectively. There was no significant difference in cooking weight, moisture absorbed and water uptake ratio of conventionally and pressure-cooked samples. The protein content of moringa kernel reduced from 41.9 for raw kernels to 40.2, 41.2 and 36.9 per cent for conventionally, pressure- and microwave-cooked samples, respectively. Pressure and microwave cooking indicated 40.1 and 39.3 g/100g of crude fat, whereas raw kernels indicated 37.1 g/100g. Conventionally and pressure-cooked kernels had lower fibre content than the raw kernels, but there was no significant difference in the ash contents of the samples. Cooking influenced the proximate composition and colour of moringa kernels. Microwave cooking indicated higher values of cookability than other cooking methods considered in this study, but no significant difference was observed in the organoleptic characteristics of moringa kernels due to the cooking methods.
Practical implications
Given the high protein and vital nutrients content which are seldom found in daily diets, moringa kernels may be considered by processors of edible nuts and kernels for food-based applications such as cooked, roasted, mixed or spiced kernels.
Originality/value
This work is perhaps the first to document moringa seeds processing by hydrothermal treatment.
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Ayodeji Bolaji Ilori, Akinloye Lawal and Olayemi Oladehinde Simeon-Oke
This study aims to examine the innovations available in the small-scale palm kernel processing firms in southwestern Nigeria. The sample population of 265 respondents was obtained…
Abstract
Purpose
This study aims to examine the innovations available in the small-scale palm kernel processing firms in southwestern Nigeria. The sample population of 265 respondents was obtained through respondent-driven sampling tools. The research tools used were questionnaire, personal observations, interviews and secondary data collection approach. The questionnaire was administered to palm kernel processors and elicited information on innovations available in the firms. Both descriptive and inferential statistical techniques were used for data analysis.
Design/methodology/approach
The study area consisted of Oyo, Ogun, Osun and Ondo states in the southwestern Nigeria, because of the abundant supply of palm kernel as well as the presence of small and medium palm kernel oil (PKO) processing firms. The study population consists of all small palm kernel processing enterprises in these states. A total of 265 firms were purposively selected for the study. The sampling procedure involved the initial purposive selection of a palm kernel processing firm in a location, from where other firms within the locality were then identified. The primary data were collected through the use of questionnaire, interview and personal observation.
Findings
The results of the study showed that only process, organisational and market innovations were recorded by the palm kernel processing firms. Apart from the sieving operation where majority of the firms (91.30 per cent) used manual method, other unit operations were done mechanically. There was evidence of one or two innovation(s) available in the unit operations of these firms. Also, improvements were carried out occasionally in the process and marketing operations; the major sources of ideas for innovation were from related firms and customers. The organisations where majority of these firms had linkage with were Nigeria Palm Kernel Processing Association, government agencies and customer/suppliers.
Research limitations/implications
The findings are limited to the south-western part of Nigeria, there is need to extend the study to other states in the southern part where palm produce is the major cash crop. This will assist in making better generalisation on the innovation and innovation capability of the processors in Nigeria.
Practical implications
The study showed that the palm kernel processing firms experienced low innovation capability which could be due to their weak interactions with the knowledge institutions. Hence, there is need for these palm kernel processing firms to establish strong linkage with the knowledge institutions where their innovation capability can be enhanced.
Social implications
The findings in this paper can serve as an input to the design of policies that can enhance the innovation capability of the various actors in the value chain. This will assist in preventing wastages, increasing the quantity and quality of products and creating job opportunities. This is because the quality of PKO depends on the processing method; hence, better process innovation will improve the characteristics of the oil and widen its application.
Originality/value
Much has been written about palm kernel processing in medium and large enterprises, but information is still scanty on the small-scale processing enterprises. This paper contributed to knowledge by examining the innovations existing in the palm kernel processing enterprises in the south-western part of Nigeria and the innovation capability possessed by these enterprises.
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In the paper the problem of designing a pattern recognition system for processing incomplete pattern vectors is considered. An efficient method of integrating the small core…
Abstract
In the paper the problem of designing a pattern recognition system for processing incomplete pattern vectors is considered. An efficient method of integrating the small core probability density function (p.d.f.) estimator employing Gaussian kernels with general parameter matrices has been proposed. As a result these general kernels satisfy the basic requirements of integrability and, therefore, they can be used in p.d.f. estimators for classification systems processing incomplete pattern vectors. In comparison with the Gaussian kernel having a diagonal parameter matrix, the general kernel is better suited for reconstructing multivariate p.d.f. of pattern vectors with correlated components. Also determination of the optimal parameters of the general kernel is much simpler for the use of the computationally demanding maximum likelihood parameter estimation method can be obviated.
Syntax-based text classification (TC) mechanisms have been overtly replaced by semantic-based systems in recent years. Semantic-based TC systems are particularly useful in those…
Abstract
Purpose
Syntax-based text classification (TC) mechanisms have been overtly replaced by semantic-based systems in recent years. Semantic-based TC systems are particularly useful in those scenarios where similarity among documents is computed considering semantic relationships among their terms. Kernel functions have received major attention because of the unprecedented popularity of SVMs in the field of TC. Most of the kernel functions exploit syntactic structures of the text, but quite a few also use a priori semantic information for knowledge extraction. The purpose of this paper is to investigate semantic kernel functions in the context of TC.
Design/methodology/approach
This work presents performance and accuracy analysis of seven semantic kernel functions (Semantic Smoothing Kernel, Latent Semantic Kernel, Semantic WordNet-based Kernel, Semantic Smoothing Kernel having Implicit Superconcept Expansions, Compactness-based Disambiguation Kernel Function, Omiotis-based S-VSM semantic kernel function and Top-k S-VSM semantic kernel) being implemented with SVM as kernel method. All seven semantic kernels are implemented in SVM-Light tool.
Findings
Performance and accuracy parameters of seven semantic kernel functions have been evaluated and compared. The experimental results show that Top-k S-VSM semantic kernel has the highest performance and accuracy among all the evaluated kernel functions which make it a preferred building block for kernel methods for TC and retrieval.
Research limitations/implications
A combination of semantic kernel function with syntactic kernel function needs to be investigated as there is a scope of further improvement in terms of accuracy and performance in all the seven semantic kernel functions.
Practical implications
This research provides an insight into TC using a priori semantic knowledge. Three commonly used data sets are being exploited. It will be quite interesting to explore these kernel functions on live web data which may test their actual utility in real business scenarios.
Originality/value
Comparison of performance and accuracy parameters is the novel point of this research paper. To the best of the authors’ knowledge, this type of comparison has not been done previously.
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Mathias Vermeulen, Tom Claessens, Benjamin Van Der Smissen, Cedric S. Van Holsbeke, Jan W. De Backer, Peter Van Ransbeeck and Pascal Verdonck
The purpose of this paper is to use rapid prototyping technology, in this case fused deposition modeling (FDM), to manufacture 2D and 3D particle image velocimetry (PIV…
Abstract
Purpose
The purpose of this paper is to use rapid prototyping technology, in this case fused deposition modeling (FDM), to manufacture 2D and 3D particle image velocimetry (PIV) compatible patient‐specific airway models.
Design/methodology/approach
This research has been performed through a case study where patient‐specific airway geometry was used to manufacture a PIV compatible model. The sacrificial kernel of the airways was printed in waterworks™ which is a support material used by Stratasys Maxum FDM devices. Transparent silicone with known refractive index was vacuum casted around the kernel and after curing out, the kernel was removed by washing out in sodium hydroxide.
Findings
The resulting PIV model was tested in an experimental PIV setup to check the PIV compatibility. The results showed that the model performs quite well when the refractive index (RI) of the silicone and the fluid are matched.
Research limitations/implications
Drawbacks such as the surface roughness, due to the size of the printing layers, and the yellowing of the silicone, due to the wash out of the kernel, need to be overcome.
Originality/value
The paper presents the manufacturing process for making complex thick walled patient‐specific PIV models starting from a strong workable sacrificial kernel. This removable kernel is obtained by switching the building and the support materials of the FDM machine. In this way, the kernel was printed in support material while the building material was used to support the kernel during printing. The model was tested in a PIV setup and the results show that the airway model is suitable for performing particle image velocimetry.
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Nianyun Liu, Jingsong Li, Quan Liu, Hang Su and Wei Wu
Higher order statistics (HOS)-based blind source separation (BSS) technique has been applied to separate data to obtain a better performance than second order statistics-based…
Abstract
Purpose
Higher order statistics (HOS)-based blind source separation (BSS) technique has been applied to separate data to obtain a better performance than second order statistics-based method. The cost function constructed from the HOS-based separation criterion is a complicated nonlinear function that is difficult to optimize. The purpose of this paper is to effectively solve this nonlinear optimization problem to obtain an estimation of the source signals with a higher accuracy than classic BSS methods.
Design/methodology/approach
In this paper, a new technique based on HOS in kernel space is proposed. The proposed approach first maps the mixture data into a high-dimensional kernel space through a nonlinear mapping and then constructs a cost function based on a higher order separation criterion in the kernel space. The cost function is constructed by using the kernel function which is defined as inner products between the images of all pairs of data in the kernel space. The estimations of the source signals is obtained through the minimizing the cost function.
Findings
The results of a number of experiments on generic synthetic and real data show that HOS separation criterion in kernel space exhibits good performance for different kinds of distributions. The proposed method provided higher signal-to-interference ratio and less sensitive to the source distribution compared to FastICA and JADE algorithms.
Originality/value
The proposed method combines the advantage of kernel method and the HOS properties to achieve a better performance than using a single one. It does not require to compute the coordinates of the data in the kernel space explicitly, but computes the kernel function which is simple to optimize. The use of nonlinear function space allows the algorithm more accurate and more robust to different kinds of distributions.
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Mohd Aaqib Sheikh, Charanjiv Singh Saini and Harish Kumar Sharma
The study was aimed to explore the potential impact of microwave heating (450 W for 2, 4, 6 and 8 min) on antioxidant activity, anti-nutritional factors, volatile and phenolic…
Abstract
Purpose
The study was aimed to explore the potential impact of microwave heating (450 W for 2, 4, 6 and 8 min) on antioxidant activity, anti-nutritional factors, volatile and phenolic compounds of the plum kernels.
Design/methodology/approach
Plum kernels are rich in proteins, lipids and bioactive compounds that are mostly underused and undervalued.
Findings
The results showed that microwave heating at 450 W for 6 min significantly (p < 0.05) increased the antioxidant activity, total phenolic and flavonoid content, while the longer treatment time (450 W for 8 min) adversely affected the phenolic compounds. Most importantly, the anti-nutritional factors like amygdalin, hydrocyanic acid, phytic acid and tannin content were reduced up to 87.1, 84.7, 20.9 and 46.2%, respectively at 450 W for 6 min treatment conditions, which was confirmed from the larger shifts observed in FT-IR spectra near 1,157 cm−1. Microwave heating at 450 W for 6 min also proved beneficial in improving the bioavailability of volatile and phenolic compounds including chlorogenic acid, gallic acid, syringic acid, (+)-catechin, caffeic acid, ß-carotene, trans-ferulic acid, rutin trihydrate, 3,4-dihydroxybenzoic acid, tannic acid and quercetin by liberating them from the plant matrix.
Originality/value
The results thus indicate that controlled microwave heating could be an effective approach for the reduction of anti-nutritional factors besides leading to an overall improvement in antioxidant potential and volatile and phenolic compounds. This novel technological approach can proliferate the use of plum kernels in different diversified food formulations.
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Pingping Xiong, Zhiqing He, Shiting Chen and Mao Peng
In recent years, domestic smog has become increasingly frequent and the adverse effects of smog have increasingly become the focus of public attention. It is a way to analyze such…
Abstract
Purpose
In recent years, domestic smog has become increasingly frequent and the adverse effects of smog have increasingly become the focus of public attention. It is a way to analyze such problems and provide solutions by mathematical methods.
Design/methodology/approach
This paper establishes a new gray model (GM) (1,N) prediction model based on the new kernel and degree of grayness sequences under the case that the interval gray number distribution information is known. First, the new kernel and degree of grayness sequences of the interval gray number sequence are calculated using the reconstruction definition of the kernel and degree of grayness. Then, the GM(1,N) model is formed based on the above new sequences to simulate and predict the kernel and degree of the grayness of the interval gray number sequence. Finally, the upper and lower bounds of the interval gray number are deduced based on the calculation formulas of the kernel and degree of grayness.
Findings
To verify further the practical significance of the model proposed in this paper, the authors apply the model to the simulation and prediction of smog. Compared with the traditional GM(1,N) model, the new GM(1,N) prediction model established in this paper has better prediction effect and accuracy.
Originality/value
This paper improves the traditional GM(1,N) prediction model and establishes a new GM(1,N) prediction model in the case of the known distribution information of the interval gray number of the smog pollutants concentrations data.
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Mehdi Habibi and Ahmad Reza Danesh
The purpose of this study is to propose a pulse width based, in-pixel, arbitrary size kernel convolution processor. When image sensors are used in machine vision tasks, large…
Abstract
Purpose
The purpose of this study is to propose a pulse width based, in-pixel, arbitrary size kernel convolution processor. When image sensors are used in machine vision tasks, large amount of data need to be transferred to the output and fed to a processor. Basic and low-level image processing functions such as kernel convolution is used extensively in the early stages of most machine vision tasks. These low-level functions are usually computationally extensive and if the computation is performed inside every pixel, the burden on the external processor will be greatly reduced.
Design/methodology/approach
In the proposed architecture, digital pulse width processing is used to perform kernel convolution on the image sensor data. With this approach, while the photocurrent fluctuations are expressed with changes in the pulse width of an output signal, the small processor incorporated in each pixel receives the output signal of the corresponding pixel and its neighbors and produces a binary coded output result for that specific pixel. The process is commenced in parallel among all pixels of the image sensor.
Findings
It is shown that using the proposed architecture, not only kernel convolution can be performed in the digital domain inside smart image sensors but also arbitrary kernel coefficients are obtainable simply by adjusting the sampling frequency at different phases of the processing.
Originality/value
Although in-pixel digital kernel convolution has been previously reported however with the presented approach no in-pixel analog to binary coded digital converter is required. Furthermore, arbitrary kernel coefficients and scaling can be deployed in the processing. The given architecture is a suitable choice for smart image sensors which are to be used in high-speed machine vision tasks.
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Saheed Adewale Omoniyi, Michael Ayodele Idowu, Abiodun Aderoju Adeola and Adekunle Ayodeji Folorunso
This paper aims to review the chemical composition and industrial benefits of oil extracted from dikanut kernels.
Abstract
Purpose
This paper aims to review the chemical composition and industrial benefits of oil extracted from dikanut kernels.
Design/methodology/approach
Several literatures on chemical composition of dikanut kernels, methods of oil extraction from dikanut kernels and chemical composition of oil extracted from dikanut kernels were critically reviewed.
Findings
The review showed that proximate composition of dikanut kernels ranged from 2.10 to 11.90 per cent, 7.70 to 9.24 per cent, 51.32 to 70.80 per cent, 0.86 to 10.23 per cent, 2.26 to 6.80 per cent and 10.72 to 26.02 per cent for moisture, crude protein, crude fat, crude fibre, ash and carbohydrate contents, respectively. The methods of oil extraction from dikanut kernels include soxhlet extraction method, novel extraction method, enzymatic extraction method and pressing method. The quality attributes of dikanut kernel oil ranged from 1.59 to 4.70 g/100g, 0.50 to 2.67 meq/Kg, 4.30 to 13.40 g/100g, 187.90 to 256.50 mg KOH/g and 3.18 to 12.94 mg KOH/g for free fatty acid, peroxide value, iodine value, saponification value and acid value, respectively. Also, the percentage compositions of oleic, myristic, stearic, linolenic, palmitic, lauric, saturated fatty acids, monosaturated fatty acids and polyunsaturated fatty acids ranging from 0.00 to 6.90, 20.50 to 61.68, 0.80 to 11.40, 0.27 to 6.40, 5.06 to 10.30, 27.63 to 40.70, 97.45 to 98.73, 1.82 to 2.12 and 0.27 to 0.49 respectively. The results showed that dikanut kernels has appreciable amount of protein, carbohydrate and high level of fat content while oil extracted from dikanut kernels have high saponification value, high myristic acid and high lauric acid.
Research limitations/implications
There are scanty information/published works on industrial products made from oil extracted from dikanut kernels.
Practical implications
The review helps in identifying different methods of extraction of oil from dikanut kernels apart from popular soxhlet extraction method (uses of organic solvent). Also, it helps to identify the domestic and industrial benefits of oil extracted from dikanut kernels.
Originality/value
The review showed that oil extracted from dikanut kernels could be useful as food additive, flavour ingredient, coating fresh citrus fruits and in the manufacture of margarine, oil creams, cooking oil, defoaming agent, cosmetics and pharmaceutical products.
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