Open Access Short Research Article

The Presence of Mycotoxins in Kenya’s Kalenjin Traditional Fermented Milk “Mursik”

K. K. Talaam, Z. W. Nga’ng’a, K. B. Lang’at, M. C. Kirui, T. Gonoi, C. C. Bii

Journal of Scientific Research and Reports, Page 1-8
DOI: 10.9734/JSRR/2018/7500

The study aimed at carrying out quantification of mycotoxins contaminating Mursik. Mursik is traditionally fermented milk prepared from freshly milked cow milk. Fermentation does not take place in controlled systems or sterilized conditions; as a result contamination with yeasts, moulds and some pathogenic bacteria would normally occur. These include species from the genus, Aspergillus, Penicillium, Fusarium and Candida. Aspergillus, Penicillium and Fusarium has been known as the producers of mycotoxins, which are secondary metabolites of fungi responsible mycoticoses in animals and humans. These mycotoxins include: aflatoxins, fumonisins and deoxynivalenol. The study was laboratory based carried out on mursik samples collected from households. All mursik samples were processed at mycology laboratory, Center for Microbiology Research (CMR), KEMRI. The research protocol was reviewed and approved by KEMRI.The study was carried in Soliat Location, Kericho County, Kenya. It was conducted between February and August, 2013, period of seven months. 194 samples were collected from farmers and 4 samples commercially sold (packet fermented milk) for controls were bought from local shops. Mycotoxins extraction was done using Envirologix procedure and was subsequently quantified by using a QuickTox kit. 99.5% of the samples were contaminated with Aflatoxins. Fumonisin toxin on quantification, 3 (1.5%) of the samples had detectable quantities, and Deoxynivalenol toxins was detected on 1 (0.5%) sample only. Aflatoxin is the major contaminant of mursik. It is clear that mycotoxins will be of increasing importance for all those involved in milk and milk products production, and food production. There is need to adopt effective strategies for mycotoxin control and mycotoxin detoxification.

Open Access Short Research Article

Evaluation of Gamma Irradiation and Storage Period Effects on Polycyclic Aromatic Hydrocarbons Load in Fava Bean (Vicia Faba) Kernels

Ayman Khalil, Khaled Aljoumaa, Mahfouz Al-Bachir

Journal of Scientific Research and Reports, Page 1-8
DOI: 10.9734/JSRR/2018/44827

In this work, gamma irradiation at doses of 1, 5, 10 and 15 kGy and storage period effects on polycyclic aromatic hydrocarbons (PAHs) contents of fava bean kernels (FBK) were investigated. PAHs were extracted from FBK (crop year 2017/2018) immediately post-harvest (Mars to late May 2017), and after six months of storage (12/05/2017 to 10/12/2017) and the PAHs, the concentration was determined at each dose using GC-MS analysis. Results demonstrated that the PAHs load in irradiated FBK was dramatically decreased as the applied dose increased. Interestingly, the decrease in the PAHs load six months post storage was less important compared with the post-harvest decrease. Moreover, the decrease in PAHs in kernels was in different trends towards irradiation used doses. Results suggest that a dose of ~ 20 kGy or higher is mandatory for preeminent hygiene of FBK from PAHs load during storage.

Open Access Original Research Article

New Exact Traveling Wave Solutions to Burgers Equation

Md. Rashed Kabir, Bimal Kumar Datta, Harun-Or-Roshid .

Journal of Scientific Research and Reports, Page 1-9
DOI: 10.9734/jsrr/2018/v21i121806

In this letter, we seek new traveling wave solutions to Burgers equation via a new approach of improved -expansion method. We handle the calculations with the aid of computer software Maple-13. As a result, many periodic and soliton like solutions have been achieved in terms of the hyperbolic functions, trigonometric functions, exp-functions and rational function solutions. The method is very simple for solving nonlinear evolution equations (NLEEs).  Further, both two and three-dimensional plots of the obtained wave solutions are also given to imagine the dynamics of the equation.

Open Access Original Research Article

Occupational Health Problems Experienced by Women Workers in Building Construction Industry

Suma Hasalkar, Spoorti Kallur, Swati Hebbal

Journal of Scientific Research and Reports, Page 1-8
DOI: 10.9734/JSRR/2018/40965

Construction industry provides job opportunity to large number of skilled as well as unskilled work force. The work force employed in the construction industry have to face several difficulties at the workplace. Several issues related to health, job stress, injuries, occupational details and work site environment at work place are the major concern of the study.  Keeping this in view a study was conducted to know occupational health problems experienced by the women workers in building construction industry. The data regarding socio personal characteristics and perceived health problems of women workers were collected by using pre tested structured interview schedule from 120 rural women workers worked in local construction sites of Dharwad taluka. The data on Musculo-skeletal problems were collected by using Corlett and Bishop [1] body map. Results revealed that more than 30 per cent of respondents had less than 5 years of work experience (35.83%) in construction, In case of type of site ground, half of the respondents experienced that the ground was too muddy (54.20%). As concerned with work place 57.50 per cent of women reported that the eating place were unhygienic. An observation into the mean scores revealed that, among the upper extremities upper back/ cervical region pain had highest mean score of 4.73 depicting very severe pain in this region. Swelling of body parts were the hazard experienced by 19.17 per cent women, followed by skin infection (6.67%) and fall / fractures (7.50%). There was significant relationship between temperature and eye pain whereas, significant relationship between humidity and shoulder joint was found and both were significant at 0.05 level.

Open Access Original Research Article

Information Criteria’s Performance in Finite Mixture Models with Mixed Features

Jaime R. S. Fonseca

Journal of Scientific Research and Reports, Page 1-9
DOI: 10.9734/jsrr/2018/v21i121826

Aims: This study is intended to determine which information criterion is more appropriate for mixture model selection when considering data sets with both categorical and numerical clustering base variables (mixed case).

Study Design:  In order to select among eleven information criteria which may support the selection of the correct number of clusters we conduct a simulation study. The generation of mixtures of both multinomial and multivariate normal data supports the proposed analysis.

Place and Duration of Study: Simulation: Instituto Superior de Ciências Sociais e Políticas (ISCSP), Universidade de Lisboa, 2012.

Methodology: The experimental design controls the number of normal (two and four) and multinomial (two and four) variables, the number of clusters (two, four and six), the level of clusters separation (ill and well), and for sample size we use three levels (400, 1200, 2000).

Thus, data sets were simulated with the following factors: two levels for the number of normal variables; two levels for the number of multinomial variables; two levels of segment separation, and three levels of number of clusters. Thus, the simulation plan uses a 23×32 factorial design with 72 cells. So with five replications (data sets) per cell, we generate a total of 23×32 ´5 = 360 experimental data sets.

Results: The best overall performance goes to AIC3 (58%), followed by AICu (56%) and AICc (54%). About AIC3, AICu and AICc, these criteria evidence a good compromise between underfit and overfit: AIC3, AIC and AICu underfit 11, 7 and 14%, and they overfit on 21, 18 and 18%, respectively. The most underfiting criterion is NEC, with 48%, and the most overfiting one is AIC, with 42%.

Conclusion: We run Friedman test for all the criteria, to test the null hypothesis that all the eleven populations distributions functions are identical We reject the null hypothesis and we accept the alternative (Monte Carlo p-value=0.000). Thus, we conclude that criteria performance was not identical for the eleven criteria, and we make multiple comparisons.

We concluded that AIC3 and AICc have significantly different performances, but AIC3 and AICu have similar performances. Thus we may conclude that AIC3 and AICu are the best information criteria for selecting the true number of clusters when dealing with finite mixture models, mixed data and information criteria for model selection.