Quality assurance and halal control points for the food industry

Purpose To determine the understanding of halal concept among food production workers and to develop a generic Halal Control Point (HCP) Plan for the manufacturing of processed foods. Design/methodology/approach A mixed method (interviews, surveys and microbiological analyses) approach was used to analyze the hygiene and halal practices of four food processing plants in Penang, Malaysia. Two hundred food production workers were surveyed (and quality assurance staff were interviewed) to determine their understanding of halal concepts and attitude towards halal food products. Adenosine Triphosphate (ATP) swabbing tests were conducted to determine the hygiene of workers and food contact surfaces. End products were sampled and enumerated for total bacterial count. Findings The swabbing tests of food contact surfaces (i.e tabletops) showed that only Company C (oat) and Company D (coffee powder) passed the adenosine triphosphate (ATP) hygiene test (≤ 10 Reflective Light Unit [RLU]). The results obtained from all workers’ hands and aprons indicated a 100% failure rate (> 30 RLU). No ATP was detected on the packaging materials from all companies. The microbiological findings indicated that the end products are satisfactory and were below detection limits as verified by the enumeration done on the food samples. Besides, from the interview sessions conducted with the Quality Assurance (QA) staff, one generic Halal Control Points (HCPs) plan and four specific HCP plan tables were developed for the manufacturing process of halal food products for each company. Originality/value The HCP plans will be of value for food industry seeking to identify potential point sources of haram contamination and halal control points for their food production processes

Accounting for Skill in Trend, Variability, and Autocorrelation Facilitates Better Multi-Model Projections: Application to the AMOC and Temperature Time Series

We present a novel quasi-Bayesian method to weight multiple dynamical models by their skill at capturing both potentially non-linear trends and first-order autocorrelated variability of the underlying process, and to make weighted probabilistic projections. We validate the method using a suite of one-at-a-time cross-validation experiments involving Atlantic meridional overturning circulation (AMOC), its temperature-based index, as well as Korean summer mean maximum temperature. In these experiments the method tends to exhibit superior skill over a trend-only Bayesian model averaging weighting method in terms of weight assignment and probabilistic forecasts. Specifically, mean credible interval width, and mean absolute error of the projections tend to improve. We apply the method to a problem of projecting summer mean maximum temperature change over Korea by the end of the 21st century using a multi-model ensemble. Compared to the trend-only method, the new method appreciably sharpens the probability distribution function (pdf) and increases future most likely, median, and mean warming in Korea. The method is flexible, with a potential to improve forecasts in geosciences and other fields

Inferring Social Media Users’ Demographics from Profile Pictures: A Face++ Analysis on Twitter Users

In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ human evaluations to manually tag both the set of images that were determined to contain faces and the set that was determined not to contain faces, comparing the results to Face++. Of the thirty percent that Face++ identified as containing a face, about 80% are more likely than not the account holder based on our manual classification, with a variety of issues in the remaining 20%. Of the images that Face++ was unable to detect a face, we isolate a variety of likely issues preventing this detection, when a face actually appeared in the image. Overall, we find the applicability of automatic facial recognition to infer demographics for system development to be problematic, despite the reported high accuracy achieved for image test collection

Who are your users?:Comparing media professionals’ preconception of users to data-driven personas

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SEMI-BATCH OPERATED CONSTRUCTED WETLANDS PLANTED WITH PHRAGMITES AUSTRALIS FOR TREATMENT OF DYEING WASTEWATER

The objective of present study is to evaluate the using of constructed wetland under semi-batch operation for the treatment of azo dye Acid Orange 7 (AO7) containing wastewater. The emergent plant selected in our study was Phragmites australis. Toxic signs were observed at the Phragmites australis after the addition of AO7 into the wetland reactors but it can adapt to the wastewater as shown in the increase of stem as the operation continue. Our result shows that the artificial aeration and the presence of Phragmites australis had a significant impact on the removal of organic matters, AO7, aromatic amines and NH4-N. The COD removal efficiency in the aerated and non-aerated wetland reactors was 95 and 62%, respectively. The NH4-N removal efficiency in the aerated wetland reactor (86%) was significantly higher than the non-aerated wetland reactor (14 %). All wetland reactors show high removal efficiency of AO7 (> 94%) but only the aerated wetland reactor perform better in the removal of aromatic amines