Fat content of chips, quality of frying fat and deep-frying practices in New Zealand fast food outlets

Objectives: To collect baseline data on the fat content of hot chips, quality (degradation) of cooking fat, deep-frying practices and related attitudes in fast food outlets in New Zealand. To identify the key determinants of the fat content of chips and quality of cooking fat. Methods: A nationally representative sample of fast food outlets (n=150, response rate 80%) was surveyed between September 1998 and March 1999. Data collected included a questionnaire, observation of cooking practices and analysis of cooked chips and frying fat. Results: Only 8% of independent operators had formal training in deep frying practices compared with 93% of chain operators. There was a wide range of fat content of chips (5%-20%, mean 11.5%). The use of thinner chips, crinkle cut chips and lower fryer fat temperature were associated with higher chip fat content. Eighty-nine per cent of chain outlets used 6–10 mm chips compared with 83% of independent outlets that used chips ≥12 mm. A wide range of frying temperatures was recorded (136–233°C) with 58% of outlets frying outside the reference range (175–190°C). As indices of fat degradation, fat acid and polar compound values above the recommended levels occurred in 54% and 5% of outlets respectively. Operators seemed willing to learn more about best practice techniques, with lack of knowledge being the main barrier to change. Conclusions and implications: Deep frying practices could be improved through operator training and certification options. Even a small decrease in the mean fat content of chips would reduce the obesogenic impact of this popular food.

Electricity load and price forecasting with influential factors in a deregulated power industry

With the emergence of smart power grid and distributed generation technologies in recent years, there is need to introduce new advanced models for forecasting. Electricity load and price forecasts are two primary factors needed in a deregulated power industry. The performances of the demand response programs are likely to be deteriorated in the absence of accurate load and price forecasting. Electricity generation companies, system operators, and consumers are highly reliant on the accuracy of the forecasting models. However, historical prices from the financial market, weekly price/load information, historical loads and day type are some of the explanatory factors that affect the accuracy of the forecasting. In this paper, a neural network (NN) model that considers different influential factors as feedback to the model is presented. This model is implemented with historical data from the ISO New England. It is observed during experiments that price forecasting is more complicated and hence less accurate than the load forecasting.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

Garlic (Allium sativum): diet based therapy of 21st century-a review

Functional and nutraceutical foods provide an opportunity to improve one's health by reducing health care costs and to support economic development in rural communities. For this reason, various phyto-based functional foods are becoming popular worldwide owing to number of evidences for their safer therapeutic applications. Garlic (Allium sativum L.,) is an essential vegetable that has been widely utilized as seasoning, flavoring, culinary and in herbal remedies. The consumption of traditional plants especially garlic has progressively increased worldwide because of their great effectiveness, fewer side effects and relatively low cost. Garlic is well known to contain an array of phytochemicals. These bioactive molecules are playing pivotal role in maintaining human health and having potential to reduce various ailments. It has distinct nutritional profile with special reference to its various bioactive components that can be used in different diet based therapies to cure various life-style related disorders. The present review is an attempt to explore the functional/nutraceutical role of garlic against various threats including dyslipidemia and hyperglycemia, cardiovascular disorders, antioxidant capacity and carcinogenic perspectives.