Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, a novel hybrid model of artificial neural networks is proposed using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks. The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks. Therefore, it can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.

An artificial neural network (p,d,q) model for timeseries forecasting

Near infrared spectroscopy: A mature analytical technique with new perspectives - A review.

Monitoring of verotoxigenic Escherichia coli (VTEC) and identification of human pathogenic VTEC types 1 Scientific Opinion of the Panel on Biological Hazards

Near infrared (NIR) spectroscopy represents an emerging analytical technique, which is enjoying increasing popularity in the food processing industry due to its low running costs, and since it does not require sample preparation. Moreover, it is a non-destructive, environmental friendly, rapid technique capable for on-line application. Therefore, this technique is predestined for implementation as an analytical tool in industrial processing. The different fields of application of NIR spectroscopy reported in the present review highlight its enormous versatility. Quantitative analyses of chemical constituents using this methodology are widespread. Moreover, a wide range of qualitative determinations, e.g. for authenticity control, sample discrimination, the assessment of sensory, rheological or technological properties, and physical attributes have been reported. Both animal- and plant-derived foodstuffs have been evaluated in this context. Highly diverse matrices such as intact solid samples, free-flowing solids, pasty, and fluid samples can by analysed by NIR spectroscopy. Sophisticated conditions for the application in industrial scale comprise among others measurements on moving conveyor belts, in continuous flows in tubes, and monitoring of fermentation processes. For such purposes, different construction designs of NIR spectrometers for hyperspectral imaging, portable devices, fibre optical and direct contact probes as well as tube integrated probes measuring through windows, and automated sample cell loading have been developed. In the present review, emphasis was put on studies dealing with on-line application of NIR spectroscopy for industrial processes in the food industry, which were categorised according to their application conditions into semi-industrial scale and industrial scale.

On-line application of near infrared (NIR) spectroscopy in food production

Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs)

Non-linear regression methods in NIRS quantitative analysis.

Feasibility in NIRS instruments for predicting internal quality in intact tomato

Miniature handheld NIR sensor for the on-site non-destructive assessment of post-harvest quality and refrigerated storage behavior in plums

Non-destructive determination of quality parameters in nectarines during on-tree ripening and postharvest storage

Testing of a local approach for the prediction of quality parameters in intact nectarines using a portable NIRS instrument

J. E. Guerrero

Papers

Non-linear regression methods in NIRS quantitative analysis.

Feasibility in NIRS instruments for predicting internal quality in intact tomato

Miniature handheld NIR sensor for the on-site non-destructive assessment of post-harvest quality and refrigerated storage behavior in plums

Non-destructive determination of quality parameters in nectarines during on-tree ripening and postharvest storage

Testing of a local approach for the prediction of quality parameters in intact nectarines using a portable NIRS instrument