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In an automated demand feeder system, underlining the parameters that contribute to fish hunger is crucial in order to facilitate an optimised food allocation to the fish. The present investigation is carried out to classify the hunger state of Lates calcarifer. A video surveillance technique is employed for data collection. The video was taken throughout the daytime, and the fish were fed through an automated feeding system. It was demonstrated through this investigation that the use of such automated system does contribute towards a higher specific growth rate percentage of body weight as well as the total length by approximately 26.00% and 15.00%, respectively against the conventional time-based method. Sixteen features were feature engineered from the raw dataset into window sizes ranging from 0.5?min, 1.0?min, 1.5?min and 2.0?min, respectively coupled with the mean, maximum, minimum and variance for each of the distinctive temporal window sizes. In addition, the extracted features were analysed through Principal Component Analysis (PCA) for dimensionality reduction as well as PCA with varimax rotation. The data were then classified using a Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Random Forest Tree models. It was demonstrated that the varimax based PCA yielded the highest classification accuracy with eight identified features. The prediction results based of the developed k-NN model on the selected features on the test data exhibited a classification rate of 96.5% was achieved suggesting that the features examined are non-trivial in classifying the fish hunger behaviour.

With this work we report on the design, development and testing of near UV LED-based systems for oxygen gas sensing. The design and developed system is an optoelectronic setup based on 405 nm LEDs which excites and measures the photoluminescence emitted from a porphyrin based luminophor. By means of an accurate optical and optoelectronic setup, the system is able to operate without the need of avalanche photodiodes, thus resulting in a compact and low energy structure. The optical setup is specifically designed to maximize both the LED light exciting the luminophor and converted light acquired from the sensor.

The control and optimization of oxygen content in wine matrices is becoming more and more important in wine production in order to guarantee their best quality. It is well known that in a first phase O2 is necessary to facilitate the development and activity of yeasts, to favor the combination of anthocyanins and color stabilization, and to help reducing the astringency of red wines. In a second phase, however, during the maturation, O2 can severely deteriorate the organoleptic characteristics of the wine. The main oenological practices commonly performed in wine cellars causes remarkable amounts of oxygen to dissolve in wine. For this reason, an instrumentation able to be used in cellars is needed to detect the amount of dissolved oxygen in the wine; as well, a plant technology is needed which is capable of eliminating excess oxygen. In this work, a new economic prototype of optical sensor for oxygen measurements in oenological matrices is developed. It is based on the sampling of the light emission of a polysulfone polymer membrane containing 5,10,15,20-Tetraphenyl-21H,23H-porphyrin platinum (II) (PtTPP). The experimental parameter used for calibration of the sensor is the life time of the PtTPP, obtained from the fitting of the emission decay profile produced by stimulation of the membrane with short pulses emitted by a 390 nm excitation LED. The membrane guarantees a linear behavior of the Stern-Volmer equation and long-lasting signal stability [1,2]. Studies on the behavior of the sensor have been performed in different environments such as air, water, synthetic wine, and also in real red and white wine samples at different temperatures from 5 ° to 20 ° C. The sensor has been suitably designed to work in food matrices and to optimize the noise signal ratio, while keeping the price of the components as low as possible. The sensor was tested for a month within a barrel of 10 m containing wine in the first fermentation phase. In particular, two equal sensors were placed at two levels of depth compared to the wine infeed level: at 0.5 and 2.5 m, respectively. The oxygen content measured during this period was always constant and equal to 0.2%. We thank Smart Future S.r.l. and the project "WOW: DEPLOYMENT OF WSAN TECHNOLOGY FOR MONITORING OXYGEN IN WINE PRODUCTS" financed by the Veneto Region ex LR 5/2001 - ex LR 9/2007.