HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS

This work presents the results of a Hybrid Neural Network (HNN) technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.

Previsão de estabilidade de óleos vegetais através da rede de inteligência artificial

Redes Neurais Artificiais são técnicas computacionais que se utilizam de um modelo matemático capaz de adquirir conhecimentos pela experiência; esse comportamento inteligente da rede provém das interações entre unidades de processamento, denominadas de neurônios artificiais. O objetivo deste trabalho foi criar uma rede neural capaz de prever a estabilidade de óleos vegetais, a partir de dados de suas composições químicas, visando um modelo para a previsão da shelf-life de óleos vegetais, tendo como parâmetros apenas dados de suas composições químicas. Os primeiros passos do processo de desenvolvimento da rede consistiram na coleta de dados relativos ao problema e sua separação em um conjunto de treinamento e outro de testes. Estes conjuntos apresentaram como variáveis dados de composição química, que incluíram os valores totais em ácidos graxos, fenóis, tocoferóis e a composição individual em ácidos graxos. O passo seguinte foi a execução do treinamento, onde o padrão de entrada apresentado à rede como parâmetro de estabilidade foi o índice de peróxido, determinado experimentalmente por um período de 16 dias de armazenagem na ausência de luz, a 65ºC. Após o treinamento foi testada a capacidade de previsão adquirida pela rede, em função do parâmetro de estabilidade adotado, mas com um novo grupo de óleos. Seguindo o teste, foi determinada a correlação linear entre os valores de estabilidade previstos pela rede e aqueles determinados experimentalmente. Com os resultados obtidos, pode-se confirmar a viabilidade de previsão da estabilidade de óleos vegetais pela rede neural, a partir de dados de sua composição química, utilizando como parâmetro de estabilidade o índice de peróxido.

Influence of temperature on the respiration rate of minimally processed organic carrots (Daucus Carota L. cv. Brasília)

The objective of this work was to study the influence of temperature on the respiration rate of minimally processed organic carrots (Daucus Carota L. cv. Brasília) with and without the application of a gelatin film. The samples were packed in flexible bags and stored at 1, 5 and 10 °C. During the five days of storage, the CO2 and O2 concentrations in the headspace of the package were monitored by gas chromatography, and the mathematical model based on enzymatic kinetics was used to estimate the respiration rate of minimally processed organic carrots. The effect of temperature on the respiration rate was evaluated by the Arrhenius equation. The results showed that the O2 concentration decreased during the storage period and the CO2 concentration increased. The lowest O2 concentrations of 2.59 and 2.66% were found for the samples stored at 10 °C with and without the film, respectively. For the CO2 concentration, the highest concentrations of 16.25 and 16.32% were again found for the temperature of 10 °C with and without the application of the film, respectively. At the temperature of 1 °C, the maximum respiratory rates for the samples without and with the film were 10.82 and 10.44 mL CO2.kg-1/hour, respectively, after 72 hours of storage. The greatest respiratory rate was obtained at 10 °C, the maximum peak being reached after 50 hours. Activation energy values were of 50.59 kJ.mol-1, for the samples with the film, and 51.88 kJ.mol-1 for the samples without the film.

Estimate of respiration rate and physicochemical changes of fresh-cut apples stored under different temperatures

In this study, the influence of storage temperature and passive modified packaging (PMP) on the respiration rate and physicochemical properties of fresh-cut Gala apples (Malus domestica B.) was investigated. The samples were packed in flexible multilayer bags and stored at 2 °C, 5 °C, and 7 °C for eleven days. Respiration rate as a function of CO2 and O2 concentrations was determined using gas chromatography. The inhibition parameters were estimated using a mathematical model based on Michaelis-Menten equation. The following physicochemical properties were evaluated: total soluble solids, pH, titratable acidity, and reducing sugars. At 2 °C, the maximum respiration rate was observed after 150 hours. At 5 °C and 7 °C the maximum respiration rates were observed after 100 and 50 hours of storage, respectively. The inhibition model results obtained showed a clear effect of CO2 on O2 consumption. The soluble solids decreased, although not significantly, during storage at the three temperatures studied. Reducing sugars and titratable acidity decreased during storage and the pH increased. These results indicate that the respiration rate influenced the physicochemical properties.

An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.

Inteligência artificial e teoria de resolução de problemas

Este artigo é uma tentativa de delinear as principais características da pesquisa numa nova área de estudos a chamada Inteligência Artificial (AI). Os itens 1 e 2 constituem um rápido histórico da AI e seus pressupostos básicos. O item 3 trata da teoria de resolução de problemas, desenvolvida por A. Newell e H. Simon. O item 4 procura mostrar a relevância da AI para a Filosofia, em especial para a filosofia da Mente e para a Teoria do Conhecimento.

Robots, intencionalidade e inteligência artificial

O artigo aborda problemas filosóficos relativos à natureza da intencionalidade e da representação mental. A primeira parte apresenta um breve histórico dos problemas, percorrendo rapidamente alguns episódios da filosofia clássica e da filosofia contemporânea. A segunda parte examina o Chinese Room Argument (Argumento do Quarto do Chinês) formulado por J. Searle. A terceira parte desenvolve alguns argumentos visando mostrar a inadequação do modelo funcionalista de mente na construção de robots. A conclusão (quarta parte) aponta algumas alternativas ao modelo funcionalista tradicional, como, por exemplo, o conexionismo.