Modeling growth of specific spoilage organisms in tilapia: Comparison Baranyi with chi-square automatic interaction detection (CHAID) model

Tilapia is an important aquatic fish, but severe spoilage of tilapia is most likely related to the global aquaculture. The spoilage is mostly caused by specific spoilage organisms (SSO). Therefore, it is very important to use microbial models to predict the growth of SSO in tilapia. This study firstly verified Pseudomonas and Vibrio as the SSO of tilapia, then established microbial growth models based on Baranyi and chi-square automatic interaction detection (CHAID) models and compared their effectiveness. The results showed that both Baranyi model and CHAID model are appropriate for predicting the growth of microorganism. Baranyi model fits the microorganism growth better than CHAID model overall though CHAID model fits well at stationary phase. CHAID model predicts the microorganism growth accurately when the rate of change of the experiment data is big.Key words: Specific spoilage organisms (SSO), tilapia, chi-square automatic interaction detection (CHAID), Baranyi, shelf-life

Prediction of the antibacterial activity of garlic extract on E. coli, S. aureus and B. subtilis by determining the diameter of the inhibition zones using artificial neural networks

The aim of this study was to devise a model that predicts the inhibition zone diameter using artificial neural networks. The concentration, temperature and the exposure time of our extract were taken as input variables. The neural architecture model 3-13-3 and a learning algorithm Quasi-Newton (BFGS) revealed a positive correlation between the experimental results and those artificially predicted, which were measured according to a mean squared error (RMSE) and an R2 coefficient of E. coli (RMSE = 1.28; R2 = 0,96), S. aureus (RMSE = 1.46; R2 = 0,97) and B. subtilis (RMSE = 1.88; R2 = 0,96) respectively. Based on these results, an external and an internal model validation were attained. A neuronal mathematical equation was created to predict the inhibition diameters for experimental data not included in the basic learning. Consequently, a good correlation was observed between the values predicted by the equation and those obtained experimentally, as demonstrated by the R2 and RMSE values. The results regarding the sensitivity analysis showed that the concentration was the most determinant parameter compared to Temperature and Time variables. Ultimately, the model developed in this study will be used reliably to predict the variation of garlic extract's inhibition diameter

Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage

Food product safety is one of the most promising areas for the application of electronic noses. During the last twenty years, these sensor-based systems have made odour analyses possible. Their application into the area of food is mainly focused on quality control, freshness evaluation, shelf-life analysis and authenticity assessment. In this paper, the performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillets stored either aerobically or under modified atmosphere packaging, at different storage temperatures. A novel multi-output fuzzy wavelet neural network model has been developed, which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the relevant quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population. Comparison results against advanced machine learning schemes indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Comparative Analysis of Machine Learning Methods to Predict Growth of F. sporotrichioides and Production of T-2 and HT-2 Toxins in Treatments with Ethylene-Vinyl Alcohol Films Containing Pure Components of Essential Oils

The efficacy of ethylene-vinyl alcohol copolymer films (EVOH) incorporating the essential oil components cinnamaldehyde (CINHO), citral (CIT), isoeugenol (IEG), or linalool (LIN) to control growth rate (GR) and production of T-2 and HT-2 toxins by Fusarium sporotrichioides cultured on oat grains under different temperature (28, 20, and 15 °C) and water activity (aw) (0.99 and 0.96) regimes was assayed. GR in controls/treatments usually increased with increasing temperature, regardless of aw, but no significant differences concerning aw were found. Toxin production decreased with increasing temperature. The effectiveness of films to control fungal GR and toxin production was as follows: EVOH-CIT > EVOH-CINHO > EVOH-IEG > EVOH-LIN. With few exceptions, effective doses of EVOH-CIT, EVOH-CINHO, and EVOH-IEG films to reduce/inhibit GR by 50%, 90%, and 100% (ED50, ED90, and ED100) ranged from 515 to 3330 µg/culture in Petri dish (25 g oat grains) depending on film type, aw, and temperature. ED90 and ED100 of EVOH-LIN were >3330 µg/fungal culture. The potential of several machine learning (ML) methods to predict F. sporotrichioides GR and T-2 and HT-2 toxin production under the assayed conditions was comparatively analyzed. XGBoost and random forest attained the best performance, support vector machine and neural network ranked third or fourth depending on the output, while multiple linear regression proved to be the worst

Implementación De Una Solución Basada En Redes Neuronales Para La Optimización Del Proceso De Refinamiento De Aceite Para Una Empresa Pesquera

La empresa “Pescamar” es una empresa pesquera líder en su sector, produciendo alimentos e ingredientes marinos de alta calidad, con un gran valor agregado y excelencia, lo cual les ha permitido convertirse en el primer productor y exportador de harina y aceite de pescado del mundo. Actualmente Pescamar cuenta con una planta que se encarga exclusivamente del refinamiento y concentración de aceite de pescado con alto contenido de Omega 3. El Omega 3 es un ácido graso esencial poliinsaturado (el organismo humano no los puede fabricar sino es a partir de otras sustancias), que se encuentran en alta proporción en los tejidos de ciertos pescados, frutos secos, aceites vegetales (tales como el aceite de canola y de girasol). Es por ello que Pescamar consciente de todos los beneficios que produce el Omega 3 (principalmente esto lo encontramos en productos farmacéuticos y nutracéuticos), le dedica mucho atención en el tratamiento y refinamiento del aceite de pescado para la obtención de nuevos aceites con altos contenidos de Omega 3. Ahora el proceso para la obtención de nuevos aceites (y que estos sean los esperados), suele ser muy complejo puesto que no existe una fórmula matemática que te diga que al someter el aceite de pescado a ciertas condiciones puedas obtener un tipo de aceite con cierta cantidad de EPA (ácido eicosapentaenoico) y DHA (ácido docosahexaenoico) deseada, siendo estos los más importantes ácidos grasos de la familia del Omega 3, mientras se obtenga mayor % de EPA y DHA el aceite de Omega 3 es más puro. Es por ello que de ahí proviene la propuesta solución y el proyecto que he realizado. Analizando el proceso que se realiza para el refinamiento del aceite verifique que el mismo es secuencial e iterativo, y que este toma demasiado tiempo y coste de recursos para poder obtener un aceite de Omega 3 deseado. Y esto es porque no se cuenta con una fórmula matemática que te diga que al someter un aceite a ciertas condiciones puedes obtener un aceite con una alta concentración de Omega 3 en la menor cantidad de pasos posibles, y sin esa información no podrán analizar todos los tipos de aceite de Omega 3 generados y los costes que implican producirlos, y por ende no se podrá aplicar una buena estrategia de venta de su producto a las demás industrias interesadas en el aceite de Omega 3. Esta problemática como tal es un proyecto de minería de datos, ya que se maneja grandes volúmenes de información, con esto me refiero a todas las posibilidades de aceites que se van a manejar para la elección de los aceites deseados. Ahora como tema central a analizar es el modelo matemático que se va a aplicar para la solución de la problemática planteada, en donde los algoritmos más cercanos para la solución de este tipo de problemas son los algoritmos predictivos (pues estos se basan principalmente en la extracción de patrones de datos históricos, y es con lo que contamos ahora sólo datos históricos) y entre los más importantes tenemos las redes neuronales, árboles de decisión y máquinas de vectores de soporte (SVMs), cada uno de estos dependiendo del proyecto te brinda un grado de error, por ello se deben analizar varios modelos para ver cuál de todos es el que te genera el menor porcentaje de error. Para este proyecto se escogió como algoritmo predictivo el perceptrón multicapa (es un tipo de algoritmo de redes neuronales), y como algoritmo de aprendizaje el backpropagation. Lo que se busca con este modelo es el poder predecir con un grado de exactitud (ya que cualquier algoritmo predictivo cuenta con un porcentaje de confiabilidad) el nuevo tipo de aceite generado basado en unas entradas (%EPA, %DHA, Temperatura y Tiempo). Finalmente a manera de reflejar los nuevos registros generados (ya que el algoritmo te va a generar esos nuevos tipos de aceites), se utilizó una herramienta reporteadora tal es el caso de QlikView).Trabajo de suficiencia profesiona

Dynamic non-linear system modelling using wavelet-based soft computing techniques

The enormous number of complex systems results in the necessity of high-level and cost-efficient modelling structures for the operators and system designers. Model-based approaches offer a very challenging way to integrate a priori knowledge into the procedure. Soft computing based models in particular, can successfully be applied in cases of highly nonlinear problems. A further reason for dealing with so called soft computational model based techniques is that in real-world cases, many times only partial, uncertain and/or inaccurate data is available. Wavelet-Based soft computing techniques are considered, as one of the latest trends in system identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based approaches to model the non-linear dynamical systems in real world problems in conjunction with possible twists and novelties aiming for more accurate and less complex modelling structure. Initially, an on-line structure and parameter design has been considered in an adaptive Neuro- Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus (Monascus ruber van Tieghem) is examined against several other approaches for further justification of the proposed methodology. By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have been introduced. Increasing the accuracy and decreasing the computational cost are both the primary targets of proposed novelties. Modifying the synoptic weights by replacing them with Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA) comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for the above challenges. These two models differ from the point of view of structure while they share the same HLA scheme. The second approach contains an additional Multiplication layer, plus its hidden layer contains several sub-WNNs for each input dimension. The practical superiority of these extensions is demonstrated by simulation and experimental results on real non-linear dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT) whole milk, and consolidated with comprehensive comparison with other suggested schemes. At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network (FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from the data by building accurate regression, but also for the identification of complex systems. The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the function approximation accuracy and general capability of the FWNN system, an efficient hybrid learning approach is used to adjust the parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the above technique

Neuro-Fuzzy Based Intelligent Approaches to Nonlinear System Identification and Forecasting

Nearly three decades back nonlinear system identification consisted of several ad-hoc approaches, which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks and the fuzzy logic combined with optimization techniques, a wider class of systems can be handled at present. Complex systems may be of diverse characteristics and nature. These systems may be linear or nonlinear, continuous or discrete, time varying or time invariant, static or dynamic, short term or long term, central or distributed, predictable or unpredictable, ill or well defined. Neurofuzzy hybrid modelling approaches have been developed as an ideal technique for utilising linguistic values and numerical data. This Thesis is focused on the development of advanced neurofuzzy modelling architectures and their application to real case studies. Three potential requirements have been identified as desirable characteristics for such design: A model needs to have minimum number of rules; a model needs to be generic acting either as Multi-Input-Single-Output (MISO) or Multi-Input-Multi-Output (MIMO) identification model; a model needs to have a versatile nonlinear membership function. Initially, a MIMO Adaptive Fuzzy Logic System (AFLS) model which incorporates a prototype defuzzification scheme, while utilising an efficient, compared to the Takagi–Sugeno–Kang (TSK) based systems, fuzzification layer has been developed for the detection of meat spoilage using Fourier transform infrared (FTIR) spectroscopy. The identification strategy involved not only the classification of beef fillet samples in their respective quality class (i.e. fresh, semi-fresh and spoiled), but also the simultaneous prediction of their associated microbiological population directly from FTIR spectra. In the case of AFLS, the number of memberships for each input variable was directly associated to the number of rules, hence, the “curse of dimensionality” problem was significantly reduced. Results confirmed the advantage of the proposed scheme against Adaptive Neurofuzzy Inference System (ANFIS), Multilayer Perceptron (MLP) and Partial Least Squares (PLS) techniques used in the same case study. In the case of MISO systems, the TSK based structure, has been utilized in many neurofuzzy systems, like ANFIS. At the next stage of research, an Adaptive Fuzzy Inference Neural Network (AFINN) has been developed for the monitoring the spoilage of minced beef utilising multispectral imaging information. This model, which follows the TSK structure, incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. In this specific case study, AFINN model was also able to predict for the first time in the literature, the beef’s temperature directly from imaging information. Results again proved the superiority of the adopted model. By extending the line of research and adopting specific design concepts from the previous case studies, the Asymmetric Gaussian Fuzzy Inference Neural Network (AGFINN) architecture has been developed. This architecture has been designed based on the above design principles. A clustering preprocessing scheme has been applied to minimise the number of fuzzy rules. AGFINN incorporates features from the AFLS concept, by having the same number of rules as well as fuzzy memberships. In spite of the extensive use of the standard symmetric Gaussian membership functions, AGFINN utilizes an asymmetric function acting as input linguistic node. Since the asymmetric Gaussian membership function’s variability and flexibility are higher than the traditional one, it can partition the input space more effectively. AGFINN can be built either as an MISO or as an MIMO system. In the MISO case, a TSK defuzzification scheme has been implemented, while two different learning algorithms have been implemented. AGFINN has been tested on real datasets related to electricity price forecasting for the ISO New England Power Distribution System. Its performance was compared against a number of alternative models, including ANFIS, AFLS, MLP and Wavelet Neural Network (WNN), and proved to be superior. The concept of asymmetric functions proved to be a valid hypothesis and certainly it can find application to other architectures, such as in Fuzzy Wavelet Neural Network models, by designing a suitable flexible wavelet membership function. AGFINN’s MIMO characteristics also make the proposed architecture suitable for a larger range of applications/problems

Redes neuronales y preprocesado de variables para modelos y sensores en bioingeniería

El propósito de esta Tesis Doctoral es proponer una alternativa viable a la aproximación de modelos y procesos en el ámbito científico y, más concretamente, en aplicaciones complejas de bioingeniería, en las cuales es imposible o muy costoso encontrar una relación directa entre las señales de entrada y de salida mediante modelos matemáticos sencillos o aproximaciones estadísticas. Del mismo modo, es interesante lograr una compactación de los datos que necesita un modelo para conseguir una predicción o clasificación en un tiempo y con un coste de implementación mínimos. Un modelo puede ser simplificado en gran medida al reducir el número de entradas o realizar operaciones matemáticas sobre éstas para transformarlas en nuevas variables. En muchos problemas de regresión (aproximación de funciones), clasificación y optimización, en general se hace uso de las nuevas metodologías basadas en la inteligencia artificial. La inteligencia artificial es una rama de las ciencias de la computación que busca automatizar la capacidad de un sistema para responder a los estímulos que recibe y proponer salidas adecuadas y racionales. Esto se produce gracias a un proceso de aprendizaje, mediante el cual se presentan ciertas muestras o �ejemplos� al modelo y sus correspondientes salidas y éste aprende a proponer las salidas correspondientes a nuevos estímulos que no ha visto previamente. Esto se denomina aprendizaje supervisado. También puede darse el caso de que tal modelo asocie las entradas con características similares entre sí para obtener una clasificación de las muestras de entrada sin necesidad de un patrón de salida. Este modelo de aprendizaje se denomina no supervisado. El principal exponente de la aplicación de la inteligencia artificial para aproximación de funciones y clasificación son las redes neuronales artificiales. Se trata de modelos que han demostrado sobradamente sus ventajas en el ámbito del modelado estadístico y de la predicción frente a otros métodos clásicos. NMateo Jiménez, F. (2012). Redes neuronales y preprocesado de variables para modelos y sensores en bioingeniería [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16702Palanci

Predictive assessment of ochratoxin A accumulation in grape juice based-medium by Aspergillus carbonarius using neural networks

Aims: To study the ability of multi-layer perceptron artificial neural networks (MLP-ANN) and radial-basis function networks (RBFNs) to predict ochratoxin A (OTA) concentration over time in grape-based cultures of Aspergillus carbonarius under different conditions of temperature, water activity (a(w)) and sub-inhibitory doses of the fungicide carbendazim. Methods and Results: A strain of A. carbonarius was cultured in a red grape juice-based medium. The input variables to the network were temperature (20-28 degrees C), a(w) (0 center dot 94-0 center dot 98), carbendazim level (0-450 ng ml(-1)) and time (3-15 days after the lag phase). The output of the ANNs was OTA level determined by liquid chromatography. Three algorithms were comparatively tested for MLP. The lowest error was obtained by MLP without validation. Performance decreased when hold-out validation was accomplished but the risk of over-fitting is also lower. The best MLP architecture was determined. RBFNs provided similar performances but a substantially higher number of hidden nodes were needed. Conclusions: ANNs are useful to predict OTA level in grape juice cultures of A. carbonarius over a range of a(w), temperature and carbendazim doses. Significance and Impact of the Study: This is a pioneering study on the application of ANNs to forecast OTA accumulation in food based substrates. These models can be similarly applied to other mycotoxins and fungal species.This work was supported by the Spanish 'Ministerio de Educacion y Ciencia' (projects AGL-2004-07549-C05-02 and AGL2007-66416-C05-01 and a research grant) and the Valencian Government 'Conselleria de Empresa, Universitat i Ciencia' (project GV04B-111 and ACOMP/2007/155 and a research grant).Mateo Jiménez, F.; Gadea Gironés, R.; Medina, A.; Mateo, R.; Jiménez, M. (2009). Predictive assessment of ochratoxin A accumulation in grape juice based-medium by Aspergillus carbonarius using neural networks. 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