Detection of Escherichia coli in packaged alfalfa sprouts with an electronic nose and an artificial neural network

A rapid method for the detection of Escherichia coli (ATCC 25922) in packaged alfalfa sprouts was developed. Volatile compounds from the headspace of packaged alfalfa sprouts, inoculated with E. coli and incubated at 10 degrees C for 1, 2, and 3 days, were collected and analyzed. Uninoculated sprouts were used as control samples. An electronic nose with 12 metal oxide electronic sensors was used to monitor changes in the composition of the gas phase of the package headspace with respect to volatile metabolites produced by E. coli. The electronic nose was able to differentiate between samples with and without E. coli. To predict the number of E. coli in packaged alfalfa sprouts, an artificial neural network was used, which included an input layer, a hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. The network was shown to be capable of correlating voltametric responses with the number of E. coli. A good prediction was possible, as measured by a regression coefficient (R2 = 0.903) between the actual and predicted data. In conjunction with the artificial neural network, the electronic nose proved to have the ability to detect E. coli in packaged alfalfa sprouts.     

An alternative freshness index method for modified atmosphere packaged abalone using an artificial neural network

An alternative freshness index method for abalone (Haliotis asinina) muscle packaged under atmospheric air (Air) and modified atmosphere (MA) of 40% CO₂: 30% O₂: 30% N₂ packaging conditions and stored at 2 ± 1 °C was developed. Biochemical indices covering pH, total volatile basic nitrogen (TVB-N), trimethylamine (TMA) and nucleotide degradation products, as well as instrumental texture and color of the packaged abalones, were determined. Sensory characteristics including odor, color and appearance were evaluated and then summarized into overall freshness scores (freshness index). The biochemical and instrumental analyses were then calibrated with the freshness index, using an artificial neural network algorithm. The neural network was shown to be capable of correlating biochemical and instrumental analyses with the freshness index. A useful prediction was possible, as measured by a low mean square error (MSE = 0.092) and a regression coefficient (R² = 0.98) between true and predicted data.     

Assessment of egg freshness by prediction of Haugh unit and albumen pH using an artificial neural network

Eggs are a good source of high quality protein and knowing their quality (physical and chemical properties) during storage is of great importance. Thus, the aim of this research was to design a computer vision system to assess egg freshness during storage time. To this end, 210 intact eggs were collected and stored for 30 days under room conditions (25?±?2 °C and 20?±?3%). After imaging, every other day, some internal and external quality characteristics including yolk height, yolk and albumen pH, yolk and albumen density and Haugh unit (HU) were measured as destructive parameters and area index (D) egg weight as non-destructive parameters. Based on Pearson correlation coefficients, area index were significantly correlated with all destructive variables (p?<?0.05). In order to predict egg freshness, artificial neural network was trained by Levenberg–Marquardt, scaled conjugate gradient, Bayesian regulation, resilient and radial basis algorithms. The best result of artificial neural network for HU and albumen pH prediction was achieved by the Levenberg–Marquardt algorithm with the correlation coefficient of 0.93 and 0.87, respectively.     

Modeling the tryptic hydrolysis of pea proteins using an artificial neural network

Experimentally determined values for the degree of hydrolysis (DH) were used with an artificial neural network (ANN) model to predict the tryptic hydrolysis of a commercially available pea protein isolate at temperatures of 40, 45, and 50 °C. Analyses were conducted using the STATISTICA Neural Networks software on a personal computer. Input data were randomized to two sets: learning and testing. Differences between the experimental and calculated DH% were slight and ranged from 0.06% to 0.24%. The performance of the educated ANN was then tested by inputting temperatures ranging from 35 to 50 °C. Very strong correlations were found between calculated DH% values obtained from the ANN and those experimentally determined at all temperatures; the determination coefficients (R²) varied from 0.9958 to 0.9997. The results so obtained will be useful to reduce the time required in the design of enzymatic reactions involving food proteins.     

Quality evaluation of chickpeas using an artificial neural network integrated computer vision system

Chickpea is one of the most consumed legumes in the world. The classification of chickpea based on the size and morphological properties is important for the market. The objective of this study is to design and implement a computer vision system (CVS) integrated with artificial neural networks (ANN) for quality evaluation of chickpeas based on their size, colour, and surface morphology. The system is composed of a flat bed scanner for acquiring digital image and software that has been developed in Matlab for image analysis. Physical properties (length, width and volume) of the samples of chickpeas as well as their colour properties and surface characteristics have been determined by using the system, and results have been validated. High correlations have been found between the results from ANN‐integrated CVS and those obtained by callipers or professionally trained inspectors based on the experiments. Overall, percentages of correct classification have been determined as 95.4%, 87.6%, and 96.0% for colour, surface morphology, and shape evaluations, respectively.     

Simulating the drawing of spunbonding nonwoven process using an artificial neural network technique

The statistical and artificial neural network (ANN) models are established for predicting the fiber diameter of spunbonded nonwovens from the processing parameters. The ANN of Bayesian frameworks produces smaller prediction errors and thus is determined to be the preferred network. Results show that the ANN model yields more accurate and stable prediction than the statistical model, and a reasonably good ANN model can be established with relatively few data points. Four methods are used to reveal the relative importance of the processing parameters in terms of their effect on the fiber diameter. It is found that the initial polymer temperature plays an most important role in reducing the fiber diameter, while the effect of the initial air temperature is not significant. Using an established ANN model, computer simulations of the effects of the processing parameter on the fiber diameter are carried out. It is found that higher polymer melt index, smaller polymer flow rate, higher initial polymer temperature, higher initial air temperature, and higher initial air velocity can all produce finer fibers. This area of research has great potential in the field of computer-assisted design in spunbonding technology.     

基于人工神经网络的纱线条干均匀度预测研究

研究了纱线条干均匀度预测问题,用HVI测试原棉指标,用USTER()TESTER 5-S400测试成纱指标,采用标准BP算法建立断裂伸长预测的模型,进行纱线的条干均匀度预测,结果表明BP模型预测速度和精度较高,可以实现棉纱条干均匀度预测.     

Antioxidant activity prediction and classification of some teas using artificial neural networks

In order to characterise and to classify some teas a simple, rapid and economical method based on composition, antioxidant activity and artificial neural networks (ANNs) is proposed. For these purpose two types of ANN based applications have been developed: one for predicting the antioxidant activity and a second one for establishing the class of the teas. The complex relationship between the total antioxidant activity (AA) depending on the total flavonoids content (F), total catechins content (C) and total methyl-xanthines content (MX) of commercial teas was revealed by the first designed feed-forward ANN. Secondly, using a probabilistic ANN, successful tea classification in various classes (green tea, black tea and express black tea) was also performed.     

A computerized mastitis decision aid using farm-based records: an artificial neural network approach

A computer module was developed and tested that used field survey and Dairy Herd Improvement Association (DHIA) data to broadly classify bacterial causes of mastitis in dairy herds. Further development of the computer model could aid interpretation of DHIA data by dairy record processing centers and herd consultants. This diagnostic module was developed with an artificial neural network, a technology that processes complex data in a manner similar to human brain function. Information describing herd management practices, quarter milk samples, and monthly DHIA data was collected from Pennsylvania dairy herds with moderate to high somatic cell counts. This information was used to develop or train an artificial neural network model that discriminated among four categories of bacterial organisms (contagious, environmental, no significant growth, and other) associated with clinical and subclinical mastitis. After training the model, new DHIA and management data were presented to the model to assess its ability to classify bacteriological etiology. When the artificial neural network was used, the probabilities of diagnosing the bacteriologic status from three randomly selected cow groups and from new untested herds ranged from 57 to 71%. Performance of the artificial neural network model was best in herds with higher frequency of minor and contagious pathogens. Prediction results for the same test data with linear discriminant analysis were less successful, ranging from 42 to 57%.     

Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network

Over use of nitrogen fertilization can result in groundwater pollution. Tools that can rapidly quantify the nitrogen status are needed for efficient fertilizer management and would be very helpful in reducing the environmental pollution caused by excessive nitrogen application. Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties. In this study, the MLR (multiple linear regression) and ANN (artificial neural network) modeling methods were applied to the monitoring of rice N (nitrogen concentration, mg nitrogen g(-1) leaf dry weight) status using leaf level hyperspectral reflectance with two different input variables, and as a result four estimation models were proposed. RMSE (root-mean-square error), REP (relative error of prediction), R2 (coefficient of determination), as well as the intercept and slope between the observed and predicted N were used to test the performance of models. Very good agreements between the observed and the predicted N were obtained with all proposed models, which was especially true for the R-ANN (artificial neural network based on reflectance selected using MLR) model. Compared to the other three models, the R-ANN model improved the results by lowering the RMSE by 14.2%, 32.1%, and 31.5% for the R-LR (linear regression based on reflectance) model, PC-LR (linear regression based on principal components scores) model, and PC-ANN (artificial neural network based on principal components scores) model, respectively. It was concluded that the ANN algorithm may provide a useful exploratory and predictive tool when applied on hyperspectral reflectance data for nitrogen status monitoring. Besides, although the performance of MLR was superior to PCA used for ANN inputs selection, the encouraging results of PC-based models indicated the promising potential of ANN combined with PCA application on hyperspectral reflectance analysis.     

食品中大肠杆菌的快速检测方法

大肠杆菌是人及各种动物肠道中的正常寄居菌,食物或水中大肠杆菌的检出意味着直接或间接的近期粪便污染。大肠杆菌作为饮水、食品等的粪源性污染卫生细菌学指标;而且他在外界存活时间与一些主要肠道病原菌相近,它的出现也可能预示某些肠道病原菌（如沙门氏菌、志贺氏菌）的存在。大肠杆菌是国际上公认的卫生监测指示菌,因此大肠杆菌的检测技术显得十分重要,相应出现了大量的大肠杆菌的各种检测方法。     

基于ANN算法的毛精纺面料织造的智能预报

介绍了在毛精纺面料织造过程中应用的神经网络技术及不同的改进算法,给出了织造预报的实际模型和试验结论,并对织机效率预报模型进行实例训练,预报结果验证了几种典型学习算法的性能.     

Predicting survival of Escherichia coli O157:H7 in dry fermented sausage using artificial neural networks

The Canadian Food Inspection Agency required the meat industry to ensure Escherichia coli O157:H7 does not survive (experiences > or = 5 log CFU/g reduction) in dry fermented sausage (salami) during processing after a series of foodborne illness outbreaks resulting from this pathogenic bacterium occurred. The industry is in need of an effective technique like predictive modeling for estimating bacterial viability, because traditional microbiological enumeration is a time-consuming and laborious method. The accuracy and speed of artificial neural networks (ANNs) for this purpose is an attractive alternative (developed from predictive microbiology), especially for on-line processing in industry. Data from a study of interactive effects of different levels of pH, water activity, and the concentrations of allyl isothiocyanate at various times during sausage manufacture in reducing numbers of E. coli O157:H7 were collected. Data were used to develop predictive models using a general regression neural network (GRNN), a form of ANN, and a statistical linear polynomial regression technique. Both models were compared for their predictive error, using various statistical indices. GRNN predictions for training and test data sets had less serious errors when compared with the statistical model predictions. GRNN models were better and slightly better for training and test sets, respectively, than was the statistical model. Also, GRNN accurately predicted the level of allyl isothiocyanate required, ensuring a 5-log reduction, when an appropriate production set was created by interpolation. Because they are simple to generate, fast, and accurate, ANN models may be of value for industrial use in dry fermented sausage manufacture to reduce the hazard associated with E. coli O157:H7 in fresh beef and permit production of consistently safe products from this raw material.     

A close to real-time prediction method of total coliform bacteria in foods based on image identification technology and artificial neural network

A prediction method of total coliform bacteria based on image identification technology in foods was proposed. In order to get the close to real-time detection results, this method used the total count of bacteria and bacilli to predict the total coliform bacteria counts because coliforms are difficult to extract the feature parameters to be recognized and enumerated, while total count of bacteria and bacilli could be enumerated by using image identification technology. An optimal artificial neural network (ANN) model was presented for prediction of total coliform bacteria counts. Several configurations were evaluated while developing the optimal ANN model. The optimal ANN model consisted two hidden layers with five neurons in each hidden layer. Results showed that predicted total coliform bacteria counts were positively correlated to the experimental total coliform bacteria counts obtained by traditional multiple-tube fermentation technique (correlation coefficient, R² = 0.9716), which predicted accuracy was much better than other predicted models (the correlation coefficient of linear regression model, second-order polynomial regression model and polynomial trend surface analysis was 39.81%, 67.17% and 78.85%, respectively).     

Monitoring the authenticity of low-fat yogurts by an artificial neural network

The growing consumption of low- and reduced-fat dairy products demands routine control of their authenticity by health agencies. The usual analyses of fat in dairy products are very simple laboratory methods; however, they require manipulation and use of reagents of a corrosive nature, such as sulfuric acid, to break the chemical bounds between fat and proteins. Additionally, they generate chemical residues that require an appropriate destination. In this work, the use of an artificial neural network based on simple instrumental analyses, such as pH, color, and hardness (inputs) is proposed for the classification of commercial yogurts in the low- and reduced-fat categories (outputs). A total of 108 strawberry-flavored yogurts (48 probiotic low-fat, 36 low-fat, and 24 full-fat yogurts) belonging to several commercial brands and from different batches were used in this research. The statistical analysis showed different features for each yogurt category; thus, a database was built and a neural model was trained with the Levenberg-Marquardt algorithm by using the neural network toolbox of the software MATLAB 7.0.1. Validation with unseen data pairs showed that the proposed model was 100% efficient. Because the instrumental analyses do not require any sample preparation and do not produce any chemical residues, the proposed procedure is a fast and interesting approach to monitoring the authenticity of these products.     

Using artificial neural networks to develop prediction models for sensory-instrumental relationships; an overview

The possibility of using neural networks for modelling instrumental-sensory relationships is investigated. The advantages and disadvantages of using artificial neural networks (ANNs) are considered and compared with those of the multivariate linear methods of principal components regression (PCR) and partial least squares regression (PLS). In particular the problem of modelling nonlinear relationships is considered. It is concluded that ANNs cannot replace PCR and PLS for linear relationships but do offer potential for modelling nonlinear relationships.     

LDA优化电子鼻传感器阵列的研究

利用PEN3电子鼻系统对6个糖酸比不同的乳饮料样品进行检测,采用线性判别分析(LDA)对传感器响应值进行分析,确定优化传感器阵列方法,并将各优化结果进行对比,最终确定阵列优化结果,使电子鼻可以用更少的传感器达到更好的分类效果,为电子鼻传感器阵列优化提供了新的思路和方法。     

A new approach for prediction of sewing performance of fabrics in apparel manufacturing using artificial neural networks

Abstract

This paper investigates the use of extended normalized radial basis function (ENRBF) neural networks to predict the sewing performance of fabrics in apparel manufacturing. In order to evaluate the performance of the ENRBF neural networks that could be emulated as human decision in the prediction of sewing performance of fabrics more effectively, it could be compared with the traditional back-propagation (BP) neural networks in terms of prediction errors. There are 109 data sets cover fabric properties measured by using a computerized measuring system, and the sewing performance of each fabric's specimen assessed by the domain experts. Of these 109 input—output data pairs, 94 were used to train the proposed ENRBF and BP neural networks for the prediction of the unknown sewing performance of a given fabric, and 15 were used to test the proposed ENRBF and BP neural networks, respectively. After 10,000 iterations of training of the ENRBF and BP neural networks, both of them converged to the minimum error level. A comparison was made between actual fabric performances during sewing, the experts' advices, and the results of predicting fabric performances during sewing for both networks. It was found that the ENRBF and BP neural networks indicate similar error levels, but the prediction made by the ENRBF neural network is better than the prediction made by the BP neural network in some areas. Both the systems provided better advice than the experts in some areas, when compared to actual sewing performance.     

A novel method for the discrimination of Hawthorn and its processed products using an intelligent sensory system and artificial neural networks

Hawthorn (CFS) has commonly been applied as an important traditional Chinese medicine and food for thousands of years. The raw material of CFS is commonly processed by stir-frying to obtain yellow (CFY), dark brown (CFD), and carbon dark (CFC) colored products, which are used for different clinical uses. In this study, an intelligent sensory system (ISS) was used to obtain the color, gas, and flavor samples data, which were further employed to develop a novel and accurate method for the identification of CFS and its processed products using principal component analysis. Moreover, this research developed a model of an artificial neural network, which could be used to predict the total organic acid, total flavonoids, citric acid, hyperin, and 5-hydroxymethyl furfural via determination of the color, odor, and taste of a sample. In conclusion, the ISS and the artificial neural network are useful tools for rapid, accurate, and effective discrimination of CFS and its processed products.     

Acoustic vibration method for food texture evaluation using an accelerometer sensor

A piezoelectric element has been used in an acoustic vibration method for measuring food texture. While it is inserted into a food sample, the piezoelectric element detects the vibration of a probe. The frequency response of the piezoelectric sensor used for the acoustic vibration method was evaluated with a laser Doppler vibrometer (LDV). The output voltage from the piezoelectric sensor, which was driven to vibrate at 23 different frequencies, was monitored and compared with the velocity signal obtained by the LDV. The output signal was substantially affected by the vibration frequencies. The output signals corresponded to displacement of the probe below 3 Hz, to velocity from 10 to 70 Hz, and to the acceleration force from 680 to 1500 Hz. These results clearly indicate that a piezoelectric sensor is impractical to use for the texture measurement and should be replaced with an accelerometer that always generates an acceleration signal irrespective of the applied vibration frequencies. The results also demonstrated that the previously defined texture index (TI) was misleading and overestimated the texture of food at probe vibration frequencies above 10 Hz. Our replacement of the sensor led us to define a new energy texture index (ETI). ETI measurement of several foods, including biscuit, Japanese cracker and vegetables were presented and the effects of water activity of cracker on the index were examined.