Spectrophotometric resolution of ternary mixtures of tryptophan, tyrosine, and histidine with the aid of principal component-artificial neural network models

A simple and sensitive spectrophotometric method to resolve ternary mixtures of tryptophan (Trp), tyrosine (Tyr), and histidine (His) in synthetic and water samples is described. It relies on the different kinetic rates of the analytes in their oxidative reaction with potassium ferricyanide (K(3)Fe(CN)(6)) in alkaline medium. The absorbance data were monitored on the analytical wavelength (420 nm) of K(3)Fe(CN)(6) spectrum. Synthetic mixtures of the three amino acids were analyzed, and the data obtained were processed by principal component-artificial neural network (PC-ANN) models. After reducing the number of spectral data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Tangent and sigmoidal transfer function were used in the hidden and output layers, respectively. The analytical performance of this method was characterized by relative standard error. The method allowed the determination of Trp, Tyr, and His at concentrations between 10 and 55, 10 and 60, and 10 and 40 microg ml(-1), respectively. The results show that the PC-ANN is an efficient method for prediction of the three analytes.     

A simultaneous procedure facilitates acquisition under an optimal interstimulus interval in artificial neural networks and rats

In a computer simulation, a neural network first received a simultaneous procedure, where the interstimulus interval (ISI) was 0 time-steps (ts). Output activations were near zero under this procedure. The network then received a forward-delay procedure where the ISI was 8 ts. Output activations increased to the near-maximum level faster than those of a control network that first received an explicitly unpaired procedure. Comparable results were obtained with rats that first received trials where a retractable lever was presented for 3s concurrently with access to water. Low-lever pressing was observed under this procedure. The rats then received trials where the lever was followed 15s after by water. Lever pressing appeared faster than a control group that received the 15-s ISI after an explicitly unpaired procedure. The model used in the simulation explains these results as connection-weight increments that promote little output activations in a simultaneous procedure, but facilitate acquisition in an optimal ISI.     

A reliable method for spectrophotometric determination of glycine betaine in cell suspension and other systems

Glycine betaine is a quaternary ammonium compound that accumulates in a large variety of species in response to different types of stress. Glycine betaine counteracts adverse effects caused by abiotic factors, preventing the denaturation and inactivation of proteins. Thus, its determination is important, particularly for scientists focused on relating structural, biochemical, physiological, and/or molecular responses to plant water status. In the current work, we optimized the periodide technique for the determination of glycine betaine levels. This modification permitted large numbers of samples taken from a chlorophyllic cell line of the grass Bouteloua gracilis to be analyzed. Growth kinetics were assessed using the chlorophyllic suspension to determine glycine betaine levels in control (no stress) cells and cells osmotically stressed with 14 or 21% polyethylene glycol 8000. After glycine extraction, different wavelengths and reading times were evaluated in a spectrophotometer to determine the optimal quantification conditions for this osmolyte. Optimal results were obtained when readings were taken at a wavelength of 290 nm at 48 h after dissolving glycine betaine crystals in dichloroethane. We expect this modification to provide a simple, rapid, reliable, and cheap method for glycine betaine determination in plant samples and cell suspension cultures.     

Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis

A method for simultaneous, nondestructive analysis of aminopyrine and phenacetin in compound aminopyrine phenacetin tablets with different concentrations has been developed by principal component artificial neural networks (PC-ANNs) on near-infrared (NIR) spectroscopy. In PC-ANN models, the spectral data were initially analyzed by principal component analysis. Then the scores of the principal components were chosen as input nodes for the input layer instead of the spectral data. The artificial neural network models using the spectral data as input nodes were also established and compared with the PC-ANN models. Four different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV), and multiplicative scatter correction) were applied to three sets of NIR spectra of compound aminopyrine phenacetin tablets. The PC-ANNs approach with SNV preprocessing spectra was found to provide the best results. The degree of approximation was performed as the selective criterion of the optimum network parameters.     

Pyrolysis patterns of 5 close Corynebacterium species analyzed by artificial neural networks

In the present study, an artificial neural network was trained with the Stuttgart Neural Networks Simulator, in order to identify Corynebacterium species by analyzing their pyrolysis patterns. An earlier study described the combination of pyrolysis, gas chromatography and atomic emission detection we used on whole cell bacteria. Carbon, sulfur and nitrogen were detected in the pyrolysis compounds. Pyrolysis patterns were obtained from 52 Corynebacterium strains belonging to 5 close species. These data were previously analyzed by Euclidean distances calculation followed by Unweighted Pair Group Method of Averages, a clustering method. With this early method, strains from 3 of the 5 species (C. xerosis, C. freneyi and C. amycolatum) were correctly characterized even if the 29 strains of C. amycolatum were grouped into 2 subgroups. Strains from the 2 remaining species (C. minutissimum and C. striatum) cannot be separated. To build an artificial neural network, able to discriminate the 5 previous species, the pyrolysis data of 42 selected strains were used as learning set and the 10 remaining strains as testing set. The chosen learning algorithm was Back-Propagation with Momentum. Parameters used to train a correct network are described here, and the results analyzed. The obtained artificial neural network has the following cone-shaped structure: 144 nodes in input, 25 and 9 nodes in 2 successive hidden layers, and then 5 outputs. It could classify all the strains in their species group. This network completes a chemotaxonomic method for Corynebacterium identification.     

Prediction of amino acid profiles in feed ingredients:: Genetic algorithm calibration of artificial neural networks

Linear regression (LR) has been used to predict the amino acid (AA) profiles of feed ingredients, given proximate analysis (PA) input. Artificial neural networks (ANN) have also been trained to predict AA levels, generally with better results. Past projects have indicated that ANN more effectively identified the complex relationship between nutrients and feed ingredients than did LR. It was shown that the maximum R² value, a measurement of the amount of variability explained by the model, was highest when a general regression neural network (GRNN) with iterative calibration (GRNNIT) was used to train the ANN. This was in comparison to LR, Ward backpropagation (WBP) or 3-layer backpropagation (3BP) architectures. The current study investigated the potential of a new, advanced method of calibration using the genetic algorithm (GA) to optimize GRNN smoothing values. Calibration of an ANN allows the neural network to generalize well and therefore provide good results on new data. A GRNN architecture (NeuroShell 2^® Software) with GA calibration (GRNNGA) was used to train an ANN to predict AA levels in maize, soya bean meal (SBM), meat and bone meal, fish meal and wheat, based on proximate analysis input. Within the GRNNGA architecture, ANN were trained with either an Euclidean or City Block distance metric and a (0,1), (−1,1), (logistic) or (tanh) input scale. Predictive performance was judged on the basis of the maximum R² value. In general, maximum R² values were higher when the GA calibration was used in comparison to LR. For example, the highest methionine (MET) R² value for SBM was 0.54 (LR), 0.81 (3BP), 0.87 (WBP), 0.92 (GRNNIT) and 0.98 (GRNNGA). Genetic algorithm calibration of GRNN architecture led to further improvements in ANN performance for AA level predictions in most of the cases studied. Exceptions were the TSAA level in SBM (0.94 with GRNNIT vs. 0.90 with GRNNGA) and the TRY level in maize (0.88 with GRNNIT vs. 0.61 with GRNNGA).     

A simulation study comparing the impact of experimental error on the performance of experimental designs and artificial neural networks used for process screening

Many variables and their interactions can affect a biotechnological process. Testing a large number of variables and all their possible interactions is a cumbersome task and its cost can be prohibitive. Several screening strategies, with a relatively low number of experiments, can be used to find which variables have the largest impact on the process and estimate the magnitude of their effect. One approach for process screening is the use of experimental designs, among which fractional factorial and Plackett–Burman designs are frequent choices. Other screening strategies involve the use of artificial neural networks (ANNs). The advantage of ANNs is that they have fewer assumptions than experimental designs, but they render black-box models (i.e., little information can be extracted about the process mechanics). In this paper, we simulate a biotechnological process (fed-batch growth of bakers yeast) to analyze and compare the effect of random experimental errors of different magnitudes and statistical distributions on experimental designs and ANNs. Except for the situation in which the error has a normal distribution and the standard deviation is constant, it was not possible to determine a clear-cut rule for favoring one screening strategy over the other. Instead, we found that the data can be better analyzed using both strategies simultaneously.     

Chemometric-assisted kinetic-spectrophotometric method for simultaneous determination of ascorbic acid, uric acid, and dopamine

A chemometric-assisted kinetic spectrophotometric method has been developed for simultaneous determination of ascorbic acid (AA), uric acid (UA), and dopamine (DA). This method relies on the difference in the kinetic rates of the reactions of analytes with a common oxidizing agent, tris(1,10-phenanthroline) and iron(III) complex (ferritin, [Fe(phen)₃]³⁺) at pH 4.4. The changes in absorbance were monitored spectrophotometrically. The data obtained from the experiments were processed by chemometric methods of artificial neural network (ANN) and partial least squares (PLS). Acceptable techniques of prediction set, randomization t test, cross-validation, and Y randomization were applied for the selection of the best chemometric method. The results showed that feedforward artificial neural network (FFANN) is more efficient than the other chemometric methods. The parameters affecting the experimental conditions were optimized, and it was found that under optimal conditions Beer’s law is followed in the concentration ranges of 4.3–74.1, 4.3–78.3, and 2.0–33.0 μM for AA, UA, and DA, respectively. The proposed method was successfully applied to the determination of analytes in serum and urine samples.     

Automata networks as preprocessing technique of artificial neural network in estimating primary production and dominating phytoplankton levels in a reservoir: An experimental work

Artificial Neural Networks (ANN) is computational architectures that can be used for estimating primary production levels and dominating phytoplankton species in reservoirs. Automata Networks (AN) were applied as a pre-processing method with subsequent ANN model development for Demirdöven Dam Reservoir. The primary purpose of using pre-processing technique was to distinguish the suitable and appropriate constituents of the input parameters' matrix, to eliminate redundancy, to enhance prediction power and calculation efficiency. The data were collected monthly over two years. The applications have yielded following results: The correlation coefficients (r values) between predicted and observed counts were as high as 0.83, 0.87, 0.83 and 0.88 for Cyclotella ocellata, Sphaerocystis schroeteri, Staurastrum longiradiatum counts, and Chlorophyll-a (Chl-a) concentrations respectively with AN. The performance of AN based pre-processing technique was compared with the performance of a well-known pre-processing technique, namely Principle Component Analysis(PCA), experimentally. r values between the predicted and observed C. ocellata, S. schroeteri and S. longiradiatum counts, and (Chl-a) were as high as 0.80, 0.86, 0.81 and 0.86 respectively with PCA.     

Use of artificial neural networks for analysis of the factors affecting particle size in mebudipine nanoemulsion

In this study, a nanoemulsion containing mebudipine [composed of ethyl oleate (oil phase), Tween 80 (T80), Span 80 (S80) (surfactants), polyethylene glycol 400, ethanol (cosurfactants), and deionized water] was prepared with the aim of improving its bioavailability for an effective antihypertensive therapy. Particle size of the formulation was measured by dynamic light scattering. Then, artificial neural networks were used in identifying factors that influence the particle size of the nanoemulsion. Three variables, namely, amount of surfactant system (T80?+?S80), amount of polyethylene glycol, and amount of ethanol as cosurfactants, were considered as input values and the particle size was used as output. The developed model showed that all the three inputs had some degrees of effect on particles size: increasing the value of each input decreased the size. Furthermore, amount of surfactant was found to be the dominant factor in controlling the final particle size of nanoemulsion.

Communicated by Ramaswamy H. Sarma     

Analysis of ethanol–glucose mixtures by two microbial sensors: application of chemometrics and artificial neural networks for data processing

Although biosensors based on whole microbial cells have many advantages in terms of convenience, cost and durability, a major limitation of these sensors is often their inability to distinguish between different substrates of interest. This paper demonstrates that it is possible to use sensors entirely based upon whole microbial cells to selectively measure ethanol and glucose in mixtures. Amperometric sensors were constructed using immobilized cells of either Gluconobacter oxydans or Pichia methanolica. The bacterial cells of G. oxydans were sensitive to both substrates, while the yeast cells of P. methanolica oxidized only ethanol. Using chemometric principles of polynomial approximation, data from both of these sensors were processed to provide accurate estimates of glucose and ethanol over a concentration range of 1.0–8.0 mM (coefficients of determination, R²=0.99 for ethanol and 0.98 for glucose). When data were processed using an artificial neural network, glucose and ethanol were accurately estimated over a range of 1.0–10.0 mM (R²=0.99 for both substrates). The described methodology extends the sphere of utility for microbial sensors.     

Anti-glycated activity prediction of polysaccharides from two guava fruits using artificial neural networks

High-efficiency ultrasonic treatment was used to extract the polysaccharides of Psidium guajava (PPG) and Psidium littorale (PPL). The aims of this study were to compare polysaccharide activities from these two guavas, as well as to investigate the relationship between ultrasonic conditions and anti-glycated activity. A mathematical model of anti-glycated activity was constructed with the artificial neural network (ANN) toolbox of MATLAB software. Response surface plots showed the correlation between ultrasonic conditions and bioactivity. The optimal ultrasonic conditions of PPL for the highest anti-glycated activity were predicted to be 256 W, 60 °C, and 12 min, and the predicted activity was 42.2%. The predicted highest anti-glycated activity of PPG was 27.2% under its optimal predicted ultrasonic condition. The experimental result showed that PPG and PPL possessed anti-glycated and antioxidant activities, and those of PPL were greater. The experimental data also indicated that ANN had good prediction and optimization capability.     

Host–parasitoid population density prediction using artificial neural networks: diamondback moth and its natural enemies

• 1 An integrated pest management (IPM) system incorporating the introduction and field release of Diadegma semiclausum (Hellén), a parasitoid of diamondback moth (DBM) Plutella xylostella (L.), comprising the worst insect pest of the cabbage family, has been developed in Kenya to replace the pesticides‐only approach.
• 2 Mathematical modelling using differential equations has been used in theoretical studies of host–parasitoid systems. Although, this method helps in gaining an understanding of the system's dynamics, it is generally less accurate when used for prediction. The artificial neural network (ANN) approach was therefore chosen to aid prediction.
• 3 The ANN methodology was applied to predict the population density of the DBM and D. semiclausum, its larval parasitoid. Two data sets, each from different release areas in the Kenya highlands, and both collected during a 3‐year period after the release of the parasitoid, were used in the present study. Two ANN models were developed using these data.
• 4 The ANN approach gave satisfactory results for DBM and for D. semiclausum. Sensitivity analysis suggested that pest populations may be naturally controlled by rainfall.
• 5 The ANN provides a powerful tool for predicting host–parasitoid population densities and made few assumptions on the field data. The approach allowed the use of data collected at any appropriate scale of the system, bypassing the assumptions and uncertainties that could have occurred when parameters are imported from other systems. The methodology can be explored with respect to the development of tools for monitoring and forecasting the population densities of a pest and its natural enemies. In addition, the model can be used to evaluate the relative effectiveness of the natural enemies and to investigate augmentative biological control strategies.

     

Deterministic convergence of chaos injection-based gradient method for training feedforward neural networks

It has been shown that, by adding a chaotic sequence to the weight update during the training of neural networks, the chaos injection-based gradient method (CIBGM) is superior to the standard backpropagation algorithm. This paper presents the theoretical convergence analysis of CIBGM for training feedforward neural networks. We consider both the case of batch learning as well as the case of online learning. Under mild conditions, we prove the weak convergence, i.e., the training error tends to a constant and the gradient of the error function tends to zero. Moreover, the strong convergence of CIBGM is also obtained with the help of an extra condition. The theoretical results are substantiated by a simulation example.     

A polarographic method for the simultaneous determination of total glucosinolates and free glucose of cruciferous material

A rapid method for the simultaneous analysis of total free glucose and total glucosinolates in aqueous extracts of cruciferous material is described. The technique, which appears suitable for plant-breeding programs as it allows the processing of more than 100 samples per day, involves the polarographic determination of O2 uptake of free glucose by a system of double-coupled enzymes, such as myrosinase-glucose oxidase. The method has advantages over current methods, because it is very rapid (4 min per analysis), allows two determinations for each analysis, and appears to be very reproducible, accurate, and sensitive.     

A novel spectrophotometric method for determination of kinetic constants of aldehyde oxidase using multivariate calibration method

Although phenanthridine has been frequently used as a specific substrate for the assessment of aldehyde oxidase activity, the use of this method is questionable due to a lower limit of detection and its validity for kinetic studies. In the present study, a novel sensitive multivariate calibration method based on partial least squares (PLS) has been developed for the measurement of aldehyde oxidase activity using phenanthridine as a substrate. Phenanthridine and phenanthridinone binary mixtures were prepared in a dynamic linear range of 0.1–30.0 μM and the absorption spectra of the solutions were recorded in the range of 210–280 nm in Sorenson's phosphate buffer (pH 7.0) containing EDTA (0.1 mM). The optimized PLS calibration model was used to calculate the concentration of each chemical in the prediction set. Hepatic rat aldehyde oxidase was partially purified and the initial oxidation rates of different concentrations of phenanthridine were calculated using the PLS method. The values were used for calculating Michaelis–Menten constants from a Lineweaver–Burk double reciprocal plot of initial velocity against the substrate concentration. The limits of detection for phenanthridine and phenanthridinone were found to be 0.04 ± 0.01 and 0.03 ± 0.01 μM (mean ± SD, n = 5), respectively. Using this method, the K_m value for the oxidation of phenanthridine was calculated as 1.72 ± 0.09 μM (mean ± SD, n = 3). Thus, this study describes a novel spectrophotometric method that provides a suitable, sensitive and easily applicable means of measuring the kinetics of phenanthridine oxidation by aldehyde oxidase without the need for expensive instrumentation.