Mehmet Serhat Odabas

Changes in the contents of main secondary metabolites in two Turkish Hypericum species during plant development.

Context: The genus Hypericum (Guttiferae) has received considerable scientific interest as a source of biologically active compounds. Objective: The study determined the morphogenetic and ontogenetic variation in the main bioactive compounds of two Hypericum species, namely, Hypericum aviculariifolium subsp. depilatum var. depilatum (Freyn and Bornm.) Robson var. depilatum and Hypericum orientale L. through HPLC analyses of whole plants as well as individual plant parts (stems, leaves, and reproductive tissues). Materials and methods: The plant materials were harvested at five phenological stages: vegetative, floral budding, full flowering, fresh fruiting, and mature fruiting; dried at room temperature, then assayed for chemical content. Results: In H. aviculariifolium, no kaempferol accumulation was observed and the highest level of hypericin, pseudohypericin, and quercitrin was reached at full flowering (0.71, 1.78, and 4.15 mg/g DW, respectively). Plants, harvested at floral budding produced the highest amount of rutin, hyperoside, and isoquercitrine (32.96, 2.42, 1.52 mg/g DW, respectively). H. orientale did not produce hypericin, pseudohypericin, or kaempferol. Rutin, hyperoside, and isoquercetine levels were the highest at floral development (1.76, 11.85, and 1.21 mg/g DW, respectively) and plants harvested at fresh fruiting produced the highest amount of quercitrine and quercetine (0.20 and 1.30 mg/g DW, respectively). Discussion: For the first time, the chemical composition of the Turkish species of Hypericum was monitored during the course of ontogenesis to determine the ontogenetic and morphogenetic changes in chemical content. Conclusions: Plant material should be harvested during flower ontogenesis for medicinal purposes in which the content of many bioactive substances tested reached their highest level.

Secondary metabolites of Hypericum orientale L. growing in Turkey: variation among populations and plant parts

The present study was conducted to determine the variation in the content of several plant chemicals, namely hyperforin, hypericin, pseudohypericin, chlorogenic acid, rutin, hyperoside, isoquercetine, kaempferol, quercitrine and quercetine among ten Hypericum orientale L. populations from Northern Turkey. The aerial parts representing a total of 30 individuals were collected at full flowering and dissected into floral, leaf and stem tissues. After dried at room temperature, the plant materials were assayed for chemical contents by HPLC. The populations varied significantly in chemical contents. Among different plant parts, the flowers were found to be the principle organ for hyperforin, hypericin, pseudohypericin and rutin accumulations while the rest of the chemicals were accumulated mainly in leaves in all growing localities. The chemical variation among the populations and plant parts is discussed as being possibly the result of different genetic, environmental and morphological factors.

Models of estimation of the content of secondary metabolites in some Hypericum species.

In the present study, models for estimation of the content of main secondary metabolites, namely hypericin, pseudohypericin, and hyperforin, were developed for Hypericum origanifolium Willd. (Guttiferae), Hypericum perfoliatum L., and Hypericum montbretii Spach., growing in Northern Turkey. Wild growing plants were harvested at vegetative, floral budding, full flowering, fresh fruiting, and mature fruiting stages and dissected into stem, leaf, and reproductive tissues. Actual secondary metabolite contents of plant materials were measured by a high performance liquid chromatography method. Multiple regression analysis using the Excel 2003 computer package was performed for each species and chemical separately to develop multiple regression models. The equation produced for predicting the content of secondary metabolites in different tissues of the species was formulized as: SMC = [a + (b1 × S) + (b2 × L) + (b3 × RP) + (b4 × S²) + (b5 × (1/RP))], where SMC is the secondary metabolite content of the whole plant, S is the secondary metabolite content of the stem, L is the secondary metabolite content of the leaf, RP is the secondary metabolite content of the reproductive parts, and a, b1, b2, b3, b4, and b5 are coefficients. The R2 coefficient values between predicted and observed contents of secondary metabolites were determined as 0.99 for H. origanifolium, 0.95–0.98 for H. perfoliatum, and 0.90–0.99 for H. montbretii. All R² values and standard errors were found to be significant at the p < 0.05 level.

The Estimation of Adult and Nymph Stages of Aphis fabae (Hemiptera: Aphididae) Using Artificial Neural Network

ABSTRACT In this research, the estimation of adult and nymph stages and adult of Aphis fabae was investigated using artificial neural network. Determining A. fabae nymph stages is difficult. Morphometric study of different parts of an insects body is needed to obtain an index to distinguish between different immature stages. The study was aimed to develop a model of A. fabae nymph stages and adult using length of hind tibia, antenna and body length. It was found that the constructed artificial neural network (ANN) exhibited high performance for predicting A. fabae nymph stages. Correlation was 99% and the estimation of the best ANN model was determined to be 0.016289 at epoch 18. Software computing techniques are very useful tools for precision agriculture and also determining which method gives the most accurate result.

Effect of Salt Stress and Irrigation Water on Growth and Development of Sweet Basil (Ocimum basilicum L.)

Abstract This study was conducted to assess the influence of different salinity and irrigation water treatments on the growth and development of sweet basil (Ocimum basilicum L.). Five salinity levels (0.4, 1.00, 2.50, 4.00 and 8.00 dSm-1) and three different irrigation water regimes (80, 100, 120% of full irrigation) were applied in a factorial design with three replications. Dry root weight, aerial part dry weight and aerial part/root ratio were determined and evaluated as experimental parameters at the end of growing period. Results revealed significant decreases in yields with increasing salinity levels. However, basil managed to survive high salt stress. With increasing salinity levels, decreases in growth were higher in roots than in leaves. Changes in the amount of irrigation water also significantly affected the evaluated parameters.

DETERMINA TION OF REFLECTANCE VALUES OF HYPERICUM'S LEAVES UNDER STRESS CONDITIONS USING ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM

The effects of water stress and salt levels on hypericums leaves were examined on greenhouse-grown plants of Hypericum perforatum L. by spectral reflectance. Salt levels and irrigation levels were applied 0, 1, 2.5 and 4 deci Siemens per meter (dS/m), 80%, 100% and 120% respectively. Adaptive Network based Fuzzy Inference System (ANFIS) was performed to estimate the effects of water stress and salt levels on spectral reflectance. As a result of ANFIS, it was found that there was close relationship between actual and predicted reflectance values in Hypericum perforatum L. leaves. Performance of ANFIS was examined under different numbers of epoch and rules. On the other hand, RMSE, correlation and analysis time values were found as outputs. Correlation was 99%. The estimation of optimal ANFIS model was determined in 3*3*3 number of rules with 400 epochs.

Multilayer Perceptron Neural Network Approach to Estimate Chlorophyll Concentration Index of Lettuce (Lactuca sativa L.)

ABSTRACT Nitrogen is an essential nutrient for greenhouse-grown lettuce (Lactuca sativa L.); however, excessive nutrient availability causes disease and detrimental effects on the leaf and root development. In this study, nitrogen content of the lettuce leaves was estimated by determining the chlorophyll concentrations of the leaves using image processing technique. The Hoagland solution was used as a fertilizer in five different doses (control, quarter of the solution, half of the solution, standard solution, and two times more of the solution). Multilayer perceptron neural network (MLPNN) model was developed based on the red, green, and blue components of the color image captured to estimate chlorophyll content and chlorophyll concentration index (SPAD values). According to the obtained results, the MLPNN model was capable of estimating the lettuce leaf chlorophyll content with a reasonable accuracy. The coefficient of determination was 0.98, and mean square error was 0.006 in validation process.

Using Artificial Neural Network and Multiple Linear Regression for Predicting the Chlorophyll Concentration Index of Saint John’s Wort Leaves

ABSTRACT This research investigates and compares artificial neural network and multiple linear regression for predicting the chlorophyll concentration index of Saint John’s wort leaves (Hypericum perforatum L.). Plants were fertilized with 0, 30, 60, 90, and 120 kg ha−1 nitrogen [34% nitrogen ammonium nitrate (NH4NO3)]. Chlorophyll concentration index of each leaf was measured using SPAD meter. Afterwards, rgb (red, green, and blue color) values of all leaf images were determined by image processing. Values obtained were modeled using both multiple regression analysis and artificial neural networks. Using multiple regression analysis R2 values were between 0.61 and 0.97. Coefficient of determination values (R2) using artificial neutral network values were found to be 0.99. Artificial neutral network modeling successfully described the relationship between actual chlorophyll concentration index values and predicted chlorophyll concentration index values.

Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions

Lack of water resources and high water salinity levels are among the most important growth-restricting factors for plants species of the world. This research investigates the effect of irrigation levels and salinity on reflectance of Saint John’s wort leaves (Hypericum perforatum L.) under stress conditions (water and salt stress) by multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Empirical and heuristics modeling methods were employed in this study to relate stress conditions to leaf reflectance. It was found that the constructed ANN model exhibited a high performance than multiple regression and ANFIS in estimating leaf reflectance accurately.

Estimation of Chlorophyll Concentration Index at Leaves using Artificial Neural Networks

In this study, the effectiveness of an SPAD-502 portable chlorophyll (Chl) meter was evaluated for estimating the Chl contents in leaves of some medicinal and aromatic plants. To predict the individual chlorophyll concentration indexes of St. John’s wort (Hypericum perforatum L.), mint (Mentha angustifolia L.), melissa (Melissa officinalis L.), thyme (Thymus sp.), and echinacea (Echinacea purpurea L.), models were developed using SPAD value. Multi-layer perceptron (MLP), adaptive neuro fuzzy inference system (ANFIS), and general regression neural network (GRNN) were used for determining the chlorophyll concentration indexes.