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Diabetes Mellitus (DM) is a metabolic disease caused by secretion and/or poor use of insulin, which produces an excess of glucose in the blood. DMs are responsible for 1.5 million deaths per year and according to the World Health Organization (OMS) it is estimated that there are 422 million people with DM worldwide, growing dramatically in low- and middle-income countries. A poor public healthcare system, a low purchasing power, and a lack of human resources are just some of the conditions that cause the diabetic population to look for supplements or adjunctive alternatives for this disease. On the other hand, the use of plants to cure and/or prevent diseases has prevailed over time. In this sense, there are various plants in popular use that have been attributed antidiabetic properties since they contain bioactive compounds of secondary metabolites such as saponins, polyphenols, terpenes, among others, which act by inhibiting enzymes involved in the adsorption of carbohydrates, improving secretion and insulin sensitivity, have antioxidant and hepatoprotective properties, among other mechanisms of action, thus helping in prevention and/or as adjunctive therapy in the treatment of blood glucose levels regulation and delaying some of the complications derived of the DM.

Mexican spices are used in the supplementation of the human diet and as medicinal herbs for the particularly high amounts of compounds capable of deactivating free radicals. In addition, these spices can have beneficial effects on chronic, no-transmissible diseases such as type II diabetes and hypertension arterial. The objective of this study is to determine the content of phenolic compounds on the antioxidant activity and inhibitory enzymes of &alpha, amylase, &alpha, glucosidase and angiotensin-converting enzyme in melissa, peppermint, thyme and mint, which are subjected to microwave drying, conventional and freeze-drying to be used as alternative treatments. Spices were evaluated to determine total phenols, flavonoids, tannins, 2,2-Diphenyl-1-picrylhydrazyl (DPPH), (2,2&prime, azino-bis- (3-ethyl benzothiazolin-6-ammonium sulphonate) (ABTS) and Ferric Reducing/Antioxidant Power (FRAP), enzymatic activity. The investigation showed that conventional drying caused a decrease in antioxidant properties and inhibitory activity, in some species, while remained preserved in microwave drying and freeze-drying. The activity of polyphenol oxides and peroxidase decreases with high temperatures and these increase with the use of cold temperatures. This study aims to determine the extent of optimal drying required to preserve phenolic compounds, and the positive effect on antioxidant activity and enzymatic activity in in vitro models, which will produce benefits for the infusion processing industry and the pharmaceutical industry.

The effect of the application of salicylic acid (SA) and hydrogen peroxide (H2O2) was evaluated on sensory quality traits of two commercial sweet bell pepper varieties, Fascinato and Orangela, by descriptive sensory evaluation, principal component analysis (PCA), and Multivariate Analysis of Variance (MANOVA). A sensory descriptive lexicon was established for the sweet pepper and an intensity score was assigned for 25 sensory attributes in all the samples. Such intensity score was performed by twelve trained panelists. Among the results it was shown that foliar application of elicitors to pepper crop produced significant differences in sensory traits of the harvested fruits. MANOVA allowed detecting differences in aroma profile among treatments and varieties, showing important changes depending on the combination of elicitors applied on each variety. The principal component analysis (PCA), showed that the PC1 explained 81.02%, while PC2 explained 9.24% of the variance in the results. A strong correlation between varieties and treatments 2 (36 mM H2O2) and 4 (0.1 mM SA and 36 mM H2O2) with PC1 was observed. Treatments 2 and 4 were related to changes in aroma and flavor traits. Fascinato was more susceptible to the effect of elicitor than Orangela. Therefore, elicitor combinations used in this experiment were capable to improve sensory quality of peppers, and not only promote an increase in shelf life as previous studies have shown.

Berries belong to the best food sources of bioactive compounds, as they are phenolic compounds (phenolic acids, flavonoids, such as anthocyanins and flavonols, and tannins), and ascorbic acid, which forms the most representative composition of these bioactive compounds. Berries with the highest number of bioactive compounds are found in the varieties Rosaceae, Ericaceae, and so on. Therefore, the research suggests an inverse correlation between fruit intake and cancer development. The functional properties in berries are currently recognized and there is a wide consumption of these fruits. Currently, these compounds, either individually or in combination, are responsible for various health benefits of berries, such as prevention of inflammatory disorders, cardiovascular diseases, or protective effects to reduce the risk of various types of cancer, no precise results on the reduction of oxidative stress by modulating protein and lipid oxidation. Berries have a range of phytochemicals that are suggested to exert anticancer effects through different mechanisms. This chapter summarizes the biological compounds active in berries in relation to the prevention and treatment of cancer. Studies on bioavailability and potential toxicity of bioactive compounds also participate in this review. Finally, it is necessary to emphasize in relation in to plant crops and use of elicitors to increase their bioactive compounds and synthesis focused on nutrition and health.

Graphical abstractDisplay Omitted FAST and Sobol' sensitivity analysis methods were compared.Genetic algorithms were performed to calibrate reduced TOMGRO model.Reduced TOMGRO model calibration allowed an adequate goodness-of-fit of the simulations.Sensitivity analysis showed the effect of growing conditions on TOMGRO parameters.Current TOMGRO structure can be used to simulate tomato growth in Central Mexico. One common constraint for using crop models for decision making in precise greenhouse crop management is the need for accurate values of model parameters depending on climate conditions, crop varieties, and management. Estimating these parameters from observed data on the crop, using a crop model, is an interesting possibility. Nevertheless, the accuracy of estimations depends on the sensitivity of the model output variables to the parameters. Therefore, this paper proposes the use of the reduced state variable TOMGRO model which describes nodes, leaf area index, total plant weight, total fruit weight, and mature fruit weight as states variables. The objective of this work was to compare EFAST and Sobol' sensitivity analysis methods to determine the most sensitive parameters for TOMGRO model outputs. A former sensitivity analysis showed that 8 parameters were the most sensitive and they were calibrated using genetic algorithms (GAs) to adapt the model to semi-arid weather conditions of Central Mexico. Genetic algorithms are important heuristic search algorithms for optimization problems and have been used to calibrate non-linear models related to control of greenhouse climate conditions. Simulation and analysis of the TOMGRO model showed that the estimations for the state variables are close to the measured data. The model could be adapted for simulating other greenhouse crops by means of sensitivity analysis and calibration.

Commonly carotenoid determinations in tomato are performed in full ripening tomatoes. In this work six tomato ripening stages were established. The relationship among color parameters (L*, a*, b*, and hue), maturity stages, and leaf area with the lycopene and β-carotene concentration was analyzed with different regression models. The R2 values were low, showing that lycopene and β-carotene content was not well correlated with color during the ripening stages. The objective of this work was to provide an ANN model including leaf area index (LAI) and color readings as inputs to solve this lack of fit of the regression models for carotenoid estimations in tomatoes. Two multilayer perceptrons (MLPs) were trained and validated, with six input variables and one output variable, to estimate the concentration of both carotenoids in tomato samples at different ripening stages. Comparing the results of the MLPs with those obtained by regression models it was concluded that when the MLPs are used within the range studied, they are able to estimate lycopene and β-carotene concentrations of tomato with accuracy and reliability, solving the lack of fit by regression models.

The study of secondary metabolism in plants is an important source for the discovery of bioactive compounds with a wide range of applications. Today these bioactive compounds derived from plants are important drugs such as antibiotics, and agrochemicals substitutes, they also have been economically important as fl avors and fragrances, dyes and pigments, and food preservatives. Many of the drugs sold today are synthetic modifi cations of naturally obtained substances. There is no rigid scheme for classifying secondary metabolites, but they can be divided into different groups based on their chemical components, function and biosynthesis: terpenoids and steroids, fatty acid-derived substances and polyketides, alkaloids, phenolic compounds, non-ribosomal polypeptides and enzyme cofactors. The increasing commercial importance of these chemical compounds has resulted in a great interest in secondary metabolism, particularly the possibility of altering the production of bioactive plant metabolites by means of tissue culture technology and metabolomics. In today’s world the use of bioactive compounds derived from plants plays an important role in pharmaceutical applications. This review presents information about these metabolites and their applications as well as their importance in agronomy and bioactive effects on human health as nutraceuticals. This review includes also the new tendencies to produce these bioactive compounds under different stresses conditions such as biotic and abiotic stress that could be included in production systems.

Modifications in growing techniques can affect the yield and nutritional quality of various cultivated plant species. Owing to its high nutritional value, sweet pepper (Capsicum annuum L.) was used in this study as a model plant to investigate the effect of abiotic stress (Electrical conductivity [EC]) and elicitor (Salicylic acid [SA]) on yield and fruit quality parameters under conditions of greenhouse. Nonetheless, the application of elicitors, are stable molecules that induce the activation of transduction cascades and hormonal pathways, which trigger induced resistance to environmental stress. The combination in metabolomics of advanced analytical methods and bioinformatics tools provides wide chemical compositional data that contributes to corroborate (or not) the substantial equivalence. The aim was evaluated the endogenous H2O2 production caused by the effect of different concentrations of (SA) in Capsicum annuum L. in production after elicitation in seedling, and the production of secondary metabolites using GC-MS (flavonoids, tannins and phenolic content as well as the antioxidant properties) in plant and fruit of Capsicum annuum L. to relate their response in metabolic pathways. Results displayed that the use SA, induced an endogenous H2O2 and enzymatic activities related with plant defense as phenylalanine ammonia lyase and catalase. This production showed results that could be an indicator for determining application opportunity uses in agriculture for maintaining plant alert systems against a stress. These results were correlated with those obtained from cultivation of capsicum annum until obtaining the fruit at ripe stage had a high content of bioactive compounds that exhibited significant antioxidant properties. The correlation of the contents of flavonoids, tannins and total phenolics and the ability to remove free radicals of the sweet pepper was significant (r = 0.99, P

Leaf area (LA) is a crucial biophysical variable that is indispensable for many physiological and agronomic models. A reliable and accurate model based on artificial neural networks (ANNs) is proposed to estimate LA of tomato growth under greenhouse conditions. The multi-layer perceptron (MLP) ANN topology was selected for the present study with 5 (ANN5) and three (ANN3) input variables, the architectures were 5-10-1 and 3-9-1, respectively. These MLPs were trained and tested to simulate the response of leaf area with linear measurements leaf length and width. In order to prove the selected topology the ANN was tested with data (leaf length and width) from different experimental growth conditions. Both models had good precision with root mean square errors (RMSEs) of 14.86 and 22.56 cm2, and mean absolute errors (MAEs) of 10.29% and 16.74%, and coefficients of determination (R2) of 0.94 and 0.89, respectively, indicating that the ANN5 model can accurately describe the complex relationship between climate factors (CO2, temperature, and radiation) in different treatments. LA development varied with the different treatments. For high levels of CO2 and temperature, the LA tends to increase highly with respect to the observed area. Variable impact analysis was performed on the input variables; width and length were the variables which impacted the most on LA estimation. Temperature and salicylic acid (SA) concentration were the variables which affect the tomato LA development during the simulations. Overall, ANN models are a useful tool in investigating and understanding the relationships between LA development and climate factors under greenhouse conditions.

La calidad del fruto es un término complejo definido por una sofisticada cadena de procesos biológicos. Dichos procesos (respiración, fotosíntesis, etc.) involucra intercambios entre el fruto y su ambiente. La integración cuantitativa de estos procesos para monitorear el comportamiento del fruto durante su desarrollo es una tarea que definitivamente debe involucrar la modelación matemática. Actualmente, el interés en la modelación matemática de los cambios en la calidad del fruto ha ido en aumento. Es posible a través de la modelación matemática evaluar la calidad de los productos finales para satisfacer las demandas del consumidor y para ajustar o mejorar las decisiones tomadas con relación a los calendarios de siembra y cosecha y para la comercialización de los frutos. Los modelos deben ser lo suficientemente mecanísticos para proporcionar una descripción representativa de los procesos fisiológicos y explicar las variaciones en los rasgos de calidad de los frutos. Los modelos actuales son más precisos y capaces de predecir el comportamiento de interacciones tan complejas como las de tipo genotipo-ambiente. Se han comenzado a hacer esfuerzos por definir aspectos importantes de la calidad de los frutos e integrarlas en los modelos de crecimiento de hortalizas. Técnicas como el análisis sensorial pueden proporcionar información importante para establecer correlaciones entre los atributos sensoriales y fisicoquímicos. Incluyendo dicha información en los modelos matemáticos puede ayudar a tener una comprensión más amplia de la interacción ambientemetabolismo y su influencia en los atributos de calidad. La agricultura de precisión ofrece la posibilidad de modificar las condiciones de clima con el propósito de mejorar las características fisicoquímicas y sensoriales de los frutos Fruit quality is a complex issue defined as a sophisticated chain of biological processes (Genard et al., 2007). These processes (transpiration, respiration, photosynthesis) involve exchanges between the fruit and its environment. Quantitative integration of these processes to monitor fruit¿s behavior is a task involving physiological modeling. Nowadays, the interest in mathematical modeling about the quality changes during fruit maturation has been increased (Wegehenkel and Mirschel, 2005). It is possible through simulation to evaluate the quality of final products in order to satisfy quality consumer demands and to adjust or improve the decision making related to harvest dates and product commercialization. Adequate models should be mechanistic enough to give a representative description of physiological processes and explain variations in quality traits. Recently, models have become more accurate and better able to predict the outcome of complex issues such as genotype-environment interactions. Furthermore, efforts have been made to define fruit quality and integrating it with crop growth models. These models are based on accurate descriptions of early stages of growth, including fresh fruit mass, dry matter content and concentration of sugars. Nevertheless, most of models do not include variables related to quality traits such as flavor and aroma. GC-O techniques could provide important information in order to establish correlations among physicochemical and sensory attributes. Including this information in mathematical models can lead to a wider comprehension of the metabolism-environment interaction and their influence on quality attributes. Precision agriculture offers the possibility to control environment with the purpose of enhance physicochemical and sensory characteristics in crops.

Mathematics is an important tool for system modeling allows us to describe the behavior of a phenomenon or system in the real world, in particular biological systems. This chapter gives an overview of mathematical models, their construction, types of models, and examples of possible applications in biosystems models. Essential for building a model is determining its scope. In addition, the mechanistic and phenomenological mathematical models are described. Applications on fish biomass estimation, quality of fruits and crops are presented.

Descriptive sensory analysis combined with a chemical analysis, provided insight regarding sensory significance for a better understanding of berrycactus' flavor. This study was based on the volatile components derived from the gas chromatography-olfactometry (GC-O) analysis. Four different ripening stages were used for the experiments: unripe, red, transition, and over-ripe. Besides red and transition stages were stored in sealed plastic bags for 7 d at 5 °C as an additional treatment in order to establish comparisons with fresh samples. GC-O analysis was performed following OSME (time-intensity) method. Moreover, chemical characterization was achieved through GC–MS analysis, each compound was tentatively identified by comparing its mass spectra with the spectra from the library NIST02, by its aroma notes and Kovats Index. The aroma descriptors found were: caramel, fruity, acetic acid, fresh, citrus, floral, and phenolics. Nine volatile compounds are among the most important in flavor of berrycactus: furfural, 5-methyl-2-furancarboxaldehyde, 2(5H)-furanone, 5-acetoxymethyl-2-furaldehyde, 2-cyclohexen-1-ol, octanoic acid ethyl ester, decanoic acid ethyl ester, octanoic acid, and phenylethyl alcohol. To better reflect the real responses of these compounds, the regression analysis of concentrations to ripening stages were performed. The 2,3-dihydro-3,5-dihydroxy-6-methyl-(4H)-pyran-4-one was found in berrycactus, it has been reported in chemical profile of other berries as a product of heat-induced reactions, however, this compound has no aroma activity. The data suggest that both furanones and esters contribute to the aroma of berrycactus. On the other hand, solid phase microextraction was found to be more useful in detecting esters and acids whereas solvent extraction was more effective in detecting furanones and ketones. Practical Application: This information could contribute to setting up favorable processing conditions; in order to retain the best sensory characteristics of berrycactus along with functional properties that would enhance commercialization and exploitation of this fruit and to promote berrycactus cultivation programs.

Fungi play a very important, but yet mostly unexplored role. Their widespread occurrence on land and in marine life makes them a challenge and a risk for humans (Brase et al., 2009). Fungi are ingenious producers of complex natural products which show a broad range of biological activities (Bohnert et al., 2010). However, a specific characteristic is the production of toxins. Mycotoxins (from “myco” fungus and toxin), are nonvolatile, relatively low-molecular weight, fungal secondary metabolic products (Brase et al., 2009). The most agriculturally important micotoxins are aflatoxins (AF) which are a group of highly toxic metabolites, studied primarly because of their negative effects on human health. Aflatoxins belong to a group of difuranocumarinic derivatives structurally related, and are produced meanly by fungi of genus Aspergillus spp. Its production depends on many factors such as substrate, temperature, pH, relative humidity and the presence of other fungi. It has been identified 18 types of aflatoxins; the most frequent in foods are B1, B2, G1, G2, M1, and M2 (Bhatnagar et al., 2002). These secondary metabolites contaminate a number of oilseed crops during growth of the fungus and this can result in severe negative economic and health impacts (Cary et al., 2009). The higher levels of aflatoxins have been found in cotton and maize seeds, peanuts, and nuts. In grains like wheat, rice, rye or barley the presence of aflatoxins is less frequent. Mycotoxins may also occur in conjugated form, either soluble (masked mycotoxins) or incorporated into/ associated with/attached to macromolecules (bound mycotoxins). These conjugated mycotoxins can emerge after metabolization by living plants, fungi and mammals or after food processing. Awareness of such altered forms of mycotoxins is increasing, but reliable analytical methods, measurement standards, occurrence, and toxicity data are still lacking (Berthiller et al., 2009). A variety of studies has been conducted in order to understand the process of crop contamination by aflatoxins. Mycotoxins are dangerous metabolites that are often carcinogenic, and they represent a serious threat to both animal and human health (Reverberi et al., 2010). Mycotoxins are considered secondary metabolites because

Maize (Zea mays L.) is one of the most important crops in the world. It is the third most important food grain crop in the developing world and is estimated that the demand for maize in developing countries will grow by 50%, from 558 million tons in 1995 to 837 million tons in 2020. Much of this increased demand will be needed by domestic supply for developing countries, which will require intensifying production on existing agricultural land (Ribaut and Ragot, 2007). Drought is one of the prime abiotic stress in crops in the world. Crop yield losses due to drought stress are considerable. Particularly in maize, as an example, drought is the major stress affecting productivity in Africa leading up to 70% or total crop loss (Muoma et al., 2010; Ashraf, 2010). Although a variety of approaches have been used to alleviate the problem of drought, plant breeding, either conventional breeding or genetic engineering, seems to be an efficient and economic means of tailoring crops to enable them to grow successfully in drought-prone environments (Ashraf, 2010). In turn, aflatoxins are found to contaminate a wide variety of important agricultural products such as corn, peanuts, tree nuts and cottonseed especially under extreme heat and drought conditions (Payne, 1998; Chen et al., 2003). Aflatoxin contamination significantly reduces the value of grain as an animal feed and export commodity (Chen et al., 2002).

A review of mathematical modeling applied to fruit quality showed that these models ranged in resolution from simple yield equations to complex representations of processes as respiration, photosynthesis and assimilation of nutrients. The latter models take into account complex genotype-environment interactions to estimate their effects on growth and yield. Recently, models are used to estimate seasonal changes in quality traits as fruit size, dry matter, water content and the concentration of sugars and acids, which are very important for flavor and aroma. These models have demonstrated their ability to generate relationships between physiological variables and quality attributes (allometric relations). This new kind of hybrid models has sufficient complexity to predict quality traits behavior. Key words: Mathematical modeling, fruit quality, respiration, photosynthesis and assimilation of nutrients.