Challenges in organic component selection and biochar as an opportunity in potting substrates: a review

Plant production in potting substrates provides maximum profit on the applied inputs, and hence, directly improving the socio-economic condition of the grower/nurserymen. The main challenge in this industry is sourcing of materials for their potting substrates. Peat and perlite have been widely preferred materials. However, recently higher prices, more restrictive legislation of many countries and wetland ecosystem destruction through its extraction has limited peat use. Nowadays, producers focus towards peat alternatives that provide good performance, are readily available, inexpensive and environment friendly to attain sustainability in potted plant production. In an effort to grasp sustainability during the last few decades, many industrial and agricultural waste materials were reviewed for their use in potting substrates. In these studies, the major focus remained on material characterization, neglecting their economics, technical aspects and environmental impacts. Thus, switching from peat and perlite to alternatives requires material exploration. In the present review, we summarize a clearer and practical approach for substituting different materials especially biochar to fulfill the need of modern potting substrate industry. Biochar has the potential to sustain the substrate production on a long-term basis.     

Effect of Freeze-Dried Red Beet (Beta vulgaris L.) Leaf Supplementation on Biochemical and Anthropometrical Parameters in Overweight and Obese Individuals: a Pilot Study

Plant Foods for Human Nutrition - Obesity is an important public health concern and is directly related with chronic noncommunicable diseases that affect the health of millions of people around the...     

Relationships between reflectance and absorbance chlorophyll indices with RGB (Red,Green, Blue) image components in seedlings of tropical tree species at nursery stage

Methods based on RGB (Red, Green, Blue) image segmentation may emerge as a new and low-cost method for estimation the quality of tree seedlings. However, the vast number of indexes based on the use of the RGB image segmentation and the lack of references in the literature still hinder the widespread use of this technology. Thus, we conducted a study aiming to test the relationships between methods based on absorbance and reflectance, widely used for the estimation of chlorophyll contents and physiological status of trees, and ten indexes based on RGB component analysis. We used leaves of five tropical tree species, belonging to different botanical families. Leaf absorbance was measured using the handheld chlorophyll meter SPAD-502, reflectance was measured using a spectrometer and the RGB indices were obtained from digitalized images of the leaves using a flatbed scanner. Modified linear regression models including all five species were used to relate RGB indices to absorbance and reflectance indices. Data collected from leaves of seedlings of five tropical tree species indicated that digital image processing technology can be a useful and rapid nondestructive method for assessment of physiological status of tree seedlings at nursery stage. Among the RGB indexes tested in this study the R, 2R*(G???B)/(G?+?B) and 2G*(G???B)/(G?+?B) are the most promising for analysis the tropical seedlings physiological status and quality.

     

Latent and active polyphenol oxidase (PPO) in red clover (Trifolium pratense) and use of a low PPO mutant to study the role of PPO in proteolysis reduction

Polyphenol oxidase (PPO) activity in leaf extracts of wild type (WT) red clover and a mutant line expressing greatly reduced levels of PPO (LP red clover) has been characterized. Both latent and active forms of PPO were present, with the latent being the predominant form. PPO enzyme and substrate (phaselic acid) levels fluctuated over a growing season and were not correlated. Protease activation of latent PPO was demonstrated; however, the rate was too low to have an immediate effect following extraction. A novel, more rapid PPO activation mechanism by the enzyme's own substrate was identified. Rates of protein breakdown and amino acid release were significantly higher in LP red clover extracts compared with WT extracts, with 20 versus 6% breakdown of total protein and 1.9 versus 0.4 mg/g FW of free amino acids released over 24 h, respectively. Inclusion of ascorbic acid increased the extent of protein breakdown. Free phenol content decreased during a 24 h incubation of WT red clover extracts, whereas protein-bound phenol increased and high molecular weight protein species were formed. Inhibition of proteolysis occurred during wilting and ensilage of WT compared with LP forage (1.9 vs 5 and 17 vs 21 g/kg of DM free amino acids for 24 h wilted forage and 90 day silage, respectively). This study shows that whereas constitutive red clover PPO occurs predominantly in the latent form, this fraction can contribute to reducing protein breakdown in crude extracts and during ensilage.     

Simulated digestion and antioxidant activity of red wine fractions separated by high speed countercurrent chromatography

Wine is an important source of dietary antioxidants because of its phenolic compound content. The antioxidant activity (AA) of pure monomer substances present in wines, such as phenolic acids, flavanols, and anthocyanins, has already been described, but the AA of polymeric phenols is still unknown. In this study, we have fractionated a red wine by countercurrent chromatography (CCC) into four fractions: fraction 1, made up of polymeric compounds; fraction 2, containing malvidin-3-glucoside; fraction 3, containing peonidin-3-glucoside; and fraction 4, containing vitisin A. The AA of these fractions was determined by oxygen radical absorbance capacity and ferric reducing ability assays. The weight of fraction 1 was the largest, so this was the largest contributor to the AA of the wine. However, the antioxidant powers (muM Trolox/g fraction) of fractions 2-4 were similar and higher than that of fraction 1. We also determined AA before and after in vitro gastric and intestinal digestions. After gastric digestion, the AA was 100-1000 times higher than the original fraction values. Gallic acid was determined in gastric and intestinal digested fractions. After intestinal digestion, the concentrations of simple phenols, such as caffeic acid, p-coumaric acid, and protocatechualdehyde, increased as they were released from the fractions under our conditions. Protocatechuic acid was determined in more intestinal digested fractions than in gastric digested fractions. These results partly explain the increase in AA after the digestion and indicate the relevance of polymeric polyphenolic compounds as precursors of smaller molecules with biological activity.     

Hypolipidemic effect in cholesterol-fed rats of a soluble fiber-rich product obtained from cocoa husks

A new soluble cocoa fiber product (SCFP), obtained after enzymatic treatment of cocoa husks, was characterized and its potential health effects studied in an animal model of dietary-induced hypercholesterolemia. The SCFP was rich in soluble dietary fiber (DF) and antioxidant polyphenols. Consumption of a cholesterol-rich diet containing the SCFP as a source of DF resulted in lower food intake and body weight gain in comparison with control groups consuming cholesterol-free or cholesterol-rich diets with cellulose as DF. The cholesterol-rich diet caused remarkable hypercholesterolemia. However, the SCFP diminished the negative impact of the cholesterol-rich diet, buffering the decrease of high density lipoprotein-cholesterol, and the increase of total and low density lipoprotein-cholesterol levels, and lipid peroxidation (malondialdehyde levels) induced by the fatty diet. The SCFP also decreased triglyceride levels to values lower than those in the group fed the cholesterol-free diet. These results put forward the potential application of the SCFP as a dietary supplement or functional food ingredient.     

Effects of fly attack (Bactrocera oleae) on the phenolic profile and selected chemical parameters of olive oil

The phenolic fraction of virgin olive oil influences both its quality and oxidative stability. One of the principal threats of the quality of olive fruit is the olive fly ( Bactrocera oleae) as it alters the chemical composition. The attack of this olive pest has been studied in order to evaluate its influence on the quality of virgin olive oil (free acidity, peroxide value, fatty acid composition, water content, oxidative stability, phenols, and antioxidant power of phenolic fraction). The study was performed using several virgin olive oils obtained from olives with different degrees of fly infestation. They were acquired in different Italian industrial mills from the Abruzzo region. Qualitative and quantitative analyses of phenolic profiles were performed by capillary electrophoresis-diode array detection, and electrochemical evaluation of the antioxidant power of the phenolic fraction was also carried out. These analyses demonstrated that the degree of fly attack was positively correlated with free acidity ( r = 0.77, p < 0.05) and oxidized products ( r = 0.58, p < 0.05), and negatively related to the oxidative stability index ( r = -0.54, p < 0.05) and phenolic content ( r = -0.50, p < 0.05), mainly with secoiridoid compounds. However, it has been confirmed that the phenolic fraction of olive oil depends on several parameters and that a clear correlation does not exist between the percentages of fly attack and phenolic content.     

Dietary Photoprotective Compounds Ameliorate UV Tolerance in Shrimp (Pleoticus muelleri) through Induction of Antioxidant Activity

The aim of this study was to determine the effects of ultraviolet radiation (UVR) on bioaccumulation of UV‐absorbing compounds acquired through the diet, in larvae and postlarvae of Pleoticus muelleri, and to assess tissue antioxidant activity, survival, and development. Mysis stage I were exposed to two artificial radiation treatments: M‐PAR (photosynthetically active radiation, range = 400–700 nm) and M‐PAR + UVR (280–700 nm). The experimental larvae received a mixed dietary treatment of Artemia persimilis and the microalga Pavlova lutheri, reared under two radiation regimes: PAR (D‐PAR) and PAR + UVR (D‐PAR + UVR). Shrimp from all treatments reached 8 d postlarval stage (PL8), except those under M‐PAR + UVR treatment fed the D‐PAR‐cultured algae, which had 0% survival. Larvae in M‐PAR + UVR and M‐PAR treatments fed with D‐PAR + UVR diet presented the highest survival rates (70 and 75%, respectively), with 37 and 41% increase in PL size. UV‐absorbing compounds were detected in microalgae and PL subject to PAR + UVR treatments. Antioxidant activity, quantified by measuring the free‐radical 1,1‐diphenyl‐2‐picrylhydrazyl in homogenates of PL8, decayed drastically under radiation treatment M‐PAR + UVR fed with algae of the D‐PAR + UVR treatment. It is concluded that the bioaccumulation of UV‐absorbing compounds and the highest antioxidant activity in PL could improve the biochemical and photophysiological responses of shrimp under UVR stress.     

The use of infrared spectroscopy and machine learning tools for detection of Meloidogyne infestations

Plant parasitic nematodes are generally soilborne pathogens that attack plants and cause economic losses in many crops. The infested plants show nonspecific symptoms or, often, are symptomless; therefore, diagnosis is performed by taking soil and root tissue samples. Here, we show that a combination of different infrared spectra analysis and machine learning algorithms can be used to detect plant parasitic nematode infestations before symptoms become visible, using leaves instead of roots and soil as samples. We found that tomato and guava plants infested with Meloidogyne enterorlobii produced different spectral patterns compared to uninfested plants. Using partial spectra from 1,450 to 900/cm as the "fingerprint region", principal component analyses indicated that after 5 (tomatoes) or 8 weeks (guava), plants with no visible symptoms of infestations were positively diagnosed. To improve the early detection response, we used machine learning modelling. A support vector machine (SVM) was used to obtain more robust, accurate models. The SVM model contained 34 support vectors, 17 for each level. The overall performance of the model was >97% and the total accuracy was significantly higher, demonstrating the absence of chance prediction. The best prediction of infestation was obtained at the second and fourth weeks for tomatoes and guavas, respectively, reducing the diagnostic time by half. The combined application of these techniques reduces the processing time from field to laboratory and shows enormous advantages by avoiding root and soil sampling.