First report on the production of phytotoxic metabolites by Mycoleptodiscus indicus under optimized conditions of submerged fermentation.

An alternative to controlling weeds resistant to conventional herbicides is the isolation of new active principles. Fungi can produce phytotoxic metabolites that may be used in the development of new herbicides. The objectives of this study were: (1) isolate, select, and identify a fungus producer of phytotoxic metabolites and (2) optimize the culture conditions of this fungus in a low-cost culture medium, with the aim of increasing the phytotoxic effects of their metabolites in weeds and commercial plants. Fungi were isolated from the leaves of Conyza sp. with disease symptoms and selected according to the production of phytotoxic metabolites in solid and submerged fermentation in a low-cost culture medium. A Plackett-Burman Design and Central Composite Rotational Design were used to optimize the conditions of temperature, agitation, pH, and concentrations of glucose and yeast extract in submerged fermentation. The phytotoxic metabolites produced under optimal conditions were tested on 10 commercial plants and weeds that are difficult to control. Of the nine fungi isolated, Mycoleptodiscus indicus UFSM54 produced higher leaf lesions. The production of phytotoxic metabolites was optimized when the fungus was cultivated at 35°C, 50 rpm, and 1.5 g L-1 of glucose in submerged fermentation. The metabolites of M. indicus caused severe phytotoxic effects on germination and seedling growth, and enhanced lesion development on detached plant leaves. The present study is the first to report on the production of phytotoxic metabolites by M. indicus, a potential producer of bioherbicides.

Using segmental bioimpedance analysis to estimate soft tissue and chemical composition of retail cuts and carcasses of lambs.

This study evaluated the potential of segmental bioimpedance analysis (SBIA) to estimate the composition of retail cuts and their predictability to infer on the carcass composition in lambs. Leg, rib, shoulder, neck, and loin from thirty-one lamb carcasses were evaluated. A single-frequency bioelectrical impedance analyzer at 50 kHz was used to perform measurements. The models for estimating soft tissue showed the highest accuracy in the retail cuts. Lean and fat weight of the lamb cuts or of the carcasses were predicted with R2 of calibration ranging from 86.6 to 99.1% and from 67.5 to 95.4%, respectively. Segmental bioimpedance analysis is an accurate technology to assess physical and chemical components in retail cuts of lamb. Despite that, shoulder was the most representative cut; all cuts evaluated through SBIA were valuable to estimate the components of the edible portion of lamb carcasses.

Assessing the composition of the soft tissue in lamb carcasses with bioimpedance and accessory measures.

Consumers are demanding additional information to support their decision-making while shopping for meat. In the lamb industry, labelling carcasses with composition information is challenging. This is due to issues with conventional analytical procedures, such as the time spent with determinations and product loss or devaluing due to sampling for analysis. The objective was to evaluate the potential use of bioimpedance analysis (BIA) to determine composition of the soft tissue portion of lamb carcasses. Thirty-one Texel and Ile-de-France crossbreed ram lambs were slaughtered at 20, 26, 32, or 38 kg of body weight. Values of resistance and reactance were collected from hot and cold carcasses, which weighed 12.4 ± 2.99 kg and 11.9 ± 2.94 kg, respectively and measured 53.9 ± 3.25 cm of length. Carcass weight and length were used to calculate other BIA variables such as impedance modulus, phase angle, bioelectrical volume, and both resistive and reactive densities. These variables were used as independent variables to predict the contents of soft tissue, moisture, ash, protein, fat, lean, and crude energy of the carcasses. Multiple regression analyses were carried out to calibrate BIA models. The leave-one-out cross-validation was performed to evaluate precision and accuracy of the BIA technique. Resistive density was the most important BIA variable to predict lamb composition of hot carcasses, which explained 83% to 92% of the variation in composition. In turn, reactive density better predicted lamb carcass composition in cold carcasses, which accounted for 81% to 92% of their variation in carcass composition. In addition, prediction models of the soft tissue portion of lamb carcasses assessed on cold carcasses showed a higher coefficient of determination and smaller root mean square error and Mallows Cp values than hot carcasses. Therefore, BIA has an excellent potential to predict lamb carcass components on either hot as cold carcass; however, higher accuracy was found with cold carcasses in comparison with hot.

Prediction of lamb body composition using in vivo bioimpedance analysis.

The objective of this study was to evaluate the potential of in vivo bioimpedance analysis (BIA) as a method to estimate body composition in lambs. Thirty-one Texel x Ile de France crossbreed ram lambs were slaughtered at pre-determined intervals of average weights of 20, 26, 32, and 38 kg. Before the slaughter of the animals, their body weight (BW) and body length (BL) were measured. The values for resistance (Rs) and reactance (Xc) were collected using a single-frequency BIA equipment (Model RJL Quantum II Bioelectrical Body Composition Analyzer). The BIA main variables such as body bioelectrical volume (V), phase angle (PA), resistive density (RsD), and reactive density (XcD) were then calculated. The soft tissue mass of the right-half cold carcass was analyzed in order to determine its chemical composition. Multiple regression analyses were performed using the lamb body composition as dependent variables and the measurements related to bioimpedance as independent variables. The best regression models were evaluated by cross-validation. The predictive model of moisture mass, which was developed by using XcD and V, accounted for 84% of its variation. Resulting models of percentage moisture (R2 = 0.79), percentage lean mass (R2 = 0.79), percentage fat (R2 = 0.79), and fat mass (R2 = 0.87) were obtained using RsD and V. Furthermore, the values of RsD regarding V, and PA in the prediction models accounted for 91% and 89% of variation in protein mass and lean mass, respectively. Bioimpedance analysis proved to be an efficient method to estimate the body composition of lambs slaughtered at different body mass stages.