Potential application of Saccharomyces cerevisiae strains for the fermentation of banana pulp

This paper aimed at evaluating the fermentation behavior of selected Saccharomyces cerevisiae strains in banana pulp and they were compared with commercial yeast (baker’s yeast) for subsequent production of distilled spirits. Five types of microorganisms were used: Four yeast strains obtained from accredited microbiology laboratories were isolated from domestic cachaça distilleries (UNICAMPV1, UFMG-A905, UFMG-A1007 and UFMG-A1240) and commercial pressed yeast (COMMERCIAL-yeast). The fermentation parameters of the studied S. cerevisiae strains were significantly different. COMMERCIAL-yeast and UNICAMP-V1 strain presented higher ethanol yield and better yeast efficiency in converting total reducing sugars (TRS) into alcohol, with ethanol yields of 83.07 and 94.06%, and yeast efficiency of 90.75 and 96.41%, respectively for UNICAMP V1 and COMMERCIAL-yeast. The higher alcohol contents of 82.26 and 78.05 mg/100 ml anhydrous alcohol were obtained by the UNICAMP-V1 and COMMERCIAL-yeast, respectively. No significant differences in fermentative parameters were observed between COMMERCIAL-yeast and UNICAMP-V1 strain. The UFMG-A1240 strain showed the lowest ethanol yield and therefore not suitable for the production of distilled spirits made of bananas, despite being useful for the production of cachaça. Methanol contents did not significantly vary among the five strains tested, except for UFMG-A1007, which produced significantly higher quantities of 0.19 ml/100 ml anhydrous alcohol. However, higher alcohols contents varied significantly between the five strains tests, with the UFMG-A1007 and UFMG-A1240 strains producing the lowest quantities of higher alcohols (30.04 and 48.69 mg/100 ml anhydrous alcohol, respectively). In conclusion, the S. cerevisiae strains UNICAMP-V1 and the COMMERCIAL-yeast showed better fermentation behavior, did not produce high methanol and higher alcohols amounts, and therefore were recommended for the production of distilled spirits made of banana in pilot-scale plants.Key words: Yeast, Saccharomyces cerevisiae, alcoholic fermentation, banana spirits

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Can the diet of the prey Tenebrio molitor (Coleoptera: Tenebrionidae) affect the development of the predator Podisus nigrispinus (Heteroptera: Pentatomidae)?

Inimigos naturais s?o importantes para o controle de pragas em culturas agr?colas e forestais. A cria??o de insetos predadores em biof?bricas deve ser de baixo custo para serem utilizados em programas de Manejo Integrado de Pragas (MIP). O objetivo deste trabalho foi avaliar o desenvolvimento de Podisus nigrispinus Dallas, 1851 (Heteroptera: Pentatomidae), alimentado com larvas de Tenebrio molitor Linnaeus, 1758 (Coleoptera: Tenebrionidae), criadas com as seguintes dietas: farelo de trigo, ra??o triturada ou peletizada para aves poedeiras e fub? de milho. Foram obtidos os par?metros de desenvolvimento e reprodu??o necess?rios para calcular a tabela de vida do predador. Os par?metros da tabela de vida revelaram crescimento populacional em todos os tratamentos. No entanto, a taxa l?quida de reprodu??o (Ro) de P. nigrispinus foi menor quando alimentados com larvas de T. molitor criadas com fub? de milho, mostrando ser a alimenta??o menos adequada para esse predador. Por proporcionar maior n?mero total de ovos, o farelo de trigo constituiu a melhor dieta para P. nigrispinus. Estudos sobre dietas de presas alternativas s?o importantes, pois podem favorecer a nutri??o de inimigos naturais e, consequentemente, melhorar o desempenho das cria??es massais em laborat?rio.Funda??o de Amparo ? Pesquisa do Estado de Minas Gerais (FAPEMIG)Empresa de Pesquisa Agropecu?ria de Minas Gerais (EPAMIG)Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico (CNPq)Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)Natural enemies are important for controlling pests in agricultural and forestry culture. The reproductions of predatory insects in biofactories should have low cost to be used in Integrated Pest Management Program (IPM). The objective of this study was to evaluate the development of Podisus nigrispinus Dallas, 1851 (Heteroptera: Pentatomidae) fed with Tenebrio molitor Linnaeus, 1758 (Coleoptera: Tenebrionidae). The larvae of T. molitor were created with the following diets: wheat bran, shredded or pelleted poultry feed layers and corn meal. Parameters for the development and reproduction of the P. nigrispinus were obtained. Parameters of the life table show population growth in all treatments. However, the net rate of reproduction (Ro) of P. nigrispinus was lower when they were fed with larvae of T. molitor created with corn meal, which proved to be the least adequate food for this predator. By providing a larger total number of eggs, wheat bran was the best diet for P. nigrispinus. Studies on alternative prey diets are important as they may favor the nutrition of natural enemies and, consequently, improve the performance in laboratory rearing

Feature Extraction and Random Forest to Identify Sheep Behavior from Accelerometer Data

Sensor technologies play an essential part in the agricultural community and many other scientific and commercial communities. Accelerometer signals and Machine Learning techniques can be used to identify and observe behaviours of animals without the need for an exhaustive human observation which is labour intensive and time consuming. This study employed random forest algorithm to identify grazing, walking, scratching, and inactivity (standing, resting) of 8 Hebridean ewes located in Cheshire, Shotwick in the UK. We gathered accelerometer data from a sensor device which was fitted on the collar of the animals. The selection of the algorithm was based on previous research by which random forest achieved the best results among other benchmark techniques. Therefore, in this study, more focus was given to feature engineering to improve prediction performance. Seventeen features from time and frequency domain were calculated from the accelerometer measurements and the magnitude of the acceleration. Feature elimination was utilised in which highly correlated ones were removed, and only nine out of seventeen features were selected. The algorithm achieved an overall accuracy of 99.43% and a kappa value of 98.66%. The accuracy for grazing, walking, scratching, and inactive was 99.08%, 99.13%, 99.90%, and 99.85%, respectively. The overall results showed that there is a significant improvement over previous methods and studies for all mutually exclusive behaviours. Those results are promising, and the technique could be further tested for future real-time activity recognition

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep