Materials and method: A comprehensive literature search was performed to identify and synthesise all relevant information, mainly from within the last decade, on the major lifestyle factors associated with male infertility and semen quality. Database searches were limited to reports published in English only. A manual search of bibliographies of the reports retrieved was conducted to identify additional relevant articles.
Results: In all, 1012 articles were identified from the database search and after reviewing the titles and abstract of the reports, 104 articles met the inclusion criteria. Of these, 30 reports were excluded as the full-text could not be retrieved and the abstract did not have relevant data. The remaining 74 reports were reviewed for data on association between a particular lifestyle factor and male infertility and were included in the present review.
Conclusion: The major lifestyle factors discussed in the present review are amongst the multiple potential risk factors that could impair male fertility. However, their negative impact may well be mostly overcome by behaviour modification and better lifestyle choices. Greater awareness and recognition of the possible impact of these lifestyle factors are important amongst couples seeking conception.
RESULTS: The relationship between dimensionless moisture content and shrinkage of sweet potato in terms of volume, surface area, perimeter and illuminated area was found to be linearly correlated. The results also demonstrated that the shrinkage of sweet potato based on computer vision and backscattered optical parameters is affected by the product thickness, drying temperature and drying time. A multilayer perceptron (MLP) artificial neural network with input layer containing three cells, two hidden layers (18 neurons), and five cells for output layer, was used to develop a model that can monitor, control and predict the shrinkage parameters and moisture content of sweet potato slices under different drying conditions. The developed ANN model satisfactorily predicted the shrinkage and dimensionless moisture content of sweet potato with correlation coefficient greater than 0.95.
CONCLUSION: Combined computer vision, laser light backscattering imaging and artificial neural network can be used as a non-destructive, rapid and easily adaptable technique for in-line monitoring, predicting and controlling the shrinkage and moisture changes of food and agricultural crops during drying. © 2017 Society of Chemical Industry.