Engineering Commons^™

Sustainable, Alginate-Based Sensor For Detection Of Escherichia Coli In Human Breast Milk, Nicholas Kikuchi, Margaret May, Matthew Zweber, Jerard Madamba, Craig M. Stephens, Unyoung Kim, Maryam Mobed-Miremadi

Bioengineering

There are no existing affordable diagnostics for sensitive, rapid, and on-site detection of pathogens in milk. To this end, an on-site colorimetric-based sustainable assay has been developed and optimized using an L₁₆ (5⁴) Taguchi design to obtain results in hours without PCR amplification. To determine the level of Escherichia coli (E. coli) contamination, after induction with 150 µL of breast milk, the B-Per bacterial protein extraction kit was added to a solution containing an alginate-based microcapsule assay. Within this 3 mm spherical novel sensor design, X-Gal (5-Bromo-4-Chloro-3-Indolyl β-_D-Galactopyranoside) was entrapped at a concentration of 2 …

Application Of Advanced Algorithms And Statistical Techniques For Weed-Plant Discrimination, Saman Akbar Zadeh

Theses: Doctorates and Masters

Precision agriculture requires automated systems for weed detection as weeds compete with the crop for water, nutrients, and light. The purpose of this study is to investigate the use of machine learning methods to classify weeds/crops in agriculture. Statistical methods, support vector machines, convolutional neural networks (CNNs) are introduced, investigated and optimized as classifiers to provide high accuracy at high vehicular speed for weed detection.

Initially, Support Vector Machine (SVM) algorithms are developed for weed-crop discrimination and their accuracies are compared with a conventional data-aggregation method based on the evaluation of discrete Normalised Difference Vegetation Indices (NDVIs) at two different …