Theses & Dissertations
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This collection is exclusively made up of theses, dissertations, and project reports submitted as a requirement for completing a graduate degree at UPR-Mayagüez.
If you are a UPRM graduate student and you are looking for information related to the deposit process, please refer to https://libguides.uprm.edu/repositorioUPRM/tesis
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PublicationCaracterísticas físico-químicas y sensoriales de un yogurt elaborado con derivados del cáñamo( 2024-05-09)Yogurt is a fermented dairy product that is highly consumed for its functional and health-beneficial properties. Within this market, fortification practices with plant-based proteins have increased. This research aims to produce a yogurt fortified with protein concentrates derived from hemp seeds. For this, a concentrate powder based on hulled hemp seeds (HPM) and a protein isolate from the seeds (HPI) was used. The HPM from Bob's Red Mill brand contains 39% crude protein while the HPI from Evo Hemp contains over 90%. Two percent of each of these derivatives were added to the Hemp Protein Meal Yogurt (HPMY) and Hemp Protein Isolate Yogurt (HPIY) formulations. The addition of these ingredients was found to increase the ash, fat and protein content of yogurts while improving water holding capacity. A decrease in pH and increase in titratable acidity were observed after a 28-day storage period. Both HPMY and HPIY were more viscous, firm, and consistent than the control. The addition of these derivatives promoted the development of LAB for the end of storage in the HPIY mainly. No contamination by coliform organisms was found, although the presence of yeast since the beginning and during storage could lead to early deterioration of the product. The sensory analysis indicated that 74% of the panelists preferred control yogurt over the treatments.
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PublicationWeather variables forecasting to reduce their impact on photovoltaic systems( 2024-03-23)Photovoltaic (PV) power generation forecasting is an important research topic, aiming to mitigate the variability caused by weather conditions and improve power generation planning. Climate factors, including solar irradiance, temperature, and cloud cover, influence the energy conversion achieved by PV systems. Long-term weather forecasting improves PV power generation planning, while short-term forecasting enhances control methods, such as managing ramp rates. The stochastic nature of weather variables poses a challenge for linear regression methods. Consequently, advanced, state-of-the-art machine learning (ML) approaches capable of handling non-linear data, such as long short-term memory (LSTM), have emerged. This paper introduces the implementation of a multivariate machine learning model to forecast PV power generation, considering multiple weather variables. A deep learning solution was implemented to analyze weather variables in a short time horizon. Utilizing a hidden Markov model for data preprocessing, an LSTM model was trained using the Alice Spring, Ambient weather, and NSRDB datasets. The proposed workflow demonstrated superior performance compared to the results obtained by other state-of-the-art methods, including support vector machine, radiation classification coordinate with LSTM (RCC-LSTM), and ESNCNN, specifically concerning the proposed multi-input single-output LSTM model. This improvement is attributed to incorporating input features such as active power, temperature, humidity, horizontal and diffuse irradiance, and wind direction, with active power as the output variable.
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PublicationRaman spectroscopy and spectrofluorometry applications in the characterization of semiconductor and biological materials( 2024-05-07)Raman spectroscopy is an analytic technique that is useful for studying the interaction of electromagnetic radiation with matter, more specific, Raman spectroscopy studies general molecular structures and vibrations. Through its characteristic Raman spectra we can obtain chemical and structural information on various substances in a few seconds. This technique is useful to study and measure chemical compounds that have diverse applications such as biomedical, electrochemical, nanoscale electronic and optoelectronics. Another important technique for the analysis of the molecular structures of chemical compounds is Spectrofluorometry. This technique allows us to measure the fluorescence of molecules by measuring the light intensity emitted by a sample after being excited with a specific wavelength. This research used optical spectroscopy, spectrofluorometry, and Raman scattering to characterize and obtain spectra of Silicon (Si), Vanadium Dioxide (VO_2), Zinc Ferrites (ZFO), lignin and Coffee Silver Skin . For the purpose of this research, we used a Raman Spectroscopy model SPEX 1403 of 0.85m Double Spectrometer with a green laser of 533 nm wavelength. In addition, we also used a spectrofluorometer of the Fluoromax-2 series that works under the control of DataMax spectroscopy software. The focus of this research is to elucidate the evolution of optical spectra of chemical and biological compounds. In addition, we seek to electronic structure of materials via observation of different optical spectra. This technique allows us to extract new information for the development and application of new technology.
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PublicationEvaluación de productos probióticos en el crecimiento del plátano y su efecto sobre poblaciones de nematodos endoparásitos migratorios( 2024-05-09)Plantains is considered one of the crops of greatest economic importance in Puerto Rico. Over the years the production has been affected by different factors, including damage caused by plant-parasitic nematodes. Radopholus similis is a migratory endoparasitic nematode that affects a diverse number of crops and causes great economic losses. Plant Probiotics are cocktails that contain a combination of microorganisms that are beneficial for the plant and soil. Different studies have been carried out that proved the effectiveness of these microorganisms not only in the benefits they confer to the plant but also in the biocontrol of plant-parasitic nematodes. In this research, two trials were carried out (in vitro and greenhouse), where four plant probiotic products were evaluated at three different concentrations (1 ppm, 3 ppm and 10 ppm). The products evaluated were: Mikrobs®, Plant Probiotics®, Bigfoot Mycorrhizae® and EM.1®. In the in vitro assay, the effect of concentrations on the mortality of J2 stages of Radopholus similis were determined. Results showed that the products Mikrobs® (10 ppm) and EM.1® (10 ppm and 3 ppm) reduced the greatest number of nematodes after 72 hours of exposure, obtaining mortality percentages of 92%, 90.67% y 86.67% respectively. Regarding the trial carried out in the greenhouse, the concentrations were evaluated at various growth parameters of banana seedlings and the suppression effect on Radopholus similis. Mikrobs® at a concentration of 10 ppm obtained the highest averages significantly in five of six growth parameters, these being: number of leaves, pseudostem diameter, root length, fresh and dry root weight. Regarding nematode control, concentrations of 3 ppm and 10 ppm obtained the lowest root damage rates. The lowest amount of nematodes/100g of root was found when Mikrobs® was applied at a concentration of 10 ppm.
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PublicationImplementation of a stream sampler for real-time blend uniformity monitoring using near-infrared and raman spectroscopy( 2024-05-06)This work describes the advancements of the implementation of the stream sampler along with Process Analytical Technology (PAT) for real-time monitoring of blend uniformity in batch and continuous manufacturing processes of tablets. This work describes two different stream sampler implementations. The first one (Chapter 2) describes the first implementation of the Raman spectrometer in the stream sampler to monitor low drug concentration in poor flowability powder blends. Raman spectra were continuously acquired as the powder blends flowed through the stream. A calibration model was developed to quantify caffeine concentration from 1.50 to 4.50% w/w. Caffeine concentration was predicted for the test set blends with a root mean square error of prediction of 0.21% w/w and a low bias of -0.03% w/w. The results showed the ability of the Raman spectrometer coupled with the stream sampler to monitor low drug concentration for poor flowability blends. The Second (Chapter 3) presents the implementation of the stream sampler device to develop a NIR calibration model for blend uniformity monitoring in a continuous manufacturing mixing process. Feeding and mixing characterizations were performed for three loss-in-weight feeders and a commercial continuous mixer to prepare powder blends of 2.5 – 7.5% w/w ibuprofen DC 85 W with a total throughput of 33 kg/h. The NIR spectral acquisition was performed after the mixing stage using a stream sampler device for flowing powders. A partial least squares regression (PLS-R) model was performed and evaluated, yielding a root-mean-square error of prediction (RMSEP) of 0.39% w/w and a bias of 0.05% w/w. Results demonstrated the promising capacity of the stream sampler coupled to a NIR probe to be implemented within continuous manufacturing processes for the real-time determination of API concentration. This study contributes to a better understanding of the analysis of Raman spectra obtained through the use of the stream sampler. Furthermore, this thesis provides insights into the implementation of the stream sampler to perform a mixing characterization to find the adequate blending speed in a continuous manufacturing process. The outcomes of this research can be used to integrate, characterize, and optimize the stream sampler for blend uniformity monitoring, whether in batch or a continuous manufacturing process.