Credit hours
In-class work per week |
Practice per week |
Credits |
Duration |
Total |
2 |
2 |
8 |
15 weeks |
120 hours |
Instructor
Tiago Bueno de Moraes
Objective
The goal of this course is to enable students to understand the fundamentals and techniques of a series of instruments and spectral sensors used in the context of biosystems engineering, discussing applications in the agricultural field, in agroindustry and in scientific research. Fundamentals of electronics, instrumentation, control and automation are presented, and how these are applied with spectral sensors in process control. Knowledge of these techniques and instruments will enable the student with a critical view of the potential application of these techniques in the field and in agroindustry.
Content
1. Fundamentals of electricity and electronics; History of applications in agriculture; Basic laws and electronic devices; types of current, resistors; capacitors; inducers; voltage sources; diode; led; transistor; microprocessors;
2. Principles of Instrumentation; Electrical measuring instruments; multimeter; oscilloscope; Quantities and units; Types of sensors and their characteristics; Destructive and non-destructive methods;
3. Fundamentals of Process Control and Automation; Types of Actuators and their characteristics; Examples of devices used in the field and in agroindustry;
4. Sensors based on optical spectroscopy; Electromagnetic radiation; Electromagnetic spectrum; UV-Vis; Principles of operation of the Instruments; Beer-Lambert law; Calibration;
5. Infrared spectroscopy sensors: IR, Near-infrared spectroscopy (NIR), Raman; Instrumentation; Atomic Emission Spectroscopy: Laser Induced Breakdown Spectroscopy (LIBS); X-ray fluorescence (XRF); Instruments; Methods and applications in the field and agroindustry;
6. Magnetic Resonance Spectroscopy Sensors: Imaging NMR, NMR, and EPR; Instrument functioning principle; Discussion of methods and applications in Biosystems;
7. Data processing, spectral and time domain signals; Applications with statistical methods, multivariate analysis and machine learning;
8. Applications in Agriculture; Analysis of soils, food and inputs; Determination of porosity; clay, organic matter, ions, pH, and nutrients;
9. Applications in plant and seed analysis; Oil and moisture content; Disease and damage detection; Applications in non-destructive food analysis, quality control and post-harvest;
10. Recent methods in the scientific literature with applications in the field and in agroindustry in the context of Digital Agriculture.
Bibliography
BALBINOT, A; BRUSAMARELLO, V.J.; Instrumentação e Fundamentos de Medidas. 2 ed.; vol. 1, Rio de Janeiro: LTC, 2010.
BALBINOT, A; BRUSAMARELLO, V.J.; Instrumentação e Fundamentos de Medidas. 2 ed.; vol. 2, Rio de Janeiro: LTC, 2011.
MORAES, C.C.; CASTRUCCI, P.L.; Engenharia de automação industrial. 2 ed. Rio de Janeiro: LTC, 2007.
Practical Spectroscopy in Agriculture and Food Science, Yuriy I. Posudin, Nacional Agricultural University, Kiev, Ukraine, ISBN 13:978-1-57808-505-7, Science Publishers, 2007.
Introdução à espectroscopia, Pavia, D; Lampman, G.; Kriz, G.; Vyvyan, J.; 2° ed., ISBN 10 85-221-2338-1, Cengage Learning, 736p., 2015.
Análises Espectroscópicas da Matéria Orgânica de Solos sob Aplicação de Águas Residuárias, Boletim de Pesquisa e Desenvolvimento: EMBRAPA Instrumentação, ISSN 1678-0434, Novembro, 2010.
NOVOTNY, E. H. Estudos espectroscópicos e cromatográficos de substâncias húmicas de solos sob diferentes sistemas de preparo. 2002. 231 f. Tese (Doutorado em Físico-Química) – Instituto de Química de São Carlos, Universidade de São Paulo, São Carlos.
Daniel H. Lysak, Myrna J. Simpson, Andre J. Simpson, Applications of nuclear magnetic resonance for the study of soils, Reference Module in Earth Systems and Environmental Sciences, Elsevier, 2022.
Colnago, L.A.; Andrade, F. D.; RMN no domínio do tempo: fundamentos e aplicações offline e online, Biotecnologia Aplicada à Agro&Indústria, Blucher, Embrapa Instrumentação, 2017.
DEMATTÊ et al. Morphological Interpretation of Reflectance Spectrum (MIRS) using libraries looking towards soil classification. Scientia Agricola, v. 71, n. 6, p. 509-520, 2014.
Ozaki, Y.; Huck, C.W.; Tsuchikawa, S.; Engelsen, S.B. (Eds.) Near-Infrared Spectroscopy; Springer: Singapore, 2021
Bec, K.B.; Grabska, J.; Huck, C.W. Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives. Foods 2022, 11, 1465. https://doi.org/10.3390/foods11101465
Cozzolino, D. Food adulteration. In Spectroscopic Methods in Food Analysis; Franca, A.S., Nollet, L., Eds.; CRC Press: Boca Raton, FL, USA, 2017; pp. 353–361.
Bec, K.B.; Grabska, J.; Huck, C.W. Portable spectroscopy applications in food, feed and agriculture. In Portable Spectroscopy and Spectrometry 2: Applications; Crocombre, R.A., Leary, P.E., Kammrath, B.W., Eds.; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2021; pp. 299–324.
VISCARRA ROSSEL, R.A. et al. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, v. 131, n. 1-2, p. 59-75, 2006.
Revistas científicas:
COMPUTERS AND ELECTRONICS IN AGRICULTURE; Elsevier Inc. Disponível em: https://www.sciencedirect.com/journal/computers-and-electronics-in-agriculture
BIOSYSTEMS ENGINEERING; Elsevier Inc. Disponível em: https://www.sciencedirect.com/journal/biosystems-engineering
Scientia Agrícola; Publicação de: Escola Superior de Agricultura "Luiz de Queiroz", Disponível em: https://www.scielo.br/j/sa/