scholarly journals Detection of Lethal Bronzing Disease in Cabbage Palms (Sabal palmetto) Using a Low-Cost Electronic Nose

Biosensors ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 188
Author(s):  
Martin J. Oates ◽  
Nawaf Abu-Khalaf ◽  
Carlos Molina-Cabrera ◽  
Antonio Ruiz-Canales ◽  
Jose Ramos ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Discrete Fourier Transform ◽  
Electronic Nose ◽  
Low Cost ◽  
Harmonic Component ◽  
Principal Component ◽  
Component Analysis ◽  
Sabal Palmetto ◽  
Cabbage Palm

Lethal Bronzing Disease (LB) is a disease of palms caused by the 16SrIV-D phytoplasma. A low-cost electronic nose (eNose) prototype was trialed for its detection. It includes an array of eight Taguchi-type (MQ) sensors (MQ135, MQ2, MQ3, MQ4, MQ5, MQ9, MQ7, and MQ8) controlled by an Arduino NANO® microcontroller, using heater voltages that vary sinusoidally over a 2.5 min cycle. Samples of uninfected, early symptomatic, moderate symptomatic, and late symptomatic infected palm leaves of the cabbage palm were processed and analyzed. MQ sensor responses were subjected to a 256 element discrete Fourier transform (DFT), and harmonic component amplitudes were reviewed by principal component analysis (PCA). The experiment was repeated three times, each showing clear evidence of differences in sensor responses between the samples of uninfected leaves and those in the early stages of infection. Within each experiment, four groups of responses were identified, demonstrating the ability of the unit to repeatedly distinguish healthy leaves from diseased ones; however, detection of the severity of infection has not been demonstrated. By selecting appropriate coefficients (here demonstrated with plots of MQ5 Cos1 vs. MQ8 Sin3), it should be possible to build a ruleset classifier to identify healthy and unhealthy samples.

scholarly journals PCA/DFT como herramienta de pronóstico para series temporales de absorbancia registradas mediante captores UV-Vis en sistemas de saneamiento urbano

Tecnura ◽  
2015 ◽  
Vol 19 (44) ◽  
pp. 47
Author(s):  
Leonardo Plazas Nossa ◽  
Andrés Torres
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Discrete Fourier Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Inverse Fast Fourier Transform ◽  
San Fernando

El objetivo de este trabajo es presentar un método de pronóstico para series de tiempo de espectrometría UV-Vis, combinando el análisis de componentes principales PCA (Principal Component Analysis), la transformada discreta de Fourier, DFT (Discrete Fourier Transform) y la transformada inversa de Fourier, IFFT (Inverse Fast Fourier Transform). Se utilizaron las correspondientes series de tiempo de absorbancia para tres diferentes sitios de estudio: (i) Planta de tratamiento de aguas residuales Salitre (PTAR) en Bogotá; (ii) Estación elevadora de Gibraltar en Bogotá; y (iii) Planta de tratamiento de aguas residuales San Fernando (PTAR) en Itagüí (parte sur de Medellín). Cada una de las series de tiempo tiene igual número de muestras (5705). Al reducir la dimensionalidad de las series de tiempo de absorbancia con PCA, se utilizan para cada sitio de estudio 3, 5 y 6 componentes principales, respectivamente; explicando en conjunto más de 97% de la variabilidad. Se utiliza en el procedimiento DFT e IFFT el armónico más importante y se remueven desde uno hasta la mitad de los valores de la longitud total de las series de tiempo. Por consiguiente, los errores de pronóstico para los tres sitios de estudio y para tres rangos de longitudes de onda propuestos (UV, Vis y UV-Vis) están comprendidos entre 0,01% y 34% para 95% de los casos. Sin embargo, para 100% de los casos los errores son inferiores a 37%, independientemente de la longitud de onda y del tiempo de pronóstico.

Comparison of discrete Fourier transform (DFT) and principal component analysis/DFT as forecasting tools for absorbance time series received by UV-visible probes installed in urban sewer systems

2014 ◽  
Vol 69 (5) ◽  
pp. 1101-1107 ◽  
Author(s):  
Leonardo Plazas-Nossa ◽  
Andrés Torres
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Fourier Transform ◽  
Discrete Fourier Transform ◽  
Treatment Plant ◽  
Water System ◽  
Principal Component ◽  
Component Analysis ◽  
Visible Range ◽  
General Terms

The objective of this work is to introduce a forecasting method for UV-Vis spectrometry time series that combines principal component analysis (PCA) and discrete Fourier transform (DFT), and to compare the results obtained with those obtained by using DFT. Three time series for three different study sites were used: (i) Salitre wastewater treatment plant (WWTP) in Bogotá; (ii) Gibraltar pumping station in Bogotá; and (iii) San Fernando WWTP in Itagüí (in the south part of Medellín). Each of these time series had an equal number of samples (1051). In general terms, the results obtained are hardly generalizable, as they seem to be highly dependent on specific water system dynamics; however, some trends can be outlined: (i) for UV range, DFT and PCA/DFT forecasting accuracy were almost the same; (ii) for visible range, the PCA/DFT forecasting procedure proposed gives systematically lower forecasting errors and variability than those obtained with the DFT procedure; and (iii) for short forecasting times the PCA/DFT procedure proposed is more suitable than the DFT procedure, according to processing times obtained.

scholarly journals Deteksi Formalin Pada Buah Tomat (Lycopersicum esculentum Mill) Dengan Teknologi Hidung Elektronik (Electronic Nose)

2019 ◽  
Vol 4 (2) ◽  
pp. 359-366
Author(s):  
Irfan Maibriadi ◽  
Ratna Ratna ◽  
Agus Arip Munawar
Keyword(s):  
Principal Component Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Lycopersicum Esculentum ◽  
Olfactory Ability ◽  
Pca Method ◽  
The Difference ◽  
Electronic Nose Technology

Abstrak,  Tujuan dari penelitian ini adalah mendeteksi kandungan dan kadar formalin pada buah tomat dengan menggunakan instrument berbasis teknologi Electronic nose. Penelitian ini menggunakan buah tomat yang telah direndam dengan formalin dengan kadar 0.5%, 1%, 2%, 3%, 4%, dan buah tomat tanpa perendaman dengan formalin (0%). Jumlah sampel yang digunakan pada penelitian ini adalah sebanyak 18 sampel. Pengukuran spektrum beras menggunakan sensor Piezoelectric Tranducer. Klasifikasi data spektrum buah tomat menggunakan metode Principal Component Analysis (PCA) dengan pretreatment nya adalah Gap Reduction. Hasil penelitian ini diperoleh yaitu: Hidung elektronik mulai merespon aroma formalin pada buah tomat pada detik ke-8.14, dan dapat mengklasifikasikan kandungan dan kadar formalin pada buah tomat pada detik ke 25.77. Hidung elektronik yang dikombinasikan dengan metode principal component analysis (PCA) telah berhasil mendeteksikandungan dan kadar formalin pada buah tomat dengan tingkat keberhasilan sebesar 99% (PC-1 sebesar 93% dan PC-2 sebesar 6%). Perbedaan kadar formalin menjadi faktor utama yang menyebabkan Elektronik nose mampu membedakan sampel buah tomat yang diuji, karena semakin tinggi kadar formalin pada buah tomat maka aroma khas dari buah tomat pun semakin menghilang, sehingga Electronic nose yang berbasis kemampuan penciuman dapat membedakannya.Detect Formaldehyde on Tomato (Lycopersicum esculentum Mill) With Electronic Nose TechnologyAbstract, The purpose of this study is to detect the contents and levels of formalin in tomatoes by using instruments based on Electronic nose technology. This study used tomatoes that have been soaked in formalin with a concentration of 0.5%, 1%, 2%, 3%, 4%, 5% and tomatoes without soaking with formalin (0%). The samples in this study were 18 samples. The measurements of the intensity on tomatoes aroma were using Piezoelectric Transducer sensors. The classification of tomato spectrum data was using the Principal Component Analysis (PCA) method with Gap Reduction pretreatment. The results of this study were obtained: the Electronic nose began to respond the smell of formalin on tomatoes at 8.14 seconds, and it could classify the content and formalin levels in tomatoes at 25.77 seconds. Electronic nose combined with the principal component analysis (PCA) method have successfully detected the content and levels of formalin in tomatoes with a success rate at 99% (PC-1 of 93% and PC-2 of 6%). The difference of grade formalin levels is the main factor that causes Electronic nose to be able to distinguish the tomato samples tested, because the higher of formalin content in tomatoes, the distinctive of tomatoes aroma is increasingly disappearing. Thereby, the Electronic nose based on  the olfactory ability can distinguish them. 

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