scholarly journals Analisis Kandungan N-Nitrogen (Amonia, Nitrit, Nitrat) dan Fosfat di Perairan Teluk Pandan Provinsi Lampung

2019 ◽  
Vol 8 (1) ◽  
pp. 57-66
Author(s):  
Reza Iklima AS ◽  
Gusti Diansyah ◽  
Andi Agussalim ◽  
Citra Mulia
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Data Analysis ◽  
Sampling Method ◽  
Principal Component ◽  
Pearl Oyster ◽  
Water Sampling ◽  
Cage Culture ◽  
Water Sampler ◽  
Pca Method

Iklima AS et al, 2019. Analysis of N-Nitrogen (Ammonia, Nitrate, and Nitric) and Phosphate at Teluk Pandan’s water territorial, Lampung Province. JLSO 8(1):57-66.Teluk Pandan’s water territorial was known to aquaculture activity such as prawn, pearl oyster and cage culture by community that lived in the area. It activities could makes water quality to be polluted.This research was purposed to known the content of nutrient (Ammonia, Nitrate, Nitric, and Phosphate) and to studied nutrient that related to other’s water quality parametric at Teluk Pandan water territorial. Sampling was determinate by 15station using purposive sampling method. Data analysis was used to studied relation between water quality’s parametric using Principal Component Analysis (PCA). Water sampling was taken at surface using water sampler. It was analyze in Oceanography and Instrumentation Laboratory, Department of Marine Science, Universitas Sriwijaya. Result of this research showing that rate of content nutrient at Teluk Pandan’s water territory ranging from 0.0007-0.0087 mg/L NO3-N, nitric ranging from 0.0001-0.0062 mg/L NO2-N, and phosphate ranging form 0,0012 – 0,0091 mg/L PO4-P. Based on result Teluk Pandan’s water territory still can be used for water’s ecosystem. Result using PCA method showing that correlation between parametric are directly proportional and inversely. Correlation that directly proportional showing by parametric group quadrant I (Temperature, Salinity, Velocity, and Abundance of Phytoplankton), quadrant II (DO, pH and nitrate) and quadrant III (Ammonia, nitric and phosphate). Inversely showing by parametric group quadrant I to parametric group quadrant III.

scholarly journals Correlation between macroalgae diversity and water quality in Southwest Maluku waters

2020 ◽  
Vol 45 (1) ◽  
pp. 25-32
Author(s):  
Marsya Jaqualine Rugebregt ◽  
Hairati Arfah ◽  
Ferdinand Pattipeilohy
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Species Composition ◽  
Diversity Index ◽  
Principal Component ◽  
Quality Parameters ◽  
Nursery Ground ◽  
Water Sampler ◽  
Phosphate Nitrate ◽  
Seawater Quality

Macroalgae play an important role in the ecosystem of the coastal area, serving as a shelter ground, nursery ground, and feeding ground. Macroalgae communities are directly influenced by water quality. This study aim was to determine the correlation between the macroalgae diversity and water quality in southwest Maluku waters. This research was conducted in September 2019 at seven research stations. Macroalgae samples were collected by transect method, while seawater quality was measured using Van Dorn Water Sampler. The macroalgae diversity, species composition, and dominance were determined. Water quality parameters analyzed were temperature, salinity, pH, phosphate, nitrate, and ammonia. Correlations between macroalgae diversity and water quality were determined using principal component analysis. This study recorded 45 species of macroalgae consisting of 15 species of red algae (Rhodophyta), 6 species of brown algae (Phaeophyta), and 24 species of green algae (Chlorophyta). Diversity Index varied ranged from low to moderate categories (0.969 - 2.345). Water quality in general is still quite good for macroalgae life. Macroalgae diversity and water quality correlate and influence each other.

Water Quality Evaluation for Tributaries of Danjiangkou Reservoir with Principal Component Analysis

2014 ◽  
Vol 955-959 ◽  
pp. 3586-3594 ◽  
Author(s):  
Xiao Kang Xin ◽  
Wei Yin ◽  
Hai Yan Jia
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Water Quality Evaluation ◽  
Mean Values ◽  
Danjiangkou Reservoir ◽  
Research Task ◽  
Pca Method ◽  
Water Quality Status

To reflect the water quality status of Danjiangkou reservoir tributaries, identify the main pollution factors, and compare pollution degree between tributaries. Principal component analysis (PCA) method is used to assess and explicate research task with annual mean values for 11 main water quality indicators of 16 priority tributaries measured in 2013. The results show that: The main pollution factors of Danjiangkou reservoir are oxygen-consuming pollutants and heavy metal, and the former is the dominant one. Shending, Sihe, Jianghe, Jianhe and Danjiang are heavily-polluted tributaries while Duhe, Jiangjun, Taogou, Hanjiang and Taohe are lightly-polluted tributaries.

Reaction Monitoring in Explorative Organic Synthesis Using Fiber-Optical NIR Spectroscopy and Principal Component Analysis

1996 ◽  
Vol 50 (12) ◽  
pp. 1541-1544 ◽  
Author(s):  
Hans-René Bjørsvik
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Latent Variables ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Quantitative Information ◽  
Chemistry Laboratory ◽  
Reaction Profile

A method of combining spectroscopy and multivariate data analysis for obtaining quantitative information on how a reaction proceeds is presented. The method is an approach for the explorative synthetic organic laboratory rather than the analytical chemistry laboratory. The method implements near-infrared spectroscopy with an optical fiber transreflectance probe as instrumentation. The data analysis consists of decomposition of the spectral data, which are recorded during the course of a reaction by using principal component analysis to obtain latent variables, scores, and loading. From the scores and the corresponding reaction time, it is possible to obtain a reaction profile. This reaction profile can easily be recalculated to obtain the concentration profile over time. This calculation is based on only two quantitative measurements, which can be (1) measurement from the work-up of the reaction or (2) chromatographic analysis from two withdrawn samples during the reaction. The method is applied to the synthesis of 3-amino-propan-1,2-diol.

The Design of Index about Corporate Governance Based on PCA Method

2014 ◽  
Vol 926-930 ◽  
pp. 4085-4088
Author(s):  
Chuan Jun Li
Keyword(s):  
Principal Component Analysis ◽  
Corporate Governance ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Contribution Rate ◽  
Variance Contribution ◽  
Pca Method

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

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. 

Sign in / Sign up

Export Citation Format

Share Document