Quality Classification of Chili Sauce Using Electronic Nose with Principal Component Analysis

2018 ◽  
Author(s):  
Danang Lelono ◽  
Deny Permana ◽  
Fandy Achmad ◽  
Triyogatama Wahyu Widodo ◽  
Muhammad Agung Bramantya ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Quality Classification

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. 

scholarly journals Deteksi Bubuk Kopi Luwak Murni dan Bubuk Kopi Luwak Campuran Dengan Teknologi Hidung Elektronik

2019 ◽  
Vol 4 (3) ◽  
pp. 105-114
Author(s):  
Mubarak Hulda ◽  
Fachruddin Fachruddin ◽  
Agus Arip Munawar
Keyword(s):  
Principal Component Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Powder Spectrum ◽  
Pca Method ◽  
Coffee Powder ◽  
Electronic Nose Technology ◽  
Spectrum Data

Abstrak. Kopi luwak merupakan kopi yang berasal dari hasil konsumsi hewan luwak (musang) yang  telah mengalami fermentasi di dalam pencernaan luwak selam 12 jam. Kopi luwak merupakan komoditi yang sangat diminati dan bernilai jual tinggi. Tujuan dari penelitian ini untuk membedakan bubuk kopi luwak murni dan bubuk kopi luwak campuran dengan memanfaatkan instrumen berbasis teknologi hidung elektronik (electronic nose). Penelitian ini menggunakan bubuk kopi luwak murni dan bubuk kopi arabika yang dicampurkan dengan perbandingan (50:50, 60:40. 70:30, 80:20 dan 90:10). Jumlah sampel yang digunakan pada penelitian ini adalah sebanyak 10 sampel. Pengukuran intensitas sinyal aroma bubuk kopi menggunakan sensor piezoelectric tranducers. Klasifikasi data spektrum bubuk kopi menggunakan metode Principal Component Analysis (PCA) dengan pretreatment nya adalah Gap Reduction. Hasil penelitian ini diperoleh yaitu: Hidung elektronik mulai merespon aroma bubuk kopi pada detik ke-5.64, dan dapat mengklasifikasikan bubuk kopi pada detik ke 11.09. Hidung elektronik yang dikombinasikan dengan metode principal component analysis (PCA) telah berhasil mendeteksi bubuk kopi luwak murni dan bubuk kopi luwak campuran dengan tingkat keberhasilan sebesar 100% (PC-1 sebesar 100% dan PC-2 sebesar 0%).Deteksi Murni Powder Kopi Luwak dan Campuran Kopi Luwak Bubuk Menggunakan Teknologi Hidung ElektronikAbstract. Civet coffee is coffee that comes from the consumption of civet animals (ferrets) that have undergone fermentation in the digestion of mongoose for 12 hours. Civet coffee is a commodity that is very popular and has a high selling value. The purpose of this study is to distinguish pure civet coffee powder and mixed civet coffee powder by using an instrument based on electronic nose technology. This study used pure civet coffee powder and arabica coffee powder mixed with comparisons (50:50, 60:40. 70:30, 80:20 and 90:10). The number of samples used in this study were 10 samples. The measurement of the intensity of coffee powder’s smell signals using piezoelectric tranducers. The classification of coffee powder spectrum data using the Principal Component Analysis (PCA) method with its pretreatment is Gap Reduction. The results of this study were obtained: The electronic nose starts responding to the smell of coffee powder at 5.85 seconds, and can classify coffee powder in 11.09 seconds. The electronic nose combined with the principal component analysis (PCA) method has succeeded in detecting pure civet coffee powder and mixed Civet coffee powder with a success rate of 100 % (PC-1 of 100% and PC-2 of 0%).     

scholarly journals Principal Component Analysis (PCA) Method for Classification of Beef and Pork Aroma Based on Electronic Nose

2019 ◽  
Vol 1 (1) ◽  
pp. 5-8 ◽  
Author(s):  
Imam Tazi ◽  
Nur Laila Isnaini ◽  
Mutmainnah Mutmainnah ◽  
Avin Ainur
Keyword(s):  
Principal Component Analysis ◽  
Electronic Nose ◽  
Meat Products ◽  
Principal Component ◽  
Component Analysis ◽  
Cumulative Number ◽  
Organoleptic Evaluation ◽  
Pca Method ◽  
Meat Samples

There are several testing processes for consuming meat products. Organoleptic evaluation is an evaluation based on color, texture, smell, and taste. This research aims to find out the response pattern of 10 gas sensor array contained in the electronic nose against the odor pattern of beef and pork base on a smell. The classification method used is using the Principal Component Analysis (PCA) method. This method is expected to simplify the test of differences in beef and pork based on the aroma. The meat used is standard beef and pork consumption that has been sold in supermarkets. Samples of beef and pork are then ground until smooth. After that, it is weighed until it reaches 1 ounce. The meat samples were tested using an electronic nose consisting of 10 gas sensors. The multivariate analysis method was used to classify the aroma of beef and pork. The results of the data processing showed that the aroma classification of beef and pork which was indexed by the electronic nose was perfect. Based on the PCA method, the proportion of PC1 is 93.4%, and PC2 is 4.9%. From the second cumulative number, the value of the first PC was obtained 98.3%. This value indicates that only with 2-dimensional data, can represent ten dimensions of data. The loading plot shows that the MQ-138 and MQ-3 sensors are the most powerful sensors in testing samples of beef and pork.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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