scholarly journals Implementation of an electronic nose for classification of synthetic flavors

2021 ◽  
Vol 10 (3) ◽  
pp. 1283-1290
Author(s):  
Radi Radi ◽  
Barokah Barokah ◽  
Dwi Noor Rohmah ◽  
Eka Wahyudi ◽  
Muhammad Danu Adhityamurti ◽  
...  
Keyword(s):  
Data Collection ◽  
Electronic Nose ◽  
Principal Component ◽  
Data Collection Process ◽  
Data Collection And Analysis ◽  
Sample Data ◽  
Three Stages ◽  
Pre Treatment ◽  
Recognition Software

Classification and identification of synthetic flavor become routine activities in the flavor and food industry due to its application. As a modern olfactory technology, electronic nose (e-nose) has the possibility to be applied in these activities. This study aimed to evaluate an e-nose for classifying synthetic flavors. In this study, an e-nose was designed with an array of gases sensors as the main sensing component and principal component analysis (PCA) for the pattern recognition software. This research was started with preparation of the hardware, continued with preparation of sample, data collection, and analysis. There were nine samples of synthetic flavors with different aroma, namely: grapes, strawberry, mocha, pandanus, mango, jackfruit, orange, melon, and durian. The data collection process includes three stages, i.e. flushing, collecting, and purging of 2 min, 3 min, 2 min respectively. These sensor responses were then analyzed for forming aroma patterns. Four pre-treatment methods were applied for the aroma pattern formation: absolute data, normalize of absolute data, relative data, and normalize of relative data. With the PCA for evaluation, the results showed that the absolute data treatment provided the best results, indicated from the distribution of aroma patterns that were grouped according to the type of samples.

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 Classification of chili powder (Capsicum annuum L.) antioxidant activity based on near infrared spectra

Food Research ◽  
2021 ◽  
Vol 5 (S2) ◽  
pp. 51-56
Author(s):  
Y.I. Aprilia ◽  
N. Khuriyati ◽  
A.C. Sukartiko
Keyword(s):  
Antioxidant Activity ◽  
Success Rate ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Near Infrared Spectra ◽  
Medium Level ◽  
Chili Powder ◽  
Pre Treatment

Testing the antioxidant activity of chili powder is often destructive; these methods are expensive, complicated, and lengthy analysis time. Meanwhile, information on antioxidant activity is needed by the industry to determine its quality class in rapid and uncomplicated handling. Therefore, this study was aimed to measure the antioxidant activity of chili powder and classify it into three quality classes, namely high, medium, and low, using Near Infrared (NIR) spectroscopy at spectral wavelengths of 1000-2500 nm and combined with chemometric techniques. The antioxidant activity of the sample was evaluated using the DPPH assay. Processing of the data started with outlier detection using Hotelling's T2 , then confirmed using leverage analysis and influence plot. The data were then processed with Smoothing-Savitzky Golay, SNV, and De-Trending. Principal Component Analysis (PCA) was performed for classifying the samples, which were validated with full crossvalidation. The results showed that the antioxidant activity was detected at a range of 1395 - 2390 nm. De-Trending was the best pre-treatment that successfully classified the low and high levels of antioxidant activity with a success rate of 100% and classified a medium level of antioxidant activity with a success rate of 96.97%.

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 Freshness assessment of tilapia fish in traditional market based on an electronic nose

2021 ◽  
Vol 10 (5) ◽  
pp. 2466-2476
Author(s):  
Radi Radi ◽  
Eka Wahyudi ◽  
Muhammad Danu Adhityamurti ◽  
Joko Purwo Leksono Yuroto Putro ◽  
Barokah Barokah ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Electronic Nose ◽  
Gas Sensors ◽  
Principal Component ◽  
Data Recording ◽  
System A ◽  
Traditional Market ◽  
Accuracy Level ◽  
Three Stages

This study evaluates an e-nose based on gas sensors to measure the freshness of tilapia. The device consists of a series of semiconductor sensors as detector, a combination of valve-vial-oxygen as sample delivery system, a microcontroller as interface and controller, and a computer for data recording and processing. The e-nose was firstly used to classify the fresh and non-fresh tilapia. A total of 48 samples of fresh tilapia and 50 samples of non-fresh tilapia were prepared and measured using the e-nose through three stages, namely: flushing, collecting, and purging. The sensor responses were processed into aroma patterns, then classified by two pattern classification softwares of principal component analysis (PCA) and neural network (NN). There were four methods for aroma patterns formation being evaluated: absolute data, normalized absolute data, relative data, normalized relative data. The results showed that the normalized absolute data method provides the best classification with the accuracy level of 93.88%. With this method, the trained NN was used to predict the freshness of 15 tilapia samples collected from a traditional market. The result showed that 60.0% of the samples are classified into fresh category, 33.3% are in the non-fresh category, and 6.7% are not included in both categories.

scholarly journals Classification of Agarwood Oil Using an Electronic Nose

Sensors ◽  
2010 ◽  
Vol 10 (5) ◽  
pp. 4675-4685 ◽  
Author(s):  
Wahyu Hidayat ◽  
Ali Yeon Md. Shakaff ◽  
Mohd Noor Ahmad ◽  
Abdul Hamid Adom
Keyword(s):  
Neural Network ◽  
Electronic Nose ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Scatter Plot ◽  
Different Types ◽  
Artificial Neural Network Ann ◽  
Distinct Separation ◽  
Agarwood Oil

Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.

Application of Electronic Nose for Classification of Fish Sauces Using Transient Responses and Learning Vector Quantization Algorithm

2014 ◽  
Vol 931-932 ◽  
pp. 1582-1586 ◽  
Author(s):  
Nitikarn Nimsuk
Keyword(s):  
Nitrogen Content ◽  
Total Nitrogen ◽  
Vector Quantization ◽  
Electronic Nose ◽  
Principal Component ◽  
Learning Vector Quantization ◽  
Total Nitrogen Content ◽  
Fish Sauce ◽  
Transient Responses

Fish sauce is one of the signature condiments in various cuisines in many countries. In this paper, fish sauces are successfully classified into groups depending on their quality indicated by the level of total nitrogen content. We introduce an electronic nose technology together with a neural network algorithm to the classification of fish sauces. The transient responses are used as features for the creation of pattern vectors for odor samples. The result of principal component analysis shows well-separated patterns of fish sauce. Furthermore, we also apply the learning vector quantization method for the classification. As a result, we obtain high accuracy of more than 90% in the classification of fish sauce based on the level of total nitrogen content.

Application of Electronic Nose for Detection of Wine-Aging Methods

2014 ◽  
Vol 875-877 ◽  
pp. 2206-2213 ◽  
Author(s):  
Yang Ji Wei ◽  
Li Li Yang ◽  
Ying Ping Liang ◽  
Jing Ming Li
Keyword(s):  
Electronic Nose ◽  
Principal Components ◽  
Euclidean Distance ◽  
Principal Component ◽  
Red Wines ◽  
Total Contribution ◽  
Distance Correlation ◽  
Linear Discrimination ◽  
Wine Aging

This study reports the application of an electronic nose for the identification and classification of red wines aged three different methods. The signals of the different wines detected by the 10 sensors present in the E-nose are significantly different from each other. The response to the signal generates a typical chemical fingerprint of the volatile compounds present in the wines. Principal Component Analysis can be applied for the dimensionality reduction of the collected signal. Since the total contribution rate of the first three principal components is up to 97.27%, different wines can be distinguished from each other by the three principal components. Euclidean distance, correlation analysis, Mahalanobis distance and linear discrimination analysis can offer 100% accuracy for known samples, and the accuracy rate can reach 88.9% for the 18 test samples. In addition, numerous advantages exist compared with sensory analysis in both authentication and quality control of wines.

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.

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