scholarly journals A comparison of sensory evaluation and an electronic nose assay in the assessment of aroma in dry hopped beers

2020 ◽  
Vol 66 (1) ◽  
Author(s):  
Jana Štefániková ◽  
Veronika Nagyová ◽  
Matej Hynšt ◽  
Dominika Kudláková ◽  
Július Árvay ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Sensory Evaluation ◽  
Electronic Nose ◽  
Control Sample ◽  
Principal Component ◽  
Aroma Profile ◽  
Basic Characteristics ◽  
The Individual ◽  
First Time ◽  
Electronic Nose Technology

The aim of this work was to compare sensory evaluation and electronic nose technology used to assess aromas in dry hopped beers. An electronic nose based on gas chromatography was used for the first time. Hops varieties Amarillo, Cascade, Chinook, Kazbek and Mandarina Bavaria were used for the production of dry hopped beers and the Sladek variety was used for the control sample. The basic characteristics of the beers were determined, and the sensory evaluation performed by selected assessors was compared to the sensory assay using an electronic nose. Assessment of the aroma profile of dry hopped beers shows that the basic flavours of these beers, such as worty, yeasty and hoppy, were suppressed. Compared to the control sample, a significant grapefruit flavour was noted by the evaluators in Kazbek and Chinook beer samples. The most prominent determinant, compared to the control sample, was in general the citrus aroma. Based on the results of the principal component analysis, it can be concluded that there were statistically significant differences between the individual dry hopped beers and between them and the control sample with the exception of beers dry hopped with hops of the Amarillo and Cascade variety, which was also confirmed by the results of sensory evaluation (approximately the same scoring of the monitored descriptors).

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 Freshness Evaluation of Three Kinds of Meats Based on the Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 605 ◽  
Author(s):  
Jun Chen ◽  
Juanhong Gu ◽  
Rong Zhang ◽  
Yuezhong Mao ◽  
Shiyi Tian
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Sensory Evaluation ◽  
Electronic Nose ◽  
Storage Time ◽  
Principal Component ◽  
Component Analysis ◽  
Set Up ◽  
Storage Times

The aim of this study was to use an electronic nose set up in our lab to detect and predict the freshness of pork, beef and mutton. Three kinds of freshness, including fresh, sub-fresh and putrid, was established by human sensory evaluation and was used as a reference for the electronic nose’s discriminant factor analysis. The principal component analysis results showed the electronic nose could distinguish well pork, beef and mutton samples with different storage times. In the PCA figures, three kinds of meats samples all presented an approximate parabola trend during 7 days’ storage time. The discriminant factor analysis showed electronic nose could distinguish and judge well the freshness of samples (accuracy was 89.5%, 84.2% and 94.7% for pork, beef and mutton, respectively). Therefore, the electronic nose is promising for meat fresh detection application.

scholarly journals Categorization of Chinese Dry-Cured Ham Based on Three Sticks’ Method by Multiple Sensory Technologies

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Dong Xia ◽  
Dan Ni Zhang ◽  
Shao Ting Gao ◽  
Le Chen ◽  
Nan Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Sensory Evaluation ◽  
Electronic Nose ◽  
Function Analysis ◽  
Principal Component ◽  
Absorption Method ◽  
Dry Cured Ham ◽  
And Cluster Analysis ◽  
Higher Sensitivity

Odor profiles of three grades of Jinhua, Xuanwei, and Rugao dry-cured hams were analyzed and distinguished by both the electronic nose and the sensory evaluation. The odor was absorbed by bamboo sticks, which is the most traditional absorption method to classify different ham grades. Then data from electronic nose was analyzed by discriminant function analysis (DFA) and cluster analysis (CA), compared with that from sensory evaluation by principal component analysis (PCA). Results showed that different grades of Jinhua, Xuanwei, and Rugao dry-cured hams could be distinguished effectively by the DFA results of electronic nose. However, sensory evaluation could not perform as well as electronic nose. It was demonstrated that intelligent sensory technology has higher sensitivity and reliability in classifying producing regions and grades of dry-cured ham.

Application of Electronic Nose in Tea Quality Recognition

2011 ◽  
Vol 422 ◽  
pp. 43-46
Author(s):  
Hong Mei Zhang ◽  
Fen Ling Chang ◽  
Yong Chang Yu ◽  
Yu Jing He ◽  
He Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Quality Control ◽  
Electronic Nose ◽  
Rapid Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Tea Quality ◽  
Electronic Nose Technology

The current study uses the electronic nose FOX 4000 to inspect Xinyang Maojian tea in three quality levels. Principal component analysis (PCA) and statistical quality control (SQC) are adopted to analyze and recognize the data. PCA shows that there is a certain difference in the odor of the tea samples in the three quality levels. PCA can evidently distinguish three kinds of samples. SQC analysis shows that X800 and X600 are located outside the controllable range, indicating that they differ from X1200 in odor. This result is consistent with the PCA result. The study shows that electronic nose technology is expected to be applied widely in the rapid detection of tea.

Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models

2010 ◽  
pp. 138-156
Author(s):  
G. A. Rekha Pai ◽  
G. A. Vijayalakshmi Pai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Models ◽  
Network Models ◽  
Principal Component ◽  
Component Analysis ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Neural Network Models ◽  
The Individual

Industrial bankruptcy is a rampant problem which does not occur overnight and when it occurs can cause acute financial embarrassment to Governments and financial institutions as well as threaten the very viability of the firms. It is therefore essential to help industries identify the impending trouble early. Several statistical and soft computing based bankruptcy prediction models that make use of financial ratios as indicators have been proposed. Majority of these models make use of a selective set of financial ratios chosen according to some appropriate criteria framed by the individual investigators. In contrast, this study considers any number of financial ratios irrespective of the industrial category and size and makes use of Principal Component Analysis to extract their principal components, to be used as predictors, thereby dispensing with the cumbersome selection procedures used by its predecessors. An Evolutionary Neural Network (ENN) and a Backpropagation Neural Network with Levenberg Marquardt’s training rule (BPN) have been employed as classifiers and their performance has been compared using Receiver Operating Characteristics (ROC) analyses. Termed PCA-ENN and PCA-BPN models, the predictive potential of the two models have been analyzed over a financial database (1997-2000) pertaining to 34 sick and 38 non sick Indian manufacturing companies, with 21 financial ratios as predictor variables.

Development of a New Tool for Managing Performance Nutrition: The Athlete Food Choice Questionnaire

2019 ◽  
Vol 29 (6) ◽  
pp. 620-627 ◽  
Author(s):  
Rachael L. Thurecht ◽  
Fiona E. Pelly
Keyword(s):  
Principal Component Analysis ◽  
Nutrition Education ◽  
Food Choice ◽  
Weight Control ◽  
Principal Component ◽  
Component Analysis ◽  
Key Factors ◽  
Dietary Interventions ◽  
Frequency Scale ◽  
The Individual

This study aimed to develop and refine an Athlete Food Choice Questionnaire (AFCQ) to determine the key factors influencing food choice in an international cohort of athletes. A questionnaire that contained 84 items on a 5-point frequency scale was developed for this study. Athletes at the 2017 Universiade, in Taiwan, were invited to participate. Principal component analysis was utilized to identify key factors and to refine the questionnaire. Completed questionnaires were received from 156 athletes from 31 countries and 17 sports. The principal component analysis extracted 36 items organized into nine factors explaining 68.0% of variation. The nine factors were as follows: nutritional attributes of the food, emotional influences, food and health awareness, influence of others, usual eating practices, weight control, food values and beliefs, sensory appeal, and performance. The overall Kaiser–Meyer–Olkin measure was 0.75, the Bartlett test of sphericity was statistically significant, χ2(666) =2,536.50, p < .001, and all of the communalities remained >0.5. Intercorrelations were detected between performance and both nutritional attributes of the food and weight control. The price of food, convenience, and situational influences did not form part of the factorial structure. This research resulted in an AFCQ that includes factors specific to athletic performance and the sporting environment. The AFCQ will enable researchers and sports dietitians to better tailor nutrition education and dietary interventions to suit the individual or team. The next phase will test the accuracy and reliability of the AFCQ both during and outside of competition. The AFCQ is a useful tool to assist with management of performance nutrition for athletes.

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