Exploratory Study on Aroma Profile of Cardamom by GC-MS and Electronic Nose

2013 ◽  
pp. 207-216 ◽  
Author(s):  
D. Ghosh ◽  
S. Mukherjee ◽  
S. Sarkar ◽  
N. K. Leela ◽  
V. K. Murthy ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Exploratory Study ◽  
Aroma Profile

Exploratory study on aroma profile of cardamom by GC- MS and Electronic nose

2012 ◽  
Author(s):  
Devdulal Ghosh ◽  
Subrata Sarkar ◽  
N. K. Leela ◽  
Subhankar Mukherjee ◽  
V. Krishna Murthy ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Exploratory Study ◽  
Aroma Profile

Piezoelectric peptide-hpDNA based electronic nose for the detection of terpenes; Evaluation of the aroma profile in different Cannabis sativa L. (hemp) samples

2020 ◽  
Vol 308 ◽  
pp. 127697 ◽  
Author(s):  
Sara Gaggiotti ◽  
Sara Palmieri ◽  
Flavio Della Pelle ◽  
Manuel Sergi ◽  
Angelo Cichelli ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Cannabis Sativa ◽  
Aroma Profile

Electronic nose systems to study shelf life and cultivar effect on tomato aroma profile

2004 ◽  
Vol 97 (2-3) ◽  
pp. 324-333 ◽  
Author(s):  
Amalia Z. Berna ◽  
Jeroen Lammertyn ◽  
Stijn Saevels ◽  
Corrado Di Natale ◽  
Bart M. Nicolaı̈
Keyword(s):  
Shelf Life ◽  
Electronic Nose ◽  
Aroma Profile

scholarly journals Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2016
Author(s):  
Claudia Gonzalez Viejo ◽  
Eden Tongson ◽  
Sigfredo Fuentes
Keyword(s):  
Machine Learning ◽  
Electronic Nose ◽  
Low Cost ◽  
High Accuracy ◽  
Quality Trait ◽  
Learning Models ◽  
High Quality ◽  
Aroma Profile ◽  
Important Quality ◽  
Machine Learning Models

Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, which is time-consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate the coffee intensity and aromas using a low-cost and portable electronic nose (e-nose) and machine learning modeling. For this purpose, triplicates of six commercial coffee samples with different intensity levels were used for this study. Two machine learning models were developed based on artificial neural networks using the data from the e-nose as inputs to (i) classify the samples into low, medium, and high-intensity (Model 1) and (ii) to predict the relative abundance of 45 different aromas (Model 2). Results showed that it is possible to estimate the intensity of coffees with high accuracy (98%; Model 1), as well as to predict the specific aromas obtaining a high correlation coefficient (R = 0.99), and no under- or over-fitting of the models were detected. The proposed contactless, nondestructive, rapid, reliable, and low-cost method showed to be effective in evaluating volatile compounds in coffee, which is a potential technique to be applied within all stages of the production process to detect any undesirable characteristics on–time and ensure high-quality products.

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 Fermentation Quality and Aroma Profile of Winter Cereals and Italian Ryegrass Plus Winter Cereal Mixture Silages

2021 ◽  
Author(s):  
Alemayehu Worku Babu ◽  
Tamás Tóth ◽  
Szilvia Orosz ◽  
Hedvig Fébel ◽  
László Kacsala ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Principal Component ◽  
Italian Ryegrass ◽  
Ethyl Esters ◽  
Winter Cereal ◽  
Aroma Profile ◽  
Linear Discriminant ◽  
Winter Cereals ◽  
Fermentation Quality ◽  
Mold And Yeast

Abstract During silage making microbial fermentation produces an array of end products which can influence the odour of the final silage and can also change many nutritive aspects of a forage. The objective of this study was to evaluate the fermentation quality and aroma profile of winter cereals and Italian ryegrass (Lolium multiflorum Lam., IRG) plus winter cereal mixture silages detected with an electronic nose. Four mixtures (mixture A: triticale, oats, barley and wheat; mixture B: triticale, barley and wheat; mixture C: IRG and oats; mixture D: IRG, oats, triticale, barley and wheat) were harvested, wilted and ensiled in laboratory-scale silos (n = 80) without additives. Mixture C had higher (P < 0.05) mold and yeast (Log10 CFU (colony forming unit)/g) counts compared to mixture B. Mixture B and C had higher acetic acid (AA) content than mixture A and D. The lactic acid (LA) content was higher for mixture B than mixture C. At the end of 90 days fermentation winter cereal mixture silages (mixture A and B) had similar aroma pattern, and mixture C was also similar to winter cereal silages. However, mixture D had different aromatic pattern than other ensiled mixtures. Both the principal component analysis (PCA) score plot for aroma profile and linear discriminant analysis (LDA) classification revealed that mixture D had different aroma profile than other mixture silages. The difference was caused by the presence of high ethanol and LA in mixture D. Ethyl esters such as ethyl 3-methyl pentanoate, 2-methylpropanal, ethyl acetate, isoamyl acetate and ethyl-3-methylthiopropanoate were found at different retention indices in mixture D silage. The low LA and higher mold and yeast count in mixture C silage caused off odour due to the presence of 3-methylbutanoic acid, a simple alcohol with unpleasant camphor-like odor. In general, the electronic nose (EN) results revealed that the ensiled mixtures were dominated by ethyl ester likely producing pleasant fruity odors which could increase the intake of ensiled mixtures. However, the technology is suitable in finding off odor compounds of ensiled forages that may likely reduce feed intake.

Optimized Neural Network for Instant Coffee Classification through an Electronic Nose

2011 ◽  
Vol 7 (6) ◽  
Author(s):  
Evandro Bona ◽  
Rui Sérgio dos Santos Ferreira da Silva ◽  
Dionísio Borsato ◽  
Denisley Gentil Bassoli
Keyword(s):  
Neural Network ◽  
Quality Control ◽  
Electronic Nose ◽  
Hybrid Algorithm ◽  
Neural Network Classifier ◽  
Index Procedure ◽  
Aroma Profile ◽  
Instant Coffee ◽  
Promising Tool ◽  
Artificial Neural Network Ann

Flavor is one of the most important features of food, especially of coffee. The evaluation of this sensory feature is complex yet indispensable in quality control of instant coffees. In this work, an artificial neural network (ANN) was developed for instant coffee classification based on an electronic nose (EN) aroma profile. To this purpose, a hybrid algorithm was developed, containing: bootstrap resample methodology; factorial design and sequential simplex optimization to tune network parameters; an ensemble multilayer perceptron (MLP) trained with backpropagation for coffee classification; and causal index procedure for knowledge extraction from the trained ANN. The produced neural network classifier correctly recognizes 100% of coffees studied. Furthermore, the causal index employment allowed inference of some rules on how the coffees were separated according to the sensors available in EN. The results indicate that the applied methodology is a promising tool for instant coffee quality control.

Classifying Disfluencies in Preschool- and School-Age Spanish–English Bilinguals Who Do Not Stutter: An Exploratory Study

2020 ◽  
Vol 5 (1) ◽  
pp. 119-130
Author(s):  
Raúl Rojas ◽  
Farzan Irani
Keyword(s):  
Exploratory Study ◽  
Evidence Base ◽  
Medium Effect ◽  
Bilingual Children ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Total Percentage ◽  
Narrative Production ◽  
Language Measures ◽  
Mean Length Of Utterance

Purpose This exploratory study examined the language skills and the type and frequency of disfluencies in the spoken narrative production of Spanish–English bilingual children who do not stutter. Method A cross-sectional sample of 29 bilingual students (16 boys and 13 girls) enrolled in grades prekindergarten through Grade 4 produced a total of 58 narrative retell language samples in English and Spanish. Key outcome measures in each language included the percentage of normal (%ND) and stuttering-like (%SLD) disfluencies, percentage of words in mazes (%MzWds), number of total words, number of different words, and mean length of utterance in words. Results Cross-linguistic, pairwise comparisons revealed significant differences with medium effect sizes for %ND and %MzWds (both lower for English) as well as for number of different words (lower for Spanish). On average, the total percentage of mazed words was higher than 10% in both languages, a pattern driven primarily by %ND; %SLDs were below 1% in both languages. Multiple linear regression models for %ND and %SLD in each language indicated that %MzWds was the primary predictor across languages beyond other language measures and demographic variables. Conclusions The findings extend the evidence base with regard to the frequency and type of disfluencies that can be expected in bilingual children who do not stutter in grades prekindergarten to Grade 4. The data indicate that %MzWds and %ND can similarly index the normal disfluencies of bilingual children during narrative production. The potential clinical implications of the findings from this study are discussed.

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