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

Shelf life determination by electronic nose: application to milk

2005 ◽  
Vol 106 (1) ◽  
pp. 199-206 ◽  
Author(s):  
Saïd Labreche ◽  
Sandrine Bazzo ◽  
Sonia Cade ◽  
Eric Chanie
Keyword(s):  
Shelf Life ◽  
Electronic Nose

Monitoring of fresh-cut Valerianella locusta Laterr. shelf life by electronic nose and VIS–NIR spectroscopy

Talanta ◽  
2014 ◽  
Vol 120 ◽  
pp. 368-375 ◽  
Author(s):  
Valentina Giovenzana ◽  
Roberto Beghi ◽  
Susanna Buratti ◽  
Raffaele Civelli ◽  
Riccardo Guidetti
Keyword(s):  
Shelf Life ◽  
Electronic Nose ◽  
Nir Spectroscopy ◽  
Fresh Cut

scholarly journals Characterization of pasteurized milk spoilage by electronic nose in relation to its selected quality parameters

2021 ◽  
Vol 10 (2) ◽  
pp. 383-397
Author(s):  
Saleem Ehsan ◽  
Zahir Al-Attabi ◽  
Nasser Al-Habsi ◽  
Michel R. G. Claereboudt ◽  
Mohammad Shafiur Rahman
Keyword(s):  
Shelf Life ◽  
Electronic Nose ◽  
Principal Component ◽  
Titratable Acidity ◽  
Bacterial Count ◽  
Quality Parameters ◽  
Accurate Estimation ◽  
Total Bacterial Count ◽  
Microbial Count ◽  
Linear Discriminant

Pasteurized fresh milk requires an accurate estimation of shelf life under various conditions to minimize the risk of spoilage and product losses. Milk samples were stored for 56 h in an oven at 25°C and for 15 days in a refrigerator at 4°C. Samples were analyzed using an electronic nose (e-nose), total bacterial count, titratable acidity and pH to determine the quality of milk. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to analyze e-nose data of milk stored at 25°C, and 4°C. A clear shift in quality was identified by the e-nose, which also appeared in the total bacterial count after 24 h and 12 days for storage at 25 and 4°C, respectively. On the other hand, titratable acidity exceeded the normal limits of 0.14 % - 0.21 % after 24 h for storage at 25°C (0.247 ± 0.006 %) and after 15 days for storage at 4°C (0.25 ± 0.01 %). If pH was a good indicator of quality for samples stored at 25°C, it showed no clear trends for samples stored at 4°C. Based on the microbial count data and e-nose output, the milk had a shelf life of 0.3 day (i.e. 8 h) when stored at 25°C. Shelf life was extended to 9 days when stored at 4°C.

Application of electronic nose in the prediction model of shelf-life of Pampus argenteus

2010 ◽  
Vol 34 (3) ◽  
pp. 367-374
Author(s):  
Yi TONG ◽  
Jing XIE ◽  
Hong XIAO ◽  
Sheng-ping YANG
Keyword(s):  
Prediction Model ◽  
Shelf Life ◽  
Electronic Nose ◽  
Pampus Argenteus

Evaluation of the Food Sniffer electronic nose for assessing the shelf life of fresh pork meat compared to physicochemical measurements of meat quality

2017 ◽  
Vol 244 (6) ◽  
pp. 1047-1055 ◽  
Author(s):  
Héctor L. Ramírez ◽  
Almudena Soriano ◽  
Sergio Gómez ◽  
Juan Ubeda Iranzo ◽  
Ana I. Briones
Keyword(s):  
Shelf Life ◽  
Meat Quality ◽  
Electronic Nose ◽  
Pork Meat ◽  
Physicochemical Measurements

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

Correlation between electronic nose signals and fruit quality indicators on shelf-life measurements with pinklady apples

2001 ◽  
Vol 80 (1) ◽  
pp. 41-50 ◽  
Author(s):  
J. Brezmes ◽  
E. Llobet ◽  
X. Vilanova ◽  
J. Orts ◽  
G. Saiz ◽  
...  
Keyword(s):  
Shelf Life ◽  
Quality Indicators ◽  
Fruit Quality ◽  
Electronic Nose

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.

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