scholarly journals Data Fusion of Electronic Nose and Electronic Tongue for Detection of Mixed Edible-Oil

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hong Men ◽  
Donglin Chen ◽  
Xiaoting Zhang ◽  
Jingjing Liu ◽  
Ke Ning
Keyword(s):  
Data Fusion ◽  
Electronic Nose ◽  
Food Processing ◽  
Electronic Tongue ◽  
Principal Component ◽  
Edible Oil ◽  
Frying Oil ◽  
Using Data ◽  
The Cost ◽  
Detection Effect

For the problem of the waste of the edible-oil in the food processing, on the premise of food security, they often need to add new edible-oil to the old frying oil which had been used in food processing to control the cost of the production. Due to the fact that the different additive proportion of the oil has different material and different volatile gases, we use fusion technology based on the electronic nose and electronic tongue to detect the blending ratio of the old frying oil and the new edible-oil in this paper. Principal component analysis (PCA) is used to distinguish the different proportion of the old frying oil and new edible-oil; on the other hand we use partial least squares (PLS) to predict the blending ratio of the old frying oil and new edible-oil. Two conclusions were proposed: data fusion of electronic nose and electronic tongue can be used to detect the blending ratio of the old frying oil and new edible-oil; in contrast to single used electronic nose or single used electronic tongue, the detection effect has increased by using data fusion of electronic nose and electronic tongue.

scholarly journals Identification of Umami Taste in Sous-Vide Beef by Chemical Analyses, Equivalent Umami Concentration, and Electronic Tongue System

Foods ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 251 ◽  
Author(s):  
Young-Hwa Hwang ◽  
Ishamri Ismail ◽  
Seon-Tea Joo
Keyword(s):  
Amino Acids ◽  
Principal Component Analysis ◽  
Food Processing ◽  
Free Amino Acids ◽  
Free Amino ◽  
Electronic Tongue ◽  
Principal Component ◽  
Chemical Analyses ◽  
Umami Taste ◽  
Sous Vide

Behaviour of umami compounds that are associated with non-volatile compounds on slow cooking regimes remains less explored. This study aims to assess the ability of the electronic tongue system on the umami taste from sous-vide beef semitendinosus. The identification was based on the taste-enhancing synergism between umami compounds 5’-nucleotides (IMP, GMP, AMP, inosine, and hypoxanthine) and free amino acids (glutamic and aspartic acid) using the estimation of equivalent umami concentration (EUC) and electronic tongue system. Sous-vide cooked at 60 and 70 °C for 6 and 12 h and cooked using the conventional method at 70 °C for 30 min (as control) were compared. The temperature had a significant effect on 5’-nucleotides, but aspartic and glutamic acid were not influenced by any treatments applied. Sous-vide cooked at 60 °C tended to have higher inosine and hypoxanthine. Meanwhile, desirable 5’-nucleotides IMP, AMP, and GMP were more intensified at the temperature of 70 °C. The principal component analysis predicted a good correlation between EUC and the electronic tongue, with sous-vide at 70 °C for 12 h presenting the most umami. Therefore, the electronic tongue system is a useful tool in food processing, particularly in determining complex sensory properties such as umami, which cannot be evaluated objectively.

scholarly journals A Novel Method for the Discrimination of Semen Arecae and Its Processed Products by Using Computer Vision, Electronic Nose, and Electronic Tongue

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Min Xu ◽  
Shi-Long Yang ◽  
Wei Peng ◽  
Yu-Jie Liu ◽  
Da-Shuai Xie ◽  
...  
Keyword(s):  
Computer Vision ◽  
Electronic Nose ◽  
Electronic Tongue ◽  
Principal Component ◽  
Accurate Method ◽  
Color Parameters ◽  
Hydroxymethyl Furfural ◽  
Novel Method ◽  
Sensory Responses ◽  
Processed Products

Areca nut, commonly known locally as Semen Arecae (SA) in China, has been used as an important Chinese herbal medicine for thousands of years. The raw SA (RAW) is commonly processed by stir-baking to yellow (SBY), stir-baking to dark brown (SBD), and stir-baking to carbon dark (SBC) for different clinical uses. In our present investigation, intelligent sensory technologies consisting of computer vision (CV), electronic nose (E-nose), and electronic tongue (E-tongue) were employed in order to develop a novel and accurate method for discrimination of SA and its processed products. Firstly, the color parameters and electronic sensory responses of E-nose and E-tongue of the samples were determined, respectively. Then, indicative components including 5-hydroxymethyl furfural (5-HMF) and arecoline (ARE) were determined by HPLC. Finally, principal component analysis (PCA) and discriminant factor analysis (DFA) were performed. The results demonstrated that these three instruments can effectively discriminate SA and its processed products. 5-HMF and ARE can reflect the stir-baking degree of SA. Interestingly, the two components showed close correlations to the color parameters and sensory responses of E-nose and E-tongue. In conclusion, this novel method based on CV, E-nose, and E-tongue can be successfully used to discriminate SA and its processed products.

scholarly journals Data Fusion of UV-Vis and FTIR Spectra Combined with Principal Component Analysis for Distinguishing of Andrographis paniculata Extracts Based on Cultivation Ages and Solvent Extraction

2021 ◽  
Vol 21 (3) ◽  
pp. 753
Author(s):  
Antonio Kautsar ◽  
Wulan Tri Wahyuni ◽  
Utami Dyah Syafitri ◽  
Syifa Muflihah ◽  
Nursifa Mawadah ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Solvent Extraction ◽  
Data Fusion ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Ftir Spectra ◽  
Andrographis Paniculata ◽  
Score Plot ◽  
Using Data

Andrographis paniculata is one of the medicinal plants used for the treatment of antidiabetic. Cultivation ages and solvent extraction affected metabolites' composition and concentration that directly cause the plant's efficacies. This research aimed to distinguish A. paniculata based on cultivation ages and solvent extraction using data fusion of UV-Vis and FTIR spectra combined with principal component analysis (PCA). A. paniculata with 2, 3, and 4 months post-planting were extracted by water, 50% ethanol, 70% ethanol, and ethanol. In each extract, we measured UV-Vis and FTIR spectra. Then, we used the data fusion from both spectra. We used UV-Vis and FTIR absorbance from 200–400 nm and 1800–400 cm–1, respectively. Each extract gives a similar pattern of UV-Vis and FTIR spectra, only differ in their intensities. PCA score plot in two and three-dimensional showed A. paniculata extracts could be distinguished based on cultivation ages and solvent extraction with a total variance of 86 and 92%, respectively. Furthermore, this study confirms the data fusion of UV-Vis and FTIR spectra could distinguished A. paniculata extracts combined with chemometrics based on cultivation ages and solvent extraction.

scholarly journals Effective discrimination of flavours and tastes of Chinese traditional fish soups made from different regions of the silver carp using an electronic nose and electronic tongue

2020 ◽  
Vol 38 (No. 2) ◽  
pp. 84-93
Author(s):  
Zhengyi Hu ◽  
Yao Tong ◽  
Anne Manyande ◽  
Hongying Du
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Electronic Nose ◽  
Theoretical Basis ◽  
Silver Carp ◽  
Electronic Tongue ◽  
Principal Component ◽  
Chemical Components ◽  
Sds Page ◽  
Carp Fish

Silver carp is a one of the most important freshwater fish species in China, and is popular when making soup in the Chinese dietary culture. In order to investigate the profile of fish soup tastes and flavours cooked using different regions of the same fish, the silver carp was cut into four different regions: head, back, abdomen, and tail. The differences in taste and flavour of the four kinds of homemade fish soup were investigated by an electronic nose and electronic tongue. The basic chemical components of the different fish regions and the SDS-PAGE profile of the fish soup samples were investigated. Two chemometrics methods (principal component analysis and discriminant factor analysis) were used to classify the odour and taste of the fish soup samples. The results showed that the electronic tongue and nose performed outstandingly in discriminating the four fish soups even though the samples were made from different regions of the same fish. The taste and flavour information of different regions of the silver carp fish could provide the theoretical basis for food intensive processing.<br /><br />

scholarly journals Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations

Foods ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1984
Author(s):  
Xiaoguang Dong ◽  
Libing Gao ◽  
Haijun Zhang ◽  
Jing Wang ◽  
Kai Qiu ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Classification Accuracy ◽  
Laying Hens ◽  
Electronic Tongue ◽  
Principal Component ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Sensory Qualities ◽  
Egg Yolks

The present study was conducted on three commercial laying breeder strains to evaluate differences of sensory qualities, including texture, smell, and taste parameters. A total of 140 eggs for each breed were acquired from Beinong No.2 (B) laying hens, Hy-Line Brown (H) laying hens, and Wuhei (W) laying hens. Sensory qualities of egg yolks and albumen from three breeds were detected and discriminated based on different algorithms. Texture profile analysis (TPA) showed that the eggs from three breeds had no differences in hardness, adhesiveness, springiness, and chewiness other than cohesiveness. The smell profiles measured by electronic nose illustrated that differences existed in all 10 sensors for albumen and 8 sensors for yolks. The taste profiles measured by electronic tongue found that the main difference of egg yolks and albumen existed in bitterness and astringency. Principal component analysis (PCA) successfully showed grouping of three breeds based on electronic nose data and failed in grouping based on electronic tongue data. Based on electronic nose data, linear discriminant analysis (LDA), fine k-nearest neighbor (KNN) and linear support vector machine (SVM) were performed to discriminate yolks, albumen, and the whole eggs with 100% classification accuracy. While based on electronic tongue data, the best classification accuracy was 96.7% for yolks by LDA and fine tree, 88.9% for albumen by LDA, and 87.5% for the whole eggs by fine KNN. The experiment results showed that three breeds’ eggs had main differences in smells and could be successfully discriminated by LDA, fine KNN, and linear SVM algorithms based on electronic nose.

scholarly journals Physicochemical parameter based estimation of discarding points for frying oil using data interpolation and principal component transformation

2020 ◽  
Vol 8 (5) ◽  
pp. 2360-2372
Author(s):  
Yumei Li ◽  
Xianbing Cao ◽  
Yanping Cao ◽  
Yuxu Feng ◽  
Jingjun Ji ◽  
...  
Keyword(s):  
Principal Component ◽  
Frying Oil ◽  
Physicochemical Parameter ◽  
Data Interpolation ◽  
Using Data

Enhancing Athlete Tracking Using Data Fusion in Wearable Technologies

2021 ◽  
Vol 70 ◽  
pp. 1-13
Author(s):  
Adnan Waqar ◽  
Iftekhar Ahmad ◽  
Daryoush Habibi ◽  
Nicolas Hart ◽  
Quoc Viet Phung
Keyword(s):  
Data Fusion ◽  
Wearable Technologies ◽  
Using Data

Improving performance: A collaborative strategy for the multi-data fusion of electronic nose and hyperspectral to track the quality difference of rice

2021 ◽  
Vol 333 ◽  
pp. 129546
Author(s):  
Yan Shi ◽  
Hangcheng Yuan ◽  
Chenao Xiong ◽  
Qi Zhang ◽  
Shuyue Jia ◽  
...  
Keyword(s):  
Data Fusion ◽  
Electronic Nose ◽  
Quality Difference
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