scholarly journals Discrimination of Chinese yellow wine from different origins based on flavor fingerprint

2020 ◽  
Vol 32 (2) ◽  
pp. 139-144
Author(s):  
Tong Chen ◽  
Xingpu Qi ◽  
Mingjie Chen ◽  
Daoli Lu ◽  
Bin Chen
Keyword(s):  
Ion Mobility ◽  
Principal Component ◽  
Classification Model ◽  
Color Model ◽  
Quadratic Discriminant Analysis ◽  
Volatile Flavor ◽  
Volatile Organic ◽  
Mixing Method ◽  
Color Mixing ◽  
Different Origins

In this study, discrimination of Chinese yellow wines from Shaoxing, Shandong, and Hubei in China has been carried out according to volatile flavor components. A total of 122 yellow wine samples were characterized by gas chromatography–ion mobility spectrometry (GC–IMS). A simple color mixing method was visually used to select characteristic peaks based on the RGB color model. Then, the volatile organic compounds corresponding to the selected characteristic peaks were identified via library searching, and the height values of those peaks were arranged for further chemometric pretreatment. Principal component analysis was employed to reveal significant differences and potential patterns between samples. Finally, quadratic discriminant analysis was applied to develop a classification model and achieved a correct classified rate of 95.35% for the prediction set. The results prove that the aroma composition combined with chemometric tools can be used as a fingerprinting technique to protect the product of origin and enable the authenticity of Chinese yellow wine.

scholarly journals Characterization of the Flavor Profile of Bigeye Tuna Slices Treated by Cold Plasma Using E-Nose and GC-IMS

Fishes ◽  
2022 ◽  
Vol 7 (1) ◽  
pp. 13
Author(s):  
Weicong Pan ◽  
Soottawat Benjakul ◽  
Chiara Sanmartin ◽  
Alessandra Guidi ◽  
Xiaoguo Ying ◽  
...  
Keyword(s):  
Ion Mobility ◽  
Cold Plasma ◽  
Principal Component ◽  
Bigeye Tuna ◽  
Volatile Flavor Compounds ◽  
Physicochemical Changes ◽  
Thunnus Obesus ◽  
Volatile Flavor ◽  
Flavor Profile

To avoid heat, treatment induces numerous physicochemical changes under severe conditions in the tuna, cold plasma (CP), as a non-thermal technology, possess objective potential on tuna processing. The effect of cold plasma on the volatile flavor compounds of bigeye tuna (Thunnus obesus) sashimi has been evaluated using electronic nose (E-nose) and gas chromatography-ion mobility spectrometry (GC-IMS). GC–IMS results revealed a total of 33 volatile compounds in tuna slices. The effect of CP treatment on tuna flavor was not significant, furthermore CP could protect volatile freshness compounds such as 1-hexanol. Principal component analysis (PCA) of the E-nose and GC–IMS results could effectively differentiate the effect of storage to tuna sashimi. There was a high correlation between the E-nose and GC–IMS results, providing a theoretical basis for establishing the flavor fingerprint of tuna sashimi.

scholarly journals Characteristic volatile organic compounds in “HeTao” melon and other cultivars grown in Hetao region analyzed by HS-GC-IMS

2021 ◽  
Vol 3 (9) ◽  
Author(s):  
Cong Liu ◽  
Yi Sun ◽  
Hong-Xi Du ◽  
Ya-Zhen Li ◽  
Ri-Ga-La Ji ◽  
...  
Keyword(s):  
Ion Mobility ◽  
Principal Component ◽  
Ethyl Formate ◽  
Organic Component ◽  
Organic Components ◽  
Alpha Pinene ◽  
Volatile Organic ◽  
Headspace Gas ◽  
The Common ◽  
First Time

AbstractThe headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was used to compare the volatile organic components of the Hetao melon and six other cultivars of melon grown in the Hetao region of China. The results showed that the common VOCs that could be qualitatively identified from the 7 different melon samples were 35 monomers and dimers of certain compounds, mainly including alcohols, esters, aldehydes, terpenes, acids and pyridines. Hexyl acetate, 3-methylbutyl acetate, ethyl acetate and ethyl formate were predominant VOCs in seven melon cultivars. Among them, Xizhoumi No. 25 (XZM25) had 3 unique volatile organic components: 3-methylbutanal, benzaldehyde and nonanal. Xizhoumi No. 17 (XZM17) had 3 unique volatile organic components: alpha-pinene, linalool and (E)-2-hexenol. Jinhongmi (JHM) had 1 unique volatile organic component: ethyl pentanoate. The Hetao melon (HLS) contained 3 unique volatile organic components: heptanal, 2-ethyl-6-methyl pyrazine and 3-methyl valeric acid. Yinmi (YM) had 2 unique volatile organic components: 3-methylbutanol and 1-butanol, and Huangjinmi (HJM) had 1 unique volatile organic component: limonene. YM, GMB2010, HLS and JHM were similar based on the principal component analysis. This research analyzed the flavor components of different melon cultivars grown in the Hetao region of China for the first time.

scholarly journals Analysis of Volatile Organic Compounds by HS-GC-IMS in Powdered Yak Milk Processed under Different Sterilization Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Duo Feng ◽  
Jing Wang ◽  
Xiao-Jiao Ji ◽  
Wen-Xiang Min ◽  
Wen-Jie Yan
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Ion Mobility ◽  
Euclidean Distance ◽  
Principal Component ◽  
Yak Milk ◽  
Ultra High Pressure ◽  
Volatile Organic ◽  
Headspace Gas ◽  
Ion Mobility Spectroscopy

Headspace-gas chromatography-ion mobility spectroscopy (HS-GC-IMS) was used to detect the volatile organic compounds (VOCs) of yak milk powders (YMPs) under ultra-high-pressure sterilization (UHPS) and thermization (TH) methods. The analyses led to the identification of several characteristic of compounds, therefore, exploitation and comparison of the different flavors. A total of 46 peaks were detected, and 17 compounds were identified, including 7 aldehydes, 5 ketones, 3 acids, 1 terpene, and 1 ester. Furthermore, principal component analysis (PCA) and fingerprint similarity analysis based on Euclidean distance compared the YMPs and found that the YMPs had certain differences, which can distinguish the YMPs with different sterilization methods. In conclusion, different sterilization methods possibly affect the flavor of YMPs, and UHPS is bettedslfr than TH. Also, aldehydes were mainly be detected in UHPS groups, whereas the ketones and acids mostly appeared in TH groups. Most importantly, UHPS can retain the original flavor of yak milk to a greater extent.

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

An energy dispersive X-ray fluorescence spectrometry approach for the identification of geographical origin of wheat flour

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Tong Chen ◽  
Xingpu Qi ◽  
Zaiyong Si ◽  
Qianwei Cheng ◽  
Hui Chen
Keyword(s):  
Principal Component Analysis ◽  
Wheat Flour ◽  
Principal Component ◽  
Component Analysis ◽  
Mineral Elements ◽  
Energy Dispersive ◽  
Fluorescence Spectrometry ◽  
Energy Channel ◽  
X Ray ◽  
Different Origins

Abstract In this work, a method was established for discriminating geographical origins of wheat flour based on energy dispersive X-ray fluorescence spectrometry (ED-XRF) and chemometrics. 68 wheat flour samples from three different origins were collected and analyzed using ED-XRF technology. Firstly, the principal component analysis method was applied to analyze the feasibility of discrimination and reduce data dimensionality. Then, Competitive Adaptive Reweighted Sampling (CARS) was used to further extract feature variables, and 12 energy variables (corresponding to mineral elements) were identified and selected to characterize the geographical attributes of wheat flour samples. Finally, a non-linear model was constructed using principal component analysis and quadratic discriminant analysis (QDA). The CARS-PCA-QDA model showed that the accuracy of five-fold cross-validation was 84.25%. The results showed that the established method was able to select important energy channel variables effectively and wheat flour could be classified based on geographical origins with chemometrics, which could provide a theoretical basis for unveiling the relationship between mineral element composition and wheat origin.

scholarly journals Influence of age and gender on the profile of exhaled volatile organic compounds analyzed by an electronic nose

2016 ◽  
Vol 42 (2) ◽  
pp. 143-145 ◽  
Author(s):  
Silvano Dragonieri ◽  
Vitaliano Nicola Quaranta ◽  
Pierluigi Carratu ◽  
Teresa Ranieri ◽  
Onofrio Resta
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Electronic Nose ◽  
Principal Component ◽  
Exhaled Breath ◽  
Age And Gender ◽  
Volatile Organic ◽  
Older Subjects ◽  
Never Smokers ◽  
And Gender

We aimed to investigate the effects of age and gender on the profile of exhaled volatile organic compounds. We evaluated 68 healthy adult never-smokers, comparing them by age and by gender. Exhaled breath samples were analyzed by an electronic nose (e-nose), resulting in "breathprints". Principal component analysis and canonical discriminant analysis showed that older subjects (≥ 50 years of age) could not be distinguished from younger subjects on the basis of their breathprints, as well as that the breathprints of males could not distinguished from those of females (cross-validated accuracy, 60.3% and 57.4%, respectively).Therefore, age and gender do not seem to affect the overall profile of exhaled volatile organic compounds measured by an e-nose.

scholarly journals 33 Aplicação da análise exploratória de dados na discriminação geográfica do quiabo do Rio Grande do Norte e Pernambuco

2018 ◽  
Vol 34 (3) ◽  
pp. 33
Author(s):  
Francisco Dos Santos Panero ◽  
Maria de Fátima Pereira Vieira ◽  
Ângela Maria Paiva Cruz ◽  
Maria de Fátima Vitória De Moura ◽  
Henrique Eduardo Bezerra Da Silva
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Rio Grande ◽  
Component Analysis ◽  
The State ◽  
Data Treatment ◽  
Different Origins

Samples of okra from Caruaru and Vitória of Santo Antão, in the State of Pernambuco, and Ceará-Mirim, Macaíba and Extremoz in the State of Rio Grande do Norte have been analysed. Two different methods were applied in the data treatment allowing to geographically discriminate samples from different origins: Principal Component Analysis - PCA and Hierarquical Cluster Analysis - HCA.

scholarly journals A proof of concept study for the differentiation of SARS-CoV-2, hCoV-NL63, and IAV-H1N1 in vitro cultures using ion mobility spectrometry

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Feuerherd ◽  
A.-K. Sippel ◽  
J. Erber ◽  
J. I. Baumbach ◽  
R. M. Schmid ◽  
...  
Keyword(s):  
Organic Compounds ◽  
Ion Mobility ◽  
Point Of Care ◽  
Respiratory Viruses ◽  
Gas Analysis ◽  
In Vitro Cultures ◽  
Proof Of Concept ◽  
Volatile Organic ◽  
Set Up

AbstractRapid, high-throughput diagnostic tests are essential to decelerate the spread of the novel coronavirus disease 2019 (COVID-19) pandemic. While RT-PCR tests performed in centralized laboratories remain the gold standard, rapid point-of-care antigen tests might provide faster results. However, they are associated with markedly reduced sensitivity. Bedside breath gas analysis of volatile organic compounds detected by ion mobility spectrometry (IMS) may enable a quick and sensitive point-of-care testing alternative. In this proof-of-concept study, we investigated whether gas analysis by IMS can discriminate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from other respiratory viruses in an experimental set-up. Repeated gas analyses of air samples collected from the headspace of virus-infected in vitro cultures were performed for 5 days. A three-step decision tree using the intensities of four spectrometry peaks correlating to unidentified volatile organic compounds allowed the correct classification of SARS-CoV-2, human coronavirus-NL63, and influenza A virus H1N1 without misassignment when the calculation was performed with data 3 days post infection. The forward selection assignment model allowed the identification of SARS-CoV-2 with high sensitivity and specificity, with only one of 231 measurements (0.43%) being misclassified. Thus, volatile organic compound analysis by IMS allows highly accurate differentiation of SARS-CoV-2 from other respiratory viruses in an experimental set-up, supporting further research and evaluation in clinical studies.

Sign in / Sign up

Export Citation Format

Share Document