Volatile-Olfactory Profiles of cv. Arbequina Olive Oils Extracted without/with Olive Leaves Addition and Their Discrimination Using an Electronic Nose

Oils from cv. Arbequina were industrially extracted together with olive leaves of cv. Arbequina or Santulhana (1%, w/w), and their olfactory and volatile profiles were compared to those extracted without leaves addition (control). The leaves incorporation resulted in green fruity oils with fresh herbs and cabbage olfactory notes, while control oils showed a ripe fruity sensation with banana, apple, and dry hay grass notes. In all oils, total volatile contents varied from 57.5 to 65.5 mg/kg (internal standard equivalents), being aldehydes followed by esters, hydrocarbons, and alcohols the most abundant classes. No differences in the number of volatiles were observed. The incorporation of cv. Arbequina or Santulhana leaves significantly reduced the total content of alcohols and esters (minus 37–56% and 10–13%, respectively). Contrary, cv. Arbequina leaves did not influence the total content of aldehydes or hydrocarbons, while cv. Santulhana leaves promoted a significant increase (plus 49 and 10%, respectively). Thus, a leaf-cultivar dependency was observed, tentatively attributed to enzymatic differences related to the lipoxygenase pathway. Olfactory or volatile profiles allowed the successful unsupervised differentiation of the three types of studied cv. Arbequina oils. Finally, a lab-made electronic nose was applied to allow the nondestructive discrimination of cv. Arbequina oils extracted with or without the incorporation of olive leaves (100% and 99 ± 5% of correct classifications for leave-one-out and repeated K-fold cross-validation variants), being a practical tool for ensuring the label correctness if future commercialization is envisaged. Moreover, this finding also strengthened that olive oils extracted with or without olive leaves incorporation possessed quite different olfactory patterns, which also depended on the cultivar of the olive leaves.

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Rapid characterization of the volatile profiles in Pu‐erh tea by gas phase electronic nose and microchamber/thermal extractor combined with TD‐GC‐MS

Journal of Food Science ◽

10.1111/1750-3841.15723 ◽

2021 ◽

Author(s):

Yanqin Yang ◽

Yuting Rong ◽

Fuqiao Liu ◽

Yongwen Jiang ◽

Yuliang Deng ◽

...

Keyword(s):

Gas Phase ◽

Electronic Nose ◽

Volatile Profiles ◽

Rapid Characterization

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Study onYang-XuUsing Body Constitution Questionnaire and Blood Variables in Healthy Volunteers

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2016/9437382 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 7

Author(s):

Hong-Jhang Chen ◽

Yii-Jeng Lin ◽

Pei-Chen Wu ◽

Wei-Hsiang Hsu ◽

Wan-Chung Hu ◽

...

Keyword(s):

Healthy Subjects ◽

Logistic Regression Model ◽

Cross Validation ◽

Blood Biomarkers ◽

Metabolic Characteristics ◽

Body Constitution ◽

Leave One Out ◽

The Relationship ◽

Fold Cross Validation ◽

Blood Variables

Traditional Chinese medicine (TCM) formulates treatment according to body constitution (BC) differentiation. Different constitutions have specific metabolic characteristics and different susceptibility to certain diseases. This study aimed to assess theYang-Xuconstitution using a body constitution questionnaire (BCQ) and clinical blood variables. A BCQ was employed to assess the clinical manifestation ofYang-Xu. The logistic regression model was conducted to explore the relationship between BC scores and biomarkers. Leave-one-out cross-validation (LOOCV) and K-fold cross-validation were performed to evaluate the accuracy of a predictive model in practice. Decision trees (DTs) were conducted to determine the possible relationships between blood biomarkers and BC scores. According to the BCQ analysis, 49% participants without any BC were classified as healthy subjects. Among them, 130 samples were selected for further analysis and divided into two groups. One group comprised healthy subjects without any BC (68%), while subjects of the other group, named as the sub-healthy group, had three BCs (32%). Six biomarkers, CRE, TSH, HB, MONO, RBC, and LH, were found to have the greatest impact on BCQ outcomes inYang-Xusubjects. This study indicated significant biochemical differences inYang-Xusubjects, which may provide a connection between blood variables and theYang-XuBC.

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IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning

BMC Bioinformatics ◽

10.1186/s12859-019-3278-3 ◽

2019 ◽

Vol 20 (S23) ◽

Cited By ~ 3

Author(s):

Cheng Yan ◽

Guihua Duan ◽

Fang-Xiang Wu ◽

Jianxin Wang

Keyword(s):

Infectious Diseases ◽

Cross Validation ◽

Sequence Similarity ◽

Least Square ◽

Computational Method ◽

Receptor Interaction ◽

Virus Receptor ◽

Receptor Interactions ◽

Leave One Out ◽

Fold Cross Validation

Abstract Background Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited. Result In this study, we propose a new computational method (IILLS) to predict virus-receptor interactions based on Initial Interaction scores method via the neighbors and the Laplacian regularized Least Square algorithm. IILLS integrates the known virus-receptor interactions and amino acid sequences of receptors. The similarity of viruses is calculated by the Gaussian Interaction Profile (GIP) kernel. On the other hand, we also compute the receptor GIP similarity and the receptor sequence similarity. Then the sequence similarity is used as the final similarity of receptors according to the prediction results. The 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) are used to assess the prediction performance of our method. We also compare our method with other three competing methods (BRWH, LapRLS, CMF). Conlusion The experiment results show that IILLS achieves the AUC values of 0.8675 and 0.9061 with the 10-fold cross validation and leave-one-out cross validation (LOOCV), respectively, which illustrates that IILLS is superior to the competing methods. In addition, the case studies also further indicate that the IILLS method is effective for the virus-receptor interaction prediction.

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Improved method for determination of waxes in olive oils: reduction of silica and use of a less hazardous solvent

OCL ◽

10.1051/ocl/2020016 ◽

2020 ◽

Vol 27 ◽

pp. 20

Author(s):

Andrea Milani ◽

Paolo Lucci ◽

Martina Sedran ◽

Erica Moret ◽

Sabrina Moret ◽

...

Keyword(s):

Silicic Acid ◽

Solid Phase ◽

Internal Standard ◽

Solvent Mixture ◽

Official Method ◽

Improved Method ◽

Capillary Gc ◽

Olive Oils ◽

Solid Phase Extraction Cartridge

The evaluation of the content of waxes is request both by IOC Trade Standard and by Regulation (EEC) 2568/91 and its further amendments. The official method uses 15 g of silicic acid and elutes several fractions by using huge volumes of dangerous solvent (n-hexane). The developed method uses 1 g of silicic acid with a different particle size and less than 20 mL of solvent mixture, substituting n-hexane with less toxic isooctane. Briefly, after spiking with a suitable internal standard, oil sample is fractionated by SPE (Solid Phase Extraction) cartridge with 1 g of silica, waxes are eluted with 14 mL of isooctane/ethyl ether 99/1 (6 mL discarded and 8 mL collected), then, after elution sample is reconstitute in 200 μL of n-heptane and analysed by capillary GC. Data of “In home” validation, (repeatability, accuracy and recovery) and relative chromatograms are reported in this paper.

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Evaluation of Filtration on Volatile Compounds in Virgin Olive Oils using an Electronic Nose

2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) ◽

10.1109/isoen.2019.8823161 ◽

2019 ◽

Author(s):

Norihito Kishimoto ◽

Ayako Kashiwagi

Keyword(s):

Volatile Compounds ◽

Electronic Nose ◽

Virgin Olive Oils ◽

Olive Oils

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Discrimination of Chinese Liquors Based on Electronic Nose and Fuzzy Discriminant Principal Component Analysis

Foods ◽

10.3390/foods8010038 ◽

2019 ◽

Vol 8 (1) ◽

pp. 38 ◽

Cited By ~ 2

Author(s):

Xiaohong Wu ◽

Jin Zhu ◽

Bin Wu ◽

Chao Zhao ◽

Jun Sun ◽

...

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Electronic Nose ◽

Nearest Neighbor ◽

Principal Component ◽

Component Analysis ◽

K Nearest Neighbor ◽

Knn Classifier ◽

Extraction Algorithm ◽

Leave One Out

The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nose system can quickly detect different types of Chinese liquors according to their aromas. In this study, an e-nose system was designed to identify six types of Chinese liquors, and a novel feature extraction algorithm, called fuzzy discriminant principal component analysis (FDPCA), was developed for feature extraction from e-nose signals by combining discriminant principal component analysis (DPCA) and fuzzy set theory. In addition, principal component analysis (PCA), DPCA, K-nearest neighbor (KNN) classifier, leave-one-out (LOO) strategy and k-fold cross-validation (k = 5, 10, 20, 25) were employed in the e-nose system. The maximum classification accuracy of feature extraction for Chinese liquors was 98.378% using FDPCA, showing this algorithm to be extremely effective. The experimental results indicate that an e-nose system coupled with FDPCA is a feasible method for classifying Chinese liquors.

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Comparison of flavour fingerprint, electronic nose and multivariate analysis for discrimination of extra virgin olive oils

Royal Society Open Science ◽

10.1098/rsos.190002 ◽

2019 ◽

Vol 6 (3) ◽

pp. 190002

Author(s):

Qi Zhou ◽

Shaomin Liu ◽

Ye Liu ◽

Huanlu Song

Keyword(s):

Multivariate Analysis ◽

Electronic Nose ◽

Corn Oil ◽

Solid Phase ◽

Partial Least Square ◽

Least Square ◽

Gas Chromatography Mass Spectrometry ◽

Virgin Olive Oils ◽

Olive Oils ◽

Coefficient Method

Flavour is a special way to discriminate extra virgin olive oils (EVOOs) from other aroma plant oils. In this study, different ratios (5, 10, 15, 20, 30, 50, 70 and 100%) of peanut oil (PO), corn oil (CO) and sunflower seed oil (SO) were discriminated from raw EVOO using flavour fingerprint, electronic nose and multivariate analysis. Fifteen different samples of EVOO were selected to establish the flavour fingerprint based on eight common peaks in solid-phase microextraction–gas chromatography–mass spectrometry corresponding to 4-methyl-2-pentanol, ( E )-2-hexenal, 1-tridecene, hexyl acetate, ( Z )-3-hexenyl acetate, ( E )-2-heptenal, nonanal and α-farnesene. Partial least square discrimination analysis (PLS-DA) was used to differentiate EVOOs and mixed oils containing more than 20% of PO, CO and SO. Furthermore, better discrimination efficiency was observed in PLS-DA than PCA (70% of CO and SO), which was equivalent to the correlation coefficient method of the fingerprint (20% of PO, CO and SO). The electronic nose was able to differentiate oil samples from samples containing 5% mixture. The discrimination method was selected based on the actual requirements of quality control.

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