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Cervicovaginal mucus is a mixture of mucins, ions, salts, and water, the proportions of which change during the reproductive cycle. It is suspected that this mucus emits an important volatile signal indicative of the reproductive state of the female. The objective of this study was to identify volatile organic compounds (VOC) in bovine cervicovaginal mucus that are modulated during the estrous cycle and could potentially be used as biomarkers of estrus and ovulation. Cervicovaginal mucus was collected from crossbred beef heifers (n = 8), which were synchronized using an 8-d controlled internal drug release (CIDR) protocol and in which onset of estrus and time of ovulation were determined by visual observation and ultrasonography, respectively. Mucus samples were collected between 0 and 96 h after CIDR removal (estrus onset occurred at 49.1 ± 3.3 h after CIDR removal). A validation study was performed on an independent group of 15 heifers from which cervicovaginal mucus samples were collected every 8 h from 40 to 80 h after CIDR removal. The VOC in mucus were identified using gas chromatography-mass spectrometry and selected compounds were quantified using selected-ion flow-tube mass spectrometry. The presence of 47 VOC was detected in mucus samples by gas chromatography-mass spectrometry with those exhibiting highest abundance including 2-butanone, acetone, 2-pentanone, 4-methyl-2-pentanone, 1-(1-methylethoxy)-2-propanone, ethanol, 2-methyl-2-propanol, and 2-butanol. All VOC peaked between 24 to 47 h after the onset of estrus (ovulation occurred 26.6 ± 5.6 h after estrus onset). Two VOC, 2-pentanone and 4-methyl-2-pentanone, exhibited a significant increase at the onset of estrus, whereas concentration of 2-butanone increased significantly just after estrus onset, indicating that these VOC may be used as putative biomarkers of estrus. The results of our study may contribute to the development of a sensor device based on VOC to aid the detection of estrus and ovulation in cattle, with particular relevance for the dairy industry where the majority of females are bred by artificial insemination. [ABSTRACT FROM AUTHOR]

This study investigated the effectiveness of SIFT-MS versus chemical profiling, both coupled to multivariate data analysis, to classify 95 Extra Virgin Argan Oils (EVAO), originating from five Moroccan Argan forest locations. The full scan option of SIFT-MS, is suitable to indicate the geographic origin of EVAO based on the fingerprints obtained using the three chemical ionization precursors (H 3 O + , NO + and O 2 + ). The chemical profiling (including acidity, peroxide value, spectrophotometric indices, fatty acids, tocopherols- and sterols composition) was also used for classification. Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and support vector machines (SVM), were compared. The SIFT-MS data were therefore fed to variable-selection methods to find potential biomarkers for classification. The classification models based either on chemical profiling or SIFT-MS data were able to classify the samples with high accuracy. SIFT-MS was found to be advantageous for rapid geographic classification. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) characterize the spoilage of seafood packaged under modified atmospheres (MAs) and could thus be used for quality monitoring. However, the VOC profile typically contains numerous multicollinear compounds and depends on the product and storage conditions. Identification of potential spoilage indicators thus calls for multivariate statistics. The aim of the present study was to define suitable statistical methods for this purpose (exploratory analysis) and to consequently characterize the spoilage of brown shrimp ( Crangon crangon ) and Atlantic cod ( Gadus morhua ) stored under different conditions (selective analysis). Hierarchical cluster analysis (HCA), principal components analysis (PCA) and partial least squares regression analysis (PLS) were applied as exploratory techniques (brown shrimp, 4 °C, 50%CO 2 /50%N 2 ) and PLS was further selected for spoilage marker identification. Evolution of acetic acid, 2,3-butanediol, isobutyl alcohol, 3-methyl-1-butanol, dimethyl sulfide, ethyl acetate and trimethylamine was frequently in correspondence with changes in the microbiological quality or sensory rejection. Analysis of these VOCs could thus enhance the detection of seafood spoilage and the development of intelligent packaging technologies. [ABSTRACT FROM AUTHOR]

[Display omitted] • SIFT-MS application in oil authentication using H 3 O+, NO+, and O 2 + reagent ions. • SIFT-MS spectra combined with chemometrics for extra virgin Argan oil (EVAO) fraud. • SIFT-MS allows the identification and quantification of volatile compounds (oil fragrance). • Classification of pure and adulterated EVAO was established. • Quantification of three adulterant oils in EVAO was done at low levels. Recognized for its nutritional and therapeutic use, extra-virgin Argan Oil (EVAO) is frequently adulterated. Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) spectra were applied to quantify adulterants (i.e., Argan oil of lower quality (LQAO), olive oil (OO), and sunflower oil (SO)) in EVAO. Four data sets, i.e., using H 3 O+, NO+, O 2 + reagent ions, and the combined data were considered. Soft independent modelling of class analogy (SIMCA), and partial least squares discriminant analysis (PLS-DA) were assessed to distinguish adulterated- from pure EVAO. The effectiveness of SIFT-MS associated with PLS and support vector machine (SVM) regression to quantify trace adulterants in EVAO was evaluated. Variable Importance in Projection (VIP), and interval-PLS (iPLS) were also investigated to extract useful features. Different models were built to predict the EVAO authenticity and the degree of adulteration. High accuracy was achieved. SIFT-MS spectra handled with the appropriate chemometric tools were found suitable for the quality evaluation of EVAO. [ABSTRACT FROM AUTHOR]

Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) has been applied in a clinical context as diagnostic tool for breath samples using target biomarkers. Exhaled breath sampling is non-invasive and therefore much more patient friendly compared to bronchoscopy, which is the golden standard for evaluating airway inflammation. In the actual pilot study, 55 exhaled breath samples of children with asthma, cystic-fibrosis and healthy individuals were included. Rather than focusing on the analysis of target biomarkers or on the identification of biomarkers, different data analysis strategies, including a variety of pretreatment, classification and discrimination techniques, are evaluated regarding their capacity to distinguish the three classes based on subtle differences in their full scan SIFT-MS spectra. Proper data-analysis strategies are required because these full scan spectra contain much external, i.e. unwanted, variation. Each SIFT-MS analysis generates three spectra resulting from ion-molecule reactions of analyte molecules with H 3 O+, NO+ and O 2 +. Models were built with Linear Discriminant Analysis, Quadratic Discriminant Analysis, Soft Independent Modelling by Class Analogy, Partial Least Squares - Discriminant Analysis, K-Nearest Neighbours, and Classification and Regression Trees. Perfect models, concerning overall sensitivity and specificity (100% for both) were found using Direct Orthogonal Signal Correction (DOSC) pretreatment. Given the uncertainty related to the classification models associated with DOSC pretreatments (i.e. good classification found also for random classes), other models are built applying other preprocessing approaches. A Partial Least Squares - Discriminant Analysis model with a combined pre-processing method considering single value imputation results in 100% sensitivity and specificity for calibration, but was less good predictive. Pareto scaling prior to Quadratic Discriminant Analysis resulted in 41/55 correctly classified samples for calibration and 34/55 for cross-validation. In future, the uncertainty with DOSC and the applicability of the promising preprocessing methods and models must be further studied applying a larger representative data set with a more extensive number of samples for each class. Nevertheless, this pilot study showed already some potential for the untargeted SIFT-MS application as a rapid pattern-recognition technique, useful in the diagnosis of clinical breath samples. Image 1 • Full scan SIFT-MS used as a non-invasive diagnostic tool for breath-sample analysis. • Spectra submitted to different pretreatment and classification approaches. • Promising PLS-DA models found. • Suitability of DOSC as data pretreatment technique needs further investigation. [ABSTRACT FROM AUTHOR]