by Keyword: Chemometrics

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Contreras, M. D. M., Jurado-Campos, N., Sánchez-Carnerero Callado, C., Arroyo-Manzanares, N., Fernández, L., Casano, S., Marco, S., Arce, L., Ferreiro-Vera, C., (2018). Thermal desorption-ion mobility spectrometry: A rapid sensor for the detection of cannabinoids and discrimination of Cannabis sativa L. chemotypes Sensors and Actuators B: Chemical 273, 1413-1424

Existing analytical techniques used for the determination of cannabinoids in Cannabis sativa L. (Cannabis) plants mostly rely on chromatography-based methods. As a rapid alternative for the direct analysis of them, thermal desorption (TD)-ion mobility spectrometry (IMS) was used for obtaining spectral fingerprints of single cannabinoids from Cannabis plant extracts and from plant residues on hands after their manipulation. The ionization source was 63Ni, with automatic switchable polarity. Although in both ionization modes there were signals in the TD-IMS spectra of the plant extracts and residues that could be assigned to concrete cannabinoids and chemotypes, most of them could not be clearly distinguished. Alternatively, the global spectral data of the plant extracts and residues were pre-processed and then, using principal component analysis (PCA)-linear discriminant analysis (LDA), grouped in function of their chemotype in a more feasible way. Using this approach, the possibility of false positive responses was also studied analyzing other non-Cannabis plants and tobacco, which were clustered in a different group to those of Cannabis. Therefore, TD-IMS, as analytical tool, and PCA-LDA, as a strategy for data reduction and pattern recognition, can be applied for on-site chemotaxonomic discrimination of Cannabis varieties and detection of illegal marijuana since the IMS equipment is portable and the analysis time is highly short.

Keywords: Cannabis sativa L., Cannabinoids, Chemometrics, ChemotypeIon mobility spectrometry

Rodríguez, R., Cortés, R., Verónica Guamán, A., Pardo, A., Torralba, Y., Gómez, F., Roca, J., Barberà, J.A., Cascante, M., Marco, S., (2018). Instrumental drift removal in GC-MS data for breath analysis: the short-term and long-term temporal validation of putative biomarkers for COPD Journal of Breath Research 12, (3), 036007

Abstract Breath analysis holds the promise of a non-invasive technique for the diagnosis of diverse respiratory conditions including COPD and lung cancer. Breath contains small metabolites that may be putative biomarkers of these conditions. However, the discovery of reliable biomarkers is a considerable challenge in the presence of both clinical and instrumental confounding factors. Among the latter, instrumental time drifts are highly relevant, as since question the short and long-term validity of predictive models. In this work we present a methodology to counter instrumental drifts using information from interleaved blanks for a case study of GC-MS data from breath samples. The proposed method includes feature filtering, and additive, multiplicative and multivariate drift corrections, the latter being based on Component Correction. Biomarker discovery was based on Genetic Algorithms in a filter configuration using Fisher´s ratio computed in the Partial Least Squares – Discriminant Analysis subspace as a figure of merit. Using our protocol, we have been able to find nine peaks that provide a statistically significant Area under the ROC Curve (AUC) of 0.75 for COPD discrimination. The method developed has been successfully validated using blind samples in short-term temporal validation. However, in the attempt to use this model for patient screening six months later was not successful. This negative result highlights the importance of increasing validation rigour when reporting biomarker discovery results.

Keywords: Instrumental shifts, Chemometrics, Biomarker validation

Rodríguez-Pérez, R., Fernández, L., Marco, S., (2018). Overoptimism in cross-validation when using partial least squares-discriminant analysis for omics data: a systematic study Analytical and Bioanalytical Chemistry 410, (23), 5981-5992

Advances in analytical instrumentation have provided the possibility of examining thousands of genes, peptides, or metabolites in parallel. However, the cost and time-consuming data acquisition process causes a generalized lack of samples. From a data analysis perspective, omics data are characterized by high dimensionality and small sample counts. In many scenarios, the analytical aim is to differentiate between two different conditions or classes combining an analytical method plus a tailored qualitative predictive model using available examples collected in a dataset. For this purpose, partial least squares-discriminant analysis (PLS-DA) is frequently employed in omics research. Recently, there has been growing concern about the uncritical use of this method, since it is prone to overfitting and may aggravate problems of false discoveries. In many applications involving a small number of subjects or samples, predictive model performance estimation is only based on cross-validation (CV) results with a strong preference for reporting results using leave one out (LOO). The combination of PLS-DA for high dimensionality data and small sample conditions, together with a weak validation methodology is a recipe for unreliable estimations of model performance. In this work, we present a systematic study about the impact of the dataset size, the dimensionality, and the CV technique used on PLS-DA overoptimism when performance estimation is done in cross-validation. Firstly, by using synthetic data generated from a same probability distribution and with assigned random binary labels, we have obtained a dataset where the true classification rate (CR) is 50%. As expected, our results confirm that internal validation provides overoptimistic estimations of the classification accuracy (i.e., overfitting). We have characterized the CR estimator in terms of bias and variance depending on the internal CV technique used and sample to dimensionality ratio. In small sample conditions, due to the large bias and variance of the estimator, the occurrence of extremely good CRs is common. We have found that overfitting peaks when the sample size in the training subset approaches the feature vector dimensionality minus one. In these conditions, the models are neither under- or overdetermined with a unique solution. This effect is particularly intense for LOO and peaks higher in small sample conditions. Overoptimism is decreased beyond this point where the abundance of noisy produces a regularization effect leading to less complex models. In terms of overfitting, our study ranks CV methods as follows: Bootstrap produces the most accurate estimator of the CR, followed by bootstrapped Latin partitions, random subsampling, K-Fold, and finally, the very popular LOO provides the worst results. Simulation results are further confirmed in real datasets from mass spectrometry and microarrays.

Keywords: Metabolomics, Mass spectrometry, Microarrays, Chemometrics, Data analysis, Classification, Method validation

Taghadomi-Saberi, S., Garcia, S. M., Masoumi, A. A., Sadeghi, M., Marco, S., (2018). Classification of bitter orange essential oils according to fruit ripening stage by untargeted chemical profiling and machine learning Sensors 18, (6), 1922

The quality and composition of bitter orange essential oils (EOs) strongly depend on the ripening stage of the citrus fruit. The concentration of volatile compounds and consequently its organoleptic perception varies. While this can be detected by trained humans, we propose an objective approach for assessing the bitter orange from the volatile composition of their EO. The method is based on the combined use of headspace gas chromatography–mass spectrometry (HS-GC-MS) and artificial neural networks (ANN) for predictive modeling. Data obtained from the analysis of HS-GC-MS were preprocessed to select relevant peaks in the total ion chromatogram as input features for ANN. Results showed that key volatile compounds have enough predictive power to accurately classify the EO, according to their ripening stage for different applications. A sensitivity analysis detected the key compounds to identify the ripening stage. This study provides a novel strategy for the quality control of bitter orange EO without subjective methods.

Keywords: Bitter orange essential oil, Headspace gas chromatography–mass spectrometry, Artificial neural network, Foodomics, Chemometrics, Feature selection

Marco, Santiago, (2014). The need for external validation in machine olfaction: emphasis on health-related applications Analytical and Bioanalytical Chemistry Springer Berlin Heidelberg 406, (16), 3941-3956

Over the last two decades, electronic nose research has produced thousands of research works. Many of them were describing the ability of the e-nose technology to solve diverse applications in domains ranging from food technology to safety, security, or health. It is, in fact, in the biomedical field where e-nose technology is finding a research niche in the last years. Although few success stories exist, most described applications never found the road to industrial or clinical exploitation. Most described methodologies were not reliable and were plagued by numerous problems that prevented practical application beyond the lab. This work emphasizes the need of external validation in machine olfaction. I describe some statistical and methodological pitfalls of the e-nose practice and I give some best practice recommendations for researchers in the field.

Keywords: Chemical sensor arrays, Pattern recognition, Chemometrics, Electronic noses, Robustness, Signal and data processing