Santiago Medina-Rodríguez

In Vitro Oxygen Sensing Using Intraocular Microrobots

We present a luminescence oxygen sensor integrated with a wireless intraocular microrobot for minimally-invasive diagnosis. This microrobot can be accurately controlled in the intraocular cavity by applying magnetic fields. The microrobot consists of a magnetic body susceptible to magnetic fields and a sensor coating. This coating embodies Pt(II) octaethylporphine (PtOEP) dyes as the luminescence material and polystyrene as a supporting matrix, and it can be wirelessly excited and read out by optical means. The sensor works based on quenching of luminescence in the presence of oxygen. The excitation and emission spectrum, response time, and oxygen sensitivity of the sensor were characterized using a spectrometer. A custom device was designed and built to use this sensor for intraocular measurements with the microrobot. Due to the intrinsic nature of luminescence lifetimes, a frequency-domain lifetime measurement approach was used. An alternative sensor design with increased performance was demonstrated by using poly(styrene-co-maleic anhydride) (PS-MA) and PtOEP nanospheres.

Comparison of different analytical classification scenarios: application for the geographical origin of edible palm oil by sterolic (NP) HPLC fingerprinting

This work shows how the best scenario, which applies two chemometric classifiers on different analytical datasets from the same sample set, could be chosen according to the classification results. To this end, several classification quality features such as sensitivity (or recall), specificity, positive (or precision) and negative predictive values, Youden index, positive and negative likelihood ratios, F-measure (or F-score), discriminant power, efficiency (or accuracy), AUC (area under the receiver operating curve), Matthews correlation coefficient, Kappa coefficient, overall agreement probability, overall agreement probability from chance and overall Kappa coefficient are described and discussed. As an application example, two sterolic chromatographic fingerprints obtained from two different normal-phase HPLC systems are used to discern the geographical origin (South-East Asia, West Africa and South America) of edible palm oil. In each case, two conventional and well-known chemometric classification methods are applied: soft independent modelling by class analogy (SIMCA) and partial least squares-discriminant analysis (PLS-DA).

High performance optical sensing nanocomposites for low and ultra-low oxygen concentrations using phase-shift measurements

The accurate and real-time measurement of low and ultra-low concentrations of oxygen using non-invasive methods is a necessity for a multitude of applications, from brewing beer to developing encapsulating barriers for optoelectronic devices. Current optical methods and sensing materials often lack the necessary sensitivity, signal intensity, or stability for practical applications. In this report we present a new optical sensing nanocomposite resulting in an outstanding overall performance when combined with the phase-shift measurement method (determination of luminescence lifetime in the frequency domain). For the first time we have incorporated the standard PtTFPP dye (PtTFPP = platinum(II) 5,10,15,20-meso-tetrakis-(2,3,4,5,6-pentafluorophenyl)-porphyrin) into AP200/19, a nanostructured aluminium oxide-hydroxide solid support. This sensing film shows an excellent sensitivity between 0 and 1% O₂ (KSV = 3102 ± 132 bar⁻¹) and between 0 and 10% O₂ (KSV = 2568 ± 614 bar⁻¹) as well as Δτ0.05% (62.53 ± 3.66%), which makes it 62 times more sensitive than PtTFPP immobilized in polystyrene and also 8 times more sensitive than PtTFPP immobilized on silica beads. Furthermore the phase-shift measurement method results in a significant improvement (about 23 times) in stability compared to the use of intensity recording methods. The film also displays full reversibility, long shelf stability (no change observed after 12 months), and it is not affected by humidity. To establish this sensing methodology and develop sensors over the full range of the visible light, we also studied three other dye-AP200/19 nanocomposites based on phosphorescent cyclometalated iridium(III) complexes.

Improved Multifrequency Phase-Modulation Method That Uses Rectangular-Wave Signals to Increase Accuracy in Luminescence Spectroscopy

We propose a novel multifrequency phase-modulation method for luminescence spectroscopy that uses a rectangular-wave modulated excitation source with a short duty cycle. It is used for obtaining more detailed information about the luminescence system: the information provided by different harmonics allows estimating a model for describing the global frequency response of the luminescent system for a wide range of analyte concentration and frequencies. Additionally, the proposed method improves the accuracy in determination of the analyte concentration. This improvement is based on a simple algorithm that combines multifrequency information provided by the different harmonics of the rectangular-wave signal, which can be easily implemented in existing photoluminescence instruments by replacing the excitation light source (short duty cycle rectangular signal instead of sinusoidal signal) and performing appropriate digital signal processing after the transducer (implemented in software). These claims have been demonstrated by using a well-known oxygen-sensing film coated at the end of an optical fiber [a Pt(II) porphyrin immobilized in polystyrene]. These experimental results show that use of the proposed multifrequency phase-modulation method (1) provides adequate modeling of the global response of the luminescent system (R(2) > 0.9996) and (2) decreases the root-mean-square error in analytical determination (from 0.1627 to 0.0128 kPa at 0.5 kPa O2 and from 0.9393 to 0.1532 kPa at 20 kPa O2) in comparison with a conventional phase-modulation method based on a sinusoidally modulated excitation source (under equal luminous power conditions).

In-Depth Two-Year Study of Phenolic Profile Variability among Olive Oils from Autochthonous and Mediterranean Varieties in Morocco, as Revealed by a LC-MS Chemometric Profiling Approach.

Olive oil phenolic fraction considerably contributes to the sensory quality and nutritional value of this foodstuff. Herein, the phenolic fraction of 203 olive oil samples extracted from fruits of four autochthonous Moroccan cultivars (“Picholine Marocaine”, “Dahbia”, “Haouzia” and “Menara”), and nine Mediterranean varieties recently introduced in Morocco (“Arbequina”, “Arbosana”, “Cornicabra”, “Frantoio”, “Hojiblanca”, “Koroneiki”, “Manzanilla”, “Picholine de Languedoc” and “Picual”), were explored over two consecutive crop seasons (2012/2013 and 2013/2014) by using liquid chromatography-mass spectrometry. A total of 32 phenolic compounds (and quinic acid), belonging to five chemical classes (secoiridoids, simple phenols, flavonoids, lignans and phenolic acids) were identified and quantified. Phenolic profiling revealed that the determined phenolic compounds showed variety-dependent levels, being, at the same time, significantly affected by the crop season. Moreover, based on the obtained phenolic composition and chemometric linear discriminant analysis, statistical models were obtained allowing a very satisfactory classification and prediction of the varietal origin of the studied oils.

A metabolic fingerprinting approach based on selected ion flow tube mass spectrometry (SIFT-MS) and chemometrics: A reliable tool for Mediterranean origin-labeled olive oils authentication

Selected Ion flow tube mass spectrometry (SIFT-MS) in combination with chemometrics was used to authenticate the geographical origin of Mediterranean virgin olive oils (VOOs) produced under geographical origin labels. In particular, 130 oil samples from six different Mediterranean regions (Kalamata (Greece); Toscana (Italy); Meknès and Tyout (Morocco); and Priego de Córdoba and Baena (Spain)) were considered. The headspace volatile fingerprints were measured by SIFT-MS in full scan with H3O+, NO+ and O2+ as precursor ions and the results were subjected to chemometric treatments. Principal Component Analysis (PCA) was used for preliminary multivariate data analysis and Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to build different models (considering the three reagent ions) to classify samples according to the country of origin and regions (within the same country). The multi-class PLS-DA models showed very good performance in terms of fitting accuracy (98.90-100%) and prediction accuracy (96.70-100% accuracy for cross validation and 97.30-100% accuracy for external validation (test set)). Considering the two-class PLS-DA models, the one for the Spanish samples showed 100% sensitivity, specificity and accuracy in calibration, cross validation and external validation; the model for Moroccan oils also showed very satisfactory results (with perfect scores for almost every parameter in all the cases).