Kinetic Determination of Acetylsalicylic Acid Using a CdTe/AgInS2 Photoluminescence Probe and Different Chemometric Models.

The combination of multiple quantum dots (QDs) in a multi-emitter nanoprobe can be envisaged as a promising sensing scheme, as it enables obtaining a collective response of individual emitters towards a given analyte and allows for achieving specific analyte-response profiles. The processing of these profiles using adequate chemometric methods empowers a more sensitive, reliable and selective determination of the target analyte. In this work, we developed a kinetic fluorometric method consisting of a dual CdTe/AgInS2 quantum dots photoluminescence probe for the determination of acetylsalicylic acid (ASA). The fluorometric response was acquired as second-order time-based excitation/emission matrices that were subsequently processed using chemometric methods seeking to assure the second-order advantage. The data obtained in this work are considered second-order data as they have a three-dimensional size, I × J × K (where I represents the samples' number, J the fluorescence emission wavelength while K represents the time). In order to select the most adequate chemometric method regarding the obtained data structure, different chemometric models were tested, namely unfolded partial least squares (U-PLS), N-way partial least squares (N-PLS), multilayer feed-forward neural networks (MLF-NNs) and radial basis function neural networks (RBF-NNs).

Chemometric-assisted surface-enhanced Raman spectroscopy for metformin determination using gold nanoparticles as substrate.

A fast, simple, and reliable method for determination of metformin was developed by coupling surface-enhanced Raman spectroscopy (SERS) with chemometric methods. This relayed on the utilization of a portable Raman spectrometer and of citrate stabilized gold nanoparticles (AuNPs) as substrate, to carry out the measurement of SERS scattering signals, thus assuring improved sensitivity. The obtained datasets were analysed using principal component analysis (PCA) and partial least squares (PLS) regression. Upon optimization of the PLS model, in terms of latent variables, spectral region and pre-processing techniques, RMSECV and R2CV values of 0.42 mg/L and 0.94, respectively, were obtained. The optimized PLS regression model was further validated with the projection of commercial pharmaceutical samples, providing good results in terms of R2P (0.97), RE (4.54 %) and analytical sensitivity (2.13 mg/L).

Cellulose-based hydrogel on quantum dots with molecularly imprinted polymers for the detection of CA19-9 protein cancer biomarker.

Molecularly imprinted polymers MIPs were successfully assembled around quantum dots (QDs), for the detection of the protein biomarker CA19-9 associated to pancreatic cancer (PC). These imprinted materials MIP@QDs were incorporated within the cellulose hydrogel with retention of its conformational structure inside the binding cavities. The concept is to use MIPs which function as the biorecognition elements, conjugated to cadmium telluride QDs as the sensing system. The excitation wavelength was set to 477 nm and the fluorescence signal was measured at its maximum intensity, with an emission range between 530 and 780 nm. The fluorescence quenching of the imprinted cellulose hydrogels occurred with increasing concentrations of CA19-9, showing linearity in the range 2.76 × 10 -2 - 5.23 × 10 2 U/ml, in a 1000-fold diluted human serum. Replicates of the imprinted hydrogel show a linear response below the cut-off values for pancreatic cancer diagnosis (< 23 U/ml), a limit of detection of 1.58 × 10 -3 U/ml and an imprinting factor (IF) of 1.76. In addition to the fact that the imprinted cellulose hydrogel displays good stability and selectivity towards CA19-9 when compared with the non-imprinted controls, the conjugation of MIPs to QDs increases the sensitivity of the system for an optical detection method towards ranges within clinical significance. This fact shows potential for the imprinted hydrogel to be applied as a sensitive, low-cost format for point-of-care tests (PoCTs).

Photoluminescent and visual determination of ibandronic acid using a carbon dots/AgInS2 quantum dots ratiometric sensing platform.

A sensing platform combining carbon dots (CDs, with blue emission) and thiomalic acid (TMA)-capped AgInS2 quantum dots (QDs, with orange emission) was developed aiming the photoluminescence (PL) ratiometric determination of ibandronic acid (IBAN), a bisphosphonate pharmaceutical. The ternary AgInS2 QDs were used for IBAN probing, undergoing a concentration-related PL quenching in its presence, whilst the PL of CDs remained practically unaffected due to its chemical inertness towards the antiresorptive drug, provided an intrinsic self-reference fluorophore. In addition, a visual sensing approach was also proposed, employing for the first time ternary QDs. This relied on RGB images acquired by means of a digital camera and seek the development of a rapid IBAN screening test. The developed sensing platforms were employed for IBAN determination in samples with pharmaceutical interest providing good results, in accordance to the reported IBAN levels, and obtaining recovery values between 98 and 103%.

Chemometric-assisted kinetic determination of oxytetracycline using AgInS2 quantum dots as PL sensing platforms.

In this work a kinetic fluorometric methodology relying on the time-based monitoring of the photoluminescence quenching of AgInS2 ternary quantum dots induced by oxytetracycline, was developed. The kinetic approach allowed not only to reduce the LOD and improve sensitivity and selectivity but also to collect second-order data that was explored for the quantification of the target analyte in the presence of uncalibrated interfering species. Upon processing the acquired second-order kinetic PL data by unfolded partial least-squares (U-PLS), oxytetracycline was quantified in commercially available pharmaceutical formulations. The obtained results, namely an R2P higher than 0.99 and RE lower than 8%, proved the suitability and accuracy of the developed approach.

Comparison of near infrared spectroscopy and Raman spectroscopy for the identification and quantification through MCR-ALS and PLS of peanut oil adulterants.

Peanut oil is considered one of the best frying oils, and, consequently there is an increasing worldwide demand. This has led to adulteration practices with unhealthy, synthetic or less expensive oils which raises concerns related with public health safety. Therefore, there is a high need for rapid, versatile, low-cost and reliable analytical methods, such as vibrational spectroscopic techniques, capable of identifying and quantifying the respective adulteration. The objective of this work focused on the application of two different vibrational spectroscopic techniques (NIR and Raman spectroscopy) for the qualitative and quantitative analysis of two adulterants in pure peanut oil, namely corn oil and vegetable oil. For the quantitative analysis two chemometric methods, namely PLS and MCR-ALS, were compared while for the qualitative analysis only MCR-ALS was tested. The analysis of peanut oil adulteration was performed by adding each adulterant individually and also by blending the peanut oil with both adulterants simultaneously. A total of 69 samples were analyzed, which was comprised by two sets of 20 samples each containing just one adulterant and another set of 29 samples containing both adulterants. Several pre-processing techniques were tested. The qualitative analysis performed by MCR-ALS allowed the identification of all the adulterants using both NIR and Raman spectra, with correlation coefficients higher than 0.99. For the quantification, none of the chemometric methods as well as the vibrational spectroscopic techniques tested showed significant better results. Nonetheless, the determination coefficients and the relative percentage errors for the validation samples for most of the developed models were higher than 0.98 and lower than 15%, respectively. Concluding, MCR-ALS was capable of correctly extracting the spectral profiles of all the adulterants in very complex mixtures (as the pure spectra of the adulterants and peanut oil are very similar) and both MCR-ALS and PLS were able to quantify the adulteration with low RE. To the best of our knowledge, it was the first time that MCR-ALS was used for the qualitative analysis of peanut oil adulteration (with all adulterants added simultaneously) and MCR-ALS and PLS were compared for the quantification of peanut oil adulteration using both NIR and Raman spectroscopy.

Determination of atenolol based on the reversion of the fluorescence resonance energy transfer between AgInS2 quantum dots and Au nanoparticles.

The present work focused on the development of a fluorescence resonance energy transfer (FRET)-based sensing platform for the monitoring of atenolol in pharmaceutical formulations. The implemented approach involved the assembly of d-penicillamine-capped AgInS2/ZnS quantum dots (QDs), as energy donors, and gold nanoparticles (AuNPs) as acceptors and the establishment of electrostatic interaction between both capping ligands at the nanoparticle surface, which induced the inhibition of the ternary QD photoluminescence (PL). The presence of a ZnS shell around the ternary QD core and the use of cysteamine (CA) as the AuNP capping ligand, instead of the typical citrate, allowed a more efficient FRET process to occur. The ability of Cd-free ternary QDs to be used as a sensing element in FRET-based assays was demonstrated, emphasizing the advantages relative to the common Cd-based QDs, when seeking the implementation of more environmentally friendly and less toxic analytical methodologies. The influence of several ß-blocker drugs on the FRET donor-acceptor assemblies was thoroughly assessed. Atenolol and nadolol caused the aggregation of CA-AuNPs via hydrogen bonding interactions which reduced the spectral overlap between the donor and acceptor, impairing the FRET process and consequently the emission of the QDs was restored. Under the optimized conditions, the obtained results exhibited a linear relationship between the QD PL recovery signal and atenolol concentration of up to 11.22 mg L-1 with a detection limit of 1.05 mg L-1. This FRET sensing platform was successfully applied in the determination of atenolol in pharmaceutical formulations with recovery values ranging from 97.4 to 104.3%.

Imprinted Fluorescent Cellulose Membranes for the On-Site Detection of Myoglobin in Biological Media.

In this work, the conjugation of molecularly imprinted polymers (MIPs) to quantum dots (QDs) was successfully applied in the assembly of an imprinted cellulose membrane [hydroxy ethyl cellulose (HEC)/MIP@QDs] for the specific recognition of the cardiac biomarker myoglobin (Myo) as a sensitive, user-friendly, and portable system with the potential for point-of-care (POC) applications. The concept is to use the MIPs as biorecognition elements, previously prepared on the surface of semiconductor cadmium telluride QDs as detection particles. The fluorescent quenching of the membrane occurred with increasing concentrations of Myo, showing linearity in the interval range of 7.39-291.3 pg/mL in a1000-fold diluted human serum. The best membrane showed a linear response below the cutoff values for myocardial infarction (23 ng/mL), a limit of detection of 3.08 pg/mL, and an imprinting factor of 1.65. The incorporation of the biorecognition element MIPs on the cellulose substrate brings an approach toward a portable and user-friendly device in a sustainable manner. Overall, the imprinted membranes display good stability and selectivity toward Myo when compared with the nonimprinted membranes (HEC/NIP@QDs) and have the potential to be applied as a sensitive system for Myo detection in the presence of other proteins. Moreover, the conjugation of MIPs to QDs increases the sensitivity of the system for an optical label-free detection method, reaching concentration levels with clinical significance.

Portable and benchtop Raman spectrometers coupled to cluster analysis to identify quinine sulfate polymorphs in solid dosage forms and antimalarial drug quantification in solution by AuNPs-SERS with MCR-ALS.

This paper proposes for the first time: (a) a qualitative analytical method based on portable and benchtop backscattering Raman spectrometers coupled to hierarchical cluster analysis (HCA) and multivariate curve resolution - alternating least-squares (MCR-ALS) to identify two polymorphs of antimalarial quinine sulfate in commercial pharmaceutical tablets in their intact forms and (b) a quantitative analytical method based on gold nanoparticles (AuNPs) as active substrates for surface-enhanced Raman scattering (SERS) in combination with MCR-ALS to quantify quinine sulfate in commercial pharmaceutical tablets in solution. The pure concentration and spectral profiles recovered by MCR-ALS proved that both formulations present different polymorphs. These results were also confirmed by two clusters observed in the HCA model, according to their similarities within and among the samples that provided useful information about the homogeneity of different pharmaceutical manufacturing processes. AuNPs-SERS coupled to MCR-ALS was able to quantify quinine sulfate in the calibration range from 150.00 to 200.00 ng mL-1 even with the strong overlapping spectral profile of the background SERS signal, proving that it is a powerful ultrahigh sensitivity analytical method. This reduced linearity was validated throughout a large calibration range from 25.00 to 175.00 µg mL-1 used in a reference analytical method based on high performance liquid chromatography with a diode array detector (HPLC-DAD) coupled to MCR-ALS for analytical validation purposes, even in the presence of a coeluted compound. The analytical methods developed herein are fast, because second-order chromatographic data and first-order SERS spectroscopic data were obtained in less than 6 and 2 min, respectively. Concentrations of quinine sulfate were estimated with low root mean square error of prediction (RMSEP) values and a low relative error of prediction (REP%) in the range 1.8-4.5%.

Dual-emission CdTe/AgInS2 photoluminescence probe coupled to neural network data processing for the simultaneous determination of folic acid and iron (II).

This work focused on the combination of CdTe and AgInS2 quantum dots in a dual-emission nanoprobe for the simultaneous determination of folic acid and Fe(II) in pharmaceutical formulations. The surface chemistry of the used QDs was amended with suitable capping ligands to obtain appropriate reactivity in terms of selectivity and sensitivity towards the target analytes. The implementation of PL-based sensing schemes combining multiple QDs of different nature, excited at the same wavelength and emitting at different ones, allowed to obtain a specific analyte-response profile. The first-order fluorescence data obtained from the whole emission spectra of the CdTe/AgInS2 combined nanoprobe upon interaction with folic acid and Fe(II) were processed by using chemometric tools, namely partial least-squares (PLS) and artificial neural network (ANN). This enabled to circumvent the selectivity issues commonly associated with the use of QDs prone to indiscriminate interaction with multiple species, which impair reliable and accurate quantification in complex matrices samples. ANN demonstrated to be the most efficient chemometric model for the simultaneous determination of both analytes in binary mixtures and pharmaceutical formulations due to the non-linear relationship between analyte concentration and fluorescence data that it could handle. The R2P and SEP% obtained for both analytes quantification in pharmaceutical formulations through ANN modelling ranged from 0.92 to 0.99 and 5.7-9.1%, respectively. The obtained results revealed that the developed approach is able to quantify, with high reliability and accuracy, more than one analyte in complex mixtures and real samples with pharmaceutical interest.