Application of NIR Spectroscopy for the Valorisation of Cork By-Products: A Feasibility Study over the Screening and Discrimination of Chemical Compounds of Interest

Quercus suber is considered a sustainable tree mainly due to its outer layer (cork) capacity to regenerate after each harvesting cycle. Cork bark is explored for several application; however, its industrial transformation generates a significant amount of waste. Recently, cork by-products have been studied as a supplier of bioactive ingredients. This work aimed to explore whether near infrared spectroscopy (NIRS), a non-destructive analysis, can be employed as a screening device for selecting cork by-products with higher potential for bioactives extraction. A total of 29 samples of cork extracts were analysed regarding their qualitative composition. Partial least squares (PLS) models were developed for quantification purposes, and R2P and RER values of 0.65 and above 4, respectively, were obtained. Discrimination models, performed through PLS-DA, yielded around 80% correct predictions, revealing that four out of five of samples were correctly discriminated, thus revealing that NIR can be successfully applied for screening purposes.

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 R²_CV 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 R²_P (0.97), RE (4.54 %) and analytical sensitivity (2.13 mg/L).

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%.

Relationship Between Gymnastic Rhythmic Practice and Body Composition, Physical Performance, and Trace Element Status in Young Girls

This study evaluates the influence on body development of doing rhythmic gymnastics in girls from 10 to 17 years of age, the results of certain strength and flexibility abilities, and the trace element status (Ca, Fe, Zn, Cu, Mn, Cr, and Ni). The subjects were divided into three groups: (a) girls who practiced rhythmic gymnastics at a competition level (competition group); (b) girls who practiced this sport at a non-competitive level (training group); and (c) girls who do not practice any sport and with a low level of physical activity (control or sedentary group). Trace element status was determined in hair and urine samples. Results showed that doing rhythmic gymnastics does not alter the normal physical development of muscle mass, and even leads to a decrease in body fat content. Furthermore, better scores in the strength and flexibility test were obtained by the participants of this sports discipline. Statistically significant differences in urine Fe, Cu, and Mn values (p < 0.05) and in hair Cr, Cu, and Mn values (p < 0.05) were found between the two rhythmic gymnastics groups and the control group, and were higher in the competition and training groups. A principal component analysis model was performed to evaluate the possibility of cluster formation among the girls. The PCA results revealed a separation between the different groups although the separation was not perfect. PLS-DA was attempted in order to verify whether it was possible to discriminate between the groups included in this study. It was clear that the competition and control ones were very well classified (around 95% of correct predictions) but 20% of the girls belonging to the training group were misclassified as belonging to the competition one.

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.

Three-way calibration using PARAFAC and MCR-ALS with previous synchronization of second-order chromatographic data through a new functional alignment of pure vectors for the quantification in the presence of retention time shifts in peak position and shape

In the present contribution is shown the application of the recently developed functional alignment of pure vectors (FAPV) as a proper algorithm to align second-order chromatographic data with severe retention time shifts in peak position and shape. FAPV decomposed a three-way chromatographic data array in their three modes (sample, spectral and elution time vectors), using a basis function to pre-process the non-linear mode (elution time) and then it aligns the functionalized pure vectors and reshapes the transformed vectors into matrices, restoring the trilinearity of second-order chromatographic data. The well-aligned three-way chromatographic data array is then successfully decomposed by advanced chemometric models such as parallel factor analysis (PARAFAC) and multivariate curve resolution - alternating least-squares (MCR-ALS) with the trilinearity constraint. The performance of this innovative analytical strategy based on PARAFAC and MCR-ALS with previous synchronization of data through FAPV algorithm is properly evaluated using real second-order chromatographic data with multiple artifacts, i.e., shifts in peak position and shape for the simultaneous quantification of amoxicillin and potassium clavulanate in commercial medicinal drugs. The present contribution compares some analytical results achieved by: (1) the usual MCR-ALS as a bilinear model applied in augmented data matrix without previous synchronization and with interval correlation optimized shifting (ICOSHIFT) and FAPV and (2) trilinear models using PARAFAC with ICOSHIFT and FAPV and trilinearity constraint in MCR-ALS with FAPV. Available results suggest that these strongly shifted and warped elution time profiles cause for the loss of trilinearity, which can be adequately restored by FAPV algorithm. PARAFAC performed a successful trilinear decomposition of three-way chromatographic data array with law values of relative prediction error (REP) in the order of 1.34-1.42% in both analytes.

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 R²_P 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.

The application of near infrared spectroscopy to wine analysis: An innovative approach using lyophilization to remove water bands interference

The implementation of near-infrared spectroscopy as an analytical method for the quantification of major wine parameters is limited due to the aqueous nature of wines. Water molecules contribute to a poor signal-to-noise ratio and to suppress important groups' vibrations frequencies, preventing the quantification of most chemical compounds present. This paper proposes an alternative approach for the quantification of major wine indicators based on near infrared spectroscopy using lyophilized wine samples. A diversity of wine samples, including red, white and rosé, were lyophilized and analyzed by NIR spectroscopy. The parameters quantified were: alcoholic degree, volumic mass, total dry extract, total sugars, total acidity, volatile acidity, pH, free sulfur dioxide and total sulfur dioxide. Calibrations using partial least squares (PLS) regression were performed against the results obtained by reference methods. Spectra collected within 10,000 to 4000 cm^-1 range were randomly divided in two sets: one for the optimization of the PLS models and the remaining for external testing. The PLS models obtained were able to accurately quantify total sugars, pH, volumic mass and total dry extract with a range-error-ratio above 10. The quantification of the remaining parameters yielded unsatisfactory results. This methodology proved to be an interesting alternative for the quantification of major wine quality descriptors by circumventing the interference of water bands. Further studies exploring different lyophilization conditions and additional wine chemical compounds present at low concentrations are needed.

Fourier transform near infrared spectroscopy as a tool to discriminate olive wastes: The case of monocultivar pomaces

Olive pomace, a wet semi-solid paste that remains after olive oil extraction, is a major waste of the process and its recovery is mandatory due to its phytotoxicity when rejected directly into the soil. Innovative applications have been studied, but simple and reliable methods that fulfil the gap between the recovery of compounds and their use by industries (contributing to the sustainability and circular economy of the chain) still need to be explored. In this work, four monocultivar olive pomaces (Arbequina, Arbosana, Oliana, and Koroneiki) were studied regarding their nutritional composition, fatty acids and vitamin E profiles, total phenolic and flavonoid contents, antioxidant activity, and Fourier Transform Near Infrared and Mid Infrared spectra. Principal Component Analysis and Partial Least Square Discriminant Analysis were used to discriminate samples. Arbosana pomace presented the highest total fat (15%, dw) and vitamin E contents (1.4 mg/g of oil), being α-tocopherol the main vitamer in all samples. Koroneiki pomace was the richest in phenolic compounds (9 g gallic acid eq./kg). The major fatty acid was oleic acid. Stearic acid, linoleic acid, and FRAP levels differed significantly among cultivars. NIR spectra showed differences in all spectral regions (best separation from 6504 to 5389 cm^-1 and 4961 to 4035 cm^-1), while MIR spectra presented differences only in some spectral regions. The results showed that Near Infrared spectroscopy together with Principal Component Analysis is a powerful tool to discriminate olive pomace cultivars, with ability to be used in an industrial context.

Antioxidant capacity of Camellia japonica cultivars assessed by near- and mid-infrared spectroscopy

Camellia japonica antioxidant capacity highly differs among its cultivars and could be successfully predicted by near- and mid-infrared spectroscopy. Camellia japonica is a Theaceae family species which are mainly used as an ornamental plant due to its colourful flowers presenting over than 32,000 recognized cultivars. However, this species have been somehow neglected due to the popular tea source, Camellia sinensis. In this study, the antioxidant profile (total phenolic and flavonoid content and total antioxidant capacity-TPC, TFC and TAC) of 31 C. japonica cultivars leaves was determined and further assessed by near- and mid-infrared spectroscopy. The leaves' antioxidant profile was revealed to be highly dependent on the cultivars analysed being in some cases distinct even for different trees of the same cultivar. Near- and mid-infrared spectroscopy proved to be suitable techniques to predict the total phenolic and flavonoid content as well as the total antioxidant capacity. The best results were obtained with near-infrared spectroscopy whose root mean square error of the prediction set samples was of 5.7 mg of gallic acid/g dry leaf; 3.5 mg catechin/g dry leaf and 3.3 mM Trolox/g dry leaf for TPC, TFC and TAC (with coefficients of the determinations equal to or higher than 0.93). Moreover, the range error ratios were higher than 15 meaning that the developed partial least-squares models are very good for calibration and quantification determinations according to the guidelines for near-infrared models development and maintenance. In this work, the antioxidant profile of several C. japonica cultivars leaves was determined for the first time, being that a rapid and low cost spectroscopic-based method was also proposed for its determination.

Varietal discrimination of hop pellets by near and mid infrared spectroscopy

Hop is one of the most important ingredients of beer production and several varieties are commercialized. Therefore, it is important to find an eco-real-time-friendly-low-cost technique to distinguish and discriminate hop varieties. This paper describes the development of a method based on vibrational spectroscopy techniques, namely near- and mid-infrared spectroscopy, for the discrimination of 33 commercial hop varieties. A total of 165 samples (five for each hop variety) were analysed by both techniques. Principal component analysis, hierarchical cluster analysis and partial least squares discrimination analysis were the chemometric tools used to discriminate positively the hop varieties. After optimizing the spectral regions and pre-processing methods a total of 94.2% and 96.6% correct hop varieties discrimination were obtained for near- and mid-infrared spectroscopy, respectively. The results obtained demonstrate the suitability of these vibrational spectroscopy techniques to discriminate different hop varieties and consequently their potential to be used as an authenticity tool. Compared with the reference procedures normally used for hops variety discrimination these techniques are quicker, cost-effective, non-destructive and eco-friendly.

Rapid assessment of bioactive phenolics and methylxanthines in spent coffee grounds by FT-NIR spectroscopy

Spent coffee grounds (SCGs) are a great source of bioactive compounds with interest to pharmaceutical and cosmetic industries. Phenolics and methylxanthines are the main health related compounds present in SCG samples. Content estimation of these compounds in SCGs is of upmost importance in what concerns their profitable use by waste recovery industries. In the present work, near-infrared spectroscopy (NIRS) was proposed as a rapid and non-destructive technique to assess the content of three main phenolics (caffeic acid, (+)-catechin and chlorogenic acid) and three methylxanthines (caffeine, theobromine and theophylline) in SCG samples obtained from different coffee brands and diverse coffee machines. The content of these compounds was determined for 61 SCG samples by HPLC coupled with diode-array detection. Partial least squares (PLS) regression based models were calibrated to correlate diffuse reflectance NIR spectra against the reference data for the six parameters obtained by HPLC. Spectral wavelength selection and number of latent variables were optimized by minimizing the cross-validation error. PLS models showed good linearity with a coefficient of determination for the prediction set (Rp(2)) of 0.95, 0.92, 0.88, 071 and 0.84 for caffeine, caffeic acid, (+)-catechin, chlorogenic acid and theophylline, respectively. The range error ratio (RER) was higher for caffeine (17.8) when compared to other compounds (12.0, 10.1, 7.6 and 9.2, respectively for caffeic acid, (+)-catechin, chlorogenic acid and theophylline). Moreover, the content of caffeine could be used to predict the antioxidant properties of SCG samples (R=0.808, n=61), despite not presenting this property itself. The results obtained confirmed that NIRS is a suitable technique to screen SCG samples unveiling those with high content of bioactive compounds, which are interesting for subsequent extraction procedures.

A review on the applications of portable near-infrared spectrometers in the agro-food industry

Industry has created the need for a cost-effective and nondestructive quality-control analysis system. This requirement has increased interest in near-infrared (NIR) spectroscopy, leading to the development and marketing of handheld devices that enable new applications that can be implemented in situ. Portable NIR spectrometers are powerful instruments offering several advantages for nondestructive, online, or in situ analysis: small size, low cost, robustness, simplicity of analysis, sample user interface, portability, and ergonomic design. Several studies of on-site NIR applications are presented: characterization of internal and external parameters of fruits and vegetables; conservation state and fat content of meat and fish; distinguishing among and quality evaluation of beverages and dairy products; protein content of cereals; evaluation of grape ripeness in vineyards; and soil analysis. Chemometrics is an essential part of NIR spectroscopy manipulation because wavelength-dependent scattering effects, instrumental noise, ambient effects, and other sources of variability may complicate the spectra. As a consequence, it is difficult to assign specific absorption bands to specific functional groups. To achieve useful and meaningful results, multivariate statistical techniques (essentially involving regression techniques coupled with spectral preprocessing) are therefore required to extract the information hidden in the spectra. This work reviews the evolution of the use of portable near-infrared spectrometers in the agro-food industry.

Flow-injection spectrophotometric determination of bromate in bottled drinking water samples using chlorpromazine reagent and a liquid waveguide capillary cell

In this work, aiming to develop a simple, inexpensive method for the determination of low bromate levels in water samples, a liquid waveguide capillary cell (LWCC) was coupled to a FIA system. The long optical path (100 cm) of the LWCC was used to improve the sensitivity and the limit of detection without resorting to any off-line or in-line preconcentration processes. The spectrophotometric determination was based on the oxidation of chlorpromazine by bromate in an acidic medium, resulting in the formation of a colored radical product. Sulfamic acid was added to the reagent for minimizing the interference of nitrite, and a chelating ion exchange resin was used to remove major cationic interferences. The developed system allowed the determination of bromate within the range between 1 - 20 μg L(-1) with a detection limit of 0.2 μg L(-1).

Sequential injection system for the enzymatic determination of ethanol in wine

A sequential injection system was developed for the enzymatic determination of ethanol in wine. The spectrophotometric determination is based on the enzymatic reaction catalyzed by alcohol dehydrogenase in the presence of NAD+. The system was applied to the determination of ethanol in a range of 0.008-0.024% (v/v) with good repeatability; RSD(n=10) < 2.3%. The results obtained with the developed system showed good agreement with those obtained by using the reference method. The determination rate was 25 h(-1); 1 micromol of NAD+, 1.1 units of enzyme, and 50 microL of sample were consumed per determination; and the waste produced was 2.2 mL per assay.