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.

Assessing the differences of two vineyards soils' by NIR spectroscopy and chemometrics.

Soil properties influence greatly the status of vine plants which consequently influences the quality of wine. Therefore, in the context of viticulture management, it is extremely important to assess the physical and chemical parameters of vineyards soils. In this study, the soils of two vineyards were analysed by near-infrared (NIR) spectroscopy and established analytical reference procedures. The main objective of this study was to verify if NIR spectroscopy is a potential tool to discriminate the soils of both vineyards as well as to quantify differences of soil's parameters. For that, a total of eight sampling spots were selected at each vineyard taking into consideration the soil type and sampled at different depths. The data analysis was performed using analysis of variance (ANOVA), principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression. The ANOVA results revealed that 12 out of the 18 parameters analysed through the reference procedures can be considered statistically different (p < 0.05). Regarding PCA, the obtained results revealed a clear separation between the scores of both vineyards either considering NIR spectra or the chemical parameters. The PLS-DA model was able to obtain 100 % of correct predictions for the discrimination of both vineyards. PLS regression analysis using NIR spectra revealed R2P and RER values higher than 0.85 and 10, respectively, for 8 (pH (H2O), N, Ca2+, Mg2+, SB, CEC, ECEC and GSB) of the 18 chemical parameters evaluated. Concluding, these results demonstrate that it is possible to discriminate the soils of the different vineyards through NIR spectroscopy as well as to quantify several chemical parameters through soils NIR spectra in a rapid, accurate, cost-effective, simple and environmentally friendly way when compared to the reference procedures.

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).

Lipid Profile Quantification and Species Discrimination of Pine Seeds through NIR Spectroscopy: A Feasibility Study.

Pine seeds are known for their richness in lipid compounds and other healthy substances. However, the reference procedures that are commonly applied for their analysis are quite laborious, time-consuming, and expensive. Therefore, it is important to develop rapid, accurate, multi-parametric, cost-effective and, essentially, environmentally friendly analytical techniques that are easily implemented at an industrial scale. The viability of using near-infrared (NIR) spectroscopy to analyse the seed lipid content and profile of three different pine species (Pinus halepensis, Pinus brutia and Pinus pinaster) was investigated. Moreover, species discrimination using NIR was also attempted. Different chemometric models, namely partial least squares (PLS) regression, for lipid analysis, and partial least square-discriminant analysis (PLS-DA), for pine species discrimination, were applied. In relation to the discrimination of pine seed species, a total of 90.5% of correct classification rates were obtained. Regarding the quantification models, most of the compounds assessed yielded determination coefficients (R2P) higher than 0.80. The best PLS models were obtained for total fat, vitamin E, saturated and monounsaturated fatty acids, C20:2, C20:1n9, C20, C18:2n6c, C18:1n9c, C18 and C16:1. Globally, the obtained results demonstrated that NIR spectroscopy is a suitable analytical technique for lipid analysis and species discrimination of pine seeds.

Oral lichen planus identification by mid-infrared spectroscopy of oral biofluids: A case-control study.

BACKGROUND AND AIMS: This study aims to access the effectiveness of mid-infrared (MIR) spectroscopy on the identification of the reticular form of OLP, following the assessment of gingival crevicular fluid (GCF) and oral mucosa transudate (OMT). MATERIAL AND METHODS: The trial follows a case-control design. Samples were characterized through MIR spectroscopy and chemometric tools were applied to distinguish between case and control participants, further identifying the spectral regions with the highest contribution to the developed models. RESULTS: MIR spectroscopy was capable to discriminate between OLP patients and controls with 95.1% and 85.4% of correct predictions, regarding GCF and OMT samples, respectively. Additionally, the spectral regions mostly contributing to the successful prediction were identified, and possibly related with the distinctive presence of amino acids/proteins and oxidative stress mediators in oral biofluids, supporting the role of the immune-inflammatory activation on OLP etiology and disease course. CONCLUSION: MIR spectroscopy analysis of GCF and OMT may be regarded as an innovative, non-invasive, low cost and sensitive technique, contributing to the identification of the reticular from of OLP.

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.

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.

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%.

Detection of melamine and sucrose as adulterants in milk powder using near-infrared spectroscopy with DD-SIMCA as one-class classifier and MCR-ALS as a means to provide pure profiles of milk and of both adulterants with forensic evidence: A short communication.

The present short communication reports a promising analytical method for authentication of milk based on first-order near-infrared (NIR) spectroscopic data coupled to data driven soft independent modeling of class analogy (DD-SIMCA). This one-class classifier was able to correctly classify all samples of genuine milk powder as members of the target class from samples of milk powder adulterated with melamine and sucrose in a concentration range of 0.8-2% (w/w) and 1-3% (w/w), respectively. Multivariate curve resolution - alternating least-squares (MCR-ALS) was applied as a complementary chemometric model to DD-SIMCA aimed at retrieving pure profiles, allowing to identify the chemical composition of samples properly attributed in the target class or not, providing further investigation from forensic point of view. In order to extend the prime focus of the present report, which was aimed at developing an appropriate chemometric model for authentication purposes, the quantification analysis was also performed. This was done by successful bilinear data decomposition of NIR spectra into pure profiles for the contributing components contained in the system studied (milk and adulterants), allowing to quantify analytes with strong overlapping profiles, even in the presence of an uncalibrated interferent, as demonstrated in this short communication using MCR-ALS under various constraints in order to decrease the rotational ambiguity.

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.

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.

Influence of Olive Pomace Blending on Antioxidant Activity: Additive, Synergistic, and Antagonistic Effects.

Food innovation is moving rapidly and comprises new categories of food products and/or ingredients with a natural and ecological origin. Monocultivar olive pomaces, individually or combined, can be a source of natural bioactive compounds suitable for food or cosmetic applications. This work aimed to assess the phenolics content and antioxidant activity of four monocultivar olive pomaces (Arbosana, Koroneiki, Oliana, and Arbequina) and forty-nine blends prepared with different proportions of each. Additive, synergistic, and antagonistic effects were studied. Among the monocultivar pomaces, Koroneiki and Arbosana were the richest in total phenolics (~15 mg gallic acid eq./g). Most of the interactions found in the blends were additive or synergistic, while very few antagonistic effects were observed. The best results were obtained for those blends where the Koroneiki variety predominated: (i) 90% Koroneiki, 4.75% Oliana, 3.75% Arbequina, 1.5% Arbosana; (ii) 65% Koroneiki, 29% Oliana, 3.25% Arbequina, 2.75% Arbosana; and (iii) 85% Koroneiki, 8.75% Arbequina, 3.5% Arbosana, 2.75% Oliana. In sum, these combinations can be advantageous in comparison to the individual use of monocultivar pomaces, presenting a higher potential to be used as functional ingredients or for bioactive compounds extraction, having in view the obtention of natural preservatives or food/cosmetic formula enhancers.

The diagnosis of eating disorders through mid-infrared spectroscopy of the gingival crevicular fluid: a pilot trial.

PURPOSE: Eating disorders (EDs) are characterized by a persistent disturbance of eating patterns, leading to poor psychological and physical health. EDs' symptoms are diverse, but their biochemical manifestations can be identified in biofluids, as the gingival crevicular fluid (GCF). The development of a rapid and accurate analytical diagnostic technique, able to provide a wider comprehension of established biochemical abnormalities, would greatly assist EDs' management. Mid-infrared (MIR) spectroscopy gathers all the referred features, and is considered a fingerprint technique. In this pilot trial, the GCF discrimination of patients with EDs and controls was accessed through MIR spectroscopy, further elucidating the relevant spectral differences between both groups. METHODS: GCF was collected from 20 women with ED diagnosis and from age-matched controls. Principal component analysis and partial least squares discriminant analysis (PLSDA) were conducted on GCF MIR spectra. Different PLSDA models were considered to address the predictive capability regarding patient identification, sampling site, and presence of EDs. RESULTS: MIR spectroscopy was capable to discriminate GCF samples, between EDs and controls, with 84.1% of correct predictions. Regression coefficient vectors' analyses revealed that major differences were related to higher protein content in EDs. CONCLUSIONS: Whether further studies are needed to validate the attained data, GCF MIR analysis may be regarded as an innovative, fast, and low-cost technique to assist on early diagnosis and clinical follow-up of EDs' patients. LEVEL OF EVIDENCE: Level IV, case-control trial.

Antioxidant Activity of Blueberry (Vaccinium spp.) Cultivar Leaves: Differences Across the Vegetative Stage and the Application of Near Infrared Spectroscopy.

Blueberries production has increased in the last few years boosted somehow by the World Health Organization (WHO) recommendations for a healthier nutrition and their recognized potential to treat several diseases. The production increase lead to high amounts of discarded leaves that could be very valuable. In this context, the antioxidant activity of Vaccinium spp. leaves, by means of the total phenolic (TPC) and flavonoid (TFC) content and total antioxidant capacity (TAC) was determined. Adult leaves of twenty-seven Vaccinium cultivars collected in three geographic regions and three seasons of the year were included. The antioxidant activity was additionally estimated with near infrared (NIR) spectroscopy and data transferability across the regions and seasons was evaluated. The TPC, TFC and TAC ranged from 39.6-272.8 mg gallic acid, 41.2-269.1 mg catechin and 22.6-124.8 mM Trolox per g of dry leaf, respectively. Globally through the seasons, the higher values of the three parameters were obtained in December. Regarding the geographic region, region A provided the cultivars with the higher antioxidant content. Titan was the cultivar with higher TPC and TAC and Misty the one with the higher TFC. NIR spectroscopy combined with the partial least squares analysis was able to successfully predict the antioxidant activity with coefficients of determination and range error ratios ranging from 0.84-0.99 and 11.2-26.8. Despite some identified limitations on data transferability, NIR spectroscopy proved to be a reliable, low cost and quick method to predict the antioxidant activity of the considered cultivar leaves.

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

MAIN CONCLUSION: 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.