FT-NIR spectroscopy for caffeine estimation in instant green tea powder and granules

Hydrolyzed urine for enhanced valorization and toxicant degradation of wet coffee processing wastes: Implications for soil contamination and health risk reductions

Coffee pulp (CP) and wastewater, from wet coffee processing plants, pollute water and soil ecosystems unless a greener management system is employed. The aim was to evaluate the effect of hydrolyzed human urine (HU) on the dynamics of total phenol, caffeine, and heavy metals during CP and coffee processing wastewater (CPWW) co-composting. The associated health risks reduction after application for cabbage production was also estimated. For the purpose, five treatments were prepared as C₀ (CP, control), C₁ (CP + CPWW), C₂ (CP + 1:1 CPWW:HU), C₃ (CP + 1:2 CPWW:HU) and C₄ (CP + 1:3 CPWW:HU). The optimum compost was applied for cabbage cultivation in comparison with mineral fertilizer and without fertilization in a greenhouse. The total phenol reduction was in the order of C₁ (77.71%) < C₀ (78.66%) < C₄ (79.89%) < C₃ (91.20%) < C₂ (91.48%), and maximum significant reduction of caffeine was also observed in C₃ (81.34%) and C₂ (82.66%). Pb and Cd were significantly reduced in C₂, and Cr in C₃ with a reduction of 4.38-15.13%, 12.50-33.00%, and 2.94-19.57%, respectively. The bio-concentration factor decreased in the order of Cd > Cr > Ni > Pb with concentrations, hazard quotient, hazard index (along with phenol) < 1, and cancer risk values below 1.00E-04, indicating very little risk. Thus, supplementing HU enhanced degradation of the anti-nutrient factors, and provide compost that enrich soil nutrients with little health risks of application.

Anharmonic DFT Study of Near-Infrared Spectra of Caffeine: Vibrational Analysis of the Second Overtones and Ternary Combinations

Anharmonic quantum chemical calculations were employed to simulate and interpret a near-infrared (NIR) spectrum of caffeine. First and second overtones, as well as binary and ternary combination bands, were obtained, accurately reproducing the lineshape of the experimental spectrum in the region of 10,000–4000 cm⁻¹ (1000–2500 nm). The calculations enabled performing a detailed analysis of NIR spectra of caffeine, including weak bands due to the second overtones and ternary combinations. A highly convoluted nature of NIR spectrum of caffeine was unveiled, with numerous overlapping bands found beneath the observed spectral lineshape. To properly reflect that intrinsic complexity, the band assignments were provided in the form of heat maps presenting the contributions to the NIR spectrum from various kinds of vibrational transitions. These contributions were also quantitatively assessed in terms of the integral intensities. It was found that the combination bands provide the decisively dominant contributions to the NIR spectrum of caffeine. The first overtones gain significant importance between 6500–5500 cm⁻¹, while the second overtones are meaningful in the higher wavenumber regions, particularly in the 10,000–7000 cm⁻¹ region. The obtained detailed band assignments enabled deep interpretation of the absorption regions of caffeine identified in the literature as meaningful for analytical applications of NIR spectroscopy focused on quantitative analysis of caffeine content in drugs and natural products.

A hybrid optimization method for sample partitioning in near-infrared analysis

The division of calibration and validation is one of the essential procedures that affect the prediction result of the calibration model in quantitative analysis of near-infrared (NIR) spectroscopy. The conventional methods are Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). These algorithms use Euclidean distance to cover as many representative samples as possible. This paper proposes an Adaptive Hybrid Cuckoo-Tabu Search (AHCTS) algorithm for partitioning samples based on optimization. The algorithm combines the characteristics of cuckoo search (CS) and tabu search (TS) and fuses with an adaptive function. For comparison, using fishmeal samples as spectral analysis data, KS, SPXY, and AHCTS algorithms were used to divide the modeling samples to establish partial least squares regression (PLSR) models. The experimental results showed that the model established by the proposed algorithm performs better than KS and SPXY. It reveals that the AHCTS method may be an advantageous alternative for quantitative analysis of NIR spectroscopy.

FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples

Daily consumption of caffeine in coffee, tea, chocolate, cocoa, and soft drinks has gained wide and plentiful public and scientific attention over the past few decades. The concentration of caffeine in vivo is a crucial indicator of some disorders—for example, kidney malfunction, heart disease, increase of blood pressure and alertness—and can cause some severe diseases including type 2 diabetes mellitus (DM), stroke risk, liver disease, and some cancers. In the present study, near-infrared spectroscopy (NIRS) coupled with partial least-squares regression (PLSR) was proposed as an alternative method for the quantification of caffeine in 25 commercially available tea samples consumed in Oman. This method is a fast, complementary technique to wet chemistry procedures as well as to high-performance liquid chromatography (HPLC) methods for the quantitative analysis of caffeine in tea samples because it is reagent-less and needs little or no pre-treatment of samples. In the current study, the partial least-squares (PLS) algorithm was built by using the near-infrared NIR spectra of caffeine standards prepared in tea samples scanned by a Frontier NIR spectrophotometer (L1280034) by PerkinElmer. Spectra were collected in the absorption mode in the wavenumber range of 10,000–4000 cm⁻¹, using a 0.2 mm path length and CaF₂ sealed cells with a resolution of 2 cm⁻¹. The NIR results for the contents of caffeine in tea samples were also compared with results obtained by HPLC analysis. Both techniques provided good results for predicting the caffeine contents in commercially available tea samples. The results of the proposed study show that the suggested FT-NIRS coupled with PLS regression algorithun has a high potential to be routinely used for the quick and reproducible analysis of caffeine contents in tea samples. For the NIR method, the limit of quantification (LOQ) was estimated as 10 times the error of calibration (root mean square error of calibration (RMSECV)) of the model; thus, RMSEC was calculated as 0.03 ppm and the LOQ as 0.3 ppm.

Determination of pH and acidity in green coffee using near-infrared spectroscopy and multivariate regression

BACKGROUND

Coffee is a raw material of global interest. Due to its relevance, this work evaluated the performance of calibration models constructed from spectral data obtained using near-infrared spectroscopy (FT-NIR) to determine the pH values and acidity in coffee beans in a practical and non-destructive way. Partial least squares regression was used during the calibration and the cross-validation to optimize the number of latent variables. The predictive capacity of the spectral pre-processing methods was also accessed.

RESULTS

The results obtained showed that the best methods of pre-processing were the first derivative for the pH variable and the standard normal variate for the acidity, which produced models with correlations of 0.78 and 0.92, ratios of prediction to deviation of 2.061 and 2.966 and biases of -0.00011 and -0.152 to test set validation, respectively. The average errors between predicted and experimental values were lower than 7%.

CONCLUSIONS

FT-NIR was successfully applied to predict properties related to the quality of coffee. The method was demonstrated to be a fast and non-destructive tool which allows the rapid inline evaluation of samples facilitating industrial and commercial processing. © 2020 Society of Chemical Industry.

Concurrence of antibiotic resistant bacteria (ARB), viruses, pharmaceuticals and personal care products (PPCPs) in ambient waters of Guwahati, India: Urban vulnerability and resilience perspective

Multi-drug resistant microbes, pathogenic viruses, metals, and pharmaceuticals and personal care products (PPCPs) in water has become the crux of urban sustainability issues. However, vulnerability due to pollutant concurrences, source apportionment, and identification of better faecal indicators needs to be better understood. The present study focuses on the vulnerability of urban Guwahati, the largest city in Northeastern India, through analyzing the concurrence of PPCPs, enteric viruses, antibiotic resistant bacteria, metal, and faecal contamination in water. The study strives to identify a relevant marker of anthropogenic pollution for the Indian scenario. Samples from the Brahmaputra River (n = 4), tributary Bharalu River (an unlined urban drain; n = 3), and Ramsar recognized Lake (Dipor Bil; n = 1) indicate caffeine > acetaminophen > theophylline > carbamazepine > crotamiton for PPCPs and pepper mild mottle virus (PMMoV) > aichi > hepatitis A > norovirus GII > norovirus GI for enteric viruses. PMMoV was the better indicator of faecal pollution due to its prevalence, specificity and ease of detection. Antibiotic resistance was neither correlated with the prevalence of PPCPs nor E. coli. As, Co and Mn appear to be inducing antibiotic resistance in E. coli. While the risk quotient of the urban drain (Bharalu River) indicates one order higher magnitude than reported for other Indian rivers, the Lake exhibited the least pollution and better resilience. The concurrence of pollutants and multi-drug resistant E. coli, owing to the complete absence of wastewater treatment, puts the city in a highly vulnerable state. Pollution is being regulated only by the dilution capability of the Brahmaputra River, which needs to be further researched for seasonal variation.

Electrodeposition of 4-Benzenesulfonic Acid onto a Graphite-Epoxy Composite Electrode for the Enhanced Voltammetric Determination of Caffeine in Beverages

Caffeine is widely present in food and drinks, such as teas and coffees, being also part of some currently commercialized medicines, but despite its enhancement on several functions of human body, its exceeding use can promote many health problems. In order to develop new fast approaches for the caffeine sensing, graphite-epoxy composite electrodes (GECE) were used as substrate, being modified by different diazonium salts, synthetized as their tetraflouroborate salts. An analytical method for caffeine quantification was developed, using sware wave voltammetry (SWV) in Britton-Robinson buffer pH 2.0. Detection limits for bare electrode and 4-benzenesulfonic modified electrode were observed circa 145 µmol·L^-1 and 1.3 µmol·L^-1, respectively. The results have shown that the modification shifts the oxidation peaks to lower potential. Kinetics of the reaction limited by diffusion was more expressive when caffeine was added to the solution, resulting in decreases of impedance, characterized by lower R _ct. All results for caffeine determination were compared to a reference chromatographic procedure (HPLC), showing no statistical difference. Analytical parameters for validation were suitably determined according to local legislation, leading to a linear behaviour from 5 to 150 µmol·L^-1; precision of 4.09% was evaluated based on the RDC 166/17, and accuracy was evaluated in comparison with the reference method, with recovery of 98.37 ± 2.58%.

Rapid determination by near infrared spectroscopy of theaflavins-to-thearubigins ratio during Congou black tea fermentation process

The theaflavin-to-thearubigin ratio (TF/TR) is an important parameter for evaluating the degree of fermentation and quality characteristics of Congou black tea. Near infrared (NIR) spectroscopy, one of the most promising techniques for evaluating large-scale tea processing quality, in association with chemometrics, can be used as a selection tool when a fast determination of the requested parameters is required. The aim of this work is to develop a unique model for the determination of TF/TR. First, 11 key wavelength variables were screened by synergy interval partial least-squares regression (SI-PLS) and competitive adaptive reweighted sampling (CARS). Based on these characteristic variables, a new extreme learning machine (ELM) combined with an adaptive boosting (ADABOOST) algorithm (ELM-ADABOOST) was applied to construct the nonlinear prediction model for TF/TR, and an independent external set was used for the validation. A determinate coefficient (R_p²) of 0.893, root mean square error of prediction (RMSEP) of 0.0044, RSD below 10%, and RPD above 3 were acquired in the prediction model. These results demonstrate that NIR can be used to rapidly determine the TF/TR value during fermentation, and it effectively simplify the model and improve the prediction accuracy when combined with the SI-CARS variable.

Quality assessment of fresh tea leaves by estimating total polyphenols using near infrared spectroscopy

This paper reports on the development of an integrated leaf quality inspecting system using near infrared reflectance (NIR) spectroscopy for quick and in situ estimation of total polyphenol (TP) content of fresh tea leaves, which is the most important quality indicator of tea. The integrated system consists of a heating system to dry the fresh tea leaves to the level of 3-4% moisture, a grinding and sieving system fitted with a 250 micron mesh sieve to make fine powder from the dried leaf. Samples thus prepared are transferred to the NIR beam and TP is measured instantaneously. The wavelength region, the number of partial least squares (PLS) component and the choice of preprocessing methods are optimized simultaneously by leave-one-sample out cross-validation during the model calibration. In order to measure polyphenol percentage in situ, the regression model is developed using PLS regression algorithm on NIR spectra of fifty-five samples. The efficacy of the model developed is evaluated by the root mean square error of cross-validation, root mean square error of prediction and correlation coefficient (R2) which are obtained as 0.1722, 0.5162 and 0.95, respectively.

Investigation of sample partitioning in quantitative near-infrared analysis of soil organic carbon based on parametric LS-SVR modeling

A framework for sample partitioning is proposed to take into account the tunable ratio of numbers of calibration and prediction samples, in consideration with the randomness, stability and robustness of calibration models.

The characterization of caffeine and nine individual catechins in the leaves of green tea (Camellia sinensis L.) by near-infrared reflectance spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used to determine the contents of caffeine and nine individual catechins in tea leaves. A total of 665 samples were scanned by NIRS, and also by high performance liquid chromatography coupled to a diode array detector to determine the contents of caffeine and nine individual catechins. The calibration models for caffeine, EGC, C, EGCG, EC, ECG, and total catechins had high r(2) (more than 0.90) and RSP (the ratio of standard deviation of reference data to SEP(C) in the external validation set) values (more than 4.1), indicating a good correlation between reference values and NIRS predicted values. In contrast, the calibration models of GC and EGCG-3Me had low r(2) and RSP values (below 0.8 and 2.0). Therefore, these results suggest that NIRS could be applied for the rapid determination of the contents of caffeine, EGC, C, EGCG, EC, ECG, and total catechins in tea leaves for breeding programs that develop high-quality tea plants.

FT-NIR spectroscopy and Whittaker smoother applied to joint analysis of duel-components for corn

Protein and total fat are two ingredients to measure the quality of corn. The aim of this study is to evaluate the quality of corn by the dual-component join determination through Fourier transform near infrared (FT-NIR) spectroscopic analysis. The calibration models were established by the systematic study performed respectively in the four regions of the whole range, the second overtone, the first overtone, and the combination. Whittaker smoother was introduced as an attractive alternative data preprocessing method. With the optimized parameters, Whittaker smoother indicates its priority for improving modeling results in any of the four regions. The predictive abilities were compared between the joint analysis of protein and total fat and the separate analysis of each single component by partial least squares (PLS) modeling. The uncertainty in parameter was further estimated for the linear models. It is suggested that the joint analysis of dual-component always leads to better predictive results, and also provided good evaluation results for the independent validation samples. For the joint analysis, the optimal region for protein was the combination (5400-4000 cm(-1)), and the optimal region for total fat was the first overtone (7200-5400 cm(-1)). The optimal PLS models also provided appreciate predictive performance for both protein and total fat. And the parameter uncertainty determination provided an acceptable estimate of the measured uncertainty for the FT-NIR analysis of corn. In general, the joint analysis of dual-component is a better strategy for FT-NIR analysis of corn, and it is hoped to be tested for other objects.

Diffuser-incorporated transmission NIR measurement for reliable analysis of packed granular samples

A diffuser-incorporated transmission near-infrared (NIR) scheme that enables direct spectral collection of packed granular samples with reliable sample representation and reproducibility has been demonstrated. The analytical utility of this method has been evaluated for the determination of polyethylene (PE) pellet density and the discrimination of the geographical origin of rice samples. Based on the preliminary observation of transmission spectral features acquired from spherical polyoxymethylene (POM) packings composed of different particle sizes as well as packing thickness, a portion of the radiation was propagated through the void space in the packing without fully interacting with the POM pellets. This type of radiation, so-called non-fully interacted radiation (NFIR), adversely affected the sample representation as well as the reproducibility of transmission measurements. To maximize the interaction of NIR radiation with granular samples, a polytetrafluoroethylene (PTFE) diffuser was positioned in front of the sample packing to introduce isotropically diffused radiation into the sample. This diffuser-incorporated scheme resulted in highly reproducible transmission spectra for both packed granular samples. Consequently, the density determination of PE pellets as well as discrimination of rice samples according to geographical origin was more accurate using the proposed scheme.

The Combined Optimization of Savitzky-Golay Smoothing and Multiplicative Scatter Correction for FT-NIR PLS Models

The combined optimization of Savitzky-Golay (SG) smoothing and multiplicative scatter correction (MSC) were discussed based on the partial least squares (PLS) models in Fourier transform near-infrared (FT-NIR) spectroscopy analysis. A total of 5 cases of separately (or combined) using SG smoothing and MSC were designed and compared for optimization. For every case, the SG smoothing parameters were optimized with the number of PLS latent variables (F), with an expanded number of smoothing points. Taking the FT-NIR analysis of soil organic matter (SOM) as an example, the joint optimization of SG smoothing and MSC was achieved based on PLS modeling. The results showed that the optimal pretreatment was successively using SG smoothing and MSC, in which the SG smoothing parameters were 4th degree of polynomial, 2nd-order derivative, and 67 smoothing points, the best corresponding F, RMSEP, and RP were 7, 0.3982 (%), and 0.8862, respectively. This result was far better than those without any pretreatment. The combined optimization of SG smoothing and MSC could obviously improve the modeling result for NIR analysis of SOM. In addition, a new method for the classification of calibration and prediction was proposed by normalization principle. The optimizations were done on this basis of this classification.

Application of chemometrics in authentication of herbal medicines: a review

INTRODUCTION

Herbal medicines (HM) and their preparations have been widely used for hundreds of years all over the world. However, they have not been officially recognised due to a lack of adequate or accepted research methodology for their evaluation.

OBJECTIVE

To present a concise overview of the recent applications of chemometrics in solving the ambiguity of herbal medicine authentication during the last two decades.

METHODOLOGY

Studies involving the applications of chemometric analysis in combination with different analytical methods were classified according to the method of analysis used including chromatographic (HPLC, GC and CE), spectroscopic (NMR, IR, UV and ICP) and genetic analysis (RAPD). The purpose of each of these studies was classified into one of three main categories: taxonomic discrimination, quality assessment or classification between plants of different geographic origins.

RESULTS

This review comprises over 150 studies, covering the past two decades, emphasising the significance of chemometric methods in the discrimination of many herbs from closely related species and from adulterants, based on the principal bioactive components and phytochemical diversity. Furthermore, the differentiation between varieties and hybrids was achieved in addition to the prediction of the active components by quantitative methods of analysis. Discrimination according to geographical origin and localities, processing methods, DNA profiling and metabolomics were also efficiently investigated.

CONCLUSION

Chemometric methods have provided an efficient and powerful tool for the quality control and authentication of different herbs.

Analysis of some selected catechins and caffeine in green tea by high performance liquid chromatography

Green tea seems to have a positive impact on health due to the catechins-found as flavanols. Thus, the present study was aimed to develop a low cost reversed phase high performance liquid chromatographic (HPLC) method for simultaneous determination of flavanol contents, namely catechin (C), epicatechin (EC), epigallocatechin (EGC), epicatechin 3-gallate (ECG) and epigallocatechin 3-gallate (EGCG) and caffeine in 29 commercial green tea samples available in a Saudi Arabian local market. A C-18 reversed-phase column, acetonitrile-trifluoroacetic acid as a mobile phase, coupled with UV detector at 205 nm, was successfully used for precise analysis of the tested analytes in boiled water of digested tea leaves. The average values of N (No. of theoretical plates), HETP (height equivalent of theoretical plates) and R(s) (separation factor) (at 10 μg ml(-1) of the catechins EC, EGC, EGCG and ECG) were 2.6×10(3)±1.2×10(3), 1.7×10(-3)±4.7×10(-4) cm and 1.7±5.53×10(-2), respectively. The developed HPLC method demonstrated excellent performance, with low limits of detection (LOD) and quantification (LOQ) of the tested catechins of 0.004-0.05 μg ml(-1) and 0.01-0.17 μg ml(-1), respectively, and recovery percentages of 96-101%. The influence of infusion time (5-30 min) and temperature on the content of the flavanols was investigated by HPLC. After a 5 min infusion of the tea leaves, the average concentrations of caffeine, catechin, EC, EGC, ECG and EGCG were found to be in the ranges 0.086-2.23, 0.113-2.94, 0.58-10.22, 0.19-24.9, 0.22-13.9 and 1.01-43.3 mg g(-1), respectively. The contents of caffeine and catechins followed the sequence: EGCG>EGC>ECG>EC>C>caffeine. The method was applied satisfactorily for the analysis of (+)-catechin, even at trace and ultra trace concentrations of catechins. The method was rapid, accurate, reproducible and ideal for routine analysis.