Determination of total antioxidant capacity in green tea by near-infrared spectroscopy and multivariate calibration

Combining non-destructive devices and multivariate analysis as a tool to quantify the fatty acid profiles of linseed genotypes

Linseed (Linum usitatissimum L.) and linseed oil, with a fatty acid profile rich in both macro and micro elements, are recognized as functional foods due to their valuable positive effects on health. Fatty acids composition (FAC) is a key indicator in assessing the quality of linseeds. The FAC of linseed is typically determined using chromatographic methods, yielding highly accurate results. However, chromatographic methods entail drawbacks such as requiring pre-chemical processes, generating chemical waste, and being both expensive and time-consuming, similar to chemical analyses. This study focused on the feasibility of colorimeter and FT-NIRS data to determine the FAC (%), protein (%) and neutral detergent fiber (NDF %) in linseed samples. By employing the PLSR analysis based on FT-NIRS, it was determined that the ratios of stearic (R2val = 0.74, RMSEP = 0.09 %), oleic (R2val = 0.75, RMSEP = 0.26 %), linoleic (R2val = 0.85, RMSEP = 0.58 %), linolenic (R2val = 0.71, RMSEP = 1.07 %), 8,11,14 eicosatrienoic (R2val = 0.77, RMSEP = 0.02 %), margaric (R2val = 0.71, RMSEP = 0.01 %), myristic (R2val = 0.75, RMSEP = 0.02 %), and behenic (R2val = 0.74, RMSEP = 1.12 %) in linseed could be successfully predicted. Furthermore, results demonstrated that the protein (R2val = 0.87, RMSEP = 0.9 %) and NDF (R2val = 0.90, RMSEP = 0.6 %) content in linseeds can be successfully predicted. PLSR demonstrated that FT-NIRS had relatively higher predictive capability compared to color models.

Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques

The global market for organic cocoa beans continues to show sturdy growth. A low-cost handheld NIR spectrometer (900-1700 nm) combined with multivariate classification algorithms was used for rapid differentiation analysis of organic cocoa beans' integrity. In this research, organic and conventionally cultivated cocoa beans were collected from different locations in Ghana and scanned nondestructively with a handheld spectrometer. Different preprocessing treatments were employed. Principal component analysis (PCA) and classification analysis, RF (random forest), KNN (K-nearest neighbours), LDA (linear discriminant analysis), and PLS-DA (partial least squares-discriminant analysis) were performed comparatively to build classification models. The performance of the models was evaluated by accuracy, specificity, sensitivity, and efficiency. Second derivative preprocessing together with PLS-DA algorithm was superior to the rest of the algorithms with a classification accuracy of 100.00% in both the calibration set and prediction set. Second derivative algorithm was found to be the best preprocessing tool. The identification rates for the calibration set and prediction set were 96.15% and 98.08%, respectively, for RF, 91.35% and 92.31% for KNN, and 90.38% and 98.08% for LDA. Generally, the results showed that a handheld NIR spectrometer coupled with an appropriate multivariate algorithm could be used in situ for the differentiation of organic cocoa beans from conventional ones to ensure food integrity along the cocoa bean value chain.

Potential role of green tea extract and epigallocatechin gallate in preventing bisphenol A-induced metabolic disorders in rats: Biochemical and molecular evidence

BACKGROUND

Bisphenol A (BPA) is an artificial chemical widely used in the production of polycarbonate plastics and epoxy resins. Accumulating evidence indicates that BPA exposure is associated with metabolic disorders. The beneficial effects of green tea and epigallocatechin gallate (EGCG), major catechin present in green tea, on alleviating BPA-induced metabolic disorders have been shown in various studies.

PURPOSE

Protective effects of green tea extract and EGCG on BPA-induced metabolic disorders and possible underlying mechanisms were investigated.

METHODS

Rats were randomly divided into control, green tea extract (50 and 100 mg/kg, IP), EGCG (20 and 40 mg/kg, IP), BPA (10 mg/kg, gavage), BPA plus green tea extract (25, 50, and 100 mg/kg, IP), BPA plus EGCG (10, 20, and 40 mg/kg, IP), and BPA plus vitamin E (200 IU/kg, IP). After two months, body weight, blood pressure, biochemical blood tests, hepatic malondialdehyde (MDA), and glutathione (GSH) were assessed. By enzyme-linked immunosorbent assay, serum levels of insulin, leptin, adiponectin, TNFα, and IL-6, and by western blotting, hepatic insulin signaling (IRS-1, PI3K, Akt) were measured.

RESULTS

BPA increased body weight, blood pressure, and MDA, decreased GSH, elevated serum levels of low-density lipoprotein cholesterol, total cholesterol, triglyceride, glucose, insulin, leptin, TNFα, IL-6, and liver enzymes including alanine aminotransferase and alkaline phosphatase, and lowered high-density lipoprotein cholesterol and adiponectin levels. In western blot, decreased phosphorylation of IRS-1, PI3K, and Akt was obtained. Administration of green tea extract, EGCG, or vitamin E with BPA reduced the detrimental effects of BPA.

CONCLUSION

These findings indicate that green tea extract and EGCG can be effective in preventing or reducing metabolic disorders induced by BPA linked to their antioxidant and anti-inflammatory activity, regulating the metabolism of lipids, and improving insulin signaling pathways.

Artificial Intelligence Applied to Flavonoid Data in Food Matrices

Increasing interest in constituents and dietary supplements has created the need for more efficient use of this information in nutrition-related fields. The present work aims to obtain optimal models to predict the total antioxidant properties of food matrices, using available information on the amount and class of flavonoids present in vegetables. A new dataset using databases that collect the flavonoid content of selected foods has been created. Structural information was obtained using a structural-topological approach called TOPological Sub-Structural Molecular (TOPSMODE). Different artificial intelligence algorithms were applied, including Machine Learning (ML) methods. The study allowed us to demonstrate the effectiveness of the models using structural-topological characteristics of dietary flavonoids. The proposed models can be considered, without overfitting, effective in predicting new values of Oxygen Radical Absorption capacity (ORAC), except in the Multi-Layer Perceptron (MLP) algorithm. The best optimal model was obtained by the Random Forest (RF) algorithm. The in silico methodology we developed allows us to confirm the effectiveness of the obtained models, by introducing the new structural-topological attributes, as well as selecting those that most influence the class variable.

Simultaneous Recognition of Species, Quality Grades, and Multivariate Calibration of Antioxidant Activities for 12 Famous Green Teas Using Mid- and Near-Infrared Spectroscopy Coupled with Chemometrics

In this paper, mid- and near-infrared spectroscopy fingerprints were combined to simultaneously discriminate 12 famous green teas and quantitatively characterize their antioxidant activities using chemometrics. A supervised pattern recognition method based on partial least square discriminant analysis (PLSDA) was adopted to classify the 12 famous green teas with different species and quality grades, and then optimized sample-weighted least-squares support vector machine (OSWLS-SVM) based on particle swarm optimization was employed to investigate the quantitative relationship between their antioxidant activities and the spectral fingerprints. As a result, 12 famous green teas can be discriminated with a recognition rate of 100% by MIR or NIR data. However, compared with individual instrumental data, data fusion was more adequate for modeling the antioxidant activities of samples with RMSEP of 0.0065. Finally, the performance of the proposed method was evaluated and validated by some statistical parameters and the elliptical joint confidence region (EJCR) test. The results indicate that fusion of mid- and near-infrared spectroscopy suggests a new avenue to discriminate the species and grades of green teas. Moreover, the proposed method also implies other promising applications with more accurate multivariate calibration of antioxidant activities.

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.

Antioxidant Phytochemicals in Fresh Produce: Exploitation of Genotype Variation and Advancements in Analytical Protocols

Horticultural commodities (fruit and vegetables) are the major dietary source of several bioactive compounds of high nutraceutical value for humans, including polyphenols, carotenoids and vitamins. The aim of the current review was dual. Firstly, toward the eventual enhancement of horticultural crops with bio-functional compounds, the natural genetic variation in antioxidants found in different species and cultivars/genotypes is underlined. Notably, some landraces and/or traditional cultivars have been characterized by substantially higher phytochemical content, i.e., small tomato of Santorini island (cv. "Tomataki Santorinis") possesses appreciably high amounts of ascorbic acid (AsA). The systematic screening of key bioactive compounds in a wide range of germplasm for the identification of promising genotypes and the restoration of key gene fractions from wild species and landraces may help in reducing the loss of agro-biodiversity, creating a healthier "gene pool" as the basis of future adaptation. Toward this direction, large scale comparative studies in different cultivars/genotypes of a given species provide useful insights about the ones of higher nutritional value. Secondly, the advancements in the employment of analytical techniques to determine the antioxidant potential through a convenient, easy and fast way are outlined. Such analytical techniques include electron paramagnetic resonance (EPR) and infrared (IR) spectroscopy, electrochemical, and chemometric methods, flow injection analysis (FIA), optical sensors, and high resolution screening (HRS). Taking into consideration that fruits and vegetables are complex mixtures of water- and lipid-soluble antioxidants, the exploitation of chemometrics to develop "omics" platforms (i.e., metabolomics, foodomics) is a promising tool for researchers to decode and/or predict antioxidant activity of fresh produce. For industry, the use of optical sensors and IR spectroscopy is recommended to estimate the antioxidant activity rapidly and at low cost, although legislation does not allow its correlation with health claims.

Application of microencapsulation for the safe delivery of green tea polyphenols in food systems: Review and recent advances

Green tea has been associated with the prevention and reduction of a wide range of severe health conditions such as cancer, immune, and cardiovascular diseases. The health benefits associated with green tea consumption have been predominantly attributed to green tea polyphenols. The functional properties of green tea polyphenols are mainly anti-oxidative, antimutagenic, anticarcinogenic, anti-microbial, etc. These excellent properties have recently gained considerable attention in the food industry. However, their application is limited by their sensitivity to factors like temperature, light, pH, oxygen, etc. More, studies have reported the occurrence of unpleasant taste and color transfer during food processing. Lastly, the production of functional food requires to maintain the stability, bioactivity, and bioavailability of the active compounds. To tackle these obstacles, technological approaches like microencapsulation have been developed and applied for the formulation of green tea-enriched food products. The present review discusses the novelty in microencapsulation techniques for the safe delivery of green tea polyphenols in food matrices. After a literature on the green tea polyphenols composition, and their health attributes, the encapsulation methods and the coating materials are presented. The application of green tea encapsulates in food matrices as well as their effect on food functional and sensory properties are also discussed.

A rapid integrated bioactivity evaluation system based on near-infrared spectroscopy for quality control of Flos Chrysanthemi

For quality control of herbal medicines or functional foods, integral activity evaluation has become more popular in recent studies. The majority of researchers focus on the relationship between chromatography/mass spectroscopy and bioactivity, but the connection with spectrum-activity is easily ignored. In this paper, the near infrared reflection spectra (NIRS) of Flos Chrysanthemi samples were collected as a representative spectrum technology, and corresponding anti-inflammation activities were utilized to illustrate the spectrum-activity study. HPLC/Q-TOF-MS identification and heat map clustering were used to select the quality markers (Q-marker) from five cultivars of Flos Chrysanthemi. Using boxplot analysis and the interval limits of detection (LODs) theory, six crucial markers, namely, chlorogenic acid, 3,5-dicaffeoylquinic acid, 1,5-dicaffeoylquinic acid, luteoloside, apigenin-7-O-β-d-glucoside, and luteolin-7-O-6-malonylglucoside were screened out. Then partial least squares regression (PLSR) calibration models combined with synergy interval partial least squares (siPLS) and 12 different spectral pretreatment methods were developed for the parameters optimization of these Q-markers in Flos Chrysanthemi powder. After comparing the relationship between Q-marker contents and anti-inflammation activity via three machine learning approaches and PLSR, back-propagation neural network (BP-ANN) displayed a more excellent non-linear fitting effect, as its R for new batches reached 0.89. These results indicated that the integrated NIRS and bioactive strategy was suitable for fast quality management in Flos Chrysanthemi, and also applied to other botanical food quality control.

Application of NIR and MIR spectroscopy for rapid determination of antioxidant activity of Radix Scutellariae from different geographical regions

INTRODUCTION

The beneficial health effects of traditional Chinese medicines are often attributed to their potent antioxidant activities, usually established in vitro. However, these wet chemical methods for determining antioxidant activities are time-consuming, laborious, and expensive.

OBJECTIVES

This study was conducted to establish a rapid determination of antioxidant activity of Radix Scutellariae using near-infrared (NIR) and mid-infrared (MIR) spectroscopy.

MATERIAL AND METHODS

Antioxidant capabilities were evaluated using 2,2-diphenyl-1-picrylhydrazyl hydrazyl (DPPH) and oxygen radical absorbance capacity (ORAC) assays. The total flavonoid contents (TFCs) of Radix Scutellariae were measured by the aluminium chloride colorimetric method. The same sample was then scanned using NIR and MIR spectroscopy. Chemometrics analysis using partial least-squares (PLS) regression was performed to establish the models for predicting the antioxidant activities of Radix Scutellariae.

RESULTS

A better predictive performance was achieved using PLS models based on NIR data. The determination coefficient (R(2)) and the residual predictive deviation (RPD) for the validation set were 0.9298 and 2.84 for DPPH, and 0.9436 and 2.66 for TFCs, respectively. MIR-PLS algorithms gave a slightly lower reliability (R(2) = 0.9090 and 0.9374, RPD = 2.01 and 2.42, for DPPH and TFC, respectively). Very comparable results for ORAC were obtained with the two methods.

CONCLUSION

The developed spectroscopic method can be successfully applied in high-throughput screening of the antioxidant capability of Radix Scutellariae samples. It can also be a viable and advantageous alternative to laborious chemical procedures.

Infrared Spectroscopy as a Versatile Analytical Tool for the Quantitative Determination of Antioxidants in Agricultural Products, Foods and Plants

Spectroscopic methods provide with very useful qualitative and quantitative information about the biochemistry and chemistry of antioxidants. Near infrared (NIR) and mid infrared (MIR) spectroscopy are considered as powerful, fast, accurate and non-destructive analytical tools that can be considered as a replacement of traditional chemical analysis. In recent years, several reports can be found in the literature demonstrating the usefulness of these methods in the analysis of antioxidants in different organic matrices. This article reviews recent applications of infrared (NIR and MIR) spectroscopy in the analysis of antioxidant compounds in a wide range of samples such as agricultural products, foods and plants.

Varietal identification of tea (Camellia sinensis) using nanofluidic array of single nucleotide polymorphism (SNP) markers

Apart from water, tea is the world's most widely consumed beverage. Tea is produced in more than 50 countries with an annual production of approximately 4.7 million tons. The market segment for specialty tea has been expanding rapidly owing to increased demand, resulting in higher revenues and profits for tea growers and the industry. Accurate varietal identification is critically important to ensure traceability and authentication of premium tea products, which in turn contribute to on-farm conservation of tea genetic diversity. Using a set of single nucleotide polymorphism (SNP) markers developed from the expressed sequence tag (EST) database of Camilla senensis, we genotyped deoxyribonucleic acid (DNA) samples extracted from a diverse group of tea varieties, including both fresh and processed commercial loose-leaf teas. The validation led to the designation of 60 SNPs that unambiguously identified all 40 tested tea varieties with high statistical rigor (p<0.0001). Varietal authenticity and genetic relationships among the analyzed cultivars were further characterized by ordination and Bayesian clustering analysis. These SNP markers, in combination with a high-throughput genotyping protocol, effectively established and verified specific DNA fingerprints for all tested tea varieties. This method provides a powerful tool for variety authentication and quality control for the tea industry. It is also highly useful for the management of tea genetic resources and breeding, where accurate and efficient genotype identification is essential.

A near-infrared reflectance spectroscopy method for direct analysis of several chemical components and properties of fruit, for example, Chinese hawthorn

Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of Chinese hawthorn (Crataegus pinnatifida Bge. var. major) fruit from three geographical regions as well as for the estimation of the total sugar, total acid, total phenolic content, and total antioxidant activity. Principal component analysis (PCA) was used for the discrimination of the fruit on the basis of their geographical origin. Three pattern recognition methods, linear discriminant analysis, partial least-squares-discriminant analysis, and back-propagation artificial neural networks, were applied to classify and compare these samples. Furthermore, three multivariate calibration models based on the first derivative NIR spectroscopy, partial least-squares regression, back-propagation artificial neural networks, and least-squares-support vector machines, were constructed for quantitative analysis of the four analytes, total sugar, total acid, total phenolic content, and total antioxidant activity, and validated by prediction data sets.

Chemometrics and vibrational spectroscopy as green tools for mine phytoremediation strategies

This study describes the use of near infrared (NIR) spectroscopy in combination with chemometrics to characterise Combretum erythrophyllum plant material to determine differences in the chemical profiles of samples harvested from mine contaminated areas and those of natural populations. The chemometric computation of near infrared vibrational spectra was used to generate principal component analysis and partial least squares models. These models were used to determine seasonal differences in the chemical matrices of samples harvested from the mine sites with different levels of contamination. Principal component analysis scatter plots illustrated clustering of phenolic profiles of samples depending on whether they originated from contaminated or uncontaminated soils. A partial least squares model was developed to link the variations in the chemical composition and levels of contamination in all samples collected in the same season (autumn). The levels of total soluble phenolic compounds in leaf extracts of C. erythrophyllum were measured using the Folin-Ciocalteau assay. Data analysis of the samples revealed that plants harvested from mine sites, particularly in summer, produced a higher level of phenolic compounds than those of the natural population, thereby displaying a good correlation with the chemometric models.

Determination of total phenolic content and antioxidant activity of garlic (Allium sativum) and elephant garlic (Allium ampeloprasum) by attenuated total reflectance-Fourier transformed infrared spectroscopy

The total phenolic contents and antioxidant activities of garlics from California, Oregon, Washington, and New York were determined by Fourier transform infrared (FT-IR) spectroscopy (400-4000 cm(-1)). The total phenolic content was quantified [Folin-Ciocalteu assay (FC)] and three antioxidant activity assays, 2,2-diphenyl-picrylhydrazyl (DPPH) assay, Trolox equivalent antioxidant capacity (TEAC) assay, and ferric reducing antioxidant power (FRAP), were employed for reference measurements. Four independent partial least-squares regression (PLSR) models were constructed with spectra from 25 extracts and their corresponding FC, DPPH, TEAC, and FRAP with values for 20 additional extracts predicted (R > 0.95). The standard errors of calibration and standard error of cross-validation were <1.45 (TEAC), 0.36 (FRAP), and 0.33 μmol Trolox/g FW (DPPH) and 0.55 mg gallic acid/g FW (FC). Cluster and dendrogram analyses could segregate garlic grown at different locations. Hydroxyl and phenolic functional groups most closely correlated with garlic antioxidant activity.

Determination of total phenolic content and antioxidant capacity of onion (Allium cepa) and shallot (Allium oschaninii) using infrared spectroscopy

Total phenolic content (TPC) and total antioxidant capacity (TAC) of four onion varieties (red, white, yellow and sweet) and shallot from selected locations (Washington, Idaho, Oregon, Texas and Georgia) were determined using Fourier transform infrared (FT-IR) spectroscopy (4000-400cm^-1). The Folin-Ciocalteu (F-C) assay was used to quantify TPC and three assays were used to determine TAC, including 2,2-diphenyl-picrylhydrazyl (DPPH) assay, Trolox equivalent antioxidant capacity (TEAC) assay and ferric reducing antioxidant power (FRAP) assay. Partial least squares regression (PLSR) with cross-validation (leave-one-out) was conducted on onion and shallot extracts (n=200) and their corresponding F-C, DPPH, TEAC and FRAP values were employed to obtain four independent calibration models for predicting TPC and TAC for the extracts. Spectra from an extra 19 independent extracts were used as an external validation set for prediction. A correlation of r>0.95 was obtained between FT-IR predicted and reference values (by F-C, DPPH, TEAC and FRAP assay) with standard errors of calibration (SEC) and standard errors of cross-validation (SECV) less than 2.85, 0.35 and 0.45μmolTrolox/g FW of extracts for TEAC, FRAP and DPPH assay, respectively; and 0.36mggallic acid/g FW of extracts for the F-C assay. In addition, cluster analysis (principal component analysis (PCA)) and discriminant function analysis (DFA) could differentiate varieties of onions and shallot based upon infrared spectral features. Loading plots for the various chemometrics models indicated that hydroxyl and phenolic functional groups were most closely correlated with antioxidant capacity. The use of mid-infrared spectroscopy to predict the total antioxidant capacity of vegetables provides a rapid and precise alternative to traditional wet chemistry analysis.

Extraction and analysis of polyphenols: recent trends

In recent years, there has been an increasing interest in diets rich in fruits and vegetables and this is mostly due to their presumed role in the prevention of various degenerative diseases, such as cancer and cardiovascular diseases. This is mainly due to the presence of bioactive compounds, such as polyphenols, carotenoids, among others. Polyphenols are one of the main classes of secondary metabolites derived from plants offering several health benefits resulting in their use as functional foods. Prior to the use of these polyphenols in specific applications, such as food, pharmaceutical, and the cosmetic industries, they need to be extracted from the natural matrices, then analyzed and characterized. The development of an efficient procedure for the extraction, proper analysis, and characterization of phenolic compounds from different sources is a challenging task due to the structural diversity of phenolic compounds, a complex matrix, and their interaction with other cellular components. In this light, this review discusses different methods of extraction, analysis, and the structural characterization of polyphenolic compounds.

Prediction and interpretation of the antioxidant capacity of green tea from dissimilar chromatographic fingerprints

Previously, multivariate calibration techniques have been successfully applied to model and predict the antioxidant activity of green tea from its chromatographic fingerprint. Since the selectivity differences between dissimilar chromatographic systems have already been valuably used in several applications, in this paper it is studied whether combining the complementary information contained in two dissimilar fingerprints can improve the predictive capacity of the multivariate calibration model. The simplest way of combining the data is concatenating both fingerprints for each sample. The resulting matrix can then be subjected to Orthogonal Projections to Latent Structures (O-PLS). Unfortunately, this approach resulted in a more complex model with a prediction error of about the average of the errors obtained with the individual fingerprints. Secondly, only the peaks with high loading and low orthogonal loading from both chromatograms were included in the O-PLS model. This resulted in a reduced complexity, but not in better predictions, probably due to a lack of complementarity of the information concerning the antioxidant capacity. Finally, the concatenated fingerprints were subjected to stepwise multiple linear regression (MLR) in order to build a model based on the variables most correlated with the antioxidant capacity. The obtained prediction error was lower than those of both previous approaches, but still higher than the error of the model based on a single analysis. This is probably again caused by a lack of complementarity in the variables. Nevertheless, it was advantageous to develop fingerprints on dissimilar system, because it enables to choose the most suited chromatographic profile to build a multivariate calibration model for the considered purpose. In contrast to what was expected, the study showed that the most simple (so the worst separated) fingerprints resulted in the best predictions. On the other hand, a more complex fingerprint in which more compounds are separated is still important to improve the interpretability of the model.

Identification of transgenic foods using NIR spectroscopy: a review

The utilization of chemometric methods in the quantitative and qualitative analysis of feeds, foods, medicine and so on has been accompanied with the great evolution in the progress and in the near infrared spectroscopy (NIRS). Hence, recently the application of NIR spectroscopy has extended on the context of genetics and transgenic products. The aim of this review was to investigate the application of NIR spectroscopy to identificate transgenic products and to compare it with the traditional methods. The results of copious researches showed that the application of NIRS technology was successful to distinguish transgenic foods and it has advantages such as fast, avoiding time-consuming, non-destructive and low cost in relation to the antecedent methods such as PCR and ELISA.

Study on discrimination of Roast green tea (Camellia sinensis L.) according to geographical origin by FT-NIR spectroscopy and supervised pattern recognition

Rapid discrimination of roast green tea according to geographical origin is crucial to quality control. Fourier transform near-infrared (FT-NIR) spectroscopy and supervised pattern recognition was attempted to discriminate Chinese green tea according to geographical origins (i.e. Anhui Province, Henan Province, Jiangsu Province, and Zhejiang Province) in this work. Four supervised pattern recognitions methods were used to construct the discrimination models based on principal component analysis (PCA), respectively. The number of principal components factors (PCs) and model parameters were optimized by cross-validation in the constructing model. The performances of four discrimination models were compared. Experimental results showed that the performance of SVM model is the best among four models. The optimal SVM model was achieved when 4 PCs were used, discrimination rates being all 100% in the training and prediction set. The overall results demonstrated that FT-NIR spectroscopy with supervised pattern recognition could be successfully applied to discriminate green tea according to geographical origins.

Prediction of dry matter, fat, pH, vitamins, minerals, carotenoids, total antioxidant capacity, and color in fresh and freeze-dried cheeses by visible-near-infrared reflectance spectroscopy

Visible-near-infrared reflectance spectroscopy was used to predict dry matter, fat, pH, retinol, alpha-tocopherol, beta-carotene, xanthophylls, sodium chloride, calcium, potassium, magnesium, zinc, total antioxidant capacity, brightness, redness, and yellowness in both fresh and freeze-dried cheeses. A total of 445 cheeses of four cheese varieties were investigated. Composition of samples was analyzed by reference methods. Samples were scanned (400-2500 nm) and predictive equations were developed using modified partial least-squares with both cross-validation and external validation. Coefficient of determination (R(2)) and residual predictive deviation (RPD) in external validation were satisfactory for dry matter (0.97; 5.99), fat (0.90; 3.22), beta-carotene (0.92; 3.43), sodium chloride (0.89; 2.94), calcium (0.95; 4.62), Zinc (0.93; 3.75), brightness (0.96; 4.88), redness (0.96; 5.23), and yellowness (0.93; 3.73) in fresh cheeses. Poor predictions were obtained for pH, retinol, alpha-tocopherol, xanthophylls, potassium, magnesium, and total antioxidant capacity (R(2) < 0.81; RPD < 3).

Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM)

Near-infrared (NIR) spectroscopy has been successfully utilized for the rapid identification of green, black and Oolong teas. The spectral features of each category are reasonably differentiated in the NIR region, and the spectral differences provided enough qualitative spectral information for identification. Support vector machine as a pattern recognition was applied to attain the differentiation of the three tea categories in this study. The top five latent variables are extracted by principal component analysis as the input of SVM classifiers. The identification results of the three tea categories were achieved by the RBF SVM classifiers and the polynomial SVM classifiers in different parameters. The best identification accuracies were up to 90%, 100% and 93.33%, respectively, when training, while, 90%, 100% and 95% when test. It was obtained using the RBF SVM classifier with sigma=0.5. The overall results ensure that NIR spectroscopy combined with SVM discrimination method can be efficiently utilized for rapid and simple identification of the different tea categories.

Beneficial effects of green tea--a review

Tea is the most consumed drink in the world after water. Green tea is a 'non-fermented' tea, and contains more catechins, than black tea or oolong tea. Catechins are in vitro and in vivo strong antioxidants. In addition, its content of certain minerals and vitamins increases the antioxidant potential of this type of tea. Since ancient times, green tea has been considered by the traditional Chinese medicine as a healthful beverage. Recent human studies suggest that green tea may contribute to a reduction in the risk of cardiovascular disease and some forms of cancer, as well as to the promotion of oral health and other physiological functions such as anti-hypertensive effect, body weight control, antibacterial and antivirasic activity, solar ultraviolet protection, bone mineral density increase, anti-fibrotic properties, and neuroprotective power. Increasing interest in its health benefits has led to the inclusion of green tea in the group of beverages with functional properties. However, although all the evidence from research on green tea is very promising, future studies are necessary to fully understand its contributions to human health, and advise its regular consumption in Western diets, in which green tea consumption is nowadays limited and sporadic.