Enhanced cross-category models for predicting the total polyphenols, caffeine and free amino acids contents in Chinese tea using NIR spectroscopy

A review: Integration of NIRS and chemometric methods for tea quality control-principles, spectral preprocessing methods, machine learning algorithms, research progress, and future directions

With the steady rise in tea production, the need for effective tea quality monitoring has become increasingly pressing. Traditional sensory evaluation and wet chemical detection methods are insufficient for real-time tea quality monitoring. As an emerging technology, near infrared spectroscopy (NIRS) offers numerous advantages, such as preserving sample integrity, generating objective results, and enabling rapid, straightforward assessments. These features make it an ideal choice for real-time tea quality testing. This paper systematically reviews the principles of NIRS, spectral preprocessing methods, statistical modeling techniques, and commonly used machine learning approaches. Furthermore, it provides an in-depth discussion of the research progress of NIRS in areas such as fresh tea leaf quality evaluation, rapid detection of tea-specific components, tea quality assessment and species identification, geographic traceability, development of NIRS equipment, and standardization. Future research directions in the tea field are also proposed. This review serves as a valuable resource for researchers aiming to understand the application and development of NIRS technology in the tea field. It offers insights to facilitate real-time tea quality monitoring and ultimately achieve intelligent quality control.

Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods

Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of traditional chemometrics and deep learning methods in identifying and quantifying adulterated CAO were compared from a statistical perspective, and no significant difference were founded in the identification of CAO at different levels of adulteration. The recognition rate of pure and adulterated CAO was 100 %, but there were misclassifications among different adulterated CAOs. The deep learning models outperformed chemometrics methods in quantitative prediction of adulteration level, with RP2, RMSEP, and RPD of the optimal ConvLSTM model achieved 0.999, 0.9 % and 31.5, respectively. The classifiers and models developed in this study based on deep learning have wide applicability and reliability, and provide a fast and accurate method for adulteration detection in CAO.

Comprehensive assessment of matcha qualities and visualization of constituents using hyperspectral imaging technology

Matcha, made from different tea leaves as raw material, exhibits diverse aromas and flavors. Therefore, there is an urgent need for a rapid, non-destructive method to assess the quality of matcha to ensure that these different characteristics are accurately assessed without compromising the integrity of the product. In this study, hyperspectral imaging technology (HSI) combined with machine learning methods enabled the first visual in situ assessment of matcha quality. The physicochemical contents of matcha were determined chemically. Qualitative and quantitative detection models for different types and grades were developed using HSI (containing Vis-NIR and NIR band). The results showed that hyperspectral data in the Vis-NIR were better than in the NIR band. The accuracy of XGBoost in modelling the classification of matcha grades reached 98.10 %. After feature selection using the random forest (RF) method, partial least squares regression (PLSR) was built to predicted the quality of matcha, which showed high prediction accuracy (test set Rp2 > 0.95). The model uses HSI to visually visualize spatial variations in constitutions (catechins, free amino acids, caffeine, soluble proteins, and soluble sugars) to show compositional differences between different types of matcha, providing a rapid non-destructive method for comprehensive assessment of matcha quality.

The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades

During food quality control, NIR technology enables the rapid and non-destructive determination of the typical quality characteristics of food categories, their origin, and the detection of potential counterfeits. Over the past 20 years, the NIR results for a variety of food groups-including meat and meat products, milk and milk products, baked goods, pasta, honey, vegetables, fruits, and luxury items like coffee, tea, and chocolate-have been compiled. This review aims to give a broad overview of the NIRS processes that have been used thus far to assist researchers employing non-destructive techniques in comparing their findings with earlier data and determining new research directions.

Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach

As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.

Rapid discrimination of quality grade of black tea based on near-infrared spectroscopy (NIRS), electronic nose (E-nose) and data fusion

For the manufacturing and sale of tea, rapid discrimination of overall quality grade is of great importance. However, present evaluation methods are time-consuming and labor-intensive. This study investigated the feasibility of combining advantages of near-infrared spectroscopy (NIRS) and electronic nose (E-nose) to assess the tea quality. We found that NIRS and E-nose models effectively identify taste and aroma quality grades, with the highest accuracies of 99.63% and 97.00%, respectively, by comparing different principal component numbers and classification algorithms. Additionally, the quantitative models based on NIRS predicted the contents of key substances. Based on this, NIRS and E-nose data were fused in the feature-level to build the overall quality evaluation model, achieving accuracies of 98.13%, 96.63% and 97.75% by support vector machine, K-nearest neighbors, and artificial neural network, respectively. This study reveals that the integration of NIRS and E-nose presents a novel and effective approach for rapidly identifying tea quality.

First ATR-FTIR Characterization of Black, Green and White Teas (Camellia sinensis) from European Tea Gardens: A PCA Analysis to Differentiate Leaves from the In-Cup Infusion

ATR-FTIR (Attenuated Total Reflectance Fourier Transform InfraRed) spectroscopy, combined with chemometric, represents a rapid and reliable approach to obtain information about the macromolecular composition of food and plant materials. With a single measurement, the chemical fingerprint of the analyzed sample is rapidly obtained. Hence, this technique was used for investigating 13 differently processed tea leaves (green, black and white) all grown and processed in European tea gardens, and their vacuum-dried tea brews, prepared using both hot and cold water, to observe how the components differ from tea leaf to the in-cup infusion. Spectra were collected in the 1800-600 cm-1 region and were submitted to Principal Component Analysis (PCA). The comparison of the spectral profiles of leaves and hot and cold infusions of tea from the same country, emphasizes how they differ in relation to the different spectral regions. Differences were also noted among the different countries. Furthermore, the changes observed (e.g., at ~1340 cm-1) due to catechin content, confirm the antioxidant properties of these teas. Overall, this experimental approach could be relevant for rapid analysis of various tea types and could pave the way for the industrial discrimination of teas and of their health properties without the need of time-consuming, lab chemical assays.

Comprehensive applications of metabolomics on tea science and technology: Opportunities, hurdles, and perspectives

With the development of metabolomics analytical techniques, relevant studies have increased in recent decades. The procedures of metabolomics analysis mainly include sample preparation, data acquisition and pre-processing, multivariate statistical analysis, as well as maker compounds' identification. In the present review, we summarized the published articles of tea metabolomics regarding different analytical tools, such as mass spectrometry, nuclear magnetic resonance, ultraviolet-visible spectrometry, and Fourier transform infrared spectrometry. The metabolite variation of fresh tea leaves with different treatments, such as biotic/abiotic stress, horticultural measures, and nutritional supplies was reviewed. Furthermore, the changes of chemical composition of processed tea samples under different processing technologies were also profiled. Since the identification of critical or marker metabolites is a complicated task, we also discussed the procedure of metabolite identification to clarify the importance of omics data analysis. The present review provides a workflow diagram for tea metabolomics research and also the perspectives of related studies in the future.

Digital Prediction of the Purchase Price of Fresh Tea Leaves of Enshi Yulu Based on Near-Infrared Spectroscopy Combined with Multivariate Analysis

In this study, near-infrared spectroscopy (NIRS) combined with a variety of chemometrics methods was used to establish a fast and non-destructive prediction model for the purchase price of fresh tea leaves. Firstly, a paired t-test was conducted on the quality index (QI) of seven quality grade fresh tea samples, all of which showed statistical significance (p < 0.05). Further, there was a good linear relationship between the QI, quality grades, and purchase price of fresh tea samples, with the determination coefficient being greater than 0.99. Then, the original near-infrared spectra of fresh tea samples were obtained and preprocessed, with the combination (standard normal variable (SNV) + second derivative (SD)) as the optimal preprocessing method. Four spectral intervals closely related to fresh tea prices were screened using the synergy interval partial least squares (si-PLS), namely 4377.62 cm-1-4751.74 cm-1, 4755.63 cm-1-5129.75 cm-1, 6262.70 cm-1-6633.93 cm-1, and 7386 cm-1-7756.32 cm-1, respectively. The genetic algorithm (GA) was applied to accurately extract 70 and 33 feature spectral data points from the whole denoised spectral data (DSD) and the four characteristic spectral intervals data (FSD), respectively. Principal component analysis (PCA) was applied, respectively, on the data points selected, and the cumulative contribution rates of the first three PCs were 99.856% and 99.852%. Finally, the back propagation artificial neural (BP-ANN) model with a 3-5-1 structure was calibrated with the first three PCs. When the transfer function was logistic, the best results were obtained (Rp2 = 0.985, RMSEP = 6.732 RMB/kg) by 33 feature spectral data points. The detection effect of the best BP-ANN model by 14 external samples were R2 = 0.987 and RMSEP = 6.670 RMB/kg. The results of this study have achieved real-time, non-destructive, and accurate evaluation and digital display of purchase prices of fresh tea samples by using NIRS technology.

Non-destructive prediction of colour and water-related properties of frozen/thawed beef meat by Raman spectroscopy coupled multivariate calibration

Freeze-thaw accelerated the colour deterioration of beef with the increase of colour b* and the decrease of colour a* values (P < 0.05). The maximum exudate loss reached 22 % after the seventh freeze-thaw. A strong correlation between the transversal relaxation time T₂₁ and thawing loss may mean that T₂₁ water contributed to the exudate loss during freeze-thaw. Afterwards, competitive adaptive reweighted sampling-partial least square (CARS-PLS) has the best prediction in thawing loss of frozen/thawed beef with correlation coefficients of prediction (R_p) of 0.971, and root mean square error of prediction (RMSEP) of 1.436. Besides, Uninformative variable elimination-partial least squares (UVE-PLS) showed good prediction effects on colour values (R_p = 0.932 - 0.994) and water content (R_p = 0.928, RMSEP = 0.582) of frozen/thawed beef. Therefore, this work demonstrated that Raman spectroscopy coupled with multivariate calibration has a good ability for non-destructive prediction in colour and water-related properties of frozen/thawed beef.

NIR Spectrometric Approach for Geographical Origin Identification and Taste Related Compounds Content Prediction of Lushan Yunwu Tea

Lushan Yunwu Tea is one of a unique Chinese tea series, and total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio models (TP/FAA) represent its most important taste-related indicators. In this work, a feasibility study was proposed to simultaneously predict the authenticity identification and taste-related indicators of Lushan Yunwu tea, using near-infrared spectroscopy combined with multivariate analysis. Different waveband selections and spectral pre-processing methods were compared during the discriminant analysis (DA) and partial least squares (PLS) model-building process. The DA model achieved optimal performance in distinguishing Lushan Yunwu tea from other non-Lushan Yunwu teas, with a correct classification rate of up to 100%. The synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS) algorithms showed considerable advantages in improving the prediction performance of TP, FAA, and TP/FAA. The siPLS algorithms achieved the best prediction results for TP (R_P = 0.9407, RPD = 3.00), FAA (R_P = 0.9110, RPD = 2.21) and TP/FAA (R_P = 0.9377, RPD = 2.90). These results indicated that NIR spectroscopy was a useful and low-cost tool by which to offer definitive quantitative and qualitative analysis for Lushan Yunwu tea.

Identification of multiple raisins by feature fusion combined with NIR spectroscopy

Varieties of raisins are diverse, and different varieties have different nutritional properties and commercial value. In this paper, we propose a method to identify different varieties of raisins by combining near-infrared (NIR) spectroscopy and machine learning algorithms. The direct averaging of the spectra taken for each sample may reduce the experimental data and affect the extraction of spectral features, thus limiting the classification results, due to the different substances of grape skins and flesh. Therefore, this experiment proposes a method to fuse the spectral features of pulp and peel. In this experiment, principal component analysis (PCA) was used to extract baseline corrected features, and linear models of k-nearest neighbor (KNN) and linear discriminant analysis (LDA) and nonlinear models of back propagation (BP), support vector machine with genetic algorithm (GA-SVM), grid search-support vector machine (GS-SVM) and particle swarm optimization with support vector machine (PSO- SVM) coupling were used to classify. This paper compared the results of four experiments using only skin spectrum, only flesh spectrum, average spectrum of skin and flesh, and their spectral feature fusion. The experimental results showed that the accuracy and Macro-F1 score after spectral feature fusion were higher than the other three experiments, and GS-SVM had the highest accuracy and Macro-F1 score of 94.44%. The results showed that feature fusion can improve the performance of both linear and nonlinear models. This may provide a new strategy for acquiring spectral data and improving model performance in the future. The code is available at https://github.com/L-ain/Source.

Rapid identification of the green tea geographical origin and processing month based on near-infrared hyperspectral imaging combined with chemometrics

The geographical origin and processing month of green tea greatly affect its economic value and consumer acceptance. This study investigated the feasibility of combining near-infrared hyperspectral imaging (NIR-HSI) with chemometrics for the identification of green tea. Tea samples produced in three regions of Chongqing (southeastern Chongqing, northeastern Chongqing, and western Chongqing) for four months (from May to August 2020) were collected. Principal component analysis (PCA) was used to reduce data dimensionality and visualize the clustering of samples in different categories. Linear partial least squares-discriminant analysis (PLS-DA) and nonlinear support vector machine (SVM) algorithms were used to develop discriminant models. The PCA-SVM models based on the first four and first five principal components (PCs) achieved the best accuracies of 97.5% and 95% in the prediction set for geographical origin and processing month of green tea, respectively. This study demonstrated the feasibility of HSI in the identification of green tea species, providing a rapid and nondestructive method for the evaluation and control of green tea quality.

The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology

Tieguanyin is one of the top ten most popular teas and the representative of oolong tea in China. In this study, a rapid and non-destructive method is developed to detect adulterated tea and its degree. Benshan is used as the adulterated tea, which is about 0%, 10%, 20%, 30%, 40%, and 50% of the total weight of tea samples, mixed with Tieguanyin. Taking the fluorescence spectra from 475 to 1000 nm, we then established the 2-and 6-class discriminant models. The 2-class discriminant models had the best evaluation index when using SG-CARS-SVM, which can reach a 100.00% overall accuracy, 100.00% specificity, 100% sensitivity, and the least time was 1.2088 s, which can accurately identify pure and adulterated tea; among the 6-class discriminant models (0% (pure Tieguanyin), 10, 20, 30, 40, and 50%), with the increasing difficulty of adulteration, SNV-RF-SVM had the best evaluation index, the highest overall accuracy reached 94.27%, and the least time was 0.00698 s. In general, the results indicated that the two classification methods explored in this study can obtain the best effects. The fluorescence hyperspectral technology has a broad scope and feasibility in the non-destructive detection of adulterated tea and other fields.

Determination of 35 Free Amino Acids in Tea Using Ultra-Performance Liquid Chromatography Coupled With Quadrupole Time-of-Flight Mass Spectrometry

The purpose of this research was to develop a simple, sensitive, and accurate method for simultaneous determination of 35 free amino acids using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). Tea samples were extracted with boiling water bath, and then separated by XBridge BEH Amide column by gradient elution. The exact mass and MS/MS spectra of the target compound was detected under the TOF–MS and Information dependent acquisition (IDA)–MS/MS mode. The results demonstrated good linearity (R² > 0.9980) in the range of 0.5–1,000 ng/mL. The limits of detection (LODs) were 0.13–25.00 mg/kg and the limits of quantitation (LOQs) were 0.25–50.00 mg/kg. The recovery rate ranged from 70.1 to 105.1% with relative standard deviations (RSDs) <11% (n = 6). This research provides a targeted strategy for developing an analysis method for amino acids in tea.

Data Science in the Food Industry

Food safety is one of the main challenges of the agri-food industry that is expected to be addressed in the current environment of tremendous technological progress, where consumers' lifestyles and preferences are in a constant state of flux. Food chain transparency and trust are drivers for food integrity control and for improvements in efficiency and economic growth. Similarly, the circular economy has great potential to reduce wastage and improve the efficiency of operations in multi-stakeholder ecosystems. Throughout the food chain cycle, all food commodities are exposed to multiple hazards, resulting in a high likelihood of contamination. Such biological or chemical hazards may be naturally present at any stage of food production, whether accidentally introduced or fraudulently imposed, risking consumers' health and their faith in the food industry. Nowadays, a massive amount of data is generated, not only from the next generation of food safety monitoring systems and along the entire food chain (primary production included) but also from the Internet of things, media, and other devices. These data should be used for the benefit of society, and the scientific field of data science should be a vital player in helping to make this possible.

Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm

In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time. The analysis showed that the PLSR with FPA method achieved better predictive results than the PLSR with full spectrum (PLSR-full). The developed simplified model using on FPA based on 12 latent variables and 89 selected wavelengths produced R-squared (R²) value and root mean squared error (RMSE) of 0.9542, 0.8794 and 0.2045, 0.3219 for calibration and prediction, respectively. For PLSR-full, the R² values of 0.9068, 0.8412 and RMSEs of 0.2916, 0.3693, were achieved for calibration and prediction. Also, the optimized model using FPA outperformed other wavelengths selection methods considered in this study. The obtained results indicated the feasibility of FPA to improve the predictability of the PLSR and reduce the model complexity. The nonlinear regression models of support vector machine regression and Gaussian process regression (GPR) were further utilized to evaluate the superiority of using the FPA in the wavelength selection. The results demonstrated that utilizing the wavelength selection method of FPA and nonlinear regression model of GPR could improve the predictive performance.

Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors

Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer was used to record the spectrum of samples. A computer vision system was used to capture the image of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of different tea samples were used to describe their appearance. A feature-level fusion strategy was used to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the softmax classification model. The histograms of the combined shape features indicated that when the softmax classification model was used, the classification accuracy was also higher than ANN. The fusion of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a single-sensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used). This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying black tea samples of different grades.

Development of ANN models based on combined UV-vis-NIR spectra for rapid quantification of physical and chemical properties of industrial hemp extracts

OBJECTIVES

The aim of this study was to develop artificial neural network (ANNs) models for prediction of physical (total dissolved solids, extraction yield) and chemical (total polyphenolic content, antioxidant activity) properties of industrial hemp extracts, prepared by two different extraction methods (solid-liquid extraction and microwave-assisted extraction) based on combined UV-VIS-NIR spectra. Spectral data were gathered for 46 samples per extraction method.

RESULTS

The PCA analysis ensured efficient separation of the samples based on the amount of ethanol in extraction solvent using NIR spectra for both conventional and microwave-assisted extraction.

CONCLUSIONS

Results showed that reliable ANN models (R² >0.7000) for describing physical, chemical, and simultaneously physical and chemical characteristics can be developed based on combined UV-VIS-NIR spectra of industrial hemp extracts without spectra pre-processing.

Determination of the concentration of total phenolic compounds in aged cachaça using two-dimensional fluorescence and mid-infrared spectroscopy

In this work, we investigate alternative quantification methods based on optical spectroscopy and variable selection approach, using as a case study the aging process of cachaça. The cachaça was aged in an Amburana barrel and the samples were analyzed with a traditional analytical method for total phenolic quantification (Folin-Ciocalteu), with 2-D fluorescence and infrared spectroscopy. We applied a methodology based on Ant Colony Optimization to optimize variable selection and model fitting to predict total phenolic content. Our results demonstrated that fluorescence spectroscopy was more sensitive than IR in the quantification of total phenolic compounds for both global and local models, presenting good results (R² superior to 0.979), significantly reducing the number of original variable (1995) for only 4 pairs of Ex/Em. Variable selection combined with spectroscopy reveals potential because this technique eliminates the need for sample preparation and allows the construction of customized sensors for application in the food industry.

Determination of 4 psychoactive substances in tea using ultra high performance liquid chromatography combined with the quadrupole time-of-flight mass spectrometry

In this study, a method for the qualification and quantification of 4 psychoactive substances in tea using ultra high performance liquid chromatography coupled with the quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) has been developed. Tea samples were extracted by a 50% (v/v) methanol-water solution, and then separated by an ACQUITY UPLC BEH Shield RP18 column using a binary solvent system by gradient elution. The analytes were determined by Q-TOF/MS in TOFMS and information-dependent acquisition (IDA)-MS/MS mode. The results showed that the mass accuracy error of the 4 psychoactive substances were lower than 5.0 × 10-6, and a good linear relationship was observed in the range of 0.5-500 μg L-1 and correlation coefficient was higher than 0.9990. The LOD was in the range of 0.005-0.020 mg kg-1 and the LOQ was in the range of 0.010-0.040 mg kg-1. The recovery of the method was in range of 80.14-93.25% with spike levels of 0.010-0.400 mg kg-1, and relative standard deviations were lower than 10%. The method was simple, specific and reliable. It has been successfully used for the detection of 4 psychoactive substances in tea samples.

Quantitative Analysis and Discrimination of Partially Fermented Teas from Different Origins Using Visible/Near-Infrared Spectroscopy Coupled with Chemometrics

Partially fermented tea such as oolong tea is a popular drink worldwide. Preventing fraud in partially fermented tea has become imperative to protect producers and consumers from possible economic losses. Visible/near-infrared (VIS/NIR) spectroscopy integrated with stepwise multiple linear regression (SMLR) and support vector machine (SVM) methods were used for origin discrimination of partially fermented tea from Vietnam, China, and different production areas in Taiwan using the full visible NIR wavelength range (400-2498 nm). The SMLR and SVM models achieved satisfactory results. Models using data from chemical constituents' specific wavelength ranges exhibited a high correlation with the spectra of teas, and the SMLR analyses improved discrimination of the types and origins when performing SVM analyses. The SVM models' identification accuracies regarding different production areas in Taiwan were effectively enhanced using a combination of the data within specific wavelength ranges of several constituents. The accuracy rates were 100% for the discrimination of types, origins, and production areas of tea in the calibration and prediction sets using the optimal SVM models integrated with the specific wavelength ranges of the constituents in tea. NIR could be an effective tool for rapid, nondestructive, and accurate inspection of types, origins, and production areas of teas.

Using near-infrared hyperspectral imaging with multiple decision tree methods to delineate black tea quality

The evaluation of tea quality tended to be subjective and empirical by human panel tests currently. A convenient analytical approach without human involvement was developed for the quality assessment of tea with great significance. In this study, near-infrared hyperspectral imaging (HSI) combined with multiple decision tree methods was utilized as an objective analysis tool for delineating black tea quality and rank. Data fusion that integrated texture features based on gray-level co-occurrence matrix (GLCM) and short-wave near-infrared spectral features were as the target characteristic information for modeling. Three different types of supervised decision tree algorithms (fine tree, medium tree, and coarse tree) were proposed for the comparison of the modeling effect. The results indicated that the performance of models was enhanced by the multiple perception feature fusion. The fine tree model based on data fusion obtained the best predictive performance, and the correct classification rate (CCR) of evaluating black tea quality was 93.13% in the prediction process. This work demonstrated that HSI coupled with intelligence algorithms as a rapid and effective strategy could be successfully applied to accurately identify the rank quality of black tea.

Rapid detection of quality index of postharvest fresh tea leaves using hyperspectral imaging

BACKGROUND

The quality of fresh tea leaves after harvest determines, to some extent, the quality and price of commercial tea. A fast and accurate method to evaluate the quality of fresh tea leaves is required.

RESULTS

In this study, the potential of hyperspectral imaging in the range of 328-1115 nm for the rapid prediction of moisture, total nitrogen, crude fiber contents, and quality index value was investigated. Ninety samples of eight tea-leaf varieties and two picking standards were tested. Quantitative partial least squares regression (PLSR) models were established using a full spectrum, whereas multiple linear regression (MLR) models were developed using characteristic wavelengths selected by a successive projections algorithm (SPA) and competitive adaptive reweighted sampling. The results showed that the optimal SPA-MLR models for moisture, total nitrogen, crude fiber contents, and quality index value yielded optimal performance with coefficients of determination for prediction (R² p) of 0.9357, 0.8543, 0.8188, 0.9168; root mean square error of 0.3437, 0.1097, 0.3795, 1.0358; and residual prediction deviation of 4.00, 2.56, 2.31, and 3.51, respectively.

CONCLUSION

The results suggested that the hyperspectral imaging technique coupled with chemometrics was a promising tool for the rapid and nondestructive measurement of tea-leaf quality, and had the potential to develop multispectral imaging systems for future online detection of tea-leaf quality. © 2020 Society of Chemical Industry.

Theae nigrae folium: Comparing the analytical performance of benchtop and handheld near-infrared spectrometers

We investigated caffeine and l-theanine, quality characteristics for camellia sinensis, in milled and ground black tea samples with near-infrared (NIR) spectroscopy giving a direct comparison between the performances of benchtop and handheld NIR spectrometers. The constructed partial least squares regression (PLSR) models for all spectrometers were validated by test-set-validation and according to the obtained root mean square errors of prediction (RMSEP). The performances of the spectrometers were as follows: The benchtop spectrometer NIRFlex N-500 (Büchi, Flawil, Switzerland) showed the best results for milled samples with a RMSEP of 0.14% for caffeine and 0.12% for l-theanine. For the ground samples, a RMSEP of 0.17% for caffeine and 0.12% for l-theanine was gained. While the handheld spectrometers MicroNIR 2200 (Viavi Solutions (former: JDS Uniphase Corporation), Milpitas, USA) and the microPHAZIR (Thermo Fisher Scientific, Waltham, USA) both provided good results for the prediction of caffeine in milled samples (RMSEP of 0.22% and 0.26%), only the microPHAZIR was able to satisfactorily determine the caffeine content in ground samples (RMSEP of 0.28%). The investigation of l-theanine with handheld spectrometers did not lead to convincing results, since R² was 0.75 for milled samples while ground samples could not be calculated. Decisive differences were concluded in how different NIR instruments capture the chemical information on caffeine vs. l-theanine. The handheld spectrometers manifested limited applicability to l-theanine. Deeper insight was obtained through the detailed NIR band assignments of caffeine and l-theanine derived from quantum mechanical simulation. Narrow working spectral region of handhelds omits the characteristic absorption bands of l-theanine. Therefore, information on l-theanine content measured by the evaluated miniaturized spectrometers is insufficient to enable its effective quantification. In contrast, the most characteristic NIR absorption of caffeine matches the working spectral regions of the handheld NIR spectrometers, hence their performance is comparable with the benchtop device.

Discrimination of white teas produced from fresh leaves with different maturity by near-infrared spectroscopy

White tea is a special tea product with increasing market demand. The assessment of white tea quality is mainly based on panel sensory by sensory evaluation experts, which is time costly and is limited by many uncertainties. This study established a rapid and accurate method for classification of white teas produced from buds and young leaves and that produced from mature leaves and shoots using near-infrared spectroscopy (NIR). Back propagation neural network modelling and support vector machine (SVM) modelling were compared with six pre-processing methods. The best performance was provided by SVM with particle swarm optimization combined with Savitzky-Golay filter pre-processing method, achieving the accuracy of 98.92% in test samples. The NIR-related chemical compounds of two categories of white teas produced from fresh leaves with different maturity were analyzed, including catechins, alkaloids, amino acids and flavonol glycosides. Compared with chemical component concentration, NIR absorbance had a distinct advantage in quick classification of white teas based on the principal components analysis. In addition, the sensory characteristics of two categories white teas produced from fresh leaves with different maturity were also assessed by panelist. The result showed that characteristics of "umami-like" and "smooth" were more likely present in white teas produced from buds and young leaves, while "woody" and "coarse" characteristics were usually present in white teas produced from mature leaves and shoots. Thus, NIR technique is a rapid and reliable method for discrimination of white teas produced from fresh leaves with different maturity, and is a potential method to discriminate sensory characteristics of white teas.

Paenibacillus polymyxa biofertilizer application in a tea plantation reduces soil N2O by changing denitrifier communities

Increasing the use of nitrogen fertilizers in tea orchards has led to intense nitrous oxide (N₂O) emissions. Foliar application of Paenibacillus polymyxa biofertilizer has been proven to be beneficial for organic tea production. In this study, tea yield and quality were significantly improved after application of P. polymyxa biofertilizer compared with the control but were not significantly different from chemical fertilizer treatments. However, the average N₂O fluxes in tea fields treated with chemical fertilizers and biofertilizers (225 kg N·ha^-1·year^-1 for both) were 50.6-973.7 and 0.6-29.1 times higher than those in the control treatment, respectively. Pot experiments conducted to explore the mechanism of N₂O reduction induced by P. polymyxa biofertilizer showed that applying P. polymyxa in addition to urea could reduce N₂O fluxes by 36.5%-73.1%. Quantitative PCR analysis suggested that a significant increase in the quantity of nirK and nosZ genes was linked to the reduction of N₂O, and high-throughput sequencing of nosZ revealed active and potentially efficient denitrifiers in different treatments. Our findings suggest that P. polymyxa biofertilizer is in line with the requirements of modern agriculture, which aims to increase product yield and quality while reducing negative environmental impacts.

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R² = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.