Rapid detection of ash content in black tea using a homemade miniature near-infrared spectroscopy.

Ash is a testing index with both health inspection value and quality decision value, and it is an essential detection item in the import and export trade of tea. To realize the rapid and effective quantitative analysis of ash content in tea, this study proposed the use of a homemade miniature near-infrared (NIR) spectroscopy combined with multivariate analysis for the rapid detection of ash content in black tea. First, NIR data of black tea samples from different countries were acquired and optimized by the spectral preprocessing method. Then, the optimized pre-processed spectral data were used as features, and four feature wavelength selection algorithms, such as competitive adaptive reweighted sampling, iteratively retaining informative variables (IRIV), variable combination population analysis (VCPA)-IRIV, and interval variable iterative space shrinkage approach (IVISSA), were utilized to optimize the feature spectra. Finally, the support vector machine regression (SVR) algorithm was employed to construct the quantitative models of ash content in black tea by combining the optimal wavelengths obtained from the four feature selection methods mentioned above. The experimental results showed that the IVISSA-SVR model had the best performance, with correlation coefficient (Rp), root mean square errors of prediction (RMSEP), and relative prediction deviation (RPD) of 0.9546, 0.0192, and 5.59 for the prediction set, respectively. The results demonstrate that a miniature NIR sensing system combined with chemometrics as an effective analytical tool can realize the rapid detection of ash content in black tea.

Combining Straw Mulch with Nitrogen Fertilizer Improves Soil and Plant Physio-Chemical Attributes, Physiology, and Yield of Maize in the Semi-Arid Region of China.

Mulching and nitrogen (N) fertilization are the main drivers for sustainable crop production. The sole use of nitrogen fertilizer threatened both the physiology and production of maize in rain-fed areas. Therefore, we proposed that wheat straw mulching with N fertilization would increase maize yield by improving soil fertility, physiology, and nitrogen use efficiency. A two-year field study evaluated the effects of CK (control), N (nitrogen application at 172 kg ha-1), HS (half wheat straw mulch, 2500 kg ha-1), HS+N (half wheat straw, 2500 kg ha-1 plus 172 kg N ha-1), FS (full wheat straw, 5000 kg ha-1), and FS+N (full wheat straw, 5000 kg ha-1 plus 172 kg N ha-1) on maize growth, physiology, and biochemistry. Compared with the control, the FS+N treatment resulted in the increase of 56% photosynthetic efficiency, 9.6% nitrogen use efficiency, 60% nitrogen uptake, 80% soluble sugar, 59% starches, 48% biomass, and 29% grain yield of maize. In addition, the FS+N regime increased 47%, 42%, and 106% of soil organic carbon and available P and N content in comparison with the control. Maize grain and biomass yields were positively correlated with N uptake, photosynthesis, soil organic carbon, and soil available N and P contents. Conclusively, the use of wheat straw at 5000 kg ha-1, along with 172 kg N ha-1, is a promising option for building a sustainable wheat-maize cropping system to achieve optimal crop yield and improved plant and soil health in a semi-arid region of China.

Regulating the Electronic Structure of Ni Sites in Ni(OH)2 by Ce Doping and Cu(OH)2 Coupling to Boost 5-Hydroxymethylfurfural Oxidation Performance.

Biomass is a sustainable and renewable resource that can be converted into valuable chemicals, reducing the demand for fossil energy. 5-Hydroxymethylfurfural (HMF), as an important biomass platform molecule, can be converted to high-value-added 2,5-furandicarboxylic acid (FDCA) via a green and renewable electrocatalytic oxidation route under mild reaction conditions, but efficient electrocatalysts are still lacking. Herein, we rationally fabricate a novel self-supported electrocatalyst of core-shell-structured copper hydroxide nanowires@cerium-doped nickel hydroxide nanosheets composite nanowires on a copper mesh (CuH_NWs@Ce:NiH_NSs/Cu) for electrocatalytically oxidizing HMF to FDCA. The integrated configuration of composite nanowires with rich interstitial spaces between them facilitates fast mass/electron transfer, improved conductivity, and complete exposure of active sites. The doping of Ce ions in nickel hydroxide nanosheets (NiH_NSs) and the coupling of copper hydroxide nanowires (CuH_NWs) regulate the electronic structure of the Ni active sites and optimize the adsorption strength of the active sites to the reactant, meanwhile promoting the generation of strong oxidation agents of Ni3+ species, thereby resulting in improved electrocatalytic activity. Consequently, the optimal CuH_NWs@Ce:NiH_NSs/Cu electrocatalyst is able to achieve a HMF conversion of 98.5% with a FDCA yield of 97.9% and a Faradaic efficiency of 98.0% at a low constant potential of 1.45 V versus reversible hydrogen electrode. Meanwhile, no activity attenuation can be found after 15 successive cycling tests. Such electrocatalytic performance suppresses most of the reported Cu-based and Ni-based electrocatalysts. This work highlights the importance of structure and doping engineering strategies for the rational fabrication of high-performance electrocatalysts for biomass upgrading.

Differential effects of petroleum-based and bio-based microplastics on anaerobic digestion: A review.

The number of plastics is increasing owing to the rapid development of the plastics industry. Microplastics (MPs) are formed during the use of both petroleum-based plastics and newly developed bio-based plastics. These MPs are inevitably released into the environment and are enriched in wastewater treatment plant sludge. Anaerobic digestion is a popular sludge stabilization method for wastewater treatment plants. Understanding the potential impacts of different MPs on anaerobic digestion is critical. This paper provides a comprehensive review of the mechanisms of petroleum-based MPs and bio-based MPs in anaerobic digestion methane production and compares their potential effects on biochemical pathways, key enzyme activities, and microbial communities. Finally, it identifies problems that must be solved in the future, proposes the focus of future research, and predicts the future development direction of the plastics industry.

Rapid Characterization of Black Tea Taste Quality Using Miniature NIR Spectroscopy and Electronic Tongue Sensors.

The taste of tea is one of the key indicators in the evaluation of its quality and is a key factor in its grading and market pricing. To objectively and digitally evaluate the taste quality of tea leaves, miniature near-infrared (NIR) spectroscopy and electronic tongue (ET) sensors are considered effective sensor signals for the characterization of the taste quality of tea leaves. This study used micro-NIR spectroscopy and ET sensors in combination with data fusion strategies and chemometric tools for the taste quality assessment and prediction of multiple grades of black tea. Using NIR features and ET sensor signals as fused information, the data optimization based on grey wolf optimization, ant colony optimization (ACO), particle swarm optimization, and non-dominated sorting genetic algorithm II were employed as modeling features, combined with support vector machine (SVM), extreme learning machine and K-nearest neighbor algorithm to build the classification models. The results obtained showed that the ACO-SVM model had the highest classification accuracy with a discriminant rate of 93.56%. The overall results reveal that it is feasible to qualitatively distinguish black tea grades and categories by NIR spectroscopy and ET techniques.

[Mineralization Characteristics of Soil Organic Carbon and Its Relationship with Organic Carbon Components in Artificial Robinia pseudoacacia Forest in Loess Hilly Region].

In order to explore the characteristics of organic carbon mineralization and the variation law of organic carbon components of an artificial forest in a loess hilly area, an artificial Robinia pseudoacacia forest restored for 13 years and the adjacent slope farmland were selected as the research objects, and indoor culture experiments under three different temperature treatments (15, 25, and 35℃) were carried out. The results indicated that the mineralization rate of soil organic carbon decreased sharply at first and then stabilized. The cumulative release of organic carbon increased rapidly in the initial stage of culture and gradually slowed in the later stage. Soil organic carbon mineralization in sloping farmland was more sensitive to temperature change, and its temperature sensitivity coefficient Q10 was 1.52, whereas that in R. pseudoacacia forest land was only 1.38. According to the fitting of the single reservoir first-order dynamic equation, the soil mineralization potential Cp of R. pseudoacacia forest land and slope farmland was between 2.02-4.32 g·kg-1 and 1.25-3.17 g·kg-1, respectively, that is, the mineralization potential of the R. pseudoacacia forest was higher. During the cultivation period, the content of various active organic carbon components decreased with time, and that in the R. pseudoacacia forest land was greater than that in the slope land. The cumulative carbon release of soil was significantly positively correlated with the contents of MBC and DOC (P<0.05), and Q10 (15-25℃) was negatively correlated with the contents of SOC, EOC, and SWC (P<0.05). These results could provide some reference for the study of soil carbon sequestration in loess hilly regions under climate change.

Assessing the determinants of teachers' job happiness in the private universities.

Teachers' job happiness in private universities is an important element for the healthy and orderly development of universities and an inevitable requirement for the construction of university faculty, and it has become a hot topic of research in the field of private higher education at present. However, there is still a lack of empirical studies on the factors influencing job happiness in private universities. This study constructs a theoretical model between professional identity, job competence, professional motivation, professional prospects, perceived fairness, job achievements and job happiness, and explores the specific drivers of teachers' job happiness in private universities based on empirical research. The results of the data analysis showed that professional identity, job competence, professional prospects, perceived fairness, job achievements, and professional motivation all had significant effects on teachers' job happiness, and the effects were decreasing. This study examined the effects of job happiness in practice in private universities, which helped private universities to enhance teachers' professional identity, strengthen organizational support for teacher development, promote teachers' teaching ability, improve job competence, and build a developmental teacher evaluation mechanism.

[Effects of Straw Returning and Fertilizer Application on Soil Nutrients and Winter Wheat Yield].

In order to explore the effects of straw returning combined with fertilizer on soil nutrients and winter wheat yield in the Guanzhong area, an experimental split plot design was utilized. The main plot consisted of no straw returning (S0) and straw returning (S). The sub-regions consisted of no fertilizer (WF), nitrogen fertilizer (NF), and nitrogen and phosphate fertilizer (NPF). Ecological stoichiometry was used to study the relationship between soil carbon, nitrogen, phosphorus content, and yield under straw returning combined with nitrogen and phosphorus fertilizer conditions. The results showed that straw and fertilization interactions had significant effects on soil organic carbon, total nitrogen, and total phosphorus contents in the surface layer (0-20 cm) (P<0.05). Compared with that in the S0WF treatment, the SNPF treatment significantly increased soil organic carbon and total nitrogen contents in the surface layer (0-20 cm) (P<0.05). The interaction between straw and year had significant effects on soil total nitrogen content in the surface layer (0-20 cm) (P<0.05). With the increase in straw returning time, the total nitrogen content of soil 0-20 cm under the SWF treatment was significantly higher than that under the S0WF treatment (P<0.05). Straw and fertilization and their interaction had no significant effects on organic carbon and total nitrogen contents in the 20-40 cm soil layer (P>0.05). Straw and straw interaction with fertilization significantly affected total P content in 20-40 cm soil (P<0.05). Compared with that in the SWF treatment, the SNPF treatment significantly increased the total phosphorus content in the 20-40 cm soil layer (P<0.05). Straw returning combined with chemical fertilizer also had a significant effect on soil stoichiometry. Compared with that in the S0WF treatment, the S0NPF treatment decreased soil C:N in the surface layer (0-20 cm) and increased soil C:P and N:P in the surface layer (0-20 cm). Compared with that in the SWF treatment, the SNF treatment reduced soil C:N in the surface layer (0-20 cm). Straw returning combined with chemical fertilizer also had a significant effect on winter wheat yield. In 2020 and 2021, the SNPF treatment increased production by 24.23% and 28.9%, respectively, compared with that of the S0WF treatment. Correlation analysis showed that yield was significantly positively correlated with C:N (P<0.05) and C:P (P<0.01). At the same time, total nitrogen and N:P were positively correlated with treatment years (P<0.001). In conclusion, straw returning and that combined with nitrogen and phosphate fertilizer (SNPF) can improve soil nutrient characteristics, change soil stoichiometric characteristics, and increase yield in the Guanzhong area. Therefore, the results of this study indicate that straw returning combined with nitrogen and phosphate fertilizer (SNPF) is an effective way to optimize regional farmland nutrient management and improve grain production capacity.

Landscape Ecological Risk Assessment Based on Land Use Change in the Yellow River Basin of Shaanxi, China.

The Yellow River Basin in Shaanxi (YRBS) has a relatively fragile ecological environment, with severe soil erosion and a high incidence of natural and geological disasters. In this study, a river basin landscape ecological risk assessment model was constructed using landscape ecology principles to investigate the temporal and spatial evolution, as well as the spatial autocorrelation characteristics of landscape ecological risks in the YRBS over a 20-year period. The main findings from the YRBS were that the land use types changed significantly over the span of 20 years, there was spatial heterogeneity of the landscape pattern, and the ecological risk value was positively correlated. The threat of landscape ecological risks in YRBS is easing, but the pressure on the ecological environment is considerable. This study provides theoretical support administrative policies for future ecological risk assessment and protection, restoration measures, and control in the Yellow River Basin of Shaanxi Province.

Redeploy manure resources to enhance the agro-pastoral cycle.

Returning manure to the land is a critical link in the internal cycle of agricultural systems, but excess manure leads to water eutrophication. The traditional manure re-use method brings pathogenic microorganisms, heavy metals, antibiotic resistance genes (ARGs), insect eggs, and other contaminants into the soil, posing a great threat to the ecological environment and human health. Clarifying the spatial distribution patterns of manure nutrient supply and farmland nutrient demand can help guide a more efficient and harmless way to return manure to farmland. This work counted data on cultivation and breeding in 356 cities on the Chinese mainland from 2015 to 2019 and calculated the livestock breeding volume (LB), total environmental capacity (C), and remaining environmental capacity (RC) accordingly. The Spatial Autocorrelation Model (SAC) was used to analyze the distribution patterns of the three. Data results show that China currently has the potential to double LB, but most cities in the west have excess manure due to the mismatched distribution of LB and C. The hot spot analysis results demonstrate the priority/general areas of manure management and the export/import areas of manure resources. The results of the outlier analysis show that some cities located at the boundary of RC Cold/Hot spot areas (e.g., Chengdu City) can perform resource replacement nearby to relieve local environmental pressure. This study analyzes the potential and realistic resistance to utilizing manure as an organic nutrient resource and provides a reference for developing manure management links.

Soil microbial nitrogen-cycling gene abundances in response to crop diversification: A meta-analysis.

Single planting structure has a significant impact on the maintenance of nitrogen in managed ecosystems. Although the effect of crop diversity on soil nitrogen-cycling microbes is mainly related to the influence of environmental factors, there is a lack of quantitative research. This study aims to determine the effect of diversified cropping mode on the abundance of functional genes in the soil nitrogen cycle based on the quantitative integration of a meta-analysis database containing 189 observation data pairs. The results show that the soil nifH (nitrogenase coding gene), nirS and nirK (nitrite reductase coding gene), and narG (nitrate reductase coding gene) abundances are positively affected by the diversity of plant species, whereas the amoA (ammonia monooxygenase coding gene) and nosZ (nitrous oxide reductase coding gene) show no response. Diversification duration and ecosystem type are important factors that regulate soil nitrogen fixation and nitrification gene abundances. Denitrification genes are mainly affected by categorical variables such as the planting pattern, soil layer, application species, duration, and soil texture. Among them, the long-term continuous diversification is mainly manifested in the reduction of soil nifH and increase of nirK abundances. Soil organic carbon and nitrogen linearly affect the responses of nifH, amoA, nirS, and nirK. Therefore, to maintain the soil ecological function, diversity of planting patterns needs to be applied flexibly by regulating the abundance of nitrogen-cycling genes. Our study draws conclusions in order to provide theoretical references for the sustainability of nitrogen and improvement of management measures in the process of terrestrial managed ecosystem diversification.

[Changes in Soil Microbial Carbon-Degrading Enzymes and Their Relationships with Carbon Pool Components During the Restoration Process of Robinia pseudoacacia].

To reveal the change in the characteristics of soil microbial C-degrading enzyme activities and the response to the components of C during the restoration process of Robinia pseudoacacia forests in the Loess Plateau, the components of the soil C pool, C-degrading enzyme activities, and microbial metabolic entropy of R. pseudoacacia in different restoration stages were studied, and the response relationship between C-degrading enzymes and soil C components was explored. The results showed that the microbial respiration (MR) first increased and then decreased with the restored years. We found that the microbial metabolic entropy (qCO2) decreased significantly with the restored years, but the microbial entropy (qMB) increased. Soil C-degrading enzymes increased significantly in the early-stage restoration of R. pseudoacacia; however, oxidizing enzymes (PO and PER) and cellobiohydrolase (CBH) decreased in the late stage of restoration. The soil organic C and recalcitrant organic C increased significantly with the restored years; however, there was no significant difference for the labile organic C. Correlation analysis and the partial least squares-path model (PLS-PM) showed that soil C-degrading enzymes and C components were significantly correlated with microbial respiration and entropy (qCO2 and qMB), respectively. The hydrolytic enzyme (BG+CBH) was significantly positively correlated with SOC, microbial biomass C, qMB, and recalcitrant and labile organic C. The oxidizing enzyme (PO+PER) was significantly positively correlated with the soil clay and qCO2. In addition, the recalcitrant organic C was the key driver of soil microbial metabolism affected by vegetation restoration. Overall, the ecosystem of R. pseudoacacia plantations would gradually stabilize with the increase in restored years and significantly increase the sequestration effect of soil C. These results will be helpful to understand the transformation rule and regulation mechanism of the soil C pool in vulnerable habitats and provide scientific basis for the restoration and management of vegetation in the Loess Plateau.

Multi-variable selection strategy based on near-infrared spectra for the rapid description of dianhong black tea quality.

The main objectives of the study are to understand and explore critical feature wavelengths of the obtained near-infrared (NIR) data relating to dianhong black tea quality categories, we propose a multi-variable selection strategy based on the variable space optimization from big to small which is the kernel idea of a variable combination of the improved genetic algorithm (IGA) and particle swarm optimization (PSO) in this study. A rapid description based on the NIR technology is implemented to assess black tea tenderness and rankings. First, 700 standard samples from dianhong black tea of seven quality classes are scanned using a NIR system. The raw spectra acquired are preprocessed by Savitzky-Golay (SG) filtering coupled with standard normal variate transformation (SNV). Then, the multi-variable selection algorithm (IGA-PSO) is applied to compare with the single method (the IGA and PSO) and search the optimal characteristic wavelengths. Finally, the identification models are developed using a decision tree (DT), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM) based on different kernel functions combined with the effective features from the above variables screening paths for the discrimination of black tea quality. The results show that the IGA-PSO-SVM model with a radial basis function achieves the best predictive results with the correct discriminant rate (CDR) of 95.28% based on selected four characteristic variables in the prediction process. The overall results demonstrate that NIR combined with a multi-variable selection method can constitute a potential tool to understand the most important features involved in the evaluation of dianhong black tea quality helping the instrument manufacturers to achieve the development of low-cost and handheld NIR sensors.

Effects of sodium selenite spray on apple production, quality, and sucrose metabolism-related enzyme activity.

Selenium is an essential trace element that improves fruit quality and nutritional value. However, the effect of sodium selenite on apple quality and its relative sucrose metabolism activity remains unclear. In this study, we investigated the roles of selenite spraying, in improving Fuji apple quality and sucrose metabolism-related enzyme activity. Results showed that foliar spraying of sodium selenite significantly (P < 0.05) increased apple fruit yield and internal quality, but no significant effects on external quality. The apple yield, vitamin C content, sugar-acid ratio and total soluble sugar increased 4.4% to 11.7%, 4.68% to 20.86%, 3.07% to 31.57%, and 4.53% to 18.89%, respectively. Se content is 9.5-fold compared to the control. Significant correlations were observed between neutral invertase, sucrose synthase activity and sucrose phosphate synthase enzymes, and sucrose phosphate synthase enzyme was most crucial. Spraying sodium selenite of 100-150 mg/L could be appropriate for improving Fuji apple yield and quality.

Evaluation of Dianhong black tea quality using near-infrared hyperspectral imaging technology.

BACKGROUND: Tea (Camellia sinensis L) is a highly nutritious beverage with commercial value globally. However, it is at risk of economic fraud. This study aims to develop a powerful evaluation method to distinguish Chinese official Dianhong tea from various other categories, employing hyperspectral imaging (HSI) technology and chemometric algorithms. RESULTS: Two matrix statistical algorithms encompassing a gray-level co-occurrence matrix (GLCM) and a gradient co-occurrence matrix (GLGCM) are used to extract HSI texture data. Three novel spectral variable screening methods are utilized to select wavenumbers of near-infrared (NIR) spectra: iteratively retaining informative variables (IRIV), interval random frog, and variable combination population analysis. Feature fusion of image texture characteristics and spectra data are the eigenvectors for model building. Authentic classification models are constructed using the extreme learning machine approach and the least squares support vector machine (LSSVM) approach, coupling them with features from wavelength extraction techniques for assessing the quality of Dianhong black tea. The results demonstrate that the LSSVM model using fused data (IRIV + GLGCM) provides the best results and achieves a predictive precision of 99.57%. CONCLUSION: This study confirms that HSI coupled with LSSVM is effective in differentiating authentic Dianhong black tea samples. © 2020 Society of Chemical Industry.

[Effects of Nitrogen Fertilizer and Straw Returning Methods on N2O Emissions in Wheat-Maize Rotational Soils].

In order to explore the impacts of nitrogen fertilizer and straw returning methods on N2O emissions, a two-factor split-zone design was adopted for experimentation under the winter wheat-summer maize rotation model in the Guanzhong area of Shanxi, China. The main areas of interest were conventional nitrogen (G) and reduced nitrogen (70% G); the sub-areas were straw no return (N), straw return (S), and straw return + biochar (SB); we analyzed their impacts on N2O emissions and crop yield, and the relationships with related impact factors. The results showed that the N2O emissions peaks appeared in the wheat season and maize season treatments within 5-16 days after fertilization, and also appeared after rainfall. The N2O flux was significantly and positively correlated with soil temperature and NH4+-N content. Regardless of the wheat season, maize season, or annual total N2O emissions, the 70% GSB treatment was the lowest and the GS treatment was the highest. At the same level of nitrogen application, S treatment increased N2O emissions, SB treatment could reduce N2O emissions, both S and SB treatments could significantly increase crop yields, and SB production increased more; 70%G-level annual N2O emissions, when compared with the G level, had been reduced by 40% to 48%, while the yield has not decreased significantly. Through comprehensive consideration, a reduction of nitrogen by 30% was achieved through the combination of straw + biochar on the basis of conventional nitrogen application, while ensuring high crop yields and the best N2O emissions reduction.

Straw mulching with inorganic nitrogen fertilizer reduces soil CO2 and N2O emissions and improves wheat yield.

Nitrogen fertilization significantly increases greenhouse gases (GHGs) emission, when applied from inorganic or organic sources. Minimizing GHGs from agroecosystems without compromising crop yield for stabilization of green production systems remains a challenge. Being an integral component of wheat production technology, the nitrogen (N) application deems to be indispensable. Thus, to reduce the application of N fertilizer and keep in view the minimization of GHGs emission, without compromising soil fertility and wheat production, field experiments were performed with treatments included maize straw mulch (S1: 0, S2: 4500, S3: 9000 kg ha-1) and nitrogen fertilizer (N1: 0, N2: 192 and N3: 240 kg ha-1) during 2015-17. Results showed that the cumulative CO2 and N2O emission from 9000 kg ha-1 of maize straw mulch with 192 kg N ha-1 (S3N2) significantly decreased by 0.67% and 33.7%, respectively, averaged over two years compared with that of 9000 kg ha-1 of maize straw mulch with 240 kg N ha-1 (S3N3). Likewise, the average soil moisture content significantly increased by 10% and 10.6% for S3N2 and S3N3 treatments at 0-10 cm soil depth, respectively, compared to S1N1. Similarly, the S3N2 and S3N3 treatments had lowered the soil temperature by 0.2 and 0.1 °C, respectively, over S1N1 in wheat grown fields. The grain yield of wheat was increased by 45% and 45% under S3N3 and S3N2 treatments than S1N1, respectively. The S3N2 treatment was more economical than S3N3 for wheat crop. Therefore, maize straw mulch (S3) combined with 20% less N fertilizer (N2) from commercial source were considered as a viable production technology to improve crop yield, and reduce soil CO2 and N2O emissions.

Quality assessment of instant green tea using portable NIR spectrometer.

Caffeine and catechin are two main components of instant green tea, and are essential components of tea quality. This paper mainly focuses on the feasibility of rapidly determining instant green tea components by using a portable near infrared (NIR) spectrometer. The two main components (caffeine and catechin) were studied. In addition, the instrument performance levels of portable and benchtop NIR spectrometers were studied and compared. Quantitative models developed using portable and benchtop spectrometers for measuring caffeine, total catechins, and four individual catechins were established and compared. After preprocessing using standard normal variate (SNV), the Rp values of the caffeine, total catechins, (-)-epigallocatechin, (-)-epigallocatechin 3-gallate, (-)-epicatechin, and (-)-epicatechin gallate in the partial least squares models for a portable NIR spectrometer were 0.974, 0.962, 0.669, 0.945, 0.942 and 0.905, respectively. For a benchtop NIR spectrometer, Rp values were 0.993, 0.958, 0.883, 0.955, 0.966 and 0.936, respectively. Passing-Bablok regression method results indicated no significant differences between the two instruments. A genetic algorithm (GA) and the successive projections algorithm (SPA) were used to screen the wavelength of the NIR spectrum and establish the model. The GA obtained more robust modeling results. This study concludes that the developed portable spectroscopy system combined with appropriate variable selection methods can be effectively used for rapid determination of caffeine, total catechins, and four individual catechins in instant green tea.

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.

Qualitative discrimination of Chinese dianhong black tea grades based on a handheld spectroscopy system coupled with chemometrics.

The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near-infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA-SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades.