Widely targeted metabolomics reveals the effect of different raw materials and drying methods on the quality of instant tea.

Introduction: Instant teas are particularly rich in tea polyphenols and caffeine and have great potential as food ingredients or additives to improve the quality of food and enhance their nutritional and commercial value. Methods: To determine the relationships between raw material, drying method, and sensory and other quality attributes, instant teas were prepared from three tea varieties, namely black, green and jasmine tea, using two drying methods, namely spray-drying (SD) and freeze-drying (FD). Results: Both the raw tea material and drying method influenced the quality of the finished instant teas. Black tea was quality stable under two drying, while green tea taste deteriorated much after SD. Jasmine tea must be produced from FD due to huge aroma deterioration after SD. FD produced instant tea with higher sensory quality, which was attributed to the lower processing temperature. Chemical compositional analysis and widely targeted metabolomics revealed that SD caused greater degradation of tea biochemical components. The flavonoids content changed markedly after drying, and metabolomics, combined with OPLS-DA, was able to differentiate the three varieties of tea. Instant tea preparations via SD often lost a large proportion of the original tea aroma compounds, but FD minimized the loss of floral and fruity aroma compounds. Changes in the tea flavonoids composition, especially during drying, contributed to the flavor development of instant tea. Discussion: These results will provide an practicle method for high-quality instant tea production through choosing proper raw tea material and lowering down drying temperature with non-thermal technologies like FD.

G-Quadruplex/Hemin-Mediated Polarity-Switchable and Photocurrent-Amplified System for Escherichia coli O157:H7 Detection.

The contamination of food by pathogens is a serious problem in global food safety, and current methods of detection are costly, time-consuming, and cumbersome. Therefore, it is necessary to develop rapid, portable, and sensitive assays for foodborne pathogens. In addition, assays for foodborne pathogens must be resistant to interference resulting from the complex food matrix to prevent false positives and negatives. In this study, hemin and reduced graphene oxide-MoS2 sheets (GMS) were used to design a near-infrared (NIR)-responsive photoelectrochemical (PEC) aptasensor with target-induced photocurrent polarity switching based on a hairpin aptamer (Hp) with a G-quadruplex motif. A ready-to-use analytical device was developed by immobilizing GMS on the surface of a commercial screen-printed electrode, followed by the attachment of the aptamer. In the presence of Escherichia coli O157:H7, the binding sites of Hp with the G-quadruplex motif were opened and exposed to hemin, leading to the formation of a G-quadruplex/hemin DNAzyme. Crucially, after binding to hemin, the charge transfer pathway of GMS changes, resulting in a switch of the photocurrent polarity. Further, G-quadruplex/hemin DNAzyme enhanced the cathodic photocurrent, and the proposed sensor exhibited a wide linear range ((25.0-1.0) × 107 CFU/mL), a low limit of detection (2.0 CFU/mL), and good anti-interference performance. These findings expand the applications of NIR-responsive PEC materials and provide versatile PEC methods for detecting biological analytes, especially for food safety testing.

Discrimination of Chinese Baijiu grades based on colorimetric sensor arrays.

In this study, a novel colorimetric sensor array based on chemo dyes including porphyrins and pH indicators were developed to analyse the volatile organic compounds of Chinese Baijiu with different grades. Ethyl acetate, ethyl butyrate and ethyl caproate appeared by significantly different concentration in different Baijiu grades measuring by gas chromatography and mass spectrometry and they were chosen as characteristic volatile organic components. The olfactory visualization system based on colorimetric sensor arrays was used to identify different Baijiu grades. The data were processed by building the principle components analysis, linear discriminant analysis and K-nearest neighbor classification models with the results of sensory evaluation and olfactory visualization system. This work presents a new-style colorimetric sensor using sensitive chemo dyes which has significant potential in quantitative analysis of volatile organic compounds, afterwards identifying different grades of Baijiu.

A novel colorimetric sensor array based on boron-dipyrromethene dyes for monitoring the storage time of rice.

A novel colorimetric sensor array based on boron-dipyrromethene (BODIPY) dyes was developed to monitor the volatile organic compounds (VOCs) of rice at different storage times. The VOCs of rice at different storage times were analyzed through GC-MS combined with multivariate analysis, and the compound 18-crown-6 was found significantly changed during rice aging process. Aimed at 18-crown-6 with particular macrocyclic structure, a series of BODIPYs were targeted synthesized for the selection of sensitive chemically responsive dyes. Four dyes were chosen to construct colorimetric sensor array based on sensitivity to VOCs of aged rice samples. Data acquired from the interactions of dyes and rice VOCs were subjected to the principal components analysis (PCA) and linear discriminant analysis (LDA). The optimal performance obtained by the LDA model was 98.75% in prediction set. Application of BODIPYs in this work has improved the sensitivity and expanded the choices of colorimetric dyes for the specific detection.

Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools.

Tea is one of the three greatest beverages in the world. In China, green tea has the largest consumption, and needle-shaped green tea, such as Maofeng tea and Sparrow Tongue tea, accounts for more than 40% of green tea (Zhu et al., 2017). The appearance of green tea is one of the important indexes during the evaluation of green tea quality. Especially in market transactions, the price of tea is usually determined by its appearance (Zhou et al., 2012). Human sensory evaluation is usually conducted by experts, and is also easily affected by various factors such as light, experience, psychological and visual factors. In the meantime, people may distinguish the slight differences between similar colors or textures, but the specific levels of the tea are hard to determine (Chen et al., 2008). As human description of color and texture is qualitative, it is hard to evaluate the sensory quality accurately, in a standard manner, and objectively. Color is an important visual property of a computer image (Xie et al., 2014; Khulal et al., 2016); texture is a visual performance of image grayscale and color changing with spatial positions, which can be used to describe the roughness and directivity of the surface of an object (Sanaeifar et al., 2016). There are already researchers who have used computer visual image technologies to identify the varieties, levels, and origins of tea (Chen et al., 2008; Xie et al., 2014; Zhu et al., 2017). Most of their research targets are crush, tear, and curl (CTC) red (green) broken tea, curly green tea (Bilochun tea), and flat-typed green tea (West Lake Dragon-well green tea) as the information sources. However, the target of the above research is to establish a qualitative evaluation method on tea quality (Fu et al., 2013). There is little literature on the sensory evaluation of the appearance quality of needle-shaped green tea, especially research on a quantitative evaluation model (Zhou et al., 2012; Zhu et al., 2017).

[Determination of taste quality of green tea using FT-NIR spectroscopy and variable selection methods].

The present paper was attempted to study the feasibility to determine the taste quality of green tea using FT-NIR spectroscopy combined with variable selection methods. Chemistry evaluation, as the reference measurement, was used to measure the total taste scores of green tea infusion. First, synergy interval PLS (siPLS) was implemented to select efficient spectral regions from SNV preprocessed spectra; then, optimal variables were selected using genetic algorithm (GA) from these selected spectral regions by siPLS, and the optimal model was achieved with Rp = 0.8908, RMSEP = 4.66 in the prediction set when 38 variables and 6 PLS factors were included. Experimental results showed that the performance of siPLS-GA model was superior to those of others. This study demonstrated that NIR spectra could be used successfully to measure taste quality of green tea and siPLS-GA algorithm has superiority to other algorithm in developing NIR spectral regression model.

[Measurement of chlorophyll content and distribution in tea plant's leaf using hyperspectral imaging technique].

Chlorophyll content and distribution in plant's leaves is an important index in estimation of plant nutrition information. In the present work, chlorophyll content and distribution in tea plant's leaves were measured by hyperspectral imaging technique. First, hyperspectral image data were captured from tea plant's leaves; then seven kinds of algorithms were used to extract the characteristic parameters from hyperspectral image; finally, seven fitted models were developed using the characteristics vectors and the reference measurements of chlorophyll contents respectively. Experimental results showed that the MSAVI2 model is superior to other models, and the results of the MSAVI2 model was achieved as follows: R = 0.843 3 and RMSE = 9.918 in the calibration set; R = 0.832 3 and RMSE = 8.601 in the prediction set. Finally, the chlorophyll content of each pixel in image was estimated by the fitted model, and the distribution of chlorophyll content in the tea plant's leaf was described by pseudo-color map. This study sufficiently demonstrated that the chlorophyll content and distribution in tea leaf can be measured by hyperspectral imaging technique.

Study on classification of soy sauce by electronic tongue technique combined with artificial neural network.

Electronic tongue as an analytical tool coupled with pattern recognition was attempted to classify 4 different brands and 2 categories (produced by different processes) of Chinese soy sauce. An electronic tongue system was used for data acquisition of the samples. Some effective variables were extracted from electronic tongue data by principal component analysis (PCA). Backpropagation artificial neural network (BP-ANN) was applied to build identification models. PCA score plots show an obvious cluster trend of different brands and different categories of soy sauce in the 2-dimensional space. The optimal BP-ANN model for different brands was achieved when principal components (PCs) were 2, and the identification rate of the discrimination model was 100% in both the calibration set and the prediction set, and the optimal BP-ANN model for different categories had the same result. This work demonstrates that electronic tongue technology combined with a suitable pattern recognition method can be successfully used in the classification of different brands and categories of soy sauce.

[Identification of egg freshness using near infrared spectroscopy and one class support vector machine algorithm].

Near infrared (NIR) spectroscopy combined with pattern recognition was attempted to discriminate the freshness of eggs. The algorithm of one-class support vector machine (OC-SVM) was employed to solve the classification problem due to imbalanced number of training samples. In this work, 86 samples of eggs (71 samples of fresh eggs and 15 samples of unfresh eggs) were surveyed by Fourier transform NIR spectroscopy. Firstly, original spectra of eggs in the wave-number range of 10 000-4 000 cm(-1) were acquired. And then, principal component analysis (PCA) was employed to extract useful information from original spectral data, and the number of PCs was optimized. Finally, OC-SVM was performed to calibrate discrimination model, and the optimal PCs were used as the input eigenvectors of model. In order to obtain a good performance, the regularization parameter v and parameter sigma of the kernel function in OC-SVM model were optimized in building model. The optimal OC-SVM model was obtained with nu = 0.5 and sigma2 = 20.3. Experimental result shows that OC-SVM got better performance than conventional two-class SVM model under the same condition. The OC-SVM model was achieved with identification rates of 80 for both fresh eggs and unfresh eggs in the independent prediction set. The identification rates of fresh eggs were 100% in two-class SVM model. However, when the two-class SVM model was used to discriminate the unfresh eggs of, the identification rates were 0% in the independent prediction set. Compared with conventional two-class SVM model, the OC-SVM model showed its superior performance in discrimination of minority unfresh eggs samples. This work shows that it is feasible to identify egg freshness using NIR spectroscopy, and OC-SVM is an excellent choice in solving the problem of imbalanced number of samples in training set.

[Study of simplification of prediction model for kiwifruit firmness using near infrared spectroscopy].

To simplify the prediction model of kiwifruit firmness, SNV was used to preprocess the near infrared (NIR) spectra (1 000-2 500 nm)of kiwifruit. PLS model simplification by optimizing spectral intervals and decreasing the number of factors through net analyte preprocessing (NAP)was carried out. Results showed that the performance of NAP/PLS model is the best. It was achieved with 5 factors in five wavenumber ranges(5 189-5 370, 4 549-4 620, 6 049-6 230, 6 999-7 730, and 6 249-6 614 cm(-1)). The optimal model was achieved with R2 = 0.819 41 and RMSECV = 0.701 77 in the calibration set and R2 = 0.780 67 and RMSEP = 0.882 71 in the prediction set. This indicates that the model not only may efficiently simplify PLS model, but also may improve precision and predictive ability.

[Study on the identification of tea using near infrared reflectance spectroscopy].

A rapid tea identification method by near infrared spectroscopy coupled with pattern recognition based on principal components analysis and Mahalanobis' distance technique was proposed. Four famous brand teas in China were studied, including Longjing tea, Biluochun tea, Maofeng tea and Tieguanyin tea in the experiment. In the spectral region between 6 500 and 5 300 cm(-1), through preprocessing method of MSC (multiplicative scatter comection), the prediction model was built. The result showed that the model was the best with 8 principal component factors. The rates of identification in calibration set samples and prediction set samples were 98.75% and 95%, respectively. A new idea about quick and precise identification of tea was offered.

Nitrogen response efficiency increased monotonically with decreasing soil resource availability: a case study from a semiarid grassland in northern China.

The concept of nutrient use efficiency is central to understanding ecosystem functioning because it is the step in which plants can influence the return of nutrients to the soil pool and the quality of the litter. Theory suggests that nutrient efficiency increases unimodally with declining soil resources, but this has not been tested empirically for N and water in grassland ecosystems, where plant growth in these ecosystems is generally thought to be limited by soil N and moisture. In this paper, we tested the N uptake and the N use efficiency (NUE) of two Stipa species (S. grandis and S. krylovii) from 20 sites in the Inner Mongolia grassland by measuring the N content of net primary productivity (NPP). NUE is defined as the total net primary production per unit N absorbed. We further distinguished NUE from N response efficiency (NRE; production per unit N available). We found that NPP increased with soil N and water availability. Efficiency of whole-plant N use, uptake, and response increased monotonically with decreasing soil N and water, being higher on infertile (dry) habitats than on fertile (wet) habitats. We further considered NUE as the product of the N productivity (NP the rate of biomass increase per unit N in the plant) and the mean residence time (MRT; the ratio between the average N pool and the annual N uptake or loss). The NP and NUE of S. grandis growing usually in dry and N-poor habitats exceeded those of S. krylovii abundant in wet and N-rich habitats. NUE differed among sites, and was often affected by the evolutionary trade-off between NP and MRT, where plants and communities had adapted in a way to maximize either NP or MRT, but not both concurrently. Soil N availability and moisture influenced the community-level N uptake efficiency and ultimately the NRE, though the response to N was dependent on the plant community examined. These results show that soil N and water had exerted a great impact on the N efficiency in Stipa species. The intraspecific differences in N efficiency within both Stipa species along soil resource availability gradient may explain the differences in plant productivity on various soils, which will be conducive to our general understanding of the N cycling and vegetation dynamics in northern Chinese grasslands.