Simultaneous determination of amino acid nitrogen and total acid in soy sauce using near infrared spectroscopy combined with characteristic variables selection.

Amino acid nitrogen and total acid are two most important quality indices to assess the quality of soy sauce in China. This work employed near infrared spectroscopy combined with synergy interval partial least square and genetic algorithm to detect amino acid nitrogen and total acid content in soy sauce. First, synergy interval partial least square was used to select efficient spectral regions from the full spectrum region; and then, genetic algorithm was used to selected variables from the efficient spectral regions, to build partial least square model. The optimal genetic algorithm synergy interval partial least square models were obtained as follows: Rc = 0.9988 and Rp = 0.9988 for amino acid nitrogen content model using 64 variables; Rc = 0.9917 and Rp = 0.9902 for total acid content model using 81 variables. Genetic algorithm synergy interval partial least square models showed superiority over the partial least square and synergy interval partial least square models. The results indicated that amino acid nitrogen and total acid content in soy sauce could be rapidly determined by near infrared spectroscopy technique. Also, the results indicated that genetic algorithm synergy interval partial least square can improve the performance in measurement of amino acid nitrogen and total acid content by near infrared spectroscopy.

Multiple Human Association and Tracking From Egocentric and Complementary Top Views.

Crowded scene surveillance can significantly benefit from combining egocentric-view and its complementary top-view cameras. A typical setting is an egocentric-view camera, e.g., a wearable camera on the ground capturing rich local details, and a top-view camera, e.g., a drone-mounted one from high altitude providing a global picture of the scene. To collaboratively analyze such complementary-view videos, an important task is to associate and track multiple people across views and over time, which is challenging and differs from classical human tracking, since we need to not only track multiple subjects in each video, but also identify the same subjects across the two complementary views. This paper formulates it as a constrained mixed integer programming problem, wherein a major challenge is how to effectively measure subjects similarity over time in each video and across two views. Although appearance and motion consistencies well apply to over-time association, they are not good at connecting two highly different complementary views. To this end, we present a spatial distribution based approach to reliable cross-view subject association. We also build a dataset to benchmark this new challenging task. Extensive experiments verify the effectiveness of our method.

[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.

[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.

[Rapid diagnostics of early phosphorus deficiency in mini-cucumber plants under protected cultivation by near infrared spectroscopy].

The morphological symptom of phosphorus deficiency at early stage is similar to the appearance of leaf aging process in preliminary phase, so that visual diagnostics of phosphorus deficiency in mini-cucumber plants at early stage is practically impossible. Near infrared reflectance spectra contain information about differences in compositions of leaf tissues between phosphorus-deficient plants and healthy plants. In the present paper, near infrared reflectance spectroscopy was used to provide diagnostic information on phosphorus deficiency of mini-cucumber plants grown under non-soil conditions. Near infrared spectra was collected from 90 leaves of mini-cucumber plants. Raw cucumber spectra was preprocessed by SNV and divided into 27 intervals. The top 10 principal components (PCs) were extracted as the input of BP-ANN classifiers by principal component analysis (PCA) while the values of nutrient deficient were used as the output variables of BP-ANN and three layers BP-ANN discrimination model was built. The best experiment results were based on the top 3 principal components of No. 7 interval when the spectra was divided into 27 intervals and identification rates of the ANN model are 100% in both training set and the prediction set. The overall results show that NIR spectroscopy combined with BP-ANN can be efficiently utilized for rapid and early diagnostics of phosphorus deficiency in mini-cucumber plants.

[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.

[A new method of characteristic wavelength sub-range selection of near infrared spectroscopy].

To improve and simplify the prediction model of carotenoid content of cucumber leaves, genetic algorithm (GA) combined with Metropolis acceptance criterion of simulated annealing algorithm (SAA) as well as interval partial least square (iPLS) were proposed and used to establish the calibration models of carotenoid content against cucumber leaves spectra. The cucumber leaves spectra data were divided into 40 intervals, among which 7 subsets, i. e. No. 3, 4, 14, 18, 21, 32 and 33, were selected by SAA-GA-iPLS. The comparison was made between SAA-GA-iPLS and traditional genetic algorithm interval partial least square (GA-iPLS), and the result of this study shows that SAA-GA-iPLS was better than traditional genetic algorithm interval partial least square (GA-iPLS).

Automated tea quality classification by hyperspectral imaging.

A hyperspectral imaging technique was attempted to classify green tea. Five grades of green tea samples were attempted. A hyperspectral imaging system was developed for data acquisition of tea samples. Principal component analysis was performed on the hyperspectral data to determine three optimal band images. Texture analysis was conducted on each optimal band image to extract characteristic variables. A support vector machine (SVM) was used to construct the classification model. The classification rates were 98% and 95% in the training and prediction sets, respectively. The SVM algorithm shows excellent performance in classification results in contrast with other pattern recognitions classifiers. Overall results show that the hyperspectral imaging technique coupled with a SVM classifier can be efficiently utilized to classify green tea.

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

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

[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.

Evaluation of Asphalt with Different Combinations of Fire Retardants.

When a fire takes place in a tunnel, the surface of the asphalt pavement will burn and release a large amount of smoke, which is toxic to human health. Thus, in order to prevent the combustion of the asphalt pavement under fire, it is necessary to propose some methods to retard its physical and chemical reaction under the high temperature. In this study, ten different combinations of fire retardants and a control case where no fire retardant was applied were prepared for evaluation. The thermogravimetric (TG)-mass spectrometry (MS) tests were used to evaluate their effect on the fire retardance from mass and energy perspectives and the Fire Dynamics Simulator (FDS) software was used to evaluate the fire retardance from temperature and smoke distribution perspectives. In experimental analysis, the TG (thermogravimetric) and DTG (differential thermogravimetric) curves were used to analyze the mass loss rate and residual mass of the asphalt and the activation energy was calculated and analyzed as well. In addition, decay rate of mass loss rate and increasing rate of activation energy were proposed to evaluate the ease of combustion of the asphalt with and without fire retardants. The results show that in laboratory experiments, the fire retardant combination which includes 48% aluminum hydroxide, 32% magnesium hydroxide, 5% expanded graphite, and 15% encapsulated red phosphorous would lead to an improved effect of fire retardance. In numerical modeling, the temperature and smoke height distribution over time were adopted to evaluate the fire retardance effect. The temperature distribution was found to be symmetrical on both sides of the combustion point and the same combination as proposed in experimental analysis was found to have the best effect on fire retardance due to the largest decrease in temperature. Additionally, because of the highest smoke height distribution, an improved effect on smoke suppression was also found when this combination was applied.

Identification of green tea's (Camellia sinensis (L.)) quality level according to measurement of main catechins and caffeine contents by HPLC and support vector classification pattern recognition.

High performance liquid chromatography (HPLC) was identified green tea's quality level by measurement of catechins and caffeine content. Four grades of roast green teas were attempted in this work. Five main catechins ((-)-epigallocatechin gallate (EGCG), (-)-epigallocatechin (EGC), (-)-epicatechin gallate (ECG), (-)-epicatechin (EC), and (+)-catechin (C)) and caffeine contents were measured simultaneously by HPLC. As a new chemical pattern recognition, support vector classification (SVC) was applied to develop identification model. Some parameters including regularization parameter (R) and kernel parameter (K) were optimized by the cross-validation. The optimal SVC model was achieved with R=20 and K=2. Identification rates were 95% in the training set and 90% in the prediction set, respectively. Finally, compared with other pattern recognition approaches, SVC algorithm shows its excellent performance in identification results. Overall results show that it is feasible to identify green tea's quality level according to measurement of main catechins and caffeine contents by HPLC and SVC pattern recognition.

Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms.

This paper attempted the feasibility to determine content total polyphenols content in green tea with near infrared (NIR) spectroscopy coupled with an appropriate multivariate calibration method. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were performed comparatively to calibrate regression model. The number of PLS components and the number of intervals were optimized according to root mean square error of cross-validation (RMSECV) in calibration set. The performance of the final model was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. Experimental results showed that the performance of siPLS model is the best in contrast to PLS and iPLS. The optimal model was achieved with R=0.9583 and RMSEP=0.7327 in prediction set. This study demonstrated that NIR spectroscopy with siPLS algorithm could be used successfully to analysis of total polyphenols content in green tea, and revealed superiority of siPLS algorithm in contrast with other multivariate calibration methods.

A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork.

This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.

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

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

Fabricating upconversion fluorescent nanoparticles modified substrate for dynamical control of cancer cells and pathogenic bacteria.

Lanthanide-doped upconversion nanoparticles (UCNPs) have attracted widespread interests in the field of biomedicine because of their unique upconverting capability by converting near infrared (NIR) excitation to visible or ultraviolet (UV) emission. Here, we developed a novel UCNP-based substrate for dynamic capture and release of cancer cells and pathogenic bacteria under NIR-control. The UCNPs harvest NIR light and convert it to ultraviolet light, which subsequently result in the cleavage of photoresponsive linker (PR linker) from the substrate, and on demand allows the release of a captured cell. The results show that after seeding cells for 5 h, the cells were efficiently captured on the surface of the substrate and ˜89.4% of the originally captured S. aureus was released from the surface after exposure to 2 W/cm2 NIR light for 30 min, and ˜92.1% of HepG2 cells. These findings provide a unique platform for exploring an entirely new application field for this promising luminescent nanomaterial.

Prediction of black tea fermentation quality indices using NIRS and nonlinear tools.

Catechin content, the ratio of tea polyphenols and free amino acids (TP/FAA), as well as the ratio of theaflavins and thearubigins (TFs/TRs) are important biochemical indicators to evaluate fermentation quality. To achieve rapid determination of such biochemical indicators, synergy interval partial least square and extreme learning machine combined with an adaptive boosting algorithm, Si-ELM-AdaBoost algorithm, were used to establish quantitative analysis models between near infrared spectroscopy (NIRS) and catechin content and between TFs/TRs and TP/FAA, respectively. The results showed that prediction performance of the Si-ELM-AdaBoost mixed algorithm is superior than that of other models. The prediction results with root-mean-square error of prediction ranged from 0.006 to 0.563, the ratio performance deviation values exceeded 2.5, and predictive correlation coefficient values exceeded 0.9 in the prediction model of each biochemical indicator. NIRS combined with Si-ELM-AdaBoost mixed algorithm could be utilized for online monitoring of black tea fermentation. Meanwhile, the AdaBoost algorithm effectively improved the accuracy of the ELM model and could better approach the nonlinear continuous function.

A magnetite/PMAA nanospheres-targeting SERS aptasensor for tetracycline sensing using mercapto molecules embedded core/shell nanoparticles for signal amplification.

Surface-enhanced Raman scattering (SERS) biosensors have promising potential in the field of antibiotics detection because of their ultrahigh detection sensitivity. This paper reports a rapid and sensitive SERS-based magnetic nanospheres-targeting strategy for sensing tetracycline (TTC) using aptamer-conjugated magnetite colloid nanocrystal clusters (MCNCs)-polymethacrylic acid (PMAA) magnetic nanospheres (MNs) as the recognition and the Au/PATP/SiO2 (APS) as the labels. Initially, MNs were fabricated and conjugated with the aptamers through condensation reaction. MNs possessed high saturation magnetization (Ms) value of 71.5emu/g and excellent biocompatibility, which facilitated the rapid and easy magnetic separation. Then, complementary DNA (cDNA) were loaded on the APS nanocarrier to produce a large amplification factor of Raman signals. The MNs-targeting aptasensor was thus fabricated by immobilizing the APS to the MNs' surfaces via the hybrid reaction between cDNA and aptamers. Sequel, TTC bound successfully to the aptamer upon its addition with the subsequent release of some cDNA-APS into the bulk solution. Under magnet attraction, the nanospheres were deposited together. Consequently, a display of strong SERS signals by supernatants of the resulting mixtures with increasing TTC concentrations was observed. The proposed aptasensor showed excellent performances for TTC detection along with wide linear range of 0.001-100ng/mL, low detection limit 0.001ng/mL, high sensitivity, and good selectivity to the general coexisted interferences.

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).

Intelligent evaluation of color sensory quality of black tea by visible-near infrared spectroscopy technology: A comparison of spectra and color data information.

Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables. In comparison with the other models, GA-BPANN models from spectra data information showed the best performance, with the correlation coefficient of 0.8935, and the root mean square error of 0.392 in the prediction set. In addition, models based on the spectra information provided better performance than that based on the color parameters. Therefore, the VIS-NIR spectroscopy technique is a promising tool for rapid and accurate evaluation of the sensory quality of black tea samples.