Real-time and accurate calibration detection of gout stones based on terahertz and Raman spectroscopy.

Gout is a metabolic disease that can result in the formation of gout stones. It is essential to promptly identify and confirm the type of gout stone to alleviate pain and inflammation in patients and prevent complications associated with gout stones. Traditional detection methods, such as X-ray, ultrasound, CT scanning, and blood uric acid measurement, have limitations in early diagnosis. Therefore, this article aims to explore the use of micro Raman spectroscopy, Fourier transform infrared spectroscopy, and Terahertz time-domain spectroscopy systems to detect gout stone samples. Through comparative analysis, Terahertz technology and Raman spectroscopy have been found to provide chemical composition and molecular structure information of different wavebands of samples. By combining these two technologies, faster and more comprehensive analysis and characterization of samples can be achieved. In the future, handheld portable integrated testing instruments will be developed to improve the efficiency and accuracy of testing. Furthermore, this article proposes establishing a spectral database of gout stones and urinary stones by combining Raman spectroscopy and Terahertz spectroscopy. This database would provide accurate and comprehensive technical support for the rapid diagnosis of gout in clinical practice.

Moxa Wool in Different Purities and Different Growing Years Measured by Terahertz Spectroscopy.

Moxa wool is a traditional Chinese herbal medicine, which can warm channels to dispel coldness. At present, there is no unified index to evaluate the purity and growing years of moxa wool in the market. Terpineol is one of the effective substances in the volatile oil of moxa wool. Here, we characterize the purity and growing years of moxa wool by studying terpineol. Gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography (HPLC) are the methods for monitoring terpineol at present, all of which have defects of complicated procedures. We established linear fitting to distinguish the different purities of moxa wool through the intensities (areas) of terpineol, the characteristic peaks, and the consequence presented; the coefficient of determination (R 2) was higher than 0.90. Furthermore, based on the characteristic peak position of standard terpineol, the correlation model with the purity and growing year of moxa wool was set up, thereby differentiating the quality of moxa wool. We have built the partial least squares (PLS) model of the growing years of moxa wool with high accuracy, and the determination coefficient is greater than 0.98. In addition, we compare the quantitative accuracy of Raman spectroscopy with terahertz technology. Finally, a new method of terahertz spectroscopy to evaluate quality of moxa wool was found. It provides a new idea for the identification of inferior moxa wool in the market and a new method for identifying the quality of moxa wool in traditional Chinese medicine.

Measuring heavy metal ions in water using nature existed microalgae as medium based on terahertz technology.

Heavy metal pollution in water seriously affects human health. The disadvantages of traditional metal ion detection methods involve long and cumbersome chemical pretreatment in the early stage, and large volume of samples. In this study, microalgae were used as the medium, and terahertz spectroscopy technology was employed to collect the changes of material components in it, so as to deduce the types and concentrations of heavy metal pollution in water. Through the partial least square(PLS), we establish the prediction model of heavy metal concentration, and the results show that the best detection time for Pb2+ is 6 h and Ni2+ is 18 h. The principal component analysis(PCA) shows that ß-carotene is the most affected substance. Afterward we collect five real surface waters in East China and verify that the judgment accuracy of Pb2+ and Ni2+ are 100% and 93.2% respectively. The results indicate that the time is shorter than the traditional pretreatment time from more than 20-6 h, the sample volume is reduced from 50 mL to 10 mL, the detection accuracy is improved from 10 ng/mL to 1 ng/mL. In a word, we provide a new fast and real-time method for biological monitoring of heavy metal pollution in water.

Lipids monitoring in Scenedesmus obliquus based on terahertz technology.

BACKGROUND: Microalgae are considered as a source of low pollution and renewable fuel due to their ability to synthesize an abundance of lipids. Conventional methods for lipid quantification are time-consuming and chemically contaminated, while spectroscopic method combined with mathematical model is much more attractive due to its ability of qualitative and quantitative analysis of material composition, in this sense, terahertz technology provides not only timely and non-destructive testing without chemical pollution, but also provides information on the functional group vibration mode and structure of the measured components. Therefore, terahertz technology is utilized in our investigation and proposed for microalgae metabolism detection. RESULTS: The aim of this study was to use terahertz spectroscopy to observe lipid content in Scenedesmus obliquus (S. obliquus). We collected the THz spectra of S. obliquus which were cultivated under nitrogen stress and terahertz spectroscopy was used to analyze changes in substance components (lipids, proteins, carbohydrates and ß-carotene). The PLS algorithm was used to model the terahertz data to distinguish the different lipid content of S. obliquus under nitrogen stress. The correlation coefficient of the prediction results of the lipid characteristic band modeling was above 0.991, and the root mean square error was less than 0.132. It indicated that terahertz technology can be used to discriminate S. obliquus cells under different nitrogen stress effectively. The correlation between the terahertz characteristic peak (9.3 THz) and the total lipid content determined by gravimetry reaches 0.960. The final results were compared with the commonly used spectroscopic methods for lipid observation (Raman spectroscopy). CONCLUSIONS: In this article, we demonstrated the effectiveness of terahertz spectroscopy to monitor changes in microalgae lipid content under nitrogen stress. Terahertz spectroscopy is more suitable for industrial production or ordinary laboratories which require intermediate result with low-frequency screening. When quantifying microalgae lipids, the constraint of terahertz spectroscopy is far less than that of Raman spectroscopy, and it is easier for operator to accurately quantify microalgae lipid. In addition, it is still in early stage for the study of microalgae using terahertz spectroscopy technology, there is still much potential for us to explore.

Publisher Correction: Study on the Visualization of Pigment in Haematococcus pluvialis by Raman Spectroscopy Technique.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Identification of heavy metal by testing microalgae using confocal Raman microspectroscopy technology.

Five copper concentrations (0, 0.5, 1, 2, and 4 mg/l) were used to stress C. pyrenoidosa continuously for five days. The biomass, chlorophyll, and carotenoids of microalgae were measured, and Raman mapping spectral data and Raman single-point spectral data of microalgae were acquired. Principal component-linear discriminant analysis, back propagation-artificial neural network (BP-ANN), and sensitive wavelengths-linear discriminant analysis were used to build models to identify different copper concentrations using the spectral data after pretreatment. The results showed that the BP-ANN model was optimal to identify copper concentrations with prediction accuracy of 92% on day 4.

Study on the Visualization of Pigment in Haematococcus pluvialis by Raman Spectroscopy Technique.

As an ideal raw material for the production of astaxanthin, H. pluvialis was drawing attention during the last few years, there are some research topics initiated to find out the synthetic pathway of astaxanthin in H. pluvialis. In this study, confocal microscopic Raman technology was utilized to analyze the point-by-point mapping for H. pluvialis, and the visualization of pigment such as carotenoid and astaxanthin content were achieved. By comparing the Raman spectra of H. pluvialis and standard product of astaxanthin, and using the C = C stretching vibration of the Raman intensity as the main indicator for carotenoids, the visual spatial distribution for the carotenoids content was obtained. The MCR-ALS was applied to analyze the Raman data of H. pluvialis, and the information of astaxanthin was extracted to achieve real-time spatial distribution. The visualization of astaxanthin content shows that MCR-ALS is very effective for extracting the information of astaxanthin content from H. pluvialis. By exploring the spatial distribution of carotenoids and astaxanthin contents, analyzing the spatial distribution rules during its growth, Raman spectroscopy technology can be utilized to investigate the internal components of the pigment (ataxanthin, etc.) in H. pluvialis, which make it as an effective methodology to monitor the accumulation and changing mechanism of pigment content in microalgae.

Detection and imaging of lipids of Scenedesmus obliquus based on confocal Raman microspectroscopy.

In this study, confocal Raman microspectroscopy was used to detect lipids in microalgae rapidly and non-destructively. Microalgae cells were cultured under nitrogen deficiency. The accumulation of lipids in Scenedesmus obliquus was observed by Nile red staining, and the total amount of lipids accumulated in the cells was measured by gravimetric method. The signals from different microalgae cells were collected by confocal Raman microspectroscopy to establish a prediction model of intracellular lipid content, and surface scanning signals for drawing pseudo color images of lipids distribution. The images can show the location of pyrenoid and lipid accumulation in cells. Analyze Raman spectrum data and build PCA-LDA model using four different bands (full bands, pigments, lipids, and mixed features). Models of full bands or pigment characteristic bands were capable of identifying S. obliquus cells under different nitrogen stress culture time. The prediction accuracy of model of lipid characteristic bands is relatively low. The correlation between the fatty acid content measured by the gravimetric method and the integral Raman intensity of the oil characteristic peak (1445 cm-1) measured by Raman spectroscopy was analyzed. There was significant correlation (R2 = 0.83), which means that Raman spectroscopy is applicable to semi-quantitative detection of microalgal lipid content.

Detection of Water Content in Rapeseed Leaves Using Terahertz Spectroscopy.

The terahertz (THz) spectra of rapeseed leaves with different water content (WC) were investigated. The transmission and absorption spectra in the range of 0.3-2 THz were measured by using THz time-domain spectroscopy. The mean transmittance and absorption coefficients were applied to analyze the change regulation of WC. In addition, the Savitzky-Golay method was performed to preprocess the spectra. Then, the partial least squares (PLS), kernel PLS (KPLS), and Boosting-PLS were conducted to establish models for predicting WC based on the processed transmission and absorption spectra. Reliable results were obtained by these three methods. KPLS generated the best prediction accuracy of WC. The prediction coefficient correlation (Rval) and root mean square error (RMSEP) of KPLS based on transmission were Rval = 0.8508, RMSEP = 0.1015, and that based on absorption were Rval = 0.8574, RMSEP = 0.1009. Results demonstrated that THz spectroscopy combined with modeling methods provided an efficient and feasible technique for detecting plant physiological information.

Identification of pesticide varieties by detecting characteristics of Chlorella pyrenoidosa using Visible/Near infrared hyperspectral imaging and Raman microspectroscopy technology.

The main goal of this research is to examine the feasibility of applying Visible/Near-infrared hyperspectral imaging (Vis/NIR-HSI) and Raman microspectroscopy technology for non-destructive identification of pesticide varieties (glyphosate and butachlor). Both mentioned technologies were explored to investigate how internal elements or characteristics of Chlorella pyrenoidosa change when pesticides are applied, and in the meantime, to identify varieties of the pesticides during this procedure. Successive projections algorithm (SPA) was introduced to our study to identify seven most effective wavelengths. With those wavelengths suggested by SPA, a model of the linear discriminant analysis (LDA) was established to classify the pesticide varieties, and the correct classification rate of the SPA-LDA model reached as high as 100%. For the Raman technique, a few partial least squares discriminant analysis models were established with different preprocessing methods from which we also identified one processing approach that achieved the most optimal result. The sensitive wavelengths (SWs) which are related to algae's pigment were chosen, and a model of LDA was established with the correct identification reached a high level of 90.0%. The results showed that both Vis/NIR-HSI and Raman microspectroscopy techniques are capable to identify pesticide varieties in an indirect but effective way, and SPA is an effective wavelength extracting method. The SWs corresponding to microalgae pigments, which were influenced by pesticides, could also help to characterize different pesticide varieties and benefit the variety identification.

[Study on the Visualization of the Biomass of Chlorella sp., Isochrysis galbana, and Spirulina sp. Based on Hyperspectral Imaging Technique].

Effective cultivation of the microalgae is the key issue for microalgal bio-energy utilization. In nutrient rich culture conditions, the microalge have a fast growth rate, but they are more susceptible to environmental pollution and influence. So to monitor the the growth process of microalgae is significant during cultivating. Hyperspectral imaging has the advantages of both spectra and image analysis. The spectra contain abundant material quality signal and the image contains abundant spatial information of the material about the chemical distribution. It can achieve the rapid information acquisition and access a large amount of data. In this paper, the authors collected the hyperspectral images of forty-five samples of Chlorella sp., Isochrysis galbana, and Spirulina sp., respectively. The average spectra of the region of interest (ROI) were extracted. After applying successive projection algorithm (SPA), the authors established the multiple linear regression (MLR) model with the spectra and corresponding biomass of 30 samples, 15 samples were used as the prediction set. For Chlorella sp., Isochrysis galbana, and Spirulina sp., the correlation coefficient of prediction (r(pre)) are 0.950, 0.969 and 0.961, the root mean square error of prediction (RMSEP) for 0.010 2, 0.010 7 and 0.007 1, respectively. Finally, the authors used the MLR model to predict biomass for each pixel in the images of prediction set; images displayed in different colors for visualization based on pseudo-color images with the help of a Matlab program. The results show that using hyperspectral imaging technique to predict the biomass of Chlorella sp. and Spirulina sp. were better, but for the Isochrysis galbana visualization needs to be further improved. This research set the basis for rapidly detecting the growth of microalgae and using the microalgae as the bio-energy.

[Identification of Microalgae Species Using Visible/Near Infrared Transmission Spectroscopy].

At present, the identification and classification of the microalgae and its biochemical analysis have become one of the hot spots on marine biology research. Four microalgae species, including Chlorella vulgaris, Chlorella pyrenoidosa, Nannochloropsis oculata, Chlamydomonas reinhardtii, were chosen as the experimental materials. Using an established spectral acquisition system, which consists of a portable USB 4000 spectrometer having transmitting and receiving fiber bundles connected by a fiber optic probe, a halogen light source, and a computer, the Vis/NIR transmission spectral data of 120 different samples of the microalgae with different concentration gradients were collected, and the spectral curves of fourmicroalgae species were pre-processed by different pre-treatment methods (baseline filtering, convolution smoothing, etc. ). Based on the pre-treated effects, SPA was applied to select effective wavelengths (EWs), and the selected EWs were introduced as inputs to develop and compare PLS, Least Square Support Vector Machines (LS-SVM), Extreme Learning Machine (ELM)models, so as to explore the feasibility of using Vis/NIR transmission spectroscopy technology for the rapid identification of four microalgae species in situ. The results showed that: the effect of Savitzky-Golay smoothing was much better than the other pre-treatment methods. Six EWs selected in the spectraby SPA were possibly relevant to the content of carotenoids, chlorophyll in the microalgae. Moreover, the SPA-PLS model obtained better performance than the Full-Spectral-PLS model. The average prediction accuracy of three methods including SPA-LV-SVM, SPA-ELM, and SPA-PLS were 80%, 85% and 65%. The established method in this study may identify four microalgae species effectively, which provides a new way for the identification and classification of the microalgae species. The methodology using Vis/NIR spectroscopy with a portable optic probe would be applicable to a diverse range of microalgae species and proves to be a rapid, real-time, non-destructive, precise method for the physiological and biochemical detection for microalgae.

Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology.

In our study, the feasibility of using visible/near infrared hyperspectral imaging technology to detect the changes of the internal components of Chlorella pyrenoidosa so as to determine the varieties of pesticides (such as butachlor, atrazine and glyphosate) at three concentrations (0.6 mg/L, 3 mg/L, 15 mg/L) was investigated. Three models (partial least squares discriminant analysis combined with full wavelengths, FW-PLSDA; partial least squares discriminant analysis combined with competitive adaptive reweighted sampling algorithm, CARS-PLSDA; linear discrimination analysis combined with regression coefficients, RC-LDA) were built by the hyperspectral data of Chlorella pyrenoidosa to find which model can produce the most optimal result. The RC-LDA model, which achieved an average correct classification rate of 97.0% was more superior than FW-PLSDA (72.2%) and CARS-PLSDA (84.0%), and it proved that visible/near infrared hyperspectral imaging could be a rapid and reliable technique to identify pesticide varieties. It also proved that microalgae can be a very promising medium to indicate characteristics of pesticides.

[Study on the Nondestructive Detection Methods for Dynamica Change of Lipid Content in Chlorella sp.].

Microalgae based biodiesel production requires a large amount of lipid accumulation in the cells, and the accumulation is greatly influenced by the environment. Therefore, it is necessary to find fast and non-destructive methods for lipid change detection. In this paper, Chlorella sp. was adopted as the objective, which was cultured under different light condition consisted of red and blue lights with different proportion. We applied the visible near-infrared spectroscopy (Vis/NIRs) technique to detect the dynamic change of lipid during the microalgae growth processes and utilized hyperspectral imaging technology for visualization of lipid distribution in the suspension. The transmittance and reflectance spectra of microalgae were acquired with Vis/NIRs and hyper-spectroscopy, respectively. In the comparison of the transmittance and reflectance spectra, they showed some different characteristics. Meanwhile it also varied in terms of the number and the area of feature wavelengths obtained by successive projections algorithm (SPA) based on the different spectra. But the established multiple linear regression (MLR) model for lipid content prediction had similar results with rpre of 0.940, RMSEP of 0.003 56 and rpre of 0.932, RMSEP of 0.004 23, respectively. Based on the predictive model, we obtained the spectra and analyzed the lipid dynamic change in microalgae in one life cycle. In the life cycle, the lipid content in Chlorella sp. was relatively stable from the beginning of inoculation to exponential phase, the increase and accumulation of lipid phenomenon occurred in the late exponential phase. Combined with the MLR model and the hypersepctral images, we studied the visualization result of microalgae suspension in the steady phase. The stimulated images showed that the microalgae with higher lipid content appeared gathering. This study compared the difference and the feasibility of the Vis/NIRs and hyperspectral imaging technique in lipid content detection applied in microalgae growing microalgae. The results are meaningful for the fast and non-destructive detection of the growth information of microalgae. It has boththeoretical and practical significance for developing microalgal culture and harvest strategy in practice.

Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging.

This study investigated the potential of using hyperspectral imaging for detecting different diseases on tomato leaves. One hundred and twenty healthy, one hundred and twenty early blight and seventy late blight diseased leaves were selected to obtain hyperspectral images covering spectral wavelengths from 380 to 1023 nm. An extreme learning machine (ELM) classifier model was established based on full wavelengths. Successive projections algorithm (SPA) was used to identify the most important wavelengths. Based on the five selected wavelengths (442, 508, 573, 696 and 715 nm), an ELM model was re-established. Then, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) at the five effective wavelengths were extracted to establish detection models. Among the models which were established based on spectral information, all performed excellently with the overall classification accuracy ranging from 97.1% to 100% in testing sets. Among the eight texture features, dissimilarity, second moment and entropy carried most of the effective information with the classification accuracy of 71.8%, 70.9% and 69.9% in the ELM models. The results demonstrated that hyperspectral imaging has the potential as a non-invasive method to identify early blight and late blight diseases on tomato leaves.

Using FT-NIR spectroscopy technique to determine arginine content in fermented Cordyceps sinensis mycelium.

This research investigated the feasibility of using Fourier transform near-infrared (FT-NIR) spectral technique for determining arginine content in fermented Cordyceps sinensis (C. sinensis) mycelium. Three different models were carried out to predict the arginine content. Wavenumber selection methods such as competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the most important wavenumbers and reduce the high dimensionality of the raw spectral data. Only a few wavenumbers were selected by CARS and CARS-SPA as the optimal wavenumbers, respectively. Among the prediction models, CARS-least squares-support vector machine (CARS-LS-SVM) model performed best with the highest values of the coefficient of determination of prediction (Rp(2)=0.8370) and residual predictive deviation (RPD=2.4741), the lowest value of root mean square error of prediction (RMSEP=0.0841). Moreover, the number of the input variables was forty-five, which only accounts for 2.04% of that of the full wavenumbers. The results showed that FT-NIR spectral technique has the potential to be an objective and non-destructive method to detect arginine content in fermented C. sinensis mycelium.

[Application of near-infrared hyperspectral imaging to predicting water content in salmon flesh].

Near-infrared hyperspectral imaging technique was employed in the present study to determine water contents in salmon flesh rapidly and nondestructively. Altogether 90 samples from different positions of salmon fish were collected for hyperspectral image scanning, and mean spectra were extracted from the region of interest (ROI) inside each image. Sixty samples were randomly selected as calibration set, and the remaining 30 samples formed prediction set. The full-spectrum and water contents were correlated using partial least squares regression (PLSR) and least-squares support vector machines (LS-SVM), which were then applied to predict water contents for prediction samples. A novel variable extraction method called random frog was applied to select effective wavelengths (EWs) from the full-spectrum. PLSR and LS-SVM calibration models were established respectively to detect water contents in salmon based on the EWs. Though the performances of EWs-based models were worse than models using full-spectrum, only 12 wavelengths were used to substitute for the original 151 wavelengths, thus models were greatly simplified and more suitable for practical application. For EWs-based PLSR and LS-SVM models, satisfactory results were achieved with correlation coefficient of prediction (Rp) of 0. 92 and 0. 93 respectively, and root mean square error of prediction (RMSEP) of 1. 31% and 1. 18% respectively. The results indicated that near-infrared hyperspectral imaging combined with chemometrics allows accurate prediction of water contents in salmon flesh, providing important reference for the rapid inspection of fish quality.

Discrimination of tomatoes bred by spaceflight mutagenesis using visible/near infrared spectroscopy and chemometrics.

Visible/near infrared spectroscopy (Vis/NIR) based on sensitive wavelengths (SWs) and chemometrics was proposed to discriminate different tomatoes bred by spaceflight mutagenesis from their leafs or fruits (green or mature). The tomato breeds were mutant M1, M2 and their parent. Partial least squares (PLS) analysis and least squares-support vector machine (LS-SVM) were implemented for calibration models. PLS analysis was implemented for calibration models with different wavebands including the visible region (400-700 nm) and the near infrared region (700-1000 nm). The best PLS models were achieved in the visible region for the leaf and green fruit samples and in the near infrared region for the mature fruit samples. Furthermore, different latent variables (4-8 LVs for leafs, 5-9 LVs for green fruits, and 4-9 LVs for mature fruits) were used as inputs of LS-SVM to develop the LV-LS-SVM models with the grid search technique and radial basis function (RBF) kernel. The optimal LV-LS-SVM models were achieved with six LVs for the leaf samples, seven LVs for green fruits, and six LVs for mature fruits, respectively, and they outperformed the PLS models. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights. The optimal LS-SVM model was achieved with SWs of 550-560 nm, 562-574 nm, 670-680 nm and 705-71 5 nm for the leaf samples; 548-556 nm, 559-564 nm, 678-685 nm and 962-974 nm for the green fruit samples; and 712-718 nm, 720-729 nm, 968-978 nm and 820-830 nm for the mature fruit samples. All of them had better performance than PLS and LV-LS-SVM, with the parameters of correlation coefficient (rp), root mean square error of prediction (RMSEP) and bias of 0.9792, 0.2632 and 0.0901 based on leaf discrimination, 0.9837, 0.2783 and 0.1758 based on green fruit discrimination, 0.9804, 0.2215 and -0.0035 based on mature fruit discrimination, respectively. The overall results indicated that ICA was an effective way for the selection of SWs, and the Vis/NIR combined with LS-SVM models had the capability to predict the different breeds (mutant M1, mutant M2 and their parent) of tomatoes from leafs and fruits.

[Microalgae Species Identification Study with Raman Microspectroscopy Technology].

Identification and classification of microalgae are basis and premise in the study of physiological and biochemical characteristics for microalgae. Microalgae cells mainly consist of five kinds of biological molecules, including proteins, carbonhydrates, lipids, nucleic acids and pigments. These five kinds of biological molecules contents with different ratio in microalgae cells can be utilized to identify microalgae species as a supplement method. This paper investigated the application of Raman microspectroscopy technology in the field of rapid identification on different algae species such as aschlorella sp. and chlamydomonas sp. . Cultivated in the same conditions of culture medium, illumination duration and intensity, these two kinds of species of microalgae cells were immobilized by using agar, and then the samples were placed under 514. 5 nm Raman laser to collect Raman spectra of different growth periods of different species. An approach to remove fluorescence background in Raman spectra called Rolling Circle Filter (RCF) algorithm was adopted to remove the fluorescent background, and then some preprocessing methods were used to offset the baseline and smooth method of Savitzky-Golay was tried to make the spectra curves of total 80 samples smoother. Then 50 samples were randomly extracted from 80 samples for modeling, and the remaining 30 samples for independent validation. This paper adopted different pretreatment methods, and used the partial least squares (PLS) to establish model between the spectral data and the microalgae species, then compared the effects of different pretreatment methods. The results showed that with Raman microspectroscopy technology, the pretreatment method of max-peak ratio standardization was a more effective identification approach which utilizes the different content ratios of pigments of different microalgae species. This method could efficiently eliminate the influence on Raman signal due to different growth stages of microalgae and decomposition of pigments contents of microalgae in vivo. Compared with other traditional classification methods, this method had significant advantages like simpler procedure and shorter testing time, and it can also avoid some subjective measurement errors caused by unskilled operations. If the threshold was set to +/- 0.5, the prediction accuracy can reach 100%, and when the threshold was +/- 0.2, the prediction accuracy reached 86.67%, which proves the proposed new method can be a good approach to identify different algae varieties.

Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant.

Visible/near-infrared (Vis/NIR) hyperspectral imaging was employed to determine the spatial distribution of total nitrogen in pepper plant. Hyperspectral images of samples (leaves, stems, and roots of pepper plants) were acquired and their total nitrogen contents (TNCs) were measured using Dumas combustion method. Mean spectra of all samples were extracted from regions of interest (ROIs) in hyperspectral images. Random frog (RF) algorithm was implemented to select important wavelengths which carried effective information for predicting the TNCs in leaf, stem, root, and whole-plant (leaf-stem-root), respectively. Based on full spectra and the selected important wavelengths, the quantitative relationships between spectral data and the corresponding TNCs in organs (leaf, stem, and root) and whole-plant (leaf-stem-root) were separately developed using partial least-squares regression (PLSR). As a result, the PLSR model built by the important wavelengths for predicting TNCs in whole-plant (leaf-stem-root) offered a promising result of correlation coefficient (R) for prediction (RP = 0.876) and root mean square error (RMSE) for prediction (RMSEP = 0.426%). Finally, the TNC of each pixel within ROI of the sample was estimated to generate the spatial distribution map of TNC in pepper plant. The achievements of the research indicated that hyperspectral imaging is promising and presents a powerful potential to determine nitrogen contents spatial distribution in pepper plant.