Rapid Detection of Total Petroleum Hydrocarbons in Soil Using Advanced Fluorescence Imaging Techniques.

Chemical methods for measuring soil organic content are often slow and yield inaccurate results due to significant errors. Simple summation of components may not accurately determine total organic content. In contrast, fluorescence imaging techniques offer rapid, in situ monitoring without complex pretreatment and demonstrate rapid and accurate assessment of soil organic content. Utilizing a soil organic pollutant fluorescence imaging in situ monitoring system that we independently developed, we conducted laboratory experiments to explore methods for acquiring fluorescence signals of petroleum hydrocarbons in soil and extracting image features. We used this monitoring system to obtain fluorescence images of crude oil in standard soil (soil properties are shown in Table S1) samples at concentrations ranging from 0 to 100 g/kg, and the coefficient of determination of the total amount inversion model reached 0.999. Simultaneously, we applied the system to a deserted petroleum storage area, and the relative standard deviation values of 16 of the 18 groups of tests were less than 1%, indicating that the monitoring system is highly stable when applied in the field. This study provides both theoretical foundation and technical support for the rapid and nondestructive detection of total petroleum hydrocarbons in soil at field sites.

Determination of phenolic compounds in water using a multivariate statistical analysis method combined with three-dimensional fluorescence spectroscopy.

Phenolic compounds are toxic chemical pollutants present in water. Three-dimensional fluorescence spectroscopy analysis is an effective and rapid method for real-time phenol monitoring in aquatic environments. However, similar chemical structures of phenols result in highly overlapping three-dimensional fluorescence spectra. Therefore, it is extremely difficult to analyze and quantify the concentration of components in a mixture system that includes two or more phenolic compounds. In this article, we study the mixed phenol system containing phenol, o-cresol, p-cresol, m-cresol, catechol, and resorcinol combined with excitation-emission matrix (EEM) fluorescence data. A multivariate statistical method called best linear unbiased prediction (BLUP) is proposed to analyze the spectra with the aim to achieve quantitative results and a trilinear decomposition algorithm called parallel factor analysis (PARAFAC) was used for comparison. Two experiments with different calibration samples were set to validate the effectiveness of BLUP through recovery, ARecovery (Average Recovery), AREP (Average Relative Error of Prediction), and RMSE (Root Mean Square Error). Overall, the average recovery of each component in experiment 1 and experiment 2 ranged from 95.91% to 111.62% and 82.91% to 129.02%, respectively. Based on the results of the experiments, the concentration of phenolic compounds in water can be quantitatively determined by combining three-dimensional fluorescence spectroscopy with the BLUP method.

Phytoplankton Image Segmentation and Annotation Method Based on Microscopic Fluorescence.

Microscopic phytoplankton segmentation is an important part of water quality assessment. The segmentation of microscopic phytoplankton still faces challenges for computer vision, such as being affected by background impurities and requiring a large number of manual annotation. In this paper, the characteristics of phytoplankton emitting fluorescence under excitation light were utilized to segment and annotate phytoplankton contours by fusing fluorescence images and bright field images. Morphological operations were used to process microscopic fluorescence images to obtain the initial contours of phytoplankton. Then, microscopic bright field images were processed by Active Contour to fine tune the contours. Seven algae species were selected as the experimental objects. Compared with manually labeling the contour in LabelMe, the recall, precision, FI score and IOU of the proposed segmentation method are 85.3%, 84.5%, 84.7%, and 74.6%, respectively. Mask-RCNN was used to verify the correctness of labels annotated by the proposed method. The average recall, precision, F1 score and IOU are 97.0%, 86.5%, 91.1%, and 84.2%, respectively, when the Mask-RCNN is trained with the proposed automatic labeling method. And the results corresponding to manual labeling are 95.3%, 86.1%, 90.3%, and 82.8% respectively. The experimental results show that the proposed method can segment the phytoplankton microscopic image accurately, and the automatically annotated contour data has the same effect as the manually annotated contour data in Mask-RCNN, which greatly reduces the manual annotation workload.

Study on Calibration Method for the Count of Living Algal Cells Detection Based on Variable Fluorescence in Ballast Water.

In the monitoring the discharge of ballast water, the count of living algal cells is of utmost significant. Variable fluorescence, denoted as Fv, stands as an optimal parameter for photosynthetic fluorescence, efficiently charactering the living algal cells count, unaffected by the ballast waters' complex background fluorescence environment. This study deeply investigates the quantitative relationship between Fv and the count of living algal cells. Observations indicate that single cell fluorescence yield (abbreviated as SCF) varies significantly across different algae species, leading to considerable errors in quantifying living algal cell count in ballast water with unknown components using the calibration relationship between Fv and the cell count. Thus, correcting SCF prior to calibration becomes necessary. The paper proposes an innovative SCF correction method based on cell cross-sectional area and an eµ factor (where µ is the expected value of the functional absorption cross-section of PSII) This method mitigates the influence of cell size and species differences on quantifying the living algal cell count. Correction operation trials revealed that dividing the SCF measurement by cell cross-sectional area and multiplying by eµ enhanced the correction effect. Comparative experiments demonstrated marked improvement: Relative errors (REs) for Chlorella pyrenoidosa and Chlorella marine, both belonging to the Chlorophyta group, fell from 92.1% and 90.6% to 37.2% and 9.5% respectively post-correction. Similarly, REs for Thalassiosira weissflogii and Nitzschia closterium minutissima, from the Bacillariophyta group, decreased from 74.7% and 68.1% to 14.3% and 19.1% respectively. The RE of Peridinium from the Pyrrophyta group dropped from 28.4% to 12.1%. The results underscore the effectiveness of cell cross-sectional area and eµ in correcting SCF, thus offering a novel correction method for swift and precise measurement of living algal cell count in ballast water, based on variable fluorescence.

Quantitative Representation of Water Quality Biotoxicity by Algal Photosynthetic Inhibition.

The method based on the photosynthetic inhibition effect of algae offers the advantages of swift response and straightforward measurement. Nonetheless, this effect is influenced by both the environment and the state of the algae themselves. Additionally, a single parameter is vulnerable to uncertainties, rendering the measurement accuracy and stability inadequate. This paper employed currently utilized photosynthetic fluorescence parameters, including Fv/Fm(maximum photochemical quantum yield), Performance Indicator (PIabs), Comprehensive Parameter Index (CPI) and Performance Index of Comprehensive Toxicity Effect (PIcte), as quantitative toxicity characteristic parameters. The paper compared the univariate curve fitting results with the multivariate data-driven model results and investigated the effectiveness of Back Propagation(BP) Neural Network and Support Vector Machine for Regression (SVR) models to enhance the accuracy and stability of toxicity detection. Using Dichlorophenyl Dimethylurea (DCMU) samples as an example, the mean Relative Root Mean Square Error (RRMSE) corresponding to the optimal parameter PIcte for the dose-effect curve fitting was 1.246 in the concentration range of 1.25-200 µg/L. On the other hand, the mean RRMSEs corresponding to the results of the BP neural network and SVR models were 0.506 and 0.474, respectively. Notably, BP neural network exhibited excellent prediction accuracy in the medium-high concentration range of 7.5-200 µg/L, with a mean RRSME of only 0.056. Regarding the stability of the results, the mean Relative Standard Deviation (RSD) of the univariate dose-effect curve results was 15.1% within the concentration range of 50-200 µg/L. In contrast, the mean RSDs for both BP neural network and SVR results were less than 5%. In the concentration range of 1.25-200 µg/L, the mean RSDs were 6.1% and 16.5%, with the BP neural network performing well. The experimental results of Atrazine were analyzed to further validate the effectiveness of the BP neural network in improving the accuracy and stability of results. These findings provided valuable insights for the development of biotoxicity detection by using the algae photosynthetic inhibition method.

A Sensitive Response Index Selection for Rapid Assessment of Heavy Metals Toxicity to the Photosynthesis of Chlorella pyrenoidosa Based on Rapid Chlorophyll Fluorescence Induction Kinetics.

Heavy metals as toxic pollutants have important impacts on the photosynthesis of microalgae, thus seriously threatening the normal material circulation and energy flow of the aquatic ecosystem. In order to rapidly and sensitively detect the toxicity of heavy metals to microalgal photosynthesis, in this study, the effects of four typical toxic heavy metals, chromium (Cr(VI)), cadmium (Cd), mercury (Hg), and copper (Cu), on nine photosynthetic fluorescence parameters (φPo, ΨEo, φEo, δRo, ΨRo, φRo, FV/FO, PIABS, and Sm) derived from the chlorophyll fluorescence rise kinetics (OJIP) curve of microalga Chlorella pyrenoidosa, were investigated based on the chlorophyll fluorescence induction kinetics technique. By analyzing the change trends of each parameter with the concentrations of the four heavy metals, we found that compared with other parameters, φPo (maximum photochemical quantum yield of photosystem II), FV/FO (photochemical parameter of photosystem II), PIABS (photosynthetic performance index), and Sm (normalized area of the OJIP curve) demonstrated the same monotonic change characteristics with an increase in concentration of each heavy metal, indicating that these four parameters could be used as response indexes to quantitatively detect the toxicity of heavy metals. By further comparing the response performances of φPo, FV/FO, PIABS, and Sm to Cr(VI), Cd, Hg, and Cu, the results indicated that whether it was analyzed from the lowest observed effect concentration (LOEC), the influence degree by equal concentration of heavy metal, the 10% effective concentration (EC10), or the median effective concentration (EC50), the response sensitivities of PIABS to each heavy metal were all significantly superior to those of φRo, FV/FO, and Sm. Thus, PIABS was the most suitable response index for sensitive detection of heavy metals toxicity. Using PIABS as a response index to compare the toxicity of Cr(VI), Cd, Hg, and Cu to C. pyrenoidosa photosynthesis within 4 h by EC50 values, the results indicated that Hg was the most toxic, while Cr(VI) toxicity was the lowest. This study provides a sensitive response index for rapidly detecting the toxicity of heavy metals to microalgae based on the chlorophyll fluorescence induction kinetics technique.

Fast online evaluation of vegetable freshness using single turnover chlorophyll fluorescence.

Vegetable freshness evaluation is of great significance to ensure the quality of vegetables and realize fine production. Existing vegetable freshness evaluation methods have difficulty realizing rapid online evaluation and industrial applications due to such disadvantages as being susceptible to subjective factors, complicated operation, large computation, and high hardware cost. To solve the above problems, a rapid online vegetable freshness evaluation method was developed based on the single turnover chlorophyll fluorescence parameters Fo', Fv'/Fm' and σPSII'. A freshness evaluation model for spinach and swamp cabbage was established based on a classification and regression tree algorithm, using Fo', Fv'/Fm' and σPSII' as sample features. The model divided the freshness of spinach and swamp cabbage into three grades: good, medium, and poor, and the leave-one-out cross validation results showed that the freshness evaluation accuracies of spinach and swamp cabbage reached 98.1% and 94.3%, respectively.

A new method for detecting mixed bacteria based on multi-wavelength transmission spectroscopy technology.

Previously, we successfully realized the identification of a single species of bacteria based on the multi-wavelength transmission spectrum of bacteria. The current research is focused on realizing the spectral analysis of mixed bacteria. Principal component analysis-Monte Carlo (PCA-MC) model was developed for the implementation of spectral separation of mixed bacteria by obtaining the ratio of components. And, the separated spectrum was regarded as the model input of the neural network concentration inversion model to obtain the concentration of each bacteria in the mix. Mean relative errors in component analysis of mixing S.aureus with K.pneumoniae, mixing S.aureus with S.typhimurium twice, mixing K.pneumoniae with S.typhimurium are 3%, 2%, 3.9% and 6.1%, respectively. The coefficient of determination (R2) of validation set and test set are 0.9947 and 0.9954 in concentration inversion model. The results show that this method can quickly and accurately determine the component ratio and concentration information in the mixed bacteria. A new method was proposed to separate the spectrum of mixed bacteria effectively and measure its concentration quickly, which makes a big step forward in the detection and online monitoring of waterborne microbial contamination based on multi-wavelength transmission spectroscopy.

A method based on a one-dimensional convolutional neural network for UV-vis spectrometric quantification of nitrate and COD in water under random turbidity disturbance scenario.

This paper proposed a novel spectrometric quantification method for nitrate and COD concentration in water using a double-channel 1-D convolution neural network for relatively long UV-vis absorption spectra data (2600 points). To improve the model's ability to resist turbidity disturbance, a new dataset augmentation method was applied and the absorption spectra of nitrate and COD under different turbidity disturbances were successfully simulated. Compared to the PLSR model, the value of RRMSEP for the CNN model was reduced from 6.1% to 1.4% in nitrate solution and 4.5% to 1.3% in COD solution. Compared to the PLSR model, the regression accuracy of the CNN model was increased from 56% to 93% in nitrate solution and 68% to 91% in COD solution. The test on the actual solution under different turbidity disturbances shows that the 1D-CNN model had a bias rate of less than 2% in both nitrate and COD solutions, while the worst bias rate in the PLSR method was 15%.

Rapid and Sensitive Detection of Water Toxicity Based on Photosynthetic Inhibition Effect.

To achieve rapid and sensitive detection of the toxicity of pollutants in the aquatic environment, a photosynthetic inhibition method with microalgae as the test organism and photosynthetic fluorescence parameters as the test endpoint was proposed. In this study, eight environmental pollutants were selected to act on the tested organism, Chlorella pyrenoidosa, including herbicides (diuron, atrazine), fungicides (fuberidazole), organic chemical raw materials (phenanthrene, phenol, p-benzoquinone), disinfectants (trichloroacetonitrile uric acid), and disinfection by-products (trichloroacetonitrile). The results showed that, in addition to specific PSII inhibitors (diuretic and atrazine), other types of pollutants could also quickly affect the photosynthetic system. The photosynthetic fluorescence parameters (Fv/Fm, Yield, α, and rP) could be used to detect the effects of pollutants on the photosynthetic system. Although the decay rate of the photosynthetic fluorescence parameters corresponding to the different pollutants was different, 1 h could be used as an appropriate toxicity exposure time. Moreover, the lowest respondent concentrations of photosynthetic fluorescence parameters to diuron, atrazine, fuberidazole, phenanthrene, P-benzoquinone, phenol, trichloroacetonitrile uric acid, and trichloroacetonitrile were 2 µg·L-1, 5 µg·L-1, 0.05 mg·L-1, 2 µg·L-1, 1.0 mg·L-1, 0.4 g·L-1, 0.1 mg·L-1, and 2.0 mg·L-1, respectively. Finally, diuron, atrazine, fuberidazole, and phenanthrene were selected for a comparison of their photosynthetic inhibition and growth inhibition. The results suggested that photosynthetic inhibition could overcome the time dependence of growth inhibition and shorten the toxic exposure time from more than 24 h to less than 1 h, or even a few minutes, while, the sensitivity of the toxicity test was not weakened. This study indicates that the photosynthetic inhibition method could be used for rapid detection of the toxicity of water pollutants and that algae fluorescence provides convenient access to toxicity data.

Artificial neural networks combined multi-wavelength transmission spectrum feature extraction for sensitive identification of waterborne bacteria.

Present research is focused on the rapid and accurate identification of bacterial species based on artificial neural networks combined with spectral data processing technology. The spectra of different bacterial species in the logarithmic growth phase were obtained. Model input features were extracted from the raw spectra using signal processing techniques, including normalization, principal component analysis (PCA) and area-based feature value extraction. The identification models based on artificial neural network of back propagation neural networks (BPNN), generalized regression neural networks (GRNN) and probabilistic neural networks (PNN) were developed using the extracted features in order to ascertain whether the different species of bacteria could be differentiated. The performance of developed models and its corresponding signal processing techniques is tested by the recognition accuracy of validation set and test set, and model error. The maximum recognition accuracy of normalized spectrum combined with BPNN was 95.5% (error: 10%, test accuracy: 100%). The total recognition accuracy of PCA-reduced features (200-400 nm) combined with GRNN resulted in 96.3%~96.8% (error: 3.3%~6.7%, test accuracy: 97.5%~100%). While the overall recognition accuracy of area-based features combined with GRNN reached 97.3% with test accuracy of 100% (model error: 5.0%). Choosing of model and signal processing techniques has a positive influence on improving classification accuracy, so as to make it possible to realize the rapid detection and online monitoring of waterborne microbial contamination.

Simultaneous determination of nitrate, chemical oxygen demand and turbidity in water based on UV-Vis absorption spectrometry combined with interval analysis.

In this paper, a new method for simultaneous determination of nitrate, COD and turbidity in water based on UV-Vis absorption spectrometry combined with interval analysis was studied. By analyzing the spectral absorption characteristics of nitrate, COD, and turbidity standard solutions and the mixtures of them, the absorption spectra in the range of 225-260 nm, 260-320 nm and 320-700 nm were selected as the characteristic spectra of nitrate, COD and turbidity, respectively. Multiplicative scatter correction was employed to compensate turbidity of the absorption spectra of the mixture solutions in the wavelength range of 225-320 nm. Then, the spectra after turbidity compensation in the range of 225-260 nm was compensated for COD using the method of spectral difference. The original spectra in the range of 320-700 nm, the turbidity compensated spectra in the range of 260-320 nm, and the COD compensated spectra in the range of 225-260 nm were analyzed by PLS algorithm in order to calculate the concentrations of nitrate, COD and turbidity in the mixture solutions. The results showed that this method could simultaneously and accurately determine the concentrations of nitrate, COD and turbidity. After interval analysis, all the correlation coefficients (R2) between the predicted values and the true values of nitrate, COD and turbidity were higher than 0.9, and root mean square error (RMSE) of predicted values were between 0.696 and 2.337.

Molecularly imprinted polymer coated Mn-doped ZnS quantum dots embedded in a metal-organic framework as a probe for selective room temperature phosphorescence detection of chlorpyrifos.

As one of the most widely used organophosphorus pesticides, chlorpyrifos (CPF) is toxic to humans. However, the rapid, effective and sensitive detection of CPF is still a challenge. In this paper, a novel molecularly imprinted phosphorescent sensor with a core-shell structure (Mn:ZnS QDs@ZIF-8@MIP) using Mn:ZnS quantum dots (QDs) as phosphorescent emitters was prepared for the highly sensitive and selective detection of CPF, and a simple and rapid room-temperature phosphorescence (RTP) detection method for CPF was proposed. For the prepared Mn:ZnS QDs@ZIF-8@MIP, Mn:ZnS QDs had good phosphorescence emission characteristics, ZIF-8 as support materials was used to improve the dispersibility of Mn:ZnS QDs, and molecularly imprinted polymer (MIP) on the surface of ZIF-8 was used to improve the selectivity of Mn:ZnS QDs for CPF. Under the optimal response conditions, the RTP intensity of Mn:ZnS QDs@ZIF-8@MIP showed a rapid response to CPF (less than 5 min), the RTP intensity ratio of P 0/P had a good linear relationship with the concentration of CPF in the range of 0-80 µM, and the detection limit of this method was 0.89 µM with the correlation coefficient of 0.99. Moreover, this simple and rapid method has been successfully used to detect CPF in real water samples with satisfactory results, and the recoveries ranged from 92% to 105% with a relative standard deviation of less than 1%. This method combines the advantages of phosphorescence emission and molecular imprinting, and greatly reduces the potential interferences of competitive substances, background fluorescence and scattered light, which opens up a broad prospect for the highly sensitive and selective detection of pollutants in water based on molecularly imprinted phosphorescent sensors.

Turbidity compensation method based on Mie scattering theory for water chemical oxygen demand determination by UV-Vis spectrometry.

In view of the problem that chemical oxygen demand (COD) measurement in water using UV-Vis spectrometry was easily affected by turbidity, this paper proposed an analytical method for determining the complex refractive index of particles in water based on Lambert-Beer's law and K-K (Kramers-Kronig) relationship. The obtained complex refractive index was used to establish the turbidity compensation model in the COD characteristic spectral region, and the COD concentration inversion were achieved by using the PLS algorithm. The results show that the turbidity compensation method based on Mie scattering theory can improve the accuracy of COD measurement by UV-Vis spectroscopy. Compared with before turbidity compensation, R2 (determination coefficient) between true values and predicted values of COD increased from 0.2274 to 0.9629, and RMSE (root mean square error) of predicted values decreased from 21.73 to 3.12 mg L-1. Compared with 350 nm PC, derivative method, and improved MSC method, the turbidity compensation method for COD measurement based on Mie scattering theory is simple, fast, and highly accurate. And the calculated spectrum can represent the scattering characteristics of the measured spectrum. The average relative error between the fitted spectrum and the original normalized spectrum in the 55 mixed solutions was 0.52%, and the maximum relative error was 6.65%. This method can be useful for online COD measurement. Graphical abstract.

Preconcentration with Chlorella vulgaris combined with energy dispersive X-ray fluorescence spectrometry for rapid determination of Cd in water.

Freshwater green algae Chlorella vulgaris was selected as an adsorbent, and a simple, rapid, economical and environmentally friendly method for the detection of heavy metal Cd in water samples based on preconcentration with C. vulgaris combined with energy dispersive X-ray fluorescence (EDXRF) spectrometry was proposed.  Chlorella vulgaris could directly and rapidly adsorb Cd2+ without any pretreatment, and the maximum adsorption efficiency could be obtained when the contact time was 1 min with an optimal pH of 10. The obtained Cd-enriched thin samples after preconcentration with C. vulgaris by suction filtration of reaction solution had very good uniformity, which could be directly measured by EDXRF spectrometry, and the net integral fluorescence intensity of Cd Kα characteristic peak had a very good linear relationship with the initial concentration of Cd in the range of 0.703-74.957 µg ml-1 with a correlation coefficient of 0.9979. When the Cd thin samples with a Cd-enriched region of 15.1 mm in diameter were formed by the developed preconcentration method with suction filtration of 10 ml reaction solution, the detection limit of this method was 0.0654 µg ml-1, which was lower than the maximum allowable discharge concentration of Cd in various industrial wastewaters. The proposed method was simple to operate, and could effectively remove the influence of matrix effect of water samples and effectively improve the sensitivity and stability of EDXRF spectrometry directly detecting heavy metals in water samples, which was successfully applied to detect Cd in real water samples with satisfactory results, and the recoveries ranged from 94.80% to 116.94%. Moreover, this method can be applied to the rapid detection and early warning of excessive Cd in discharged industrial wastewaters. This work will provide a methodological basis for the development of rapid and online monitoring technology and instrument of heavy metal pollutants in water.

Optimal chlorophyll fluorescence parameter selection for rapid and sensitive detection of lead toxicity to marine microalgae Nitzschia closterium based on chlorophyll fluorescence technology.

Heavy metal pollution as one of the most serious pollution problems of marine environment, seriously threatens the safety of marine organism and human health, and will lead to potential risks for the marine ecological environment. In order to develop a rapid and sensitive toxicity detection method for marine heavy metals, in this study, marine diatom Nitzschia closterium was used as the test organism, and the effects of different concentrations of lead (Pb) on the five chlorophyll fluorescence parameters of N. closterium including the maximum photochemical quantum yield of PSII (Fv/Fm), the effective quantum yield of PSII photochemical energy conversion (ΦPSII), the effective absorption cross section of PSII photochemistry (σPSII'), the relative electron transfer rate of PSII (rP), and the PSII electron flux per unit volume (JVPII) at different exposure times were investigated based on chlorophyll fluorescence technology. By comparing with the photosynthetic activity fluorescence parameter Fv/Fm which is commonly used for toxicity analysis of pollutants using algae as test organisms, the optimal chlorophyll fluorescence parameter that could rapidly and sensitively determine Pb toxicity to N. closterium was selected. The results indicate that all the five chlorophyll fluorescence parameters of Fv/Fm, ΦPSII, σPSII', rP and JVPII showed good dose-response relationships with Pb within 8 h exposure time, and they all could be used as endpoints to rapidly determine Pb toxicity to N. closterium. Among the five chlorophyll fluorescence parameters, JVPII was the most sensitive fluorescence parameter for detecting the toxicity of Pb to N. closterium within 6 h exposure. And for JVPII, the median effective concentration (EC50) values of Pb at 2, 4 and 6 h were 0.329, 0.068 and 0.040 mmol L-1, respectively. However, when the exposure time was 8 h, ΦPSII was the most sensitive fluorescence parameter for the toxicity detection of Pb, and the EC50 value of Pb at 8 h was 0.038 mmol L-1. This study will provide an important basis for the development of a rapid and sensitive detection method for the biological toxicity of marine heavy metals, and those results will be helpful for ecological risk assessment in marine environment.

On-line analysis of algae in water by discrete three-dimensional fluorescence spectroscopy.

In view of the problem of the on-line measurement of algae classification, a method of algae classification and concentration determination based on the discrete three-dimensional fluorescence spectra was studied in this work. The discrete three-dimensional fluorescence spectra of twelve common species of algae belonging to five categories were analyzed, the discrete three-dimensional standard spectra of five categories were built, and the recognition, classification and concentration prediction of algae categories were realized by the discrete three-dimensional fluorescence spectra coupled with non-negative weighted least squares linear regression analysis. The results show that similarities between discrete three-dimensional standard spectra of different categories were reduced and the accuracies of recognition, classification and concentration prediction of the algae categories were significantly improved. By comparing with that of the chlorophyll a fluorescence excitation spectra method, the recognition accuracy rate in pure samples by discrete three-dimensional fluorescence spectra is improved 1.38%, and the recovery rate and classification accuracy in pure diatom samples 34.1% and 46.8%, respectively; the recognition accuracy rate of mixed samples by discrete-three dimensional fluorescence spectra is enhanced by 26.1%, the recovery rate of mixed samples with Chlorophyta 37.8%, and the classification accuracy of mixed samples with diatoms 54.6%.

Phytoplankton photosynthetic rate measurement using tunable pulsed light induced fluorescence kinetics.

The photosynthetic process of phytoplankton is the basis of the material circulation and energy flow of the ecosystem. The rapid and accurate measurement of phytoplankton photosynthesis rate is of great significance to water ecological environment monitoring, marine resource assessment and global climate change prediction. On the basis of "Bio-Optical" model, a photosynthetic rate measurement method based on tunable pulsed light induced fluorescence kinetics was put forward in this paper. The chlorophyll fluorescence was used as the probe of photosynthesis process, and the phytoplankton photosynthetic rate was evaluated by the photosynthetic electron transport rate. Comparative experiment results showed that the photosynthetic electron transport rate measured by fluorescence kinetic method under different conditions of DCMU, culture light and nutrients (nitrogen) were consistent with the photosynthetic oxygen evolution rate measured by oxygen evolution method, and the correlation coefficient R2 were 0.934, 0.957 and 0.955 respectively.

Underdetermined blind separation of three-way fluorescence spectra of PAHs in water.

In this work, underdetermined blind decomposition method is developed to recognize individual components from the three-way fluorescent spectra of their mixtures by using sparse component analysis (SCA). The mixing matrix is estimated from the mixtures using fuzzy data clustering algorithm together with the scatters corresponding to local energy maximum value in the time-frequency domain, and the spectra of object components are recovered by pseudo inverse technique. As an example, using this method three and four pure components spectra can be blindly extracted from two samples of their mixture, with similarities between resolved and reference spectra all above 0.80. This work opens a new and effective path to realize monitoring PAHs in water by three-way fluorescence spectroscopy technique.

[Rapid Quantitative Detection of Bacterial in Water Based on Multi-Wavelength Scattering Spectra].

The structures of bacterial cells are analyzed in this paper. The scattering components of individual cell were divided into two parts including external structure and internal structure. The interpretation model of bacteria about scattering light is established. The model is used to analyze the scattering light of Escherichia coli in the region of 400~900 nm. The average size of external structure and the internal structure can be obtained, and the ratio of the two parts is also obtained. According to the relationship of the optical density of single cell and the overall measurement, the concentration of bacterial can be obtained quickly. The maximum difference in all the concentrations of the bacteria repeated measurements is 1.83%; compared with the plate culture method, the measurement results were in the same order of magnitude, with relative error of 3.43%. The scattering light of Escherichia coli and Klebsiella pneumoniae are analyzed in different growth stages, the curves of the concentration and the size of the two species bacteria over time are obtained. The results can provide a quick way for the study of bacterial growth and technical support for rapid detection of bacteria in the water.