Spatio-temporal variation of water salinity in mangroves revealed by continuous monitoring and its relationship to floristic diversity.

Salinity is among the most critical factors limiting the growth and species distribution of coastal plants. Water salinity in estuarine ecosystems varies temporally and spatially, but the variation patterns across different time scales and salinity fluctuation have rarely been quantified. The effects of salinity on floristic diversity in mangroves are not fully understood due to the temporal and spatial heterogeneity of salinity. In this study, we monitored water salinity at an interval of 10-min over one year in three mangrove catchment areas representing the outer part, middle part, and inner part respectively of Dongzhai Bay, Hainan, China. The number of mangrove community types and dominant mangrove species of the three catchment areas were also investigated. We found that the diurnal variation and dry-season intra-month variation in water salinity were driven by tidal cycles. The seasonal variation in water salinity was mainly driven by rainfall with higher salinity occurring in the dry season and lower salinity occurring in the wet season. Spatially, water salinity was highest at the outer part, intermediate at the middle part, and lowest at the inner part of the bay. The intra-month and annual fluctuations of water salinity were highest at the middle part and lowest at the outer part of the bay. The number of mangrove community types and dominant species were lowest at the outer part, intermediate at the middle part, and highest at the inner part of the bay. These results suggest that the temporal variation of water salinity in mangroves is driven by different factors at different time scales and therefore it is necessary to measure water salinity at different time scales to get a complete picture of the saline environment that mangroves experience. Spatially, lower salinity levels benefit mangrove species richness within a bay landscape, however, further research is needed to distinguish the effects of salinity fluctuation and salinity level in affecting mangrove species richness.

Increasing carbon and nutrient burial rates in mangroves coincided with coastal aquaculture development and water eutrophication in NE Hainan, China.

Mangroves sequester and store large area-specific quantities of blue carbon (C) and essential nutrients such as nitrogen (N) and phosphorous (P). Quantifying C and nutrient burial rates in mangroves across a centennial time span and relating these rates to mangrove habitat is fundamental for elucidating the role of mangroves in carbon and nutrient budgets and their responses to environmental changes. However, relevant data are very limited in China. In this study, we used the radionuclides (210Pb and 137Cs) to determine chronologies and C, N and P burial rates in two mangrove forests located at different geomorphologic settings in NE Hainan Island, China. We found that the temporal patterns of C, N and P burial rates since 1900 fitted a quadratic function with a notable increase after 1960s in both mangroves, which coincided with the rapid development of coastal aquaculture since 1960s in NE Hainan and the subsequent coastal water eutrophication in this area. Sediment accretion rate (SAR) and mass accumulation rate (MAR) stayed relatively steady in the open-coastal mangroves, while they increased exponentially in the estuarine mangroves since 1900. The estuarine mangroves had significantly higher SAR and C, N and P burial rates than the open-coastal mangroves. C, N and P burial rates averaged at 141.52 g m-2 a-1, 6.27 g m-2 a-1 and 1.14 g m-2 a-1, respectively in the estuarine core, and these rates averaged at 61.71 g m-2 a-1, 3.71 g m-2 a-1 and 0.43 g m-2 a-1, respectively in the open-coastal core. The results suggest that estuarine mangroves may be more capable of surviving accelerating sea level rise under climate change and play a greater role in C accumulation and nutrient filtering under anthropogenic nutrient enrichment than marine-dominated mangroves. Blue C burial may be enhanced by coastal water eutrophication, but such a relationship needs to be tested in further studies.

Leaf hydraulics coordinated with leaf economics and leaf size in mangrove species along a salinity gradient.

Independence among leaf economics, leaf hydraulics and leaf size confers plants great capability in adapting to heterogeneous environments. However, it remains unclear whether the independence of the leaf traits revealed across species still holds within species, especially under stressed conditions. Here, a suite of traits in these dimensions were measured in leaves and roots of a typical mangrove species, Ceriops tagal, which grows in habitats with a similar sunny and hot environment but different soil salinity in southern China. Compared with C. tagal under low soil salinity, C. tagal under high soil salinity had lower photosynthetic capacity, as indicated directly by a lower leaf nitrogen concentration and higher water use efficiency, and indirectly by a higher investment in defense function and thinner palisade tissue; had lower water transport capacity, as evidenced by thinner leaf minor veins and thinner root vessels; and also had much smaller single leaf area. Leaf economics, hydraulics and leaf size of the mangrove species appear to be coordinated as one trait dimension, which likely stemmed from co-variation of soil water and nutrient availability along the salinity gradient. The intraspecific leaf trait relationship under a stressful environment is insightful for our understanding of plant adaption to the multifarious environments.

Fourier transform infrared spectroscopy and chemometrics for the discrimination of animal fur types.

Rapid and reliable animal fur identification has remained a challenge for customs inspection. The accurate distinction between fur types has a significant meaning in implementing the correct tariff policy. A variety of analytical methods have been applied to work on distinguishing animal fur types, with tools of microscopy, molecular testing, mass spectrometry, Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. In this research, the capability of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) combined with pattern recognition methods was investigated for the discrimination of animal fur in six types. This work was to explore the non-destructive application of ATR-FTIR technique in discriminant analysis of animal fur. All spectra were collected by ATR-FTIR of the wavenumber ranging from 4000 to 650 cm-1. Data pretreatments included moving average smoothing and multiplicative scatter correction (MSC). Four supervised classification algorithms were chosen to categorize the types of fur: soft independent modeling of class analogy (SIMCA), principal component analysis linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM). PLS-DA and LS-SVM were both effective approaches, with a 100% classification accuracy rate. The accuracy of PCA-LDA and SIMCA was 98.33% and 99.44%, respectively. Furthermore, LS-SVM model obtained using Monte-Carlo sampling method also obtained 100% prediction accuracy, while all other methods produced misclassification. LS-SVM corrected the non-linearities for the animal fur FTIR data but also remarkably improved the prediction performance level. The results of this study revealed that the combination of ATR-FTIR and chemometrics has a huge potential for animal fur discrimination.

Feature selection of infrared spectra analysis with convolutional neural network.

Data-driven deep learning analysis, especially for convolution neural network (CNN), has been developed and successfully applied in many domains. CNN is regarded as a black box, and the main drawback is the lack of interpretation. In this study, an interpretable CNN model was presented for infrared data analysis. An ascending stepwise linear regression (ASLR)-based approach was leveraged to extract the informative neurons in the flatten layer from the trained model. The characteristic of CNN network was employed to visualize the active variables according to the extracted neurons. Partial least squares (PLS) model was presented for comparison on the performance of extracted features and model interpretation. The CNN models yielded accuracies with extracted features of 93.27%, 97.50% and 96.65% for Tablet, meat, and juice datasets on the test set, while the PLS-DA models obtained accuracies with latent variables (LVs) of 95.19%, 95.50% and 98.17%. Both the CNN and PLS models demonstrated the stable patterns on active variables. The repeatability of CNN model and proposed strategies were verified by conducting the Monte-Carlo cross-validation.

Non-destructive analysis the dating of paper based on convolutional neural network.

A non-destructive method based on Fourier Transformed Infrared Spectroscopy (FT-IR) was proposed to estimate the date of paper from different years in this article. For the paper samples, dated from 1940 to 1980, naturally aged and conserved in library. Partial least squares-discriminate analysis (PLS-DA), Logistic regression and convolutional neural network (CNN), were employed to evaluate the date of paper, with the accuracy 60.74%, 95.31% and 98.77%, respectively. Based on the characteristics of CNN model and with the help of network localization, active variables could be recognized in the whole spectrum. Although the localization of active variables showed a discriminative pattern, the selected spectral regions were similar. Most important variables focused on the 1700-1400 cm-1, were corresponding to cellulose crystallinity, which was consisted with the ageing processing. The present work gave the potential of FT-IR combined with chemometric techniques could estimate the dating of unknown paper. Meanwhile, the analysis of active variables obtained further indicated the worthy of CNN model for document dating.

A new hybrid filter/wrapper algorithm for feature selection in classification.

Feature selection can greatly enhance the performance of a learning algorithm when dealing with a high dimensional data set. The filter method and the wrapper method are the two most commonly approaches. However, these approaches have limitations. The filter method uses independent evaluation to evaluate and select features, which is computationally efficient but less accurate than the wrapper method. The wrapper method uses a predetermined classifier to compute the evaluation, which can afford high accuracy for particular classifiers, but is computationally expensive. In this study, we introduce a new feature selection method that we refer to as the large margin hybrid algorithm for feature selection (LMFS). In this method, we first utilize a new distance-based evaluation function, in which ideally samples from the same class are close together, whereas samples from other classes are far apart, and a weighted bootstrapping search strategy to find a set of candidate feature subsets. Then, we use a specific classifier and cross-validation to select the final feature subset from the candidate feature subsets. Six vibrational spectroscopic data sets and three different classifiers, namely k-nearest neighbors, partial least squares discriminant analysis and least squares support vector machine were used to validate the performance of the LMFS method. The results revealed that LMFS can effectively overcome the over-fitting between the optimal feature subset and a given classifier. Compared with the filter and wrapper methods, the features selected by the LMFS method have better classification performance and model interpretation. Furthermore, LMFS can effectively overcomes the impact of classifier complexity on computational time, and distance-based classifiers were found to be more suitable for selecting the final subset in LMFS.

Bootstrapping search margin-based nearest neighbor method for qualitative spectroscopic analysis.

Qualitative spectroscopic analysis depends in one way or another on comparing spectra of the specimens to be identified with spectra of "known" or "standard" samples. The k-nearest neighbor (k-NN) method is one of the oldest and simplest techniques for performing such comparisons. In this study, we present a new k-NN algorithm for qualitative spectroscopic analysis, which we refer to as the bootstrapping search margin-based nearest neighbor (BSMNN) method. This method consists of two phases. In the first phase, we attempt to find a feature space in which samples with different labels produce large margins, such that the classification has high confidence, by maximizing a margin-based objective function using a weighted bootstrap sampling search strategy. In the subsequent classification phase, we compute local Euclidean distances between different samples under the feature space. A new instance x is classified by the label of its nearest neighbor. Six widely used vibrational spectroscopic data sets were used to validate the performance of the BSMNN method. The results showed that, despite its simplicity, BSMNN yields better results compared with commonly used k-NN algorithms including Relief, neighborhood components analysis, neighborhood component feature selection, and large margin nearest neighbor. Furthermore, BSMNN can be used to identify important spectral regions. It is worth mentioning that the margin-based objective function used in BSMNN is proposed for the first time for measuring the quality of features. Although in this study the margin-based objective function is focused on k-NN classification, it also can be used for other distance-based classifiers, such as soft independent modeling of class analogies and least squares support vector machine.

Bioaccumulation and translocation of tetrabromobisphenol A and hexabromocyclododecanes in mangrove plants from a national nature reserve of Shenzhen City, South China.

Brominated flame retardants (BFRs) such as tetrabromobisphenol A (TBBPA) and hexabromocyclododecanes (HBCDs) are of ecological concern due to their ubiquitous presence and adverse effects. There is a paucity of data on environmental fate of such compounds in mangrove wetlands, which are unique ecosystems in coastal intertidal areas and act as natural sinks for many pollutants. In this study, mangrove plants and sediments were collected from an urban nature reserve in South China to investigate bioaccumulation and translocation of TBBPA and HBCDs. The mean (range) concentrations of TBBPA and ΣHBCD in roots, stems and leaves were 67 (

The roots of blue carbon: responses of mangrove stilt roots to variation in soil bulk density.

Mangroves harbour large soil organic carbon (C) pools. These C stocks are attributed to the production and slow decomposition of the below-ground biomass. Novel in-growth containers were used to assess the effect of soil bulk density (BD: 0.4, 0.8 and 1.2 g cm-3) on morphological, anatomical and chemical traits of the below-ground fraction of aerial roots of the mangrove Rhizophora stylosa. Dense soils increased total root biomass and primary root diameter, while the primary root length decreased. Furthermore, high soil BD reduced aerenchyma lacunae and led to the formation of structural features such as fibrous strands. These morphological and anatomical changes were not reflected in tissue chemistry, with lignin levels averaging 17.0 ± 0.6%, although roots grown in high BD had higher nitrogen levels. This likely affects decomposition rates. Thus, variation in soil BD has major implications for C sequestration in Rhizophora-dominated mangroves.

Halogenated flame retardants in mangrove sediments from the Pearl River Estuary, South China: Comparison with historical data and correlation with microbial community.

Polybrominated diphenyl ethers (PBDEs), decabromodiphenyl ethane (DBDPE), 1,2-bis(2,4,6-tribromophenoxy)ethane (BTBPE), tetrabromobisphenol A (TBBPA), hexabromocyclododecane (HBCDD) and dechlorane plus (DP) were measured in sediments collected from three mangrove wetlands of the Pearl River Estuary (PRE) in South China. This study aims to investigate the distribution of these halogenated flame retardants (HFRs) and the correlations between HFRs and microbial community structure in mangrove sediments. Concentrations of PBDEs, DBDPE, BTBPE, TBBPA, HBCDD and DP in mangrove sediments ranged from 6.97 to 216.1, 3.70-26.0, 0.02-0.73, 0.02-37.5, 0.44-127.5 and 0.07-2.23 ng/g dry weight, respectively. Higher levels of PBDEs, BTBPE, HBCDD and DP were observed in sediments from Futian mangrove wetland of Shenzhen, the only nature reserve located in the downtown of China. The highest concentration of TBBPA found in mangrove sediments from Guangzhou was proximate to a ferry terminal and a dockyard where TBBPA is widely used in the coatings. PBDEs were the predominant HFRs in mangrove sediments, with an average contribution of 63.0%. Mangrove sediments from Guangzhou and Zhuhai showed an enrichment of (-)-α-HBCDD, (-)-ß-HBCDD and (-)-γ-HBCDD. Concentrations of HFRs in mangrove sediments from Guangzhou increased significantly from 2012 to 2015, which was probably due to the establishment and rapid development of Nansha Free Trade Zone of Guangzhou. Redundancy analysis showed that HFRs may cause a shift of microbial community structure in mangrove sediments and the variations were significantly correlated with TBBPA, syn-DP and BTBPE.

Fourier transform infrared spectroscopy and chemometrics for the discrimination of paper relic types.

The paper relic identification is a pending issue to be resolved in the field of cultural heritage. As we all known, heritage paper has significant importance in archaeological research. Nowadays, there are a variety of research methodologies focuses on the analysis of inks for dating documents. While the paper analysis attained little attention. This work is to explore the non-destructive application of ATR-FTIR technique in discrimination of paper relics. 15 types of paper spectra were collected by ATR-FTIR, which wavenumber range were range from 4000 to 650 cm-1. And the moving average smoothing and normalization was used for pretreatment analysis. Five different classification algorithms, principal component analysis-linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), least squares-support vector machine (LS-SVM), partial least squares-linear discriminant analysis (PLS-LDA) were selected to classify the types of paper. PLS-LDA and LS-SVM are effective techniques with 100% classification accuracy. PCA-LDA, PLS-DA and SIMCA give accuracy of 98.67%, 97.33% and 95.56%, respectively. The present experiment suggested that ATR-FTIR combining with chemometrics will be highly useful in paper identification of cultural heritage.

An ensemble variable selection method for vibrational spectroscopic data analysis.

Wavelength selection is a critical factor for pattern recognition of vibrational spectroscopic data. Not only does it alleviate the effect of dimensionality on an algorithm's generalization performance, but it also enhances the understanding and interpretability of multivariate classification models. In this study, a novel partial least squares discriminant analysis (PLSDA)-based wavelength selection algorithm, termed ensemble of bootstrapping space shrinkage (EBSS), has been devised for vibrational spectroscopic data analysis. In the algorithm, a set of subsets are generated from a data set using random sampling. For an individual subset, a feature space is determined by maximizing the expected 10-fold cross-validation accuracy with a weighted bootstrap sampling strategy. Then an ensemble strategy and a sequential forward selection method are applied to the feature spaces to select characteristic variables. Experimental results obtained from analysis of real vibrational spectroscopic data sets demonstrate that the ensemble wavelength selection algorithm can reserve stable and informative variables for the final modeling and improve predictive ability for multivariate classification models.

A modification of the bootstrapping soft shrinkage approach for spectral variable selection in the issue of over-fitting, model accuracy and variable selection credibility.

In this study, we proposed a new computational method stabilized bootstrapping soft shrinkage approach (SBOSS) for variable selection based on bootstrapping soft shrinkage approach (BOSS) which can enhance the analysis of chemical interest from the massive variables among the overlapped absorption bands. In SBOSS, variable is selected by the index of stability of regression coefficients instead of regression coefficients absolute value. In each loop, a weighted bootstrap sampling (WBS) is applied to generate sub-models, according to the weights update by conducting model population analysis (MPA) on the stability of regression coefficients (RC) of these sub-models. Finally, the subset with the lowest RMSECV is chosen to be the optimal variable set. The performance of the SBOSS was evaluated by one simulated dataset and three NIR datasets. The results show that SBOSS can select the fewer variables and supply the least RMSEP and latent variable number of the PLS model with the best stability comparing with methods of Monte Carlo uninformative variables elimination (MCUVE), genetic algorithm (GA), competitive reweighted sampling (CARS), stability of competitive adaptive reweighted sampling (SCARS) and BOSS.

Exotic earthworms maintain soil biodiversity by altering bottom-up effects of plants on the composition of soil microbial groups and nematode communities.

Bottom-up effects of plants on soil communities can be modified by the activity of exotic earthworms, by altering resource availability for soil food webs through feeding, burrowing, and casting activities. The present study explored effects of plants (planting of shrubs) on soil micro-food webs (composition of soil microbial and nematode communities), and whether these effects were altered by the activity of exotic earthworms (exotic earthworms addition). Planted shrubs resulted in a non-significant increase of bacterial biomass and significantly increased the abundance of different nematode trophic groups and total nematode biomass, indicating that planted shrubs had significant bottom-up effects on soil bacteria and nematodes. Planted shrubs decreased nematode diversity, evenness, and richness, but increased nematode dominance in the plots where the abundance of exotic earthworms was not amended. By contrast, these effects of shrub presence on soil biodiversity were not found in the plots that received exotic earthworms. In addition, planted shrubs increased the total energy flux to the nematode community. By contrast, the elevated activity of exotic earthworms mitigated the increase in total energy flux to nematodes in the presence of shrubs, and increased the ratio of fungal to bacterial PLFAs. Both of these changes indicate reduced energy flux in the plots with added exotic earthworms. Nematode diversity decreased, while nematode dominance increased with increasing total energy flux to nematodes, probably because few species benefited from high energy flux. Our study indicates that exotic earthworms can maintain soil biodiversity by reducing the energy flux through soil food webs.

Soil inorganic carbon in mangroves of tropical China: patterns and implications.

Soil inorganic carbon (IC) is neglected in most blue carbon studies despite the globally significant role of the calcium carbonate cycle in ocean C balance and climate change. We sampled soils to 1 m depth from seven mangrove reserves in Hainan Island, China. Only 45 out of 509 samples were rich in IC (greater than 10 mg cm-3). Most of the IC-rich samples were found at the outer part of Qinglan Bay, which is adjacent to the largest coral reef zone of Hainan Island. Soil IC concentration ranged from 0 to 66 g kg-1 (or 0-67 mg cm-3), accounting for 0-92% of total C. IC concentration increased with soil depth where it was abundant. Soil pH was low (2.36-6.59) in IC-depleted soils, but increased to 5.67-7.99 in IC-rich soils. Soil total C stock and IC stock in mangroves of Hainan amounted to 0.76×106 and 0.12×106 Mg, respectively, with IC accounting for 16% of total C. Our study finds that carbonate concentrations can be high in mangrove soils but their spatial distribution indicates they are largely allochthonous in origin. Evidence of carbonate dissolution in mangroves suggests mangroves may increase total alkalinity to buffer acidification in seawater.

Towards improvement in prediction of iodine value in edible oil system based on chemometric analysis of portable vibrational spectroscopic data.

Iodine value (IV) is a significant parameter to illustrate the quality of edible oil. In this study, three portable spectroscopy devices were employed to determine IV in mixed edible oil system, a new Micro-Electro-Mechanical-System (MEMS) Fourier Transform Infrared Spectrometer (MEMS-FTIR), a MicroNIRTM1700 and an i-Raman Plus-785S. Quantitative model was built by Partial least squares (PLS) regression model and four variable selection methods were applied before PLS model, which are Monte Carlo uninformative variables elimination (MCUVE), competitive reweighted sampling (CARS), bootstrapping soft shrinkage approach (BOSS) and variable combination population analysis (VCPA). The coefficient of determination (R2), and the root mean square error prediction (RMSEP) were used as indicators for the predictability of the PLS models. In MicroNIRTM1700 dataset, MCUVE gave the lowest RMSEP (2.3440), in MEMS-FTIR dataset, CARS showed the best performance with RMSEP (2.2185), in i-Raman Plus-785S dataset, BOSS gave the lowest RMSEP (2.5058). They all had great improvements than full spectrum PLS model. Four variable selection methods take a smaller number of variables and perform significant superiority in prediction accuracy. It was demonstrated that three new portable instruments would be suitable for the on-site determination of edible oil quality in infrared and Raman field.

Mangrove vegetation enhances soil carbon storage primarily through in situ inputs rather than increasing allochthonous sediments.

The role of soil carbon (C) in coastal wetlands as a net sink is related to the relative abundance of autochthonous versus allochthonous C. We aimed to investigate soil C sources and the pathways by which mangrove vegetation enhances soil C accumulation. We sampled soil to 1 m depth in seven oceanic mangrove forests and an adjacent un-vegetated mudflat at Dongzhai Bay, China. Stable C isotope technique was used to separate autochthonous and allochthonous C sources. Autochthonous C accounted for 27-97% of soil C stock in the top meter. Soil C density was 1.1-3.6 times higher in mangroves than in the mudflat. Among the increased soil C in mangroves relative to mudflat, autochthonous C accounted for 65-100% of the increments. The results suggest that mangrove vegetation enhances soil C storage primarily through in situ inputs, therefore the substantial soil C stocks commonly found in mangroves play an important role in sequestering atmospheric CO2.

Soil carbon storage in mangroves is primarily controlled by soil properties: A study at Dongzhai Bay, China.

Coastal wetlands are well known for their considerable capacity to store carbon (C). However, the spatial patterns and major controls of soil C concentration and C density in coastal wetlands remain poorly known. We measured soil total C concentration up to one meter depth and assessed environmental and biological factors influencing soil C input and decomposition processes across various geomorphologic settings and mangrove forest types at Dongzhai Bay, China. Structural equation modeling (SEM) was used to determine the causal pathways of influencing factors on soil C concentration. We found that the variation pattern of soil C concentration across geomorphologic settings and forest types was mirrored by soil properties. From 68 to 94% (varying with soil depth) variations of soil C concentration were explained by the inter-related influencing factors included in SEM. In the upper 60cm soil layers, soil moisture was the most important factor affecting soil C concentration. In the 60-100cm subsoil zone, the proportion of finer soil particles was the primary control of soil C concentration variation. In contrast, aboveground biomass and nearness of sampling site to the open water, which affect autochthonous and allochthonous C inputs, had relatively weak effects on soil C concentration compared to soil properties, which affect C decomposition. Soil C concentration was a good predictor of soil C density at all soil depths. The results suggest that top- and subsoil C concentrations in mangroves are subjected to different environmental controls, but taken together, mangrove soil C storage may be primarily controlled by soil property-mediated C decomposition rate. Subsoil C deserves more attention since it may respond differently to environmental changes than the better-known topsoil C.

Quantitative determination of additive Chlorantraniliprole in Abamectin preparation: Investigation of bootstrapping soft shrinkage approach by mid-infrared spectroscopy.

A novel method, mid-infrared (MIR) spectroscopy, which enables the determination of Chlorantraniliprole in Abamectin within minutes, is proposed. We further evaluate the prediction ability of four wavelength selection methods, including bootstrapping soft shrinkage approach (BOSS), Monte Carlo uninformative variable elimination (MCUVE), genetic algorithm partial least squares (GA-PLS) and competitive adaptive reweighted sampling (CARS) respectively. The results showed that BOSS method obtained the lowest root mean squared error of cross validation (RMSECV) (0.0245) and root mean squared error of prediction (RMSEP) (0.0271), as well as the highest coefficient of determination of cross-validation (Qcv2) (0.9998) and the coefficient of determination of test set (Q2test) (0.9989), which demonstrated that the mid infrared spectroscopy can be used to detect Chlorantraniliprole in Abamectin conveniently. Meanwhile, a suitable wavelength selection method (BOSS) is essential to conducting a component spectral analysis.