Pretreatment and Integrated Analysis of Spectral Data Reveal

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Pretreatment and Integrated Analysis of Spectral Data Reveal Seaweed Similarities Based on Chemical Diversity Feifei Wei, Kengo Ito, Kenji Sakata, Yasuhiro Date, and Jun Kikuchi Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/ac504211n • Publication Date (Web): 03 Feb 2015 Downloaded from http://pubs.acs.org on February 11, 2015

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Analytical Chemistry

1

Pretreatment and Integrated Analysis of Spectral

2

Data Reveal Seaweed Similarities Based on

3

Chemical Diversity

4

Feifei Wei†, Kengo Ito‡, Kenji Sakada†, Yasuhiro Date†, ‡ and Jun Kikuchi*,†,‡,§,¶

5 6 7 8 9 10 11 12 13 14

†

RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku,

Yokohama 235-0045, Japan ‡

Graduate School of Medical Life Science, Yokohama City University, 1-7-29

Suehirocho, Tsurumi-ku, Yokohama 230-0045, Japan §

Biomass Engineering Research Program, RIKEN Research Cluster for Innovation, 2-1

Hirosawa, Wako 351-0198, Japan ¶

Graduate School of Bioagricultural Sciences and School of Agricultural Sciences,

Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

15 16

[email protected]; [email protected]; [email protected];

17

[email protected]; [email protected]

18 19

* To whom correspondence should be addressed.

20

Tel: +49(89)63641603. Fax: +49(89)63646881. E-mail: [email protected]

21 22 1

ACS Paragon Plus Environment


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ABSTRACT

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Extracting useful information from high dimensionality and large data sets is a major

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challenge for data-driven approaches. The present study was aimed at developing novel

26

integrated analytical strategies for comprehensively characterizing seaweed similarities

27

based on chemical diversity. The chemical compositions of 107 seaweed and 2 seagrass

28

samples were analyzed using multiple techniques, including Fourier transform infrared

29

(FT-IR) and solid- and solution-state nuclear magnetic resonance (NMR) spectroscopy,

30

thermogravimetry-differential thermal analysis (TG-DTA), inductively coupled

31

plasma-optical emission spectrometry (ICP-OES), CHNS/O total elemental analysis, and

32

isotope ratio mass spectrometry (IR-MS). The spectral data were preprocessed using

33

non-negative matrix factorization (NMF) and NMF combined with multivariate curve

34

resolution-alternating least-squares (MCR-ALS) methods in order to separate individual

35

component information from the overlapping and/or broad spectral peaks. Integrated

36

analysis of the preprocessed chemical data demonstrated distinct discrimination of

37

differential seaweed species. Further network analysis revealed a close correlation

38

between the heavy metal elements and characteristic components of brown algae such as

39

cellulose, alginic acid, and sulfated mucopolysaccharides, providing a componential

40

basis for its metal-sorbing potential. These results suggest that this integrated analytical

41

strategy is useful for extracting and identifying the chemical characteristics of diverse

42

seaweeds based on large chemical data sets, particularly complicated overlapping

43

spectral data.

44 45

KEYWORDS

46

spectral data preprocessing, integrated analysis, seaweed diversity, peak separation 2


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INTRODUCTION

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With the technological advances in high-throughput analysis, it is becoming

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increasingly essential to understand complicated global biological and ecological systems

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in both a hypothesis-driven and a data-driven manner.1,2 Data-driven approaches based

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on bioinformatics tools and computational technologies enable researchers to

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prospectively assess variables simultaneously rather than independently without explicit

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knowledge.3-14 One of the main problems with data-driven research, however, is the need

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to understand very large amounts of different data types (called “big data”) from a holistic

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view at a high abstraction level. Big data means more than just mountains of data.

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Therefore, it becomes particularly important to develop novel methodologies, such as

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dimension reduction, to mine hidden patterns, unknown correlations, and other useful

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information in high dimensionality and large data sets.

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Seaweed has been used as a traditional food and an important source of fiber, minerals,

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vitamins, polysaccharides, and iodine in eastern Asia for several centuries. Increasing

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attention has recently been focused on the biomedical and pharmaceutical applications of

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seaweed in drug development, because it is rich in bioactive metabolites with

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anticoagulant, antiviral, antioxidant, antiallergic, anti-inflammatory, antiobesity, and

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anticancer properties.15,16 Seaweed has also been widely used in the removal of

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contaminants from industrial effluents due to its biosorptive capacity and metal-sorbing

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potential.17 On the other hand, accumulation of toxic substances such as arsenic (As)

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makes seaweed consumption a potential risk for human health.18 It seems likely that

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further comprehensive characterization of seaweed will enhance our understanding of the

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functional biological properties of seaweed and shed light on the development of natural

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health foods. 3



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Several measurement instruments have been applied to the chemical composition

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analysis of seaweed, such as Fourier transform infrared (FT-IR) and solid- and

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solution-state

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thermogravimetry-differential thermal analysis (TG-DTA), inductively coupled

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plasma-optical emission spectrometry (ICP-OES), CHNS/O total elemental analysis. Our

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group recently investigated the temporal changes in the chemical composition of the

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brown algae Sargassum fusiforme (S. fusiforme) during seasonal fluctuations based on a

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variety of heterogeneous measurements of organic and inorganic chemical data.19-22 To

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perform this analysis, an integrated analytical strategy was devised by combining a data

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processing step for isolation of pure peaks via removal of noise and the separation of

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overlapping signals with a pool of statistical analyses, including principle component

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analysis (PCA), self-organizing maps (SOMs), and correlation network analysis, in order

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to extract temporal signatures for the composition of S. fusiforme and explore multiple

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biological interactions between components and the environment within aquatic

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ecosystems.

nuclear

magnetic

resonance

(NMR)

spectroscopy,

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We are also interested in the comprehensive characterization and evaluation of

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seaweed biodiversity using integrated analytical approaches. Compared to the tracking of

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temporal variations in the chemical composition of a single species, composition analysis

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of diverse seaweed species is more complicated. The differentiation in species

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composition may result in serious overlapping of spectral signals originally derived from

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differential components, which may lead to erroneous data and misinterpretation in the

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integrated data analysis. Therefore, the present study was aimed at further developing

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novel data preprocessing techniques for integrated data analysis using various

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measurement techniques in order to characterize huge data sets collected for seaweeds 4


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with species diversity.

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MATERIALS AND METHODS

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Samples. The 107 seaweed samples, including red, brown, and green algae, as well as

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2 seagrass samples used in this study were collected from an intertidal area at Aburatsubo

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in Miura City (35°16′ N, 139°62′ E) and Tenjin Island in Yokosuka City (35°22′ N,

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139°60′ E), Kanagawa, Japan between August 2010 and April 2012, as shown in Table 1.

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The pretreatment conditions for the samples have been described previously.19

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Biomass Measurements. As a follow-up study, all of the samples were analyzed in the

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same manner as previously reported.19,20,23-26 With more details, the 13C solid-state NMR

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spectra were observed using a DRX-500 spectrometer (500 MHz Bruker-BioSpin,

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Billerica, MA) operating at 125 MHz with a Bruker MAS VTN 500SB BL4 probe, using

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a 4 mm cross polarization magic angle spinning (CP-MAS) probe head; the 1H-NMR of

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Watergate spectra were acquired at 298 K on a 700 MHz Bruker Biospin NMR

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instrument (AVENCEII-700) equipped with an inverse (proton coils closest to the

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sample) gradient 5 mm Cryo 1H/13C/15N probe (Bruker Biospin, Rheinstetten, Germany);

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the FT-IR spectra (650-4000 cm−1) were obtained using a Nicolet 6700 FT-IR

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spectrometer (Thermo Fisher Scientific Inc., Waltham, MA) with KBr disks; the

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thermogravimetric analysis was conducted using an EXSTAR TG/DTA 6300 (SII

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Nanotechnology Inc., Tokyo, Japan) instrument; the ICP-OES analysis was conducted

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using an SPS 5510 (SII Nanotechnology Inc., Tokyo, Japan) instrument with CCD

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detector, with a range of wavelengths from 167 to 785 nm and 74 applicable elements; the

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elemental analysis was performed with a CHNS/O analyzer (Vario Micro cube,

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Elementar Analysensysteme GmbH, Hanau, Germany) using helium as the carrier gas; 5



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the isotope ratio mass spectrometry (IR-MS) analysis was performed on an IsoPrime 100

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(Jasco international Co., Ltd., Tokyo, Japan) in combination with an elemental analyzer

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(Vario MICRO cube) in “CN mode,” and isotopic ratios of carbon and nitrogen in the

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samples were measured as CO2 and N2 gases using IR-MS.

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Multivariate Spectral Decomposition. The 1H and 13C CP-MAS NMR spectra were

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manually phased and baseline collected. The 1H NMR spectra were normalized to the

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DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid) intensity. The

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spectra were normalized to the total integral area. Intensity scores for the NMR spectra

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were extracted from relative peaks using Topspin software (Bruker Biospin). The

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non-negative matrix factorization (NMF) method was then applied in order to separate

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overlapping spectral peaks, followed by multivariate curve resolution-alternating

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least-squares (MCR-ALS) analysis using “R” software (http://www.r-project.org/). The

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FT-IR spectra were normalized to the intensity of the 650 cm−1 peak and then produced

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using the NMF and MCR-ALS methods. The TG-DTA data set was processed using the

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NMF method for spectral decomposition.

13

C CP-MAS NMR

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Statistical Analyses of the Integrated Data. PCA and partial least squares

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discriminant analysis (PLS-DA) were used to explore any biomass component clustering

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of R-based on intrinsic biochemical similarities between the seaweed species. Pearson’s

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correlations for the integrated data were calculated using R software, and high correlation

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coefficients (|r| > 0.95 between seaweed samples; |r| > 0.7 between multiple

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measurements) were collected from all of the correlation coefficients and transformed

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into a matrix of connections between sources and targets. The transformed data matrix

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was imported to the network analysis software Gephi (https://gephi.org/). SOMs are

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considered to be a nonlinear mapping technique that identifies clusters in an unsupervised 6


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way within data sets without the rigid assumptions of linearity or normality associated

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with traditional statistical techniques. In this study, all SOM calculations were performed

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using the Kohonen library on the R platform.

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RESULTS AND DISCUSSION

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Spectral Data Pretreatment. The solid-state NMR and FT-IR data were used to

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evaluate intact biomass components; the TG-DTA data were used to characterize the

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thermal decomposition profiles; the solution-state NMR data were used to evaluate the

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metabolic profiles; and the ICP-OES, CHNS/O analysis, and IR-MS data were used to

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examine the elemental contents of the samples.27-31 Before integrated data analysis, data

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pre-processing was performed on the derivative thermogravimetry (DTG), FT-IR,

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CP-MAS, and 1H-NMR spectral data to separate individual component information.

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Spectral-editing techniques have been used as an effective method to detect different

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functional chemical groups with overlapping chemical shifts.32,33 In addition to this, it

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has been discussed that editing pulse sequence in solid-state NMR experiment allows to

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be possible for absolute quantification of spectral components.34 However, it should be

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noted that the absolute quantification is not definitely needed to identify the chemical

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characteristics of diverse seaweeds based on large chemical data sets. In contrast, the

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relative quantification based on the entire carbon information with less information loss

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will lead to more comprehensive understanding of the complicated biological diversity

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of the ecological systems.19,20,29-31 Therefore, in the present study, entire carbon

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information, but not partial structure information from selective pulse sequences, was

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obtained by decomposing peaks using NMF or NMF combined with MCR-ALS

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methods. 7



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Regarding the wave-fitting methods to separate spectral peaks, MCR-ALS with a

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Gaussian shape constraint was previously applied for the multivariate spectral

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decomposition of FT-IR, DTG, and CP-MAS data in order to monitor the temporal

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variation in the chemical composition of the brown algae S. fusiforme.19,35 However, in a

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complicated system including diverse seaweed species, MCR-ALS with a Gaussian

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shape exhibited poor performance since the peak centers of the same component derived

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from different seaweed species shifted obviously in different spectra (data not shown). In

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addition, the independent components analysis (ICA) was also considered as a peak

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separation method.36 However, a problem of ICA is that it will lead to negative values in

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the decomposed results, which in fact have no meaning in the interpretation of NMR or

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FT-IR spectral data. In the present study, the seaweed species diversity resulted in serious

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overlapping of the signals originally derived from multiple measurements, which

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required reasonable and effective data pretreatment. The NMF method is an algorithm

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that decomposes multivariate data into a smaller number of basis functions and

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encodings using non-negative constraints.37 It was first introduced by Paatero and

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Tapper as a positive matrix factorization concept for estimating errors in widely varying

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environmental data.38,39 Lee and Seung represented parts-based objects using NMF as

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an effective multiplicative algorithm.40 Due to the non-negativity and sparseness

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constraints, NMF has also been widely used in multidimensional data analyses.41-45

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Here, we applied the parts-based NMF method for the first time to spectral data

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pretreatment for the integrated analysis of a huge data set representing biodiversity.

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For NMF models, it is important to determine the number of components in order to

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capture the true underlying trends in big data. Several approximate and heuristic

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techniques are available for obtaining the number of components, although in the 8


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present study, parameters residual sum of squares (RSS) and Durbin–Watson (DW)

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criterion were calculated.36,46 As the name suggests, RSS is the sum of squared residuals

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between an original and a reconstructed matrix for each model. Therefore, the model

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with the lowest RSS value is expected to be the most representative. Meanwhile, the

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DW criterion has been proposed as a measure of the signal/noise ratio, the value of

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which approaches 0 when there is no noise in the signal and 2 when the signal contains

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only noise.

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The spectral decomposition of the DTG data is shown in Figure 1(A). The original

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DTG spectrum appeared to be simple, but indeed contained both a large number of

200

peaks and noise due to the differential components in the complex mixture of seaweeds.

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After spectral decomposition using NMF, the number of components were determined

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to be 18 (DW ≤ 1.4, see Figure S1 in the Supporting Information), which indicated that

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18 peaks were extracted and only a little noise was left, as revealed by RSS value. These

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results confirmed the superiority of the NMF approach for extracting individual

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component information from broad spectral peaks, which may be important but often

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overlooked and unappreciated in data pretreatment using methods such as binning.

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Furthermore, NMF coupled with MCR-ALS exhibited good performance for peak

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separation of the FT-IR, CP-MAS, and 1H-NMR spectral data, which were much more

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complicated than that of the DTG. As shown in Figures 1(B), (C), and (D), 92

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component peaks, including broad peaks, were extracted from the FT-IR spectra, while

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the CP-MAS and 1H-NMR spectra were decomposed into 62 and 54 peaks, respectively.

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These spectral decomposition results obtained using NMF or NMF followed by

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MCR-ALS indicated a thorough extraction of the peaks due to the main components,

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and the spectral data were converted in a part-based manner. The component 9



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information from the broad spectral peaks thus became acquirable, which provided

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informative and appropriate input data for the following integrated data analyses.

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Integrated Analysis of Seaweed Variety. The present study is aimed to develop

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novel integrated analytical strategies to extract useful information from not only broad

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signals but also overlapped peaks in the spectrum. Until recently, multivariate analysis

220

such as PCA has been widely applied using binning data without spectral decomposition.

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However, if two components varied in opposite directions in overlapped peaks, no matter

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how narrow the bin size was set they would be treated as one bin ignoring the inside

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variation. In contrast, overlapped signals would be decomposed from the overlapping

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parts as the wave manner, which is similar to their original way, and be treated as

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different components by using NMF and/or MCR-ALS method. As shown in Figure S2,

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a better discrimination of all seaweed species was observed in the score plots based on

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the integrated data matrix with NMF/MCR-ALS peak decomposition (Figure S2(B)) than

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that using unprocessed data matrix (bin data) (Figure S2(A)). Furthermore, the partial

229

least squares-discriminant analysis (PLS-DA) were performed on the preprocessed

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chemical composition data matrix for all of the seaweed samples. As shown in Figure

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S2 (C) in the Supporting Information, clear discrimination of the seaweed samples was

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observed in the score plots, indicating that pretreatment of each spectral data set enabled

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extraction and identification of the chemical characteristics of each sample. Pearson's

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correlation coefficients between all of the seaweed samples were then calculated in

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order to evaluate their biological similarity. As shown in Figure 2, seaweed samples

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from the same species showed high correlation coefficients (|r| ≥ 0.95). For example, as

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a species of brown algae, samples of S. fusiforme (aqua) together showed relatively high

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correlation coefficients with the other brown algae samples (gray). The present results 10


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indicated that the proposed integrated analytical strategy using a variety of chemical

240

data can be effective for evaluating the biological similarity of diverse seaweeds.

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Comprehensive Feature Extraction for Seaweed Similarity Based on Chemical

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Diversity. The mathematical modeling in the present study provides a powerful approach

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for promoting a deep understanding and comprehensive characterization of the

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biodiversity and biological similarity of seaweeds. Because elemental stoichiometry may

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be related to metabolomic changes,47 the CP-MAS, 1H-NMR, and ICP data were

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expanded and highlighted in Figure 3. As shown in this figure, the correlation network

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analysis for all of the brown seaweed components identified using CP-MAS, 1H-NMR,

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and ICP analyses indicated that Cd had strong relationships with the 1H-NMR peaks at

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3.14 and 3.78 ppm and the CP-MAS NMR peaks at 72.92, 73.06, 73.21, 73.35, 73.50,

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73.79, and 73.94 ppm (|r| > 0.7). According to our previous study, these NMR spectral

251

peaks are possibly derived from characteristic components of brown algae such as

252

cellulose, alginic acid, and sulfated mucopolysaccharides.20 These results suggested that

253

the polysaccharide components of brown algae are closely related to its selective

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biosorption capability for heavy metals.

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Biosorption is a term that describes the removal of heavy metals via the passive

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binding to nonliving biomass from an aqueous solution.15 Selective adsorption of toxic

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heavy metals by brown algae has gained increasing interest due to its high efficiency.

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Mechanical studies have emphasized the cell wall properties of brown algae such as

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alginate and fucoidan, because both electrostatic attraction and complexation can

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contribute to heavy metal chelation.20,48 Specific attention has been focused on the

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Fucales, a major order of brown algae, because it is abundant in nature and includes the

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most structurally complex seaweeds.49 As shown in Figure 4(A), brown algae samples 11



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belonging to the order Fucales (labels 1, 3, 7, 9, 10, and 12) were assigned with similar

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colors, indicating high values in the SOM plot based on their relationships with the heavy

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metal cadmium (Cd). These data suggest that brown algae belonging to the order Fucales

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have similar tendencies with respect to the selective biosorption of Cd, which is

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consistent with the conclusions of previous studies.15

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Furthermore, PCA and PLS-DA analyses demonstrated a very clear distinction

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between the chemical composition of S. fusiforme and that of other brown algae species

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(data shown in Figures S3 (A) and (B) of the Supporting Information). The loading plots

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(see Figures S3 (C), (D) and (E)) revealed that mannitol, laminaran, fucoidan, alginate,

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As, and Cd were identified as characteristic components of S. fusiforme, suggesting a

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higher biosorption capability for Cd and As. Indeed, as can be seen in Figure 4(B), the

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SOM plot based on the relationship with As indicated that the order Fucales, particularly

275

the species S. fusiforme (label 1), has a high biosorption capability for As. More

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specifically, alginate is a family of linear polysaccharides containing 1,4-linked

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β-D-mannuronic and α-L-guluronic acid residues arranged in a nonregular, blockwise

278

order along the chain.50 The unique composition of the alginates present in S. fusiforme

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may represent a distinct advantage over other brown algae with respect to the binding of

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divalent heavy metal ions.51 According to these structural characteristics, this seaweed

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can concentrate more than 80% natural inorganic As and thus can be used as an effective

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heavy metal sorbent.52,53

283 284

CONCLUSIONS AND OUTLOOK

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Spectroscopic data such as NMR and FT-IR spectra have the advantage of enabling

286

fully quantitative analyses that provide quick, direct, and comprehensive observations, 12


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and these large quantities of information lead to a better understanding of complicated

288

global biological and ecological systems. However, one major problem is that

289

overlapping and broad spectral peaks derived from diverse components in a chemical

290

mixture lead to erroneous data and misinterpretations in integrated data analyses. The

291

current spectral partitioning process (the so called binning method) often overlooks

292

these complicated peaks, and thus much useful information is lost. In the present study,

293

a novel data preprocessing approach was developed based on NMF and MCR-ALS

294

methods for the extraction of valuable chemical information from spectral data in a

295

parts-based manner. Further multivariate statistical analysis and correlation network

296

analysis of the preprocessed data revealed seaweed similarities based on chemical

297

diversity. These findings suggest that the proposed analytical strategy is useful for

298

extracting and identifying individual component features from large chemical data sets,

299

which will shed light on the links between chemical data and the biological

300

consequences.

301 302

ASSOCIATED CONTENT

303

Supporting Information

304

Additional information as noted in the text. This material is available free of charge via

305

the Internet at http://pubs.acs.org.

306 307

AUTHOR INFORMATION

308

Corresponding Author

309

*E-mail: [email protected]. Tel: +49(89)63641603. Fax: +49(89)63646881.

310

Author Contributions 13



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The manuscript was written with equal contribution from all of the authors. All the

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authors have approved the final version of the manuscript.

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Funding

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This research was supported in part by Grants-in-Aid for Scientific Research (Grant No.

315

25513012) (to J.K.), and also partially supported by Council for Science, Technology and

316

Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP),

317

“Technologies for creating next-generation agriculture, forestry and fisheries” funded

318

from Bio-oriented Technology Research Advancement Institution, NARO)

319

Notes

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The authors have no conflict of interest to declare.

321 322

ACKNOWLEDGMENTS

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The authors wish to thank Drs. Jiro Tanaka (Tokyo University of Marine Science and

324

Technology) and Yuji Omori (Yokosuka City Museum) for their valuable advice on

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identification of a variety of seaweed species. The 252 numerical data used for the

326

integrated analysis can be provided on request.

327

14


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REFERENCES

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(1) Pe'er, D.; Hacohen, N. Cell 2011, 2011 144, 864-873.

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Table 1. Algae Samples Used in the Study. sample ID

a

species (class No.)

b

order

sampling position

sampling date

F1

Sargassum fusiforme (1)

Fucales

Aburatsubo

2011.05.20

F2

S. fusiforme (1)

Fucales

Aburatsubo

2011.06.01

F3

S. fusiforme (1)

Fucales

Aburatsubo

2011.06.01

F4

S. fusiforme (1)

Fucales

Aburatsubo

2011.06.16

F5

S. fusiforme (1)

Fucales

Aburatsubo

2011.07.05

F6

S. fusiforme (1)

Fucales

Aburatsubo

2011.07.14

F7

S. fusiforme (1)

Fucales

Aburatsubo

2011.08.01

F8

S. fusiforme (1)

Fucales

Aburatsubo

2011.08.15

F9

S. fusiforme (1)

Fucales

Aburatsubo

2011.08.30

F10

S. fusiforme (1)

Fucales

Aburatsubo

2011.09.30

F11

S. fusiforme (1)

Fucales

Aburatsubo

2011.10.12

F12

S. fusiforme (1)

Fucales

Aburatsubo

2011.11.24

F13

S. fusiforme (1)

Fucales

Aburatsubo

2011.12.14

F14

S. fusiforme (1)

Fucales

Aburatsubo

2012.01.24

F15

S. fusiforme (1)

Fucales

Aburatsubo

2012.02.20

F16

S. fusiforme (1)

Fucales

Aburatsubo

2012.03.21

F17

S. fusiforme (1)

Fucales

Aburatsubo

2012.04.09

B1

Sargassum ringgoldianum (3)

Fucales

Aburatsubo

2011.06.16

B2


Fucales

Aburatsubo

2011.07.05

B3

Sargassum patens (7)

Fucales

Aburatsubo

2011.07.14

B4


Fucales

Aburatsubo

2011.07.14

B5

Sargassum hemiphyllum (9)

Fucales

Aburatsubo

2011.08.01

B6

Sargassum fulvellum (10)

Fucales

Aburatsubo

2011.08.01

B7


Fucales

Aburatsubo

2011.08.01

B8

Sargassum thunbergii (12)

Fucales

Tenjin Island

2010.08.11

B9

Padina arborescens (4)

Dictyotales

Aburatsubo

2011.07.05

B10

Dictyopteris undulata (6)

Dictyotales

Aburatsubo

2011.07.05

B11


Dictyotales

Aburatsubo

2011.07.14

B12


Dictyotales

Aburatsubo

2011.08.01

B13


Dictyotales

Aburatsubo

2011.08.01

B14


Dictyotales

Aburatsubo

2011.08.01

(to be continued)

18


Page 19 of 27

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(continued) B15

Dictyopteris prolifera (11)

Dictyotales

Aburatsubo

2011.08.01

B16


Dictyotales

Aburatsubo

2011.08.01

B17


Dictyotales

Aburatsubo

2011.06.16

B18

Ishige okamurae (2)

Ishigeales

Aburatsubo

2011.06.16

B19

Ishige okamurae (2)

Ishigeales

Aburatsubo

2011.07.05

B20

Ishige okamurae (2)

Ishigeales

Aburatsubo

2011.07.14

B21

Ishige okamurae (2)

Ishigeales

Aburatsubo

2011.08.01

B22

Eisenia bicyclis (5)

Laminariales

Aburatsubo

2011.07.05

B23


Laminariales

Aburatsubo

2011.07.14

B24


Laminariales

Aburatsubo

2011.08.01

B25

Ecklonia cava (13)

Laminariales

Aburatsubo

2011.06.16

B26

Colpomenia sinuosa (8)

Scytosiphonales

Aburatsubo

2011.07.14

B27

Colpomenia sinuosa (8)

Scytosiphonales

Aburatsubo

2011.06.16

R1

Chondrus verrucosus

Gigartinales

Aburatsubo

2011.06.16

R2

Chondrus verrucosus

Gigartinales

Aburatsubo

2011.06.16

R3

Chondrus ocellatus

Gigartinales

Aburatsubo

2011.06.16

R4

Chondrus ocellatus

Gigartinales

Aburatsubo

2011.06.16

R5

Chondrus verrucosus

Gigartinales

Aburatsubo

2011.06.16

R6

Chondrus verrucosus

Gigartinales

Aburatsubo

2011.07.05

R7

Chondrus verrucosus

Gigartinales

Aburatsubo

2011.07.05

R8

Chondrus verrucosus

Gigartinales

Aburatsubo

2011.07.05

R9

Chondrus verrucosus

Gigartinales

Aburatsubo

2011.07.05

R10

Chondrus verrucosus

Gigartinales

Aburatsubo

2011.08.01

R11

Chondrus verrucosus

Gigartinales

Aburatsubo

2011.08.01

R12

Chondrus verrucosus

Gigartinales

Aburatsubo

2011.07.14

R13

Prionitis cornea

Gigartinales

Aburatsubo

2011.06.16

R14

Prionitis cornea

Gigartinales

Aburatsubo

2011.06.16

R15

Prionitis cornea

Gigartinales

Aburatsubo

2011.07.05

R16

Prionitis cornea

Gigartinales

Aburatsubo

2011.07.05

R17

Grateloupia asiatica

Gigartinales

Aburatsubo

2011.07.05

R18

Grateloupia elliptica

Gigartinales

Aburatsubo

2011.07.05

R19

Prionitis cornea

Gigartinales

Aburatsubo

2011.07.14

R20

Grateloupia chiangii

Gigartinales

Aburatsubo

2011.07.14

(to be continued)

19



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Page 20 of 27

(continued) R21

Prionitis cornea

Gigartinales

Aburatsubo

2011.08.01

R22

Prionitis cornea

Gigartinales

Aburatsubo

2011.08.01

R23

Meristotheca papulosa

Gigartinales

Aburatsubo

2011.07.14

R24

Meristotheca papulosa

Gigartinales

Aburatsubo

2011.07.14

R25

Gelidium elegans

Gelidiales

Aburatsubo

2011.07.14

R26

Gelidium elegans

Gelidiales

Aburatsubo

2011.08.01

R27

Gelidium elegans

Gelidiales

Tenjin Island

2010.08.11

R28

Gelidium elegans

Gelidiales

Aburatsubo

2011.08.01

R29

Gracilaria textorii

Gracilariales

Aburatsubo

2011.06.16

R30

Gracilaria textorii

Gracilariales

Aburatsubo

2011.06.16

R31

Gracilaria textorii

Gracilariales

Aburatsubo

2011.07.05

R32

Gracilaria textorii

Gracilariales

Aburatsubo

2011.07.05

R33

Amphiroa zonata

Corallinales

Aburatsubo

2011.07.05

R34

Amphiroa zonata

Corallinales

Aburatsubo

2011.08.01

R35

Amphiroa zonata

Corallinales

Aburatsubo

2011.06.16

R36

Martensia jejuensis

Ceramiales

Aburatsubo

2011.07.14

R37

Martensia jejuensis

Ceramiales

Aburatsubo

2011.08.01

R38

Laurencia intermedia

Ceramiales

Aburatsubo

2011.08.01

R39

Lomentaria catenata

Rhodymeniales

Aburatsubo

2011.06.16

G1

Ulva pertusa

Ulvales

Aburatsubo

2011.06.16

G2

Ulva pertusa

Ulvales

Aburatsubo

2011.07.05

G3

Ulva pertusa

Ulvales

Aburatsubo

2011.07.14

G4

Ulva pertusa

Ulvales

Aburatsubo

2011.07.14

G5

Ulva pertusa

Ulvales

Aburatsubo

2011.08.01

G6

Ulva pertusa

Ulvales

Aburatsubo

2011.08.01

G7

Codium cylindricum

Codiales

Aburatsubo

2011.06.16

G8

Codium fragile

Codiales

Aburatsubo

2011.06.16

G9

Codium latum

Codiales

Aburatsubo

2011.07.05

G10

Codium fragile

Codiales

Aburatsubo

2011.07.05

G11

Codium cylindricum

Codiales

Aburatsubo

2011.07.05

G12

Codium fragile

Codiales

Aburatsubo

2011.07.14

G13

Codium fragile

Codiales

Aburatsubo

2011.07.14

G14

Codium fragile

Codiales

Aburatsubo

2011.07.14

(to be continued)

20


Page 21 of 27

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(continued)

408 409 410

a

G15

Codium cylindricum

Codiales

Aburatsubo

2011.07.14

G16

Codium subtubulosum

Codiales

Aburatsubo

2011.07.14

G17

Codium cylindricum

Codiales

Aburatsubo

2011.08.01

G18

Codium fragile

Codiales

Aburatsubo

2011.08.01

G19

Codium fragile

Codiales

Aburatsubo

2011.06.16

G20

Codium latum

Codiales

Aburatsubo

2011.06.16

G21

Codium cylindricum

Codiales

Aburatsubo

2011.07.05

G22

Chaetomorpha crassa

Cladophorales

Aburatsubo

2011.07.14

G23

Chaetomorpha crassa

Cladophorales

Aburatsubo

2011.08.01

G24

Codium lucasii

Bryopsidales

Aburatsubo

2011.06.16

S1

Zostera marina

Helobiales

Aburatsubo

2011.07.05

S2

Zostera marina

Helobiales

Aburatsubo

2011.07.14

“F”, “B”, “R”, “G”, and “S” mean S. fusiforme, brown algae, brown algae, red algae,

green algae, and seagrass, respectively. b

the class No. of only brown algae is consistent with Figure 4.

411

21



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412

Page 22 of 27

FIGURE LEGENDS

413 414

Figure 1. Multivariate spectral decomposition of (A) DTG, (B) FT-IR, (C) CP-MAS, and

415

(D) 1H-NMR spectra of S. fusiforme using NMF or NMF/MCR-ALS methods. Lines in

416

black: original data; lines in red: basis functions (loading plots) of the reconstructed

417

NMF or NMF/MCR-ALS models; lines in blue: RSS values for the original and

418

reconstructed models.

419 420

Figure 2. Similarity network for various seaweeds based on Pearson’s correlation

421

coefficients (|r| > 0.95).

422 423 424

Figure 3. Correlation network for brown algae components obtained using CP-MAS, 1

H-NMR, and ICP data (|r| > 0.7).

425 426

Figure 4. SOM plots of brown algae components based on their relationships with (A) Cd

427

and (B) As. The class number of brown algae samples corresponds to Table 1.

428

22


Page 23 of 27

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429 430

Figure 1. Wei et al. 23



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Page 24 of 27

431 432 433

434 435

Figure 2. Wei et al.

436

24


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437 438 439

440 441


442

25



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Page 26 of 27

443 444

445 446


447 448

26


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449

For TOC only

450 451 452

27


Pretreatment and Integrated Analysis of Spectral Data Reveal

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