Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the contents of three (3) heavy metals (Cu, Cd, and Pb) in Fritillaria thunbergii. Table 1 lists the statistics of heavy metal contents in different samples. A number of procedures are involved in the ICP-MS analysis of the samples, including digestion of the sample, filtration and purification of the digestion solution, and detection of the digestion solution using ICP-MS (ELAN DRC-e, Perkin Elmer, USA). The pH value as well as all steps in the ICP-MS analysis were carried out by experimental technicians at the Zhejiang College of Biosystems Engineering and Food Science, Zhejiang University. This was a preliminary attempt to detect multiple heavy metals (Cd, Cu and Pb) in Fritillaria thubergii using the LIBS technique. The pH value was determined by measuring the electrical conductivity of the sample solution with a pH meter. The ICP-MS analysis was performed using a quadrupole-based ICP-MS system which allowed the technicians to accurately measure the concentrations of elements in the sample solution. Similar steps are described by Su et al. [37 (link)]. Unscrambler X, version 10.1 (CAMO Software AS, Oslo, Norway, 2011) was used for the descriptive statistics (file imported in MATLAB format).
Unscrambler x version 10
Manufactured by CAMO Software
Sourced in Norway
Unscrambler X version 10.5 is a software application designed for multivariate data analysis. It provides tools for processing, visualizing, and modeling data from various sources.
Automatically generated - may contain errors
Lab products found in correlation
Spss statistics 23, by IBM (4 mentions)
Opus software version 6, by Bruker (2 mentions)
Matlab r2019a version 9, by MathWorks (2 mentions)
Elan drc e, by PerkinElmer (1 mentions)
Spss statistics version 25, by IBM (1 mentions)
Pls toolbox, by Eigenvector Research (1 mentions)
Matlab, by MathWorks (1 mentions)
Matrix f spectrometer, by Bruker (1 mentions)
Originpro 8 sr2, by OriginLab (1 mentions)
Originpro software 2018, by OriginLab (1 mentions)
Ir prestige 21, by Shimadzu (1 mentions)
Omnic 9, by Thermo Fisher Scientific (1 mentions)
Microphazir rx analyzer, by Thermo Fisher Scientific (1 mentions)
Origin 6, by OriginLab (1 mentions)
27 protocols using unscrambler x version 10
1
Quantifying Heavy Metals in Fritillaria thunbergii
2
Antioxidant Analysis by PCA and Correlation
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The statistical analysis of the results (spectrophotometric and chromatographic data) was performed by principal component analysis (PCA) with cross-validation (full model size and center data). In order for all of the variables included in the analysis to have an equal chance to influence the model, the standardization was used as the scaling technique. All of the statistical analyses were performed using Unscrambler X Version 10.1 software (CAMO Software AS, Oslo, Norway).
Linear Pearson correlation coefficient was also used to analyze the strength of correlation between the results that were obtained for antioxidants assays.
Linear Pearson correlation coefficient was also used to analyze the strength of correlation between the results that were obtained for antioxidants assays.
3
PCA Analysis of DNA Samples
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The software Unscrambler® X Version 10.1 (Camo Software AS., Oslo, Norway) was used to perform the principal component analysis (PCA). The pre-processing of the DNA samples database consisted of using multiplicative scattering correction (MSC) prior to the PCA analysis.
4
Multivariate analysis of FT-IR spectra
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The FT-IR spectra were analyzed by multivariate analysis. The principal component analysis (PCA) was performed by using an Unscrambler X, version 10.5 (CAMO software, Oslo, Norway), wherein the FT-IR spectral peak height was utilized as a function of wavenumber as the input parameter. Full cross-validation was applied and singular value decomposition was used as an algorithm. The PCA score plot was prepared using PC-1 and PC-2. The loading plot as a function of wavenumber was also used to interpret the PCA score plot based on PC-1 and PC-2 in accordance with the literature [35 (link)].
5
Aloe Species Characterization via PCA
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PCA analysis. For the characterisation of the studied Aloe species, the data obtained from chromatographic and spectrophotometric analyses was subjected to principal component analysis (PCA) with cross-validation (full model size and centre data), using Unscrambler X version 10.5 software (CAMO Software AS, Oslo, Norway).
All extractions and chromatographic analyses were performed in triplicate. The results for HPLC, GC-MS analyses and antioxidant assays are presented in tables as the mean±standard deviation. Signi cant differences between samples were analysed with one-way ANOVA post-hoc tests and pairwise multiple comparisons were conducted using Tukey's test. Signi cant differences were reported based on P<0.05. Statistical analyses were performed using the SPSS Statistics 23.0.
All extractions and chromatographic analyses were performed in triplicate. The results for HPLC, GC-MS analyses and antioxidant assays are presented in tables as the mean±standard deviation. Signi cant differences between samples were analysed with one-way ANOVA post-hoc tests and pairwise multiple comparisons were conducted using Tukey's test. Signi cant differences were reported based on P<0.05. Statistical analyses were performed using the SPSS Statistics 23.0.
6
Aloe Species Characterization via PCA
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PCA analysis. For the characterisation of the studied Aloe species, the data obtained from chromatographic and spectrophotometric analyses was subjected to principal component analysis (PCA) with cross-validation (full model size and centre data), using Unscrambler X version 10.5 software (CAMO Software AS, Oslo, Norway).
All extractions and chromatographic analyses were performed in triplicate. The results for HPLC, GC-MS analyses and antioxidant assays are presented in tables as the mean±standard deviation. Signi cant differences between samples were analysed with one-way ANOVA post-hoc tests and pairwise multiple comparisons were conducted using Tukey's test. Signi cant differences were reported based on P<0.05. Statistical analyses were performed using the SPSS Statistics 23.0.
All extractions and chromatographic analyses were performed in triplicate. The results for HPLC, GC-MS analyses and antioxidant assays are presented in tables as the mean±standard deviation. Signi cant differences between samples were analysed with one-way ANOVA post-hoc tests and pairwise multiple comparisons were conducted using Tukey's test. Signi cant differences were reported based on P<0.05. Statistical analyses were performed using the SPSS Statistics 23.0.
7
Aloe Species Characterization via PCA
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PCA analysis. For the characterisation of the studied Aloe species, the data obtained from chromatographic and spectrophotometric analyses was subjected to principal component analysis (PCA) with cross-validation (full model size and centre data), using Unscrambler X version 10.5 software (CAMO Software AS, Oslo, Norway).
All extractions and chromatographic analyses were performed in triplicate. The results for HPLC, GC-MS analyses and antioxidant assays are presented in tables as the mean±standard deviation. Signi cant differences between samples were analysed with one-way ANOVA post-hoc tests and pairwise multiple comparisons were conducted using Tukey's test. Signi cant differences were reported based on P<0.05. Statistical analyses were performed using the SPSS Statistics 23.0.
All extractions and chromatographic analyses were performed in triplicate. The results for HPLC, GC-MS analyses and antioxidant assays are presented in tables as the mean±standard deviation. Signi cant differences between samples were analysed with one-way ANOVA post-hoc tests and pairwise multiple comparisons were conducted using Tukey's test. Signi cant differences were reported based on P<0.05. Statistical analyses were performed using the SPSS Statistics 23.0.
8
Spectral Data Preprocessing for Polymer Analysis
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The collected raw spectra were transferred into the Unscrambler® X Version 10.5 (CAMO Software, Oslo, Norway). Before spectral analysis, firstly the spectra were recalculated from reflectance R into absorbance A, by applying a negative common logarithm (log 1/R). A linear offset correction was then used as a pretreatment method; it enables direct comparison of all measurements and polymers. For most of the analysis methods, the spectral dataset was averaged to one spectrum per concentration, except for PCA and PLSR analysis, where no sample averaging was used. All plots were generated with OriginPro® 2020. Noteworthily, the spectra below 4500 cm−1 should be considered less reliable, as the complete absorption phenomenon occurred for several samples. However, this region was not used for the purpose of this study, nor are any discussions in this work based on this fragment of spectra. Nonetheless, throughout this manuscript, full spectral data are presented (i.e., in the region of 10,000–4000 cm−1), as they may be found useful by the readers for qualitative (i.e., rough) assessment.
9
Metabolic Profiling of Seaweed Effects
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A Principal Component Analysis (PCA) of the spectroscopic data was performed to determine the differences in the metabolic profiles between reference and seaweed-treated samples as follows. First, all the spectra were normalized to an average mass of extracted tissue and volume of extractions. The spectra of the TCA extracts were manually binned into non-regular buckets in Amix-Viewer, version 4.0 (Bruker, Rheinstetten, Germany). The table containing the integral values of the defined buckets for all samples, was further transported to IBM SPSS Statistics version 25 (Armonk, New York, USA) for an ANOVA analysis (p-value < 0.05). This analysis allowed to define the buckets or spectral regions whose values significantly differed between the RS and STS samples. The buckets for which the p-values exceeded 0.05 were removed from the following PCA analysis. The reduced table was further imported to Unscrambler X version 10.5 (CAMO Software AS, Oslo, Norway), where the PCA analysis was performed.
10
Spectral Analysis of Muscle Samples
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The Unscrambler® X version 10.5 (CAMO Software, Oslo, Norway) and PLS_Toolbox (Version 8.6.2, Eigenvector Research, Inc., Manson, WA USA) data analysis software packages were used to analyse the spectra. The spectral range was reduced from 908-1700 nm to 908-1680 nm to remove the spectral noise segments. As each muscle was scanned three times at different points, spectra were averaged to obtain one spectrum per sample.
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