match_spec() joins two OpenSpecy objects and their metadata
based on similarity.
cor_spec() correlates two OpenSpecy objects, typically one with
knowns and one with unknowns.
ident_spec() retrieves the top match values from a correlation matrix
and formats them with metadata.
get_metadata() retrieves metadata from OpenSpecy objects.
max_cor_named() formats the top correlation values from a correlation
matrix as a named vector.
filter_spec() filters an Open Specy object.
fill_spec() adds filler values to an OpenSpecy object where it doesn't have intensities.
os_similarity() EXPERIMENTAL, returns a single similarity metric between two OpenSpecy objects based on the method used.
Usage
cor_spec(x, ...)
# Default S3 method
cor_spec(x, ...)
# S3 method for class 'OpenSpecy'
cor_spec(x, library, na.rm = T, conform = F, type = "roll", ...)
match_spec(x, ...)
# Default S3 method
match_spec(x, ...)
# S3 method for class 'OpenSpecy'
match_spec(
x,
library,
na.rm = T,
conform = F,
type = "roll",
top_n = NULL,
order = NULL,
add_library_metadata = NULL,
add_object_metadata = NULL,
fill = NULL,
...
)
ident_spec(
cor_matrix,
x,
library,
top_n = NULL,
add_library_metadata = NULL,
add_object_metadata = NULL,
...
)
get_metadata(x, ...)
# Default S3 method
get_metadata(x, ...)
# S3 method for class 'OpenSpecy'
get_metadata(x, logic, rm_empty = TRUE, ...)
max_cor_named(cor_matrix, na.rm = T)
filter_spec(x, ...)
# Default S3 method
filter_spec(x, ...)
# S3 method for class 'OpenSpecy'
filter_spec(x, logic, ...)
ai_classify(x, ...)
# Default S3 method
ai_classify(x, ...)
# S3 method for class 'OpenSpecy'
ai_classify(x, library, fill = NULL, ...)
fill_spec(x, ...)
# Default S3 method
fill_spec(x, ...)
# S3 method for class 'OpenSpecy'
fill_spec(x, fill, ...)
os_similarity(x, ...)
# Default S3 method
os_similarity(x, ...)
# S3 method for class 'OpenSpecy'
os_similarity(x, y, method = "hamming", na.rm = T, ...)Arguments
- x
an
OpenSpecyobject, typically with unknowns.- library
an
OpenSpecyorglmnetobject representing the reference library of spectra or model to use in identification.- na.rm
logical; indicating whether missing values should be removed when calculating correlations. Default is
TRUE.- conform
Whether to conform the spectra to the library wavenumbers or not.
- type
the type of conformation to make returned by
conform_spec()- top_n
integer; specifying the number of top matches to return. If
NULL(default), all matches will be returned.- order
an
OpenSpecyused for sorting, ideally the unprocessed one;NULLskips sorting.- add_library_metadata
name of a column in the library metadata to be joined;
NULLif you don't want to join.- add_object_metadata
name of a column in the object metadata to be joined;
NULLif you don't want to join.- fill
an
OpenSpecyobject with a single spectrum to be used to fill missing values for alignment with the AI classification.- cor_matrix
a correlation matrix for object and library, can be returned by
cor_spec()- logic
a logical or numeric vector describing which spectra to keep.
- rm_empty
logical; whether to remove empty columns in the metadata.
- y
an
OpenSpecyobject to perform similarity search against x.- method
the type of similarity metric to return.
- ...
additional arguments passed
cor().
Value
match_spec() and ident_spec() will return
a data.table-class() containing correlations
between spectra and the library.
The table has three columns: object_id, library_id, and
match_val.
Each row represents a unique pairwise correlation between a spectrum in the
object and a spectrum in the library.
If top_n is specified, only the top top_n matches for each
object spectrum will be returned.
If add_library_metadata is is.character, the library metadata
will be added to the output.
If add_object_metadata is is.character, the object metadata
will be added to the output.
filter_spec() returns an OpenSpecy object.
fill_spec() returns an OpenSpecy object.
cor_spec() returns a correlation matrix.
get_metadata() returns a data.table-class()
with the metadata for columns which have information.
os_similarity() returns a single numeric value representing the type
of similarity metric requested. 'wavenumber' similarity is based on the
proportion of wavenumber values that overlap between the two objects,
'metadata' is the proportion of metadata column names,
'hamming' is something similar to the hamming distance where we discretize
all spectra in the OpenSpecy object by wavenumber intensity values and then
relate the wavenumber intensity value distributions by mean difference in
min-max normalized space. 'pca' tests the distance between the OpenSpecy
objects in PCA space using the first 4 component values and calculating the
max-range normalized distance between the mean components. The first two
metrics are pretty straightforward and definitely ready to go, the 'hamming'
and 'pca' metrics are pretty experimental but appear to be working under our
current test cases.
See also
adj_intens() converts spectra;
get_lib() retrieves the Open Specy reference library;
load_lib() loads the Open Specy reference library into an R
object of choice
Examples
data("test_lib")
unknown <- read_extdata("ftir_ldpe_soil.asp") |>
read_any() |>
conform_spec(range = test_lib$wavenumber,
res = spec_res(test_lib)) |>
process_spec()
cor_spec(unknown, test_lib)
#> intensity
#> 00002e4e3fac430aa1fdfea6e26f85e4 0.5716650527
#> 00061dd49cbb71549cf3d530b894ba92 -0.0524107035
#> 0008a60c1af45a76ffb91b9cbe1a32e4 0.2120038819
#> 000902ae526db452960c5782843081b4 -0.0574671312
#> 00133c1f1531821f815431f72871eae9 0.3424853396
#> 0016677a99407c18717bf5d3eea9a0a2 0.1169383083
#> 00185609a3205a19225e1530ca98084e -0.0059672577
#> 001d3eb19976c785442a3e6fa8094bff -0.0063433127
#> 0026eee747d277edf308194c81179cb0 -0.0563867464
#> 0031bb13faea1e04b52ffbeca009e8ab 0.6675896750
#> 003b2e57d47a4225b8ea041c946cfc0a -0.0204737255
#> 0046ff947759e247f85dc63a2a25e097 0.0286048318
#> 0054792a45a11ccf7158fdf2c8873125 0.0889031245
#> 005c6a81975d747fe493032cac9dfaa2 0.2070905137
#> 0062d71f901713f2ac7a89c72fb186d4 0.2847788255
#> 00904e33ccbaa20fb68c1943d8303d82 0.0103191160
#> 00931a5ccadb549d463293381c974d79 -0.0182594153
#> 00b9f87c7d7c82675cb945fe4da37917 -0.0874221226
#> 00f4b09b9823187caf65b781b8be87b9 -0.0943789974
#> 010bfd4be6c24ced909f6de717c7b04e 0.0756006101
#> 015e8a21344a60a020226cfef344c584 0.0279306259
#> 016961c0d7def12f68239756d23a79a6 -0.0739530912
#> 019b8f3e4839f8fda1441f7d712ff12d -0.0088904495
#> 01b2fea6718855567e2e468a792b457f 0.0519834480
#> 02b8194bd714469666151a7c6b90b36c 0.0687712268
#> 03f1fa06cd6bc468ae5e406ca1d58dbb -0.0560522947
#> 041771facd71667d6bd6c9269d3701be 0.0502373982
#> 0481958e37c72c71f88d80e4dd69ac59 0.0405530434
#> 058a9af32053d14e876703a60a37498f -0.0172377431
#> 05c3cde45098e01cc44da3f72ada913e -0.0586996007
#> 06a2ed935a9b37b653717ff4a0ea62f7 0.0486159272
#> 06f7950194c7a42714cec488e378a820 0.1062184336
#> 087b8dc9fe95b7cab9d037304f0d39c8 -0.0071308393
#> 09b69fcfcf3c695299dd0f88743cf14a 0.0693075642
#> 0b10e6cc01c8e53f2221647a9c828164 0.5492755145
#> 0b8b5f10d7a9b2bd7d702cd4fdfd8a29 0.0198909283
#> 0e815860f0115ac34e2b46ac91e0c096 0.0219596516
#> 101b6ae86864958ddd95445d0ab01fe4 0.4308809802
#> 14ee1d85d0b1ee84fab0773329c45bd2 -0.0552288672
#> 1a31cfc8e25332f4771d03e8a862e217 0.0257980970
#> 23069d13c090ee5235874bd632467f9c 0.0239585112
#> 252b6f9c5fb83ec82246bc041cabd221 -0.0160335235
#> 263479214867d17e135a0d599b1f8354 0.5000294168
#> 2fa630bec93db824509be188494d79ad 0.1018184192
#> 35ce7ae774d57362dacb92cacd53b01c 0.0057562573
#> 3d35fd9a2766afaf4c088b615dffc350 -0.0009768496
#> 4762edfa07e6b28a4b90db83594f2b3d -0.0004834032
#> 4f1d701f4080b4c74acdaa04dea52dd1 0.0682211684
#> 5961a72282786fe43d8704780782f459 0.0629534493
#> 65792c9e80934e4692540a3e0fbfb552 0.0457114187
#> 6e013532253522149a0deb04373b79a2 0.1339615512
#> 7167a50e76f4a34dbb1d354babd674b0 0.0387929426
#> 76b3af06ab610907cea4dbe3aa4ec67e 0.0237972406
#> 889adce6238a677669b6523d74635d03 0.1267896304
#> 9f91f6441608ecca35f69d298e5351bd 0.0018181040
#> a692fd0b8b3b80b640ce4bbea8105570 0.0423592870
#> ec69350b1b7de35fd6b8cf65048e5fc0 0.1076174781
match_spec(unknown, test_lib, add_library_metadata = "sample_name",
top_n = 1)
#> Key: <library_id>
#> library_id object_id match_val x y
#> <char> <fctr> <num> <int> <int>
#> 1: 0031bb13faea1e04b52ffbeca009e8ab intensity 0.6675897 37 1
#> SpectrumID Organization SpectrumType SpectrumIdentity
#> <char> <char> <char> <char>
#> 1: HDPE_Sarah_#18_1s_20ac_10x_25mW J. Lynch, NIST Raman HDPE
#> Polymer.Category LibraryType OWNER SpectralCollectionMode Preprocessing
#> <char> <char> <char> <char> <char>
#> 1: polyolefins Polymers <NA> <NA> <NA>
#> InstrumentUsed RRUFFID IDEAL CHEMISTRY LOCALITY SOURCE STATUS URL
#> <char> <char> <char> <char> <char> <char> <char>
#> 1: <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> MEASURED CHEMISTRY OtherInformation JCAMP-DX YFACTOR YUNITS InstrumentMode
#> <char> <char> <char> <char> <char> <char>
#> 1: <NA> <NA> <NA> <NA> <NA> <NA>
#> DELTAX Sponsor Color Cell ID Comment cell_plate_id
#> <char> <char> <char> <char> <char> <char> <char>
#> 1: <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> Form..film..foam.pliable..hard. Brand Item Location Notes
#> <char> <char> <char> <char> <char>
#> 1: <NA> <NA> <NA> <NA> <NA>
#> Longest.dimension Width Depth Particle.mass..mg. Tg..oC. Tm..oC. Onset....
#> <char> <char> <char> <char> <char> <char> <char>
#> 1: <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> Enthalpy..J.g. Substance Form Colour Filename SourceDatabase Method...49
#> <char> <char> <char> <char> <char> <char> <char>
#> 1: <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> File QA/QC Natural /Synthetic Plastic/other framework Abbreviation
#> <char> <char> <char> <char> <char> <char>
#> 1: <NA> <NA> <NA> <NA> <NA> <NA>
#> Source ID Method...57 morphology color final_polymer_assignment Citation
#> <char> <char> <char> <char> <char> <char>
#> 1: <NA> <NA> <NA> <NA> <NA> <NA>
#> Plate Itteration SpectralResolution InstrumentAccessories Wavenumber_Range
#> <int> <int> <char> <char> <char>
#> 1: NA NA <NA> <NA> <NA>
#> PIN_ID ORIENTATION ID merged database
#> <char> <char> <int>
#> 1: <NA> <NA> NA
#> Database ID WWTP Paper/Kirstie/FLOPPE...70
#> <int>
#> 1: NA
#> Database ID WWTP Paper/Kirstie/FLOPPE...71 ID (ESM1) Longest_dimension width
#> <int> <int> <int> <int>
#> 1: NA NA NA NA
#> Cluster2018/WWTP PRESSURE TEMPERATURE MaterialForm MaterialProducer
#> <int> <int> <num> <char> <char>
#> 1: NA NA NA <NA> <NA>
#> NumberofAccumulations TIME Ratio against background Truncated
#> <int> <char> <char> <char>
#> 1: NA <NA> <NA> <NA>
#> ElementMultiply or ElementDivide Collection length Apodization
#> <char> <char> <char>
#> 1: <NA> <NA> <NA>
#> Bench serial number Subtraction or Addition Baseline Correction Smooth
#> <char> <char> <char> <char>
#> 1: <NA> <NA> <NA> <NA>
#> %Transmittance->Absorbance Sample gain Number of background scans
#> <char> <num> <int>
#> 1: <NA> NA NA
#> Background gain Sample spacing Number of scan points Resolution points
#> <num> <num> <int> <int>
#> 1: NA NA NA NA
#> Number of FFT points ZPD Library ID Shortform Grating Hole size (nm)
#> <int> <int> <char> <int> <int>
#> 1: NA NA <NA> NA NA
#> Slit (um) Filter (%) Delay (s) DATE MOLFORM $NIST SOURCE $NIST DOC FILE
#> <int> <int> <int> <char> <char> <char> <char>
#> 1: NA NA NA <NA> <NA> <NA> <NA>
#> $NIST PSD FILE APERTURE BEAMSPLITTER DETECTOR (DIA. DET. PORT IN SPHERE)
#> <char> <char> <char> <char>
#> 1: <NA> <NA> <NA> <NA>
#> SPHERE DIAMETER ACQUISITION MODE SCANNER SPEED PHASE CORRECTION ZEROFILLING
#> <char> <char> <char> <char> <char>
#> 1: <NA> <NA> <NA> <NA> <NA>
#> WAVENUMBER ACCURACY LOW PASS FILTER SWITCH GAIN ON $NIST ID COADDED SCANS
#> <char> <char> <char> <char> <int>
#> 1: <NA> <NA> <NA> <NA> NA
#> PHASE RESOLUTION MW ContactInfo CASNumber MaterialQuality LaserLightUsed
#> <num> <num> <char> <char> <char> <char>
#> 1: NA NA <NA> <NA> <NA> <NA>
#> TotalAcquisitionTime_s DataProcessingProcedure
#> <char> <char>
#> 1: <NA> <NA>
#> LevelofConfidenceinIdentification smoother baseline DENSITY new_label
#> <char> <int> <int> <num> <char>
#> 1: <NA> NA NA NA <NA>
#> polymer_class plastic_or_not
#> <char> <char>
#> 1: Polyolefins (POLYALKENES) plastic
#> url_polymer_class
#> <char>
#> 1: https://www.polymerdatabase.com/polymer%20index/polyalkenes.html
#> polymer url_polymer
#> <char> <char>
#> 1: POLY(ETHYLENE) https://www.polymerdatabase.com/polymers/polyethylene.html
#> url_more_info
#> <char>
#> 1: https://www.polymerdatabase.com/polymer%20classes/Polyolefin%20type.html
#> snr_deriv file_id
#> <num> <char>
#> 1: 1339.684 77f837c1640910f2879184d61ef1df48