---
title: "Open Specy Package Tutorial"
author: >
  Win Cowger, Zacharias Steinmetz, Rachel Kozloski, Aleksandra Karapetrova
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Open Specy Package Tutorial}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE
)

data.table::setDTthreads(2)
```

# Document Overview 

This document outlines a common workflow for using the Open Specy package and
highlights some topics that users are often requesting a tutorial on. If the
document is followed sequentially from beginning to end, the user will have a
better understanding of every procedure involved in using the Open Specy R
package as a tool for interpreting spectra. It takes approximately 45 minutes to
read through and follow along with this standard operating procedure the first
time. Afterward, knowledgeable users should be able to thoroughly analyze
spectra at an average speed of 1 min^-1^ or faster with the new batch and
automated procedures.

The Open Specy R package is the backbone of the Shiny app. The choice is yours
as to which you start with, we use both on a regular basis. The tutorial will
talk through the R functions you can use to programatically analyze spectra. If
you are looking for a tutorial about how to use the app see [this
tutorial](http://wincowger.com/OpenSpecy-package/articles/app.html).

# Installation

After installing the R package, you just need to read in the library.

```{r setup}
library(OpenSpecy)
```

# Running the App

To get started with the Open Specy user interface, access
[https://openanalysis.org/openspecy/](https://openanalysis.org/openspecy/) or
start the Shiny GUI directly from your own computer in R. If you are looking for
a tutorial about how to use the app see [this
tutorial](http://wincowger.com/OpenSpecy-package/articles/app.html).

```{r, eval=FALSE}
run_app()
```

# Read Data

The following line of code will read in your data when using the package and
interprets which reading function to use based on the file extension.

```{r, eval=FALSE}
read_any("path/to/your/data")
```

These file type specific functions will also read in spectral
data accordingly if you have a particular format in mind.

```{r, eval=FALSE}
read_text(".csv")
read_asp(".asp")
read_opus(".0")
```

Open Specy allows for upload of native Open Specy .y(a)ml, .json, or .rds files.
In addition, .csv, .asp, .jdx, .0, .spa, .spc, and .zip files can be imported.
.zip  files can either contain multiple files with individual spectra in them of
the non-zip formats or it can contain a .hdr and .dat file that form an ENVI
file for a spectral map. Open Specy and .csv files should always load correctly
but the other file types are still in development, though most of the time these
files work perfectly. If uploading a .csv file, label the column with the
wavenumbers `wavenumber` and name the column with the intensities `intensity`.
Wavenumber units must be cm^-1^. Any other columns are not used by the software.
Always keep a copy of the original file before alteration to preserve metadata
and raw data for your records.

It is best practice to cross check files in the proprietary software they came
from and Open Specy before use in Open Specy. Due to the complexity of some
proprietary file types, we haven't been able to make them fully compatible yet.
If your file is not working, please contact the administrator and share the file
so that we can work on integrating it.

The specific steps to converting your instrument's native files to .csv can be
found in its software manual or you can check out
[Spectragryph](https://www.effemm2.de/spectragryph/), which supports many
spectral file conversions. For instructions, see [Spectragryph
Tutorial](http://wincowger.com/OpenSpecy-package/articles/spectragryph.html).

If you don't have your own data, you can use a test dataset.

```{r}
data("raman_hdpe")
```

We also have many onboard files that you can call to test different formats:

```{r}
spectral_map <- read_extdata("CA_tiny_map.zip") |> 
  read_any() # preserves some metadata
asp_example <- read_extdata("ftir_ldpe_soil.asp") |> 
  read_any() 
ps_example <- read_extdata("ftir_ps.0") |> 
  read_any() # preserves some metadata
csv_example <- read_extdata("raman_hdpe.csv") |> 
  read_any()
json_example <- read_extdata("raman_hdpe.json") |> 
  read_any() # read in exactly as an OpenSpecy object
```

You will notice now that the R package reads in files into an object with class
`OpenSpecy`. This is a class we created for high throughput spectral analysis
which now also preserves spectral metadata. You can even create these from
scratch if you'd like.

```{r}
scratch_OpenSpecy <- as_OpenSpecy(x = seq(1000,2000, by = 5), 
                                  spectra =  data.frame(runif(n = 201)), 
                                  metadata = list(file_name = "fake_noise")) 
```

Open Specy objects are lists with three components, `wavenumber` is a vector of
the wavenumber values for the spectra and corresponds to the rows in `spectra`
which is a `data.table` where each column is a set of spectral intensities.
`metadata` is a data.table which holds additional information about the spectra.
Each row corresponds to a column in `spectra`.

```{r}
# Access the wavenumbers
scratch_OpenSpecy$wavenumber
```

```{r}
# Access the spectra
scratch_OpenSpecy$spectra
```

```{r}
# Access the metadata
scratch_OpenSpecy$metadata
```

```{r}
# Performs checks to ensure that OpenSpecy objects are adhering to our standards;
# returns TRUE if it passes. 
check_OpenSpecy(scratch_OpenSpecy)

# Checks only the object type to make sure it has OpenSpecy type
is_OpenSpecy(scratch_OpenSpecy)
```

We have some generic functions built for inspecting the spectra:

```{r}
print(scratch_OpenSpecy) # shows the raw object
```

```{r}
summary(scratch_OpenSpecy) # summarizes the contents of the spectra
```

```{r}
head(scratch_OpenSpecy) # shows the top wavenumbers and intensities
```

# Save Data

Open Specy objects can be saved most accurately as .rds, .yml, or .json files.
.rds will be the most reproducible as it is a native R file format and floating
point errors can happen with .json or .yml.

```{r, eval=F}
write_spec(scratch_OpenSpecy, "test_scratch_OpenSpecy.yml", digits = 5)
write_spec(scratch_OpenSpecy, "test_scratch_OpenSpecy.json", digits = 5)
```

# Format Conversions

Another great spectroscopy R package is
[hyperSpec](https://CRAN.R-project.org/package=hyperSpec). We
actually depend on their functions for several of ours. They are currently
making some awesome new features and we want to integrate well with them so that
both packages can be easily used together. That is why we created the
`as_hyperSpec` function and made sure that `as_OpenSpecy` can convert from
`hyperSpec` objects.

```{r, eval=F}
hyperspecy <- as_hyperSpec(scratch_OpenSpecy)
```

# Visualization

## Spectra

In R, we have two ways to visualize your spectra, one is quick and efficient and
the other is interactive. Here is an example of quick and efficient plotting.

```{r, fig.align="center", fig.width=5}
plot(scratch_OpenSpecy) # quick and efficient
```

This is an example of an interactive plot. You can plot two different datasets
simultaneously to compare. 

```{r, fig.align="center", out.width="100%"}
# This will min-max normalize your data even if it isn't already but are not
# changing your underlying data
plotly_spec(scratch_OpenSpecy, json_example)
```

## Maps

Spectral maps can also be visualized as overlaid spectra but in addition the
spatial information can be plotted as a heatmap. It is important to
note that when multiple spectra are uploaded in batch they are prescribed `x`
and `y` coordinates, this can be helpful for visualizing summary statistics and
navigating vast amounts of data but shouldn't be confused with data which
actually has spatial coordinates.

In R the user can control what values form the colors of the heatmap by
specifying `z`, it is handy to put the information you want in the metadata for
this reason. This example just shows the x values of the spectra.

```{r, eval=F}
heatmap_spec(spectral_map,
             z = spectral_map$metadata$x)
```

An interactive plot of the heatmap and spectra overlayed can be generated with
the `interactive_plot()` function. A user can hover over the heatmap to identify
the next row id they are interested in and update the value of select to see
that spectrum.

```{r, fig.align="center", out.width="100%"}
interactive_plot(spectral_map, select = 100, z = spectral_map$metadata$x)
```
<br>

# Combining OpenSpecy Objects

Sometimes you have several OpenSpecy objects that you want to combine into one.
The easiest way to do that is by having spectra which all are in the same exact
format with the same series of wavenumbers. The default settings of `c_spec`
assume that is the case.

If you have different wavenumber ranges for the spectra you want to combine,
you can set  `range = "common"` and `res` equal to the wavenumber resolution you
want and the function will collapse all the spectra to whatever their common
range is using linear interpolation.

```{r, fig.align="center", fig.width=5}
c_spec(list(asp_example, ps_example), range = "common", res = 5) |> 
  plot()
```

# Filtering OpenSpecy Objects

OpenSpecy objects can have any number of spectra in them. To create an OpenSpecy
with a subset of the spectra that is in an Open Specy object you can use the
`filter_spec` function. Filtering is allowed by index number, name, or using a
logical vector. Filtering will update the `spectra` and `metadata` items of the
`OpenSpecy` but not the `wavenumber`.

```{r}
# Extract the 150th spectrum. 
filter_spec(spectral_map, 150)
```

```{r}
# Extract the spectrum with column name "8_5". 
filter_spec(spectral_map, "8_5") |> 
  print()
```

```{r, fig.align="center", fig.width=5}
# Extract the spectra with a logical argument based on metadata
filter_spec(spectral_map, spectral_map$metadata$y == 1) |>
  plot()
```

# Sampling OpenSpecy Objects

Sometimes you have a large suite of examples of spectra of the same material and
you want to reduce the number of spectra you run through the analysis for
simplicity or you are running simulations or other procedures that require you
to first sample from the spectra contained in your OpenSpecy objects before
doing analysis. the `sample_spec` function serves this purpose.

```{r, fig.align="center", fig.width=5}
sample_spec(spectral_map, size = 5) |>
  plot()
```

# Processing

The goal of this all processing is to increase the signal to noise ratio (S/N)
of the spectra without distorting the shape, position, or relative size of the
peaks. After loading data, you can process the data using intensity
adjustment, baseline subtraction, smoothing, flattening, and range selection.
The default settings is an absolute derivative transformation, it is kind of
magic, it does something similar to smoothing, baseline subtraction, and
intensity correction simultaneously and really quickly.

The `process_spec()` function is a monolithic function for all
processing procedures which is optimized by default to result in high signal to
noise in most cases, same as the app.

```{r}
processed <- process_spec(raman_hdpe,
                          active = TRUE,
                          adj_intens = FALSE,
                          adj_intens_args = list(type = "none"),
                          conform_spec = TRUE,
                          conform_spec_args = list(range = NULL, res = 5,
                                                   type = "interp"),
                          restrict_range = FALSE,
                          restrict_range_args = list(min = 0, max = 6000),
                          flatten_range = FALSE,
                          flatten_range_args = list(min = 2200, max = 2420),
                          subtr_baseline = FALSE,
                          subtr_baseline_args = list(type = "polynomial",
                                                     degree = 8, raw = FALSE,
                                                     baseline = NULL),
                          smooth_intens = TRUE,
                          smooth_intens_args = list(polynomial = 3, window = 11,
                                                    derivative = 1, abs = TRUE),
                          make_rel = TRUE)
summary(processed)
summary(raman_hdpe)
```

You can compare the processed and unprocessed data in an overlay plot.

```{r eval=FALSE}
plotly_spec(raman_hdpe, processed)
```

We want people to use the `process_spec()` function for most processing
operations. All other processing functions can be tuned using its parameters in
the single function and we have set defaults and nested the processing functions
in a way that provides typically quality results. However, we recognize that
nesting of functions and order of operations can be useful for users to control
so you can also use individual functions for each operation if you'd like. See
explanations of each processing sub-function below.

## Threshold Signal-Noise

Considering whether you have enough signal to analyze spectra is important.
Classical spectroscopy would recommend your highest peak to be at least 10 times
the baseline of your processed spectra before you begin analysis. If your
spectra is below that threshold even after processing, you may want to consider
recollecting it. In practice, we are rarely able to collect spectra of that good
quality and more often use 5. The "run_sig_over_noise" `metric` searches your
spectra for high and low regions and conducts division on them to derive the
signal to noise ratio and `step` specifies the number of intensities to search.
This is a good way to automatically calculate the signal to noise ratio. In the
example below you can see that our signal to noise ratio is increased by the
processing, the goal of processing is generally to maximize this.
"sig_times_noise" `metric` multiplies the mean signal by the standard deviation
of the signal and "tot_sig" `metric` sums the intensities. The latter can be
really useful for thresholding spectral maps to identify particles which we will
discuss later. If you know where your signal region and noise regions are you
can specify them with `sig_min`, `sig_max`, `noise_min`, and `noise_max`.

```{r, eval=F}
sig_noise(processed, metric = "run_sig_over_noise") > 
  sig_noise(raman_hdpe, metric = "run_sig_over_noise")
```

If analyzing spectra in batch, we recommend looking at the heatmap and
optimizing the percent of spectra that are above your signal to noise threshold
to determine the correct settings instead of looking through spectra
individually. Setting the `min_sn` will threshold the heatmap image to only
color spectra which have a `sn` value over the threshold.

```{r, out.width="100%"}
spectral_map_p <- spectral_map |>
  process_spec(flatten_range = T)

spectral_map_p$metadata$sig_noise <- sig_noise(spectral_map_p)

heatmap_spec(spectral_map_p, sn = spectral_map_p$metadata$sig_noise, min_sn = 5)
```

## Intensity Adjustment

Open Specy assumes that intensity units are in absorbance units but Open Specy
can adjust reflectance or transmittance spectra to absorbance units. The
transmittance adjustment uses the $\log_{10} 1/T$ calculation which does not
correct for system or particle characteristics. The reflectance adjustment use
the Kubelka-Munk equation $\frac{(1-R)^2}{2R}$.

This is the respective R code for a scenario where the spectra doesn't need
intensity adjustment:

```{r, fig.align="center", out.width="100%"}
trans_raman_hdpe <- raman_hdpe
trans_raman_hdpe$spectra <- 2 - trans_raman_hdpe$spectra^2
    
rev_trans_raman_hdpe <- trans_raman_hdpe |> 
  adj_intens(type = "transmittance")

plotly_spec(trans_raman_hdpe, rev_trans_raman_hdpe)
```

## Conforming

Conforming spectra is essential before comparing to a reference library and can
be useful for summarizing data when you don't need it to be highly resolved
spectrally. We set the default spectral resolution to 5 because this tends to be
pretty good for a lot of applications and is in between 4 and 8 which are
commonly used wavenumber resolutions. There are several ways that this function
can be specified.

```{r}
conform_spec(raman_hdpe) |> # default convert res to 5 wavenumbers.
  summary()

# Force one spectrum to have the exact same wavenumbers as another
conform_spec(asp_example, range = ps_example$wavenumber, res = NULL) |> 
  summary()

# Specify the wavenumber resolution and use a rolling join instead of linear
# approximation (faster for large datasets). 
conform_spec(spectral_map, range = ps_example$wavenumber, res = 10,
             type = "roll") |> 
  summary()
```

## Smoothing

The [Savitzky-Golay
filter](https://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter) is used for
smoothing. Higher polynomial numbers lead to more wiggly fits and thus less
smoothing, lower numbers lead to more smooth fits. The SG filter is fit to a
moving window of 11 data points by default where the center point in the window
is replaced with the polynomial estimate. Larger windows will produce smoother
fits. The derivative order is set to 1 by default which transforms the spectra
to their first derivative. A zero order derivative will have no derivative
transformation. When smoothing is done well, peak shapes and relative heights
should not change. The absolute value is primarily useful for first derivative
spectra where the absolute value results in an absorbance-like spectrum which is
why we set it as the default. You'll notice a new function we are using
`c_spec()` which is used to combine spectral objects into one OpenSpecy object.

Examples of smoothing:

```{r smooth_intens, fig.cap = "Sample `raman_hdpe` spectrum with different smoothing polynomials.", fig.width=5, fig.align="center"}
none <- make_rel(raman_hdpe)
p1 <- smooth_intens(raman_hdpe, polynomial = 1, derivative = 0, abs = F)
p4 <- smooth_intens(raman_hdpe, polynomial = 4, derivative = 0, abs = F)

c_spec(list(none, p1, p4)) |> 
  plot()
```

Derivative transformation can happen with the same function. 

```{r compare_derivative, fig.cap = "Sample `raman_hdpe` spectrum with different derivatives.", fig.width=5, fig.align="center"}
none <- make_rel(raman_hdpe)
d1 <- smooth_intens(raman_hdpe, derivative = 1, abs = T)
d2 <- smooth_intens(raman_hdpe, derivative = 2, abs = T)

c_spec(list(none, d1, d2)) |> 
  plot()
```

## Baseline Correction

The goal of baseline correction is to get all non-peak regions of the spectra to
zero absorbance. The higher the polynomial order, the more wiggly the fit to the
baseline. If the baseline is not very wiggly, a more wiggly fit could remove
peaks which is not desired. The baseline correction algorithm used in Open Specy
is called "iModPolyfit" (Zhao et al. 2007). This algorithm iteratively fits
polynomial equations of the specified order to the whole spectrum. During the
first fit iteration, peak regions will often be above the baseline fit. The data
in the peak region is removed from the fit to make sure that the baseline is
less likely to fit to the peaks. The iterative fitting terminates once the
difference between the new and previous fit is small. An example of a good
baseline fit below. Manual baseline correction can also be specified by
providing a baseline `OpenSpecy` object.

```{r subtr_baseline, fig.cap = "Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020).", fig.width=5, fig.align="center"}
alternative_baseline <- smooth_intens(raman_hdpe, polynomial = 1, window = 51,
                                      derivative = 0, abs = F, make_rel = F) |>
  flatten_range(min = 2700, max = 3200, make_rel = F)

none <- make_rel(raman_hdpe)
d2 <- subtr_baseline(raman_hdpe, type = "manual",
                     baseline = alternative_baseline)
d8 <- subtr_baseline(raman_hdpe, degree = 8)

c_spec(list(none, d2, d8)) |> 
  plot()
```

## Range Selection

Sometimes an instrument operates with high noise at the ends of the spectrum
and, a baseline fit produces distortions, or there are regions of interest for
analysis. Range selection accomplishes those goals. You should look into the
signal to noise ratio of your specific instrument by wavelength to determine
what wavelength ranges to use. Distortions due to baseline fit can be assessed
from looking at the process spectra. Additionally, you can restrict the range
to examine a single peak or a subset of peaks of interests. Multiple ranges can
be specified simultaneously.

```{r restrict_range, fig.cap = "Sample `raman_hdpe` spectrum with different degrees of range restriction.", fig.width=5, fig.align="center"}
none <- make_rel(raman_hdpe)
r1 <- restrict_range(raman_hdpe, min = 1000, max = 2000)
r2 <- restrict_range(raman_hdpe, min = c(1000, 1800), max = c(1200, 2000))

compare_ranges <- c_spec(list(none, r1, r2), range = "common")
# Common argument crops the ranges to the most common range between the spectra
# when joining. 

plot(compare_ranges)
```

## Flattening Ranges

Sometimes there are peaks that really shouldn't be in your spectra and can
distort your interpretation of the spectra but you don't necessarily want to
remove the regions from the analysis because you believe those regions should be
flat instead of having a peak. One way to deal with this is to replace the peak
values with the mean of the values around the peak. This is the purpose of the
`flatten_range` function. By default it is set to flatten the CO2 region for
FTIR spectra because that region often needs to be flattened when atmospheric
artifacts occur in spectra. Like `restrict_range`, the R function can accept
multiple ranges.

```{r flatten_range, fig.cap = "Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020).", fig.width=5, fig.align="center"}
single <- filter_spec(spectral_map, 120) # Function to filter spectra by index
                                         # number or name or a logical vector. 
none <- make_rel(single)
f1 <- flatten_range(single) #default flattening the CO2 region. 
f2 <- flatten_range(single, min = c(1000, 2500), max = c(1200, 3000))

compare_flats <- c_spec(list(none, f1, f2))

plot(compare_flats)
```

## Min-Max Normalization

Often we regard spectral intensities as arbitrary and min-max normalization
allows us to view spectra on the same scale without drastically distorting their
shapes or relative peak intensities. In the package, most of the processing
functions will min-max transform your spectra by default if you do not specify
otherwise. This plot shows two plots that are nearly identical except for the
min-max transformed spectrum has a y axis that ranges from 0-1.

```{r make_rel, fig.cap = "Sample `raman_hdpe` spectrum with one being relative and the other untransformed."}
relative <- make_rel(raman_hdpe)
```

# Identifying Spectra 

## Reading Libraries

Reference libraries are spectra with known identities. The Open Specy library
now has over 10,000 spectra in it an is getting so large that we cannot fit it
within the R package size limit of 5 MB. We host the reference libraries on
[OSF](https://osf.io/x7dpz/) and have a function to pull the libraries down
automatically. Running get_lib by itself will download all libraries to your
package director or you can specify which libraries you want and where you want
them.

```{r, eval=F}
get_lib(type = "derivative")
```

After download the libraries you can load them into your active environment. You
should load them one at a time. Libraries are also `OpenSpecy` objects so you
can use any Open Specy object as a library.

```{r, eval=F}
lib <- load_lib(type = "derivative")
```

## Matches

Before attempting to use a reference library to identify spectra it is really
important to understand what format the reference library is in. All the
OpenSpecy reference libraries are in Absorbance units. `derivative` has been
absolute first derivative transformed, `nobaseline` has been baseline corrected,
`raw` is the rawest form of the reference spectra (not recommended except for
advanced uses). The previously mentioned libraries all have Raman and FTIR
spectra in them. `mediod` is the mediod compressed library version of the
absolute first derivative for FTIR, `model` is an exception because it is a
multinomial regression approach for FTIR to identification of absolute
derivative spectra. 
In this example we use the `data("test_lib")` which is a subsampled version of
the absolute derivative library and `data("raman_hdpe")` which is an unprocessed
Raman spectrum in absorbance units of HDPE plastic. With single spectra it is
easy to look at the spectra but when doing in batch, also refer to the
Thresholding Signal-Noise section for guidance on making sure your batch spectra
are processed to quality specs.

```{r, eval = F}
data("test_lib")
data("raman_hdpe")

processed <- process_spec(x = raman_hdpe, 
                          conform_spec = F, #We will conform during matching. 
                          smooth_intens = T #Conducts the default derivative transformation. 
                          )

# Check to make sure that the signal to noise ratio of the processed spectra is
# greater than 10.
print(sig_noise(processed) > 10)
plotly_spec(raman_hdpe, processed)
```

After your spectra is processed similarly to the library specifications, you can
identify the spectra using `match_spec()`. All of the libraries have wavenumbers
at 5 cm^-1 resolution. Whichever library you choose, you need to get your
spectra into a similar enough format to use for comparison. That means
conforming the wavenumbers to the same values using `conform = T`.
Alternatively, you could have done the conformation during the processing. The
`add_library_metadata` and `add_object_metadata` options specify the column name
in the metadata that you want to add metadata from and `top_n` specifies how
many matches you want. In this example we just identified a single spectrum with
the library but you can also send an OpenSpecy object with multiple spectra. The
output `matches` is a data.table with at least 3 columns, `object_id` tells you
the column names of the spectra in `x`, `library_id` tells you the column names
from the library that it matched to. `match_val` is the value of the Pearson
correlation coefficient (default) or other correlation if specified in `...` or
if using the model identification option `match_val` will be the model
confidence. The output in this example returned the correct material type, HDPE,
as the top match. If using Pearson correlation, 0.7 is a good threshold to use
for a positive ID. In this example, only our top match is greater than the
threshold so we would disregard the other matches. If no matches were above our
threshold, we would proclaim that the spectrum is of an unknown identity. You'll
also notice in this example that we matched to a library with both Raman and
FTIR spectra but the Raman spectra had the highest hits, this is the rationale
for lazily matching to a library with both. If you want to just match to a
library with FTIR or Raman spectra, you can first filter the library using
`filter_spec()` using `SpectrumType`.

```{r eval=FALSE}
matches <- match_spec(x = processed, library = test_lib, conform = T,
                      add_library_metadata = "sample_name", top_n = 5)
print(matches[,c("object_id", "library_id", "match_val", "SpectrumType",
                 "SpectrumIdentity")])
```

## Library Metadata

The libraries we have created have over 100 variables of metadata in them and
this can be onerous to read through especially given that many of the variables
are `NA` values. We created `get_metadata()` to remedy this by removing columns
from the metadata which are all blank values. The function below will return the
metadata for the top match in `matches`. Remember, similar `filter_spec()`, you
can specify `logic` for more than one thing at a time.

```{r eval=FALSE}
get_metadata(x = test_lib, logic = matches[[1,"library_id"]], rm_empty = T)
```

## Plot Matches

Overlaying unknown spectra and the best matches can be extremely useful to
identify peaks that don't fit to the reference library which may need further
investigation. The example below shows great correspondence between the best
match and the unknown spectrum. All major peaks are accounted for and the
correct relative height. There are two small peaks in the unknown spectrum near
500 that are not accounted for which could be investigated further but we would
call this a positive id to HDPE.

```{r eval=FALSE}
plotly_spec(processed, filter_spec(test_lib, logic = matches[[1,"library_id"]]))
```

## Sharing Reference Data

If you have reference data or AI models that you think would be useful for
sharing with the spectroscopy community through OpenSpecy please contact the
package administrator to discuss options for collaborating.

# Characterizing Particles

Sometimes the spectroscopy task we want to perform is to identify particles in a
spectral map. This is especially common for microplastic analysis where a
spectral map is used to image a sample and spectral information is used to
differentiate microplastic particles from nonplastic particles. In addition to
the material id, one often wants to measure the shape and size of the particles.
In a brute force technique, one could first identify every spectrum in the map,
then use thresholding and image analysis to measure the particles. However, more
often than not, particles are well separated on the image surface and background
spectra is quite different from particle spectra and therefore we can use
thresholding a priori to identify and measure the particles, then pass an
exemplary spectrum for each particle to the identification routine. It is
important to note here that this is at the bleeding edge of theory and technique
so we may be updating these functions in the near future.

## Brute Force

```{r, eval = F}
data("test_lib")
test_map <- read_any(read_extdata("CA_tiny_map.zip"))

test_map_processed <- process_spec(test_map, conform_spec_args = list(
  range = test_lib$wavenumber, res = NULL)
  )

identities <- match_spec(test_map_processed, test_lib, order = test_map,
                         add_library_metadata = "sample_name", top_n = 1)

features <- ifelse(identities$match_val > 0.7,
                   tolower(identities$polymer_class), "unknown")

id_map <- def_features(x = test_map_processed, features = features)

id_map$metadata$identities <- features
# Also should probably be implemented automatically in the function when a
# character value is provided. 

# Collapses spectra to their median for each particle
test_collapsed <- collapse_spec(id_map)
```

## A Priori Particle Thresholding

```{r, eval = F}
data("test_lib")
test_map <- read_any(read_extdata("CA_tiny_map.zip"))

# Characterize the total signal as a threshold value. 
snr <- sig_noise(test_map,metric = "log_tot_sig")

# Use this to find your particles and the sig_noise value to use for
# thresholding. 
heatmap_spec(test_map, z = snr)

# Set define the feature regions based on the threshold. 400 appeared to be
# where I suspected my particle to be in the previous heatmap. 
id_map <- def_features(x = test_map, features = snr > 400) 

# Check that the thresholding worked as expected.
heatmap_spec(id_map, z = id_map$metadata$feature_id)

# Collapse the spectra to their medians based on the threshold. Important to
# note here that the particles with id -88 are anything from the FALSE values
# so they should be your background. 
collapsed_id_map <- id_map |>
  collapse_spec()

# Process the collapsed spectra. 
id_map_processed <- process_spec(collapsed_id_map, conform_spec_args = list(
  range = test_lib$wavenumber, res = NULL)
  )

# Check the spectra.
plot(id_map_processed)

# Get the matches of the collapsed spectra for the particles.
matches <- match_spec(id_map_processed, test_lib,
                      add_library_metadata = "sample_name", top_n = 1)
```

# References

Chabuka BK, Kalivas JH (2020). “Application of a Hybrid Fusion Classification
Process for Identification of Microplastics Based on Fourier Transform Infrared
Spectroscopy.” *Applied Spectroscopy*, **74**(9), 1167–1183. doi:
[10.1177/0003702820923993](https://doi.org/10.1177/0003702820923993).

Cowger W, Gray A, Christiansen SH, De Frond H, Deshpande AD, Hemabessiere L, Lee
E, Mill L, et al. (2020). “Critical Review of Processing and Classification
Techniques for Images and Spectra in Microplastic Research.” *Applied
Spectroscopy*, **74**(9), 989–1010. doi:
[10.1177/0003702820929064](https://doi.org/10.1177/0003702820929064).

Cowger W, Steinmetz Z, Gray A, Munno K, Lynch J, Hapich H, Primpke S,
De Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral Classification
Needs an Open Source Community: Open Specy to the Rescue!”
*Analytical Chemistry*, **93**(21), 7543–7548. doi:
[10.1021/acs.analchem.1c00123](https://doi.org/10.1021/acs.analchem.1c00123).

Primpke S, Wirth M, Lorenz C, Gerdts G (2018). “Reference Database Design for
the Automated Analysis of Microplastic Samples Based on Fourier Transform
Infrared (FTIR) Spectroscopy.” *Analytical and Bioanalytical Chemistry*,
**410**(21), 5131–5141. doi: 
[10.1007/s00216-018-1156-x](https://doi.org/10.1007/s00216-018-1156-x).

Renner G, Schmidt TC, Schram J (2017). “A New Chemometric Approach for Automatic
Identification of Microplastics from Environmental Compartments Based on FT-IR
Spectroscopy.” *Analytical Chemistry*, **89**(22), 12045–12053. doi: 
[10.1021/acs.analchem.7b02472](https://doi.org/10.1021/acs.analchem.7b02472).

Savitzky A, Golay MJ (1964). “Smoothing and Differentiation of Data by
Simplified Least Squares Procedures.” *Analytical Chemistry*, **36**(8),
1627–1639.

Zhao J, Lui H, McLean DI, Zeng H (2007). “Automated Autofluorescence Background
Subtraction Algorithm for Biomedical Raman Spectroscopy.”
*Applied Spectroscopy*, **61**(11), 1225–1232. doi:
[10.1366/000370207782597003](https://doi.org/10.1366/000370207782597003).
