Link first tutorial

Author: philipp-baumann

README.Rmd

@@ -1,151 +1,80 @@
 `simplerspec` teaching material for infrared spectroscopy applications
 ================
 
-# Getting started with infrared spectroscopy
-
-First, you should read some scientific literature. You are probably
-curious what infrared spectroscopy is based on and what it serves to in
-an agronomic context. You will find out by reading the recommended
-literature at the bottom of this document.
-
------
-
-Secondly, you may want to be prepared to measure, process and model
-plant or soil samples spectrally, and finally predict properties of new
-samples to reduce the extent of labourious reference analyses. To achive
-this, one needs some statistics.
-
-R has become a very popular environment for statistical computing. There
-are alternative languages that are equally good, better or worse,
-depending on the field of application and personal opinion. Here, we
-solely focus on R.
-
-  - **Minimum recommended R knowledge**: Prior to working with spectral
-    data using the R environment, you should improve your R
-    scripting/programming skills. You can for example consider [**this
-    document**](https://github.com/philipp-baumann/simplerspec-teaching/blob/master/00_R-basics-spectro.md)
-    in this repo or [**this pdf
-    file**](https://github.com/philipp-baumann/simplerspec-teaching/blob/master/00_R-basics-spectro.pdf).
-    I would highly recommend to do this because being familiar with R
-    basics helps to avoid frustration in the first place and to
-    hopefully enjoy the opportunities spectral analysis provides.
-
------
-
-# Recommended literature
-
-## Principles and applications of soil spectroscopy
-
-  - Awiti, A. O., Walsh, M. G., Shepherd, K. D., & Kinyamario, J.
-    (2008). Soil condition classification using infrared spectroscopy: A
-    proposition for assessment of soil condition along a tropical
-    forest-cropland chronosequence. Geoderma, 143(1–2), 73–84.
-    <https://doi.org/10.1016/j.geoderma.2007.08.021>
-
-  - Chang, C.-W., Laird, D. A., Mausbach, M. J., & Hurburgh, C. R.
-    (2001). Near-infrared reflectance spectroscopy–principal components
-    regression analyses of soil properties. Soil Science Society of
-    America Journal, 65(2), 480–490.
-
-  - Clairotte, M., Grinand, C., Kouakoua, E., Thébault, A., Saby, N. P.
-    A., Bernoux, M., & Barthès, B. G. (2016). National calibration of
-    soil organic carbon concentration using diffuse infrared reflectance
-    spectroscopy. Geoderma, 276, 41–52.
-    <https://doi.org/10.1016/j.geoderma.2016.04.021>
-
-  - Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., Bachmann,
-    M., Barthès, B., … Wetterlind, J. (2015). Chapter Four - Soil
-    Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring.
-    In D. L. Sparks (Ed.), Advances in Agronomy (Vol. 132, pp. 139–159).
-    Academic Press. Retrieved from
-    <http://www.sciencedirect.com/science/article/pii/S0065211315000425>
-
-  - Rinnan, Å. (2014). Pre-processing in vibrational spectroscopy –
-    when, why and how. Analytical Methods, 6(18), 7124.
-    <https://doi.org/10.1039/C3AY42270D>
-
-  - Rinnan, Å., Berg, F. van den, & Engelsen, S. B. (2009). Review of
-    the most common pre-processing techniques for near-infrared spectra.
-    TrAC Trends in Analytical Chemistry, 28(10), 1201–1222.
-    <https://doi.org/10.1016/j.trac.2009.07.007>
-
-  - Shepherd, K. D., & Markus G. Walsh. (n.d.). Infrared
-    spectroscopy—enabling an evidence-based diagnostic surveillance
-    approach to agricultural and environmental management in developing
-    countries. <https://doi.org/10.1255/jnirs.716>
-
-  - Sila, A. M., Shepherd, K. D., & Pokhariyal, G. P. (2016). Evaluating
-    the utility of mid-infrared spectral subspaces for predicting soil
-    properties. Chemometrics and Intelligent Laboratory Systems, 153,
-    92–105. <https://doi.org/10.1016/j.chemolab.2016.02.013>
-
-  - Terhoeven-Urselmans, T., Vagen, T.-G., Spaargaren, O., & Shepherd,
-    K. D. (2010). Prediction of Soil Fertility Properties from a
-    Globally Distributed Soil Mid-Infrared Spectral Library. Soil
-    Science Society of America Journal, 74(5), 1792.
-    <https://doi.org/10.2136/sssaj2009.0218>
-
-  - Viscarra Rossel, R. A., Behrens, T., Ben-Dor, E., Brown, D. J.,
-    Demattê, J. A. M., Shepherd, K. D., … Ji, W. (2016). A global
-    spectral library to characterize the world’s soil. Earth-Science
-    Reviews, 155, 198–230.
-    <https://doi.org/10.1016/j.earscirev.2016.01.012>
-
-  - Viscarra Rossel, R. A., Walvoort, D. J. J., McBratney, A. B., Janik,
-    L. J., & Skjemstad, J. O. (2006). Visible, near infrared, mid
-    infrared or combined diffuse reflectance spectroscopy for
-    simultaneous assessment of various soil properties. Geoderma,
-    131(1–2), 59–75. <https://doi.org/10.1016/j.geoderma.2005.03.007>
-
-  - Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a
-    basic tool of chemometrics. Chemometrics and Intelligent Laboratory
-    Systems, 58(2), 109–130.
-    <https://doi.org/10.1016/S0169-7439(01)00155-1>
-
-## Principles and application of plant infrared spectroscopy
-
-  - Lebot, V, Malapa, R., & Jung, M. (2013). Use of NIRS for the rapid
-    prediction of total N, minerals, sugars and starch in tropical root
-    and tuber crops. New Zealand Journal of Crop and Horticultural
-    Science, 41(3), 144–153.
-    <https://doi.org/10.1080/01140671.2013.798335>
-
-  - Lebot, Vincent, Champagne, A., Malapa, R., & Shiley, D. (2009). NIR
-    Determination of Major Constituents in Tropical Root and Tuber Crop
-    Flours. Journal of Agricultural and Food Chemistry, 57(22),
-    10539–10547. <https://doi.org/10.1021/jf902675n>
-
-  - van Maarschalkerweerd, M., & Husted, S. (2015). Recent developments
-    in fast spectroscopy for plant mineral analysis. Frontiers in Plant
-    Science, 6. <https://doi.org/10.3389/fpls.2015.00169>
-
-## Statistical modeling based on spectroscopy
-
-  - Chong, I.-G., & Jun, C.-H. (2005). Performance of some variable
-    selection methods when multicollinearity is present. Chemometrics
-    and Intelligent Laboratory Systems, 78(1–2), 103–112.
-    <https://doi.org/10.1016/j.chemolab.2004.12.011>
-
-  - Eriksson, L., Trygg, J., & Wold, S. (2014). A chemometrics toolbox
-    based on projections and latent variables: A chemometrics toolbox
-    based on projections and latent variables. Journal of Chemometrics,
-    28(5), 332–346. <https://doi.org/10.1002/cem.2581>
-
-  - Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of
-    statistical learning (Vol. 1). Springer series in statistics
-    Springer, Berlin. Retrieved from
-    <http://statweb.stanford.edu/~tibs/book/preface.ps>
-
-  - Kuhn, M. (2008). Building Predictive Models in R Using the caret
-    Package. Journal of Statistical Software, Articles, 28(5), 1–26.
-    <https://doi.org/10.18637/jss.v028.i05>
-
-  - Stevens, A., & Ramirez–Lopez, L. (2014). An introduction to the
-    prospectr package. R Package Vignette, Report No.: R Package Version
-    0.1, 3. Retrieved from
-    <ftp://200.236.31.2/CRAN/web/packages/prospectr/vignettes/prospectr-intro.pdf>
-
-  - Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a
-    basic tool of chemometrics. Chemometrics and Intelligent Laboratory
-    Systems, 58(2), 109–130.
-    <https://doi.org/10.1016/S0169-7439(01)00155-1>
+Getting started with infrared spectroscopy
+==========================================
+
+First, you should read some scientific literature. You are probably curious what infrared spectroscopy is based on and what it serves to in an agronomic context. You will find out by reading the recommended literature at the bottom of this document.
+
+------------------------------------------------------------------------
+
+Secondly, you may want to be prepared to measure, process and model plant or soil samples spectrally, and finally predict properties of new samples to reduce the extent of labourious reference analyses. To achive this, one needs some statistics.
+
+R has become a very popular environment for statistical computing. There are alternative languages that are equally good, better or worse, depending on the field of application and personal opinion. Here, we solely focus on R.
+
+-   **Minimum recommended R knowledge**: Prior to working with spectral data using the R environment, you should improve your R scripting/programming skills. You can for example consider [**this document**](https://github.com/philipp-baumann/simplerspec-teaching/blob/master/00_R-basics-spectro.md) in this repo or [**this pdf file**](https://github.com/philipp-baumann/simplerspec-teaching/blob/master/00_R-basics-spectro.pdf). I would highly recommend to do this because being familiar with R basics helps to avoid frustration in the first place and to hopefully enjoy the opportunities spectral analysis provides.
+
+------------------------------------------------------------------------
+
+Spectroscopy tutorials
+======================
+
+Below you find specific sections that cover the spectroscopy processing and modeling workflow (click on links):
+
+1.  [**Read, filter and transform spectra and metadata**](https://github.com/philipp-baumann/simplerspec-read-filter-transform): Learn how to use the R core data structures and opterations such as subsetting to explore and transform spectral data.
+
+------------------------------------------------------------------------
+
+Recommended literature
+======================
+
+Principles and applications of soil spectroscopy
+------------------------------------------------
+
+-   Awiti, A. O., Walsh, M. G., Shepherd, K. D., & Kinyamario, J. (2008). Soil condition classification using infrared spectroscopy: A proposition for assessment of soil condition along a tropical forest-cropland chronosequence. Geoderma, 143(1–2), 73–84. <https://doi.org/10.1016/j.geoderma.2007.08.021>
+
+-   Chang, C.-W., Laird, D. A., Mausbach, M. J., & Hurburgh, C. R. (2001). Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties. Soil Science Society of America Journal, 65(2), 480–490.
+
+-   Clairotte, M., Grinand, C., Kouakoua, E., Thébault, A., Saby, N. P. A., Bernoux, M., & Barthès, B. G. (2016). National calibration of soil organic carbon concentration using diffuse infrared reflectance spectroscopy. Geoderma, 276, 41–52. <https://doi.org/10.1016/j.geoderma.2016.04.021>
+
+-   Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., Bachmann, M., Barthès, B., … Wetterlind, J. (2015). Chapter Four - Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring. In D. L. Sparks (Ed.), Advances in Agronomy (Vol. 132, pp. 139–159). Academic Press. Retrieved from <http://www.sciencedirect.com/science/article/pii/S0065211315000425>
+
+-   Rinnan, Å. (2014). Pre-processing in vibrational spectroscopy – when, why and how. Analytical Methods, 6(18), 7124. <https://doi.org/10.1039/C3AY42270D>
+
+-   Rinnan, Å., Berg, F. van den, & Engelsen, S. B. (2009). Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends in Analytical Chemistry, 28(10), 1201–1222. <https://doi.org/10.1016/j.trac.2009.07.007>
+
+-   Shepherd, K. D., & Markus G. Walsh. (n.d.). Infrared spectroscopy—enabling an evidence-based diagnostic surveillance approach to agricultural and environmental management in developing countries. <https://doi.org/10.1255/jnirs.716>
+
+-   Sila, A. M., Shepherd, K. D., & Pokhariyal, G. P. (2016). Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties. Chemometrics and Intelligent Laboratory Systems, 153, 92–105. <https://doi.org/10.1016/j.chemolab.2016.02.013>
+
+-   Terhoeven-Urselmans, T., Vagen, T.-G., Spaargaren, O., & Shepherd, K. D. (2010). Prediction of Soil Fertility Properties from a Globally Distributed Soil Mid-Infrared Spectral Library. Soil Science Society of America Journal, 74(5), 1792. <https://doi.org/10.2136/sssaj2009.0218>
+
+-   Viscarra Rossel, R. A., Behrens, T., Ben-Dor, E., Brown, D. J., Demattê, J. A. M., Shepherd, K. D., … Ji, W. (2016). A global spectral library to characterize the world’s soil. Earth-Science Reviews, 155, 198–230. <https://doi.org/10.1016/j.earscirev.2016.01.012>
+
+-   Viscarra Rossel, R. A., Walvoort, D. J. J., McBratney, A. B., Janik, L. J., & Skjemstad, J. O. (2006). Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131(1–2), 59–75. <https://doi.org/10.1016/j.geoderma.2005.03.007>
+
+-   Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130. <https://doi.org/10.1016/S0169-7439(01)00155-1>
+
+Principles and application of plant infrared spectroscopy
+---------------------------------------------------------
+
+-   Lebot, V, Malapa, R., & Jung, M. (2013). Use of NIRS for the rapid prediction of total N, minerals, sugars and starch in tropical root and tuber crops. New Zealand Journal of Crop and Horticultural Science, 41(3), 144–153. <https://doi.org/10.1080/01140671.2013.798335>
+
+-   Lebot, Vincent, Champagne, A., Malapa, R., & Shiley, D. (2009). NIR Determination of Major Constituents in Tropical Root and Tuber Crop Flours. Journal of Agricultural and Food Chemistry, 57(22), 10539–10547. <https://doi.org/10.1021/jf902675n>
+
+-   van Maarschalkerweerd, M., & Husted, S. (2015). Recent developments in fast spectroscopy for plant mineral analysis. Frontiers in Plant Science, 6. <https://doi.org/10.3389/fpls.2015.00169>
+
+Statistical modeling based on spectroscopy
+------------------------------------------
+
+-   Chong, I.-G., & Jun, C.-H. (2005). Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems, 78(1–2), 103–112. <https://doi.org/10.1016/j.chemolab.2004.12.011>
+
+-   Eriksson, L., Trygg, J., & Wold, S. (2014). A chemometrics toolbox based on projections and latent variables: A chemometrics toolbox based on projections and latent variables. Journal of Chemometrics, 28(5), 332–346. <https://doi.org/10.1002/cem.2581>
+
+-   Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1). Springer series in statistics Springer, Berlin. Retrieved from <http://statweb.stanford.edu/~tibs/book/preface.ps>
+
+-   Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, Articles, 28(5), 1–26. <https://doi.org/10.18637/jss.v028.i05>
+
+-   Stevens, A., & Ramirez–Lopez, L. (2014). An introduction to the prospectr package. R Package Vignette, Report No.: R Package Version 0.1, 3. Retrieved from <ftp://200.236.31.2/CRAN/web/packages/prospectr/vignettes/prospectr-intro.pdf>
+
+-   Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130. <https://doi.org/10.1016/S0169-7439(01)00155-1>