references.bib

@article{clairotte_national_2016,
	title = {National calibration of soil organic carbon concentration using diffuse infrared reflectance spectroscopy},
	volume = {276},
	issn = {00167061},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S001670611630180X},
	doi = {10.1016/j.geoderma.2016.04.021},
	language = {en},
	urldate = {2016-08-08},
	journal = {Geoderma},
	author = {Clairotte, Michaël and Grinand, Clovis and Kouakoua, Ernest and Thébault, Aurélie and Saby, Nicolas P.A. and Bernoux, Martial and Barthès, Bernard G.},
	month = aug,
	year = {2016},
	pages = {41--52},
	file = {Clairotte et al. - 2016 - National calibration of soil organic carbon concen.pdf:/Users/baumanph/Zotero/storage/3RV7IJMG/Clairotte et al. - 2016 - National calibration of soil organic carbon concen.pdf:application/pdf}
}

@article{shepherd_infrared_nodate,
	title = {Infrared spectroscopy—enabling an evidence-based diagnostic surveillance approach to agricultural and environmental management in developing countries},
	doi = {10.1255/jnirs.716},
	author = {Shepherd, Keith D. and Markus G. Walsh}
}

@article{terhoeven-urselmans_prediction_2010,
	title = {Prediction of {Soil} {Fertility} {Properties} from a {Globally} {Distributed} {Soil} {Mid}-{Infrared} {Spectral} {Library}},
	volume = {74},
	issn = {1435-0661},
	url = {https://www.soils.org/publications/sssaj/abstracts/74/5/1792},
	doi = {10.2136/sssaj2009.0218},
	language = {en},
	number = {5},
	urldate = {2016-08-09},
	journal = {Soil Science Society of America Journal},
	author = {Terhoeven-Urselmans, Thomas and Vagen, Tor-Gunnar and Spaargaren, Otto and Shepherd, Keith D.},
	year = {2010},
	pages = {1792},
	file = {Terhoeven-Urselmans et al. - 2010 - Prediction of Soil Fertility Properties from a Glo.pdf:/Users/baumanph/Zotero/storage/XWNBEED8/Terhoeven-Urselmans et al. - 2010 - Prediction of Soil Fertility Properties from a Glo.pdf:application/pdf}
}

@article{sila_evaluating_2016,
	title = {Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties},
	volume = {153},
	issn = {01697439},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0169743916300351},
	doi = {10.1016/j.chemolab.2016.02.013},
	language = {en},
	urldate = {2016-08-08},
	journal = {Chemometrics and Intelligent Laboratory Systems},
	author = {Sila, Andrew M. and Shepherd, Keith D. and Pokhariyal, Ganesh P.},
	month = apr,
	year = {2016},
	pages = {92--105},
	file = {Sila et al. - 2016 - Evaluating the utility of mid-infrared spectral su.pdf:/Users/baumanph/Zotero/storage/H2PS99WC/Sila et al. - 2016 - Evaluating the utility of mid-infrared spectral su.pdf:application/pdf}
}

@article{viscarra_rossel_global_2016,
	title = {A global spectral library to characterize the world's soil},
	volume = {155},
	issn = {00128252},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0012825216300113},
	doi = {10.1016/j.earscirev.2016.01.012},
	language = {en},
	urldate = {2016-08-09},
	journal = {Earth-Science Reviews},
	author = {Viscarra Rossel, R.A. and Behrens, T. and Ben-Dor, E. and Brown, D.J. and Demattê, J.A.M. and Shepherd, K.D. and Shi, Z. and Stenberg, B. and Stevens, A. and Adamchuk, V. and Aïchi, H. and Barthès, B.G. and Bartholomeus, H.M. and Bayer, A.D. and Bernoux, M. and Böttcher, K. and Brodský, L. and Du, C.W. and Chappell, A. and Fouad, Y. and Genot, V. and Gomez, C. and Grunwald, S. and Gubler, A. and Guerrero, C. and Hedley, C.B. and Knadel, M. and Morrás, H.J.M. and Nocita, M. and Ramirez-Lopez, L. and Roudier, P. and Campos, E.M. Rufasto and Sanborn, P. and Sellitto, V.M. and Sudduth, K.A. and Rawlins, B.G. and Walter, C. and Winowiecki, L.A. and Hong, S.Y. and Ji, W.},
	month = apr,
	year = {2016},
	pages = {198--230},
	file = {Viscarra Rossel et al. - 2016 - A global spectral library to characterize the worl.pdf:/Users/baumanph/Zotero/storage/ZTQ4JMHK/Viscarra Rossel et al. - 2016 - A global spectral library to characterize the worl.pdf:application/pdf}
}

@article{chong_performance_2005,
	title = {Performance of some variable selection methods when multicollinearity is present},
	volume = {78},
	issn = {01697439},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0169743905000031},
	doi = {10.1016/j.chemolab.2004.12.011},
	language = {en},
	number = {1-2},
	urldate = {2015-12-24},
	journal = {Chemometrics and Intelligent Laboratory Systems},
	author = {Chong, Il-Gyo and Jun, Chi-Hyuck},
	month = jul,
	year = {2005},
	pages = {103--112},
	file = {Chong and Jun - 2005 - Performance of some variable selection methods whe.pdf:/Users/baumanph/Zotero/storage/DXMMGNSA/Chong and Jun - 2005 - Performance of some variable selection methods whe.pdf:application/pdf}
}

@article{lebot_nir_2009,
	title = {{NIR} {Determination} of {Major} {Constituents} in {Tropical} {Root} and {Tuber} {Crop} {Flours}},
	volume = {57},
	issn = {0021-8561, 1520-5118},
	url = {http://pubs.acs.org/doi/abs/10.1021/jf902675n},
	doi = {10.1021/jf902675n},
	language = {en},
	number = {22},
	urldate = {2014-11-14},
	journal = {Journal of Agricultural and Food Chemistry},
	author = {Lebot, Vincent and Champagne, Antoine and Malapa, Roger and Shiley, Dan},
	month = nov,
	year = {2009},
	pages = {10539--10547},
	file = {jf902675n.pdf:/Users/baumanph/Zotero/storage/EDA7CB3T/jf902675n.pdf:application/pdf}
}

@article{viscarra_rossel_visible_2006,
	title = {Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties},
	volume = {131},
	issn = {0016-7061},
	url = {http://www.sciencedirect.com/science/article/pii/S0016706105000728},
	doi = {10.1016/j.geoderma.2005.03.007},
	abstract = {Historically, our understanding of the soil and assessment of its quality and function has been gained through routine soil chemical and physical laboratory analysis. There is a global thrust towards the development of more time- and cost-efficient methodologies for soil analysis as there is a great demand for larger amounts of good quality, inexpensive soil data to be used in environmental monitoring, modelling and precision agriculture. Diffuse reflectance spectroscopy provides a good alternative that may be used to enhance or replace conventional methods of soil analysis, as it overcomes some of their limitations. Spectroscopy is rapid, timely, less expensive, non-destructive, straightforward and sometimes more accurate than conventional analysis. Furthermore, a single spectrum allows for simultaneous characterisation of various soil properties and the techniques are adaptable for ‘on-the-go’ field use. The aims of this paper are threefold: (i) determine the value of qualitative analysis in the visible (VIS) (400–700 nm), near infrared (NIR) (700–2500 nm) and mid infrared (MIR) (2500–25,000 nm); (ii) compare the simultaneous predictions of a number of different soil properties in each of these regions and the combined VIS–NIR–MIR to determine whether the combined information produces better predictions of soil properties than each of the individual regions; and (iii) deduce which of these regions may be best suited for simultaneous analysis of various soil properties. In this instance we implemented partial least-squares regression (PLSR) to construct calibration models, which were independently validated for the prediction of various soil properties from the soil spectra. The soil properties examined were soil pHCa, pHw, lime requirement (LR), organic carbon (OC), clay, silt, sand, cation exchange capacity (CEC), exchangeable calcium (Ca), exchangeable aluminium (Al), nitrate–nitrogen (NO3–N), available phosphorus (PCol), exchangeable potassium (K) and electrical conductivity (EC). Our results demonstrated the value of qualitative soil interpretations using the loading weight vectors from the PLSR decomposition. The MIR was more suitable than the VIS or NIR for this type of analysis due to the higher incidence spectral bands in this region as well as the higher intensity and specificity of the signal. Quantitatively, the accuracy of PLSR predictions in each of the VIS, NIR, MIR and VIS–NIR–MIR spectral regions varied considerably amongst properties. However, more accurate predictions were obtained using the MIR for pH, LR, OC, CEC, clay, silt and sand contents, P and EC. The NIR produced more accurate predictions for exchangeable Al and K than any of the ranges. There were only minor improvements in predictions of clay, silt and sand content using the combined VIS–NIR–MIR range. This work demonstrates the potential of diffuse reflectance spectroscopy using the VIS, NIR and MIR for more efficient soil analysis and the acquisition of soil information.},
	number = {1–2},
	urldate = {2016-09-06},
	journal = {Geoderma},
	author = {Viscarra Rossel, R. A. and Walvoort, D. J. J. and McBratney, A. B. and Janik, L. J. and Skjemstad, J. O.},
	month = mar,
	year = {2006},
	keywords = {Diffuse infrared reflectance spectroscopy, MIR partial least-squares regression, NIR, Soil analysis, Visible spectroscopy},
	pages = {59--75},
	file = {ScienceDirect Snapshot:/Users/baumanph/Zotero/storage/WFNKNDXM/S0016706105000728.html:text/html;Viscarra Rossel et al. - 2006 - Visible, near infrared, mid infrared or combined d.pdf:/Users/baumanph/Zotero/storage/TJIMZ2TT/Viscarra Rossel et al. - 2006 - Visible, near infrared, mid infrared or combined d.pdf:application/pdf}
}

@incollection{nocita_chapter_2015,
	title = {Chapter {Four} - {Soil} {Spectroscopy}: {An} {Alternative} to {Wet} {Chemistry} for {Soil} {Monitoring}},
	volume = {132},
	shorttitle = {Chapter {Four} - {Soil} {Spectroscopy}},
	url = {http://www.sciencedirect.com/science/article/pii/S0065211315000425},
	abstract = {The soil science community is facing a growing demand of regional, continental, and worldwide databases in order to monitor the status of the soil. However, the availability of such data is very scarce. Cost-effective tools to measure soil properties for large areas (e.g., Europe) are required. Soil spectroscopy has shown to be a fast, cost-effective, environmental-friendly, nondestructive, reproducible, and repeatable analytical technique. The main aim of this paper is to describe the state of the art of soil spectroscopy as well as its potential to facilitating soil monitoring. The factors constraining the application of soil spectroscopy as an alternative to traditional laboratory analyses, together with the limits of the technique, are addressed. The paper also highlights that the widespread use of spectroscopy to monitor the status of the soil should be encouraged by (1) the creation of a standard for the collection of laboratory soil spectra, to promote the sharing of spectral libraries, and (2) the scanning of existing soil archives, reducing the need for costly sampling campaigns. Finally, routine soil analysis using soil spectroscopy would be beneficial for the end users by a reduction in analytical costs, and an increased comparability of results between laboratories. This ambitious project will materialize only through (1) the establishment of local and regional partnerships among existent institutions able to generate the necessary technical competence, and (2) the support of international organizations. The Food and Agriculture Organization (FAO) of United Nations and the Joint Research Centre of the European Commission are well placed to promote the use of laboratory and field spectrometers for monitoring the state of soils.},
	urldate = {2016-09-06},
	booktitle = {Advances in {Agronomy}},
	publisher = {Academic Press},
	author = {Nocita, Marco and Stevens, Antoine and van Wesemael, Bas and Aitkenhead, Matt and Bachmann, Martin and Barthès, Bernard and Ben Dor, Eyal and Brown, David J. and Clairotte, Michael and Csorba, Adam and Dardenne, Pierre and Demattê, Jose A. M. and Genot, Valerie and Guerrero, Cesar and Knadel, Maria and Montanarella, Luca and Noon, Carole and Ramirez-Lopez, Leonardo and Robertson, Jean and Sakai, Hiro and Soriano-Disla, Jose M. and Shepherd, Keith D. and Stenberg, Bo and Towett, Erick K. and Vargas, Ronald and Wetterlind, Johanna},
	editor = {Sparks, Donald L.},
	year = {2015},
	keywords = {Alternative, Common methodology, Monitoring, Perspectives, Review, Spectroscopy},
	pages = {139--159},
	file = {Nocita et al. - 2015 - Chapter Four - Soil Spectroscopy An Alternative t.pdf:/Users/baumanph/Zotero/storage/QWH3E45N/Nocita et al. - 2015 - Chapter Four - Soil Spectroscopy An Alternative t.pdf:application/pdf;ScienceDirect Snapshot:/Users/baumanph/Zotero/storage/FBR8JKSE/S0065211315000425.html:text/html}
}

@article{wold_pls-regression:_2001,
	series = {{PLS} {Methods}},
	title = {{PLS}-regression: a basic tool of chemometrics},
	volume = {58},
	issn = {0169-7439},
	shorttitle = {{PLS}-regression},
	url = {http://www.sciencedirect.com/science/article/pii/S0169743901001551},
	doi = {10.1016/S0169-7439(01)00155-1},
	abstract = {PLS-regression (PLSR) is the PLS approach in its simplest, and in chemistry and technology, most used form (two-block predictive PLS). PLSR is a method for relating two data matrices, X and Y, by a linear multivariate model, but goes beyond traditional regression in that it models also the structure of X and Y. PLSR derives its usefulness from its ability to analyze data with many, noisy, collinear, and even incomplete variables in both X and Y. PLSR has the desirable property that the precision of the model parameters improves with the increasing number of relevant variables and observations.

This article reviews PLSR as it has developed to become a standard tool in chemometrics and used in chemistry and engineering. The underlying model and its assumptions are discussed, and commonly used diagnostics are reviewed together with the interpretation of resulting parameters.

Two examples are used as illustrations: First, a Quantitative Structure–Activity Relationship (QSAR)/Quantitative Structure–Property Relationship (QSPR) data set of peptides is used to outline how to develop, interpret and refine a PLSR model. Second, a data set from the manufacturing of recycled paper is analyzed to illustrate time series modelling of process data by means of PLSR and time-lagged X-variables.},
	number = {2},
	urldate = {2016-09-06},
	journal = {Chemometrics and Intelligent Laboratory Systems},
	author = {Wold, Svante and Sjöström, Michael and Eriksson, Lennart},
	month = oct,
	year = {2001},
	keywords = {Latent variables, multivariate analysis, PLS, PLSR, Two-block predictive PLS},
	pages = {109--130},
	file = {ScienceDirect Snapshot:/Users/baumanph/Zotero/storage/MARF8NV9/S0169743901001551.html:text/html;Wold et al. - 2001 - PLS-regression a basic tool of chemometrics.pdf:/Users/baumanph/Zotero/storage/S85FFAG2/Wold et al. - 2001 - PLS-regression a basic tool of chemometrics.pdf:application/pdf}
}

@article{rinnan_pre-processing_2014,
	title = {Pre-processing in vibrational spectroscopy – when, why and how},
	volume = {6},
	issn = {1759-9660, 1759-9679},
	url = {http://xlink.rsc.org/?DOI=C3AY42270D},
	doi = {10.1039/C3AY42270D},
	language = {en},
	number = {18},
	urldate = {2016-11-28},
	journal = {Analytical Methods},
	author = {Rinnan, Åsmund},
	month = jun,
	year = {2014},
	pages = {7124},
	file = {Rinnan - 2014 - Pre-processing in vibrational spectroscopy – when,.pdf:/Users/baumanph/Zotero/storage/PSKTJJNV/Rinnan - 2014 - Pre-processing in vibrational spectroscopy – when,.pdf:application/pdf}
}

@article{awiti_soil_2008,
	title = {Soil condition classification using infrared spectroscopy: {A} proposition for assessment of soil condition along a tropical forest-cropland chronosequence},
	volume = {143},
	issn = {0016-7061},
	shorttitle = {Soil condition classification using infrared spectroscopy},
	url = {https://www.sciencedirect.com/science/article/pii/S0016706107002625},
	doi = {10.1016/j.geoderma.2007.08.021},
	abstract = {Soil fertility depletion in smallholder agricultural systems in sub-Saharan Africa presents a formidable challenge both for food production and environmental sustainability. A critical constraint to managing soils in sub-Saharan Africa is poor targeting of soil management interventions. This is partly due to lack of diagnostic tools for screening soil condition that would lead to a robust and repeatable spatially explicit case definition of poor soil condition. The objectives of this study were to: (i) evaluate the ability of near infrared spectroscopy to detect changes in soil properties across a forest-cropland chronosequence; and (ii) develop a heuristic scheme for the application of infrared spectroscopy as a tool for case definition and diagnostic screening of soil condition for agricultural and environmental management. Soil reflectance was measured for 582 topsoil samples collected from forest-cropland chronosequence age classes namely; forest, recently converted, RC (17 years) and historically converted, HC (ca.70 years). 130 randomly selected samples were used to calibrate soil properties to soil reflectance using partial least-squares regression (PLSR). 64 randomly selected samples were withheld for validation. A proportional odds logistic model was applied to chronosequence age classes and 10 principal components of spectral reflectance to determine three soil condition classes namely; “good”, “average” and “poor” for 194 samples. Discriminant analysis was applied to classify the remaining 388 “unknown” samples into soil condition classes using the 194 samples as a training set. Validation r2 values were: total C, 0.91; total N, 0.90; effective cation exchange capacity (ECEC), 0.90; exchangeable Ca, 0.85; clay content, 0.77; silt content, 0.77 exchangeable Mg, 0.76; soil pH, 0.72; and K, 0.64. A spectral based definition of “good”, “average” and “poor” soil condition classes provided a basis for an explicitly quantitative case definition of poor or degraded soils. Estimates of probabilities of membership of a sample in a spectral soil condition class presents an approach for probabilistic risk-based assessments of soil condition over large spatial scales. The study concludes that reflectance spectroscopy is rapid and offers the possibility for major efficiency and cost saving, permitting spectral case definition to define poor or degraded soils, leading to better targeting of management interventions.},
	number = {1–2},
	urldate = {2017-02-09},
	journal = {Geoderma},
	author = {Awiti, Alex O. and Walsh, Markus G. and Shepherd, Keith D. and Kinyamario, Jenesio},
	month = jan,
	year = {2008},
	keywords = {Case definition, Chronosequence, infrared spectroscopy, Probabilistic risk-based assessment, Soil condition class, Tropical rainforest},
	pages = {73--84},
	file = {ScienceDirect Full Text PDF:/Users/baumanph/Zotero/storage/3NSMUBDF/Awiti et al. - 2008 - Soil condition classification using infrared spect.pdf:application/pdf;ScienceDirect Snapshot:/Users/baumanph/Zotero/storage/ZSPVXNQ2/S0016706107002625.html:text/html}
}

@article{chang_near-infrared_2001,
	title = {Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties},
	volume = {65},
	url = {https://dl.sciencesocieties.org/publications/sssaj/abstracts/65/2/480},
	number = {2},
	urldate = {2017-04-26},
	journal = {Soil Science Society of America Journal},
	author = {Chang, Cheng-Wen and Laird, David A. and Mausbach, Maurice J. and Hurburgh, Charles R.},
	year = {2001},
	pages = {480--490},
	file = {Chang et al. - 2001 - Near-infrared reflectance spectroscopy–principal c.pdf:/Users/baumanph/Zotero/storage/HD52X3IF/Chang et al. - 2001 - Near-infrared reflectance spectroscopy–principal c.pdf:application/pdf}
}

@article{van_maarschalkerweerd_recent_2015,
	title = {Recent developments in fast spectroscopy for plant mineral analysis},
	volume = {6},
	issn = {1664-462X},
	url = {http://www.frontiersin.org/Plant_Nutrition/10.3389/fpls.2015.00169/abstract},
	doi = {10.3389/fpls.2015.00169},
	urldate = {2017-05-18},
	journal = {Frontiers in Plant Science},
	author = {van Maarschalkerweerd, Marie and Husted, Søren},
	month = mar,
	year = {2015},
	file = {van Maarschalkerweerd and Husted - 2015 - Recent developments in fast spectroscopy for plant.pdf:/Users/baumanph/Zotero/storage/ETC84JZG/van Maarschalkerweerd and Husted - 2015 - Recent developments in fast spectroscopy for plant.pdf:application/pdf}
}

@article{rinnan_review_2009,
	title = {Review of the most common pre-processing techniques for near-infrared spectra},
	volume = {28},
	issn = {01659936},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0165993609001629},
	doi = {10.1016/j.trac.2009.07.007},
	language = {en},
	number = {10},
	urldate = {2017-06-02},
	journal = {TrAC Trends in Analytical Chemistry},
	author = {Rinnan, Åsmund and Berg, Frans van den and Engelsen, Søren Balling},
	month = nov,
	year = {2009},
	pages = {1201--1222},
	file = {Rinnan et al. - 2009 - Review of the most common pre-processing technique.pdf:/Users/baumanph/Zotero/storage/S5QJCM2C/Rinnan et al. - 2009 - Review of the most common pre-processing technique.pdf:application/pdf}
}

@article{lebot_use_2013,
	title = {Use of {NIRS} for the rapid prediction of total {N}, minerals, sugars and starch in tropical root and tuber crops},
	volume = {41},
	issn = {0114-0671, 1175-8783},
	url = {http://www.tandfonline.com/doi/abs/10.1080/01140671.2013.798335},
	doi = {10.1080/01140671.2013.798335},
	language = {en},
	number = {3},
	urldate = {2017-06-05},
	journal = {New Zealand Journal of Crop and Horticultural Science},
	author = {Lebot, V and Malapa, R and Jung, M},
	month = sep,
	year = {2013},
	pages = {144--153},
	file = {Lebot et al. - 2013 - Use of NIRS for the rapid prediction of total N, m.pdf:/Users/baumanph/Zotero/storage/TMB7SCMZ/Lebot et al. - 2013 - Use of NIRS for the rapid prediction of total N, m.pdf:application/pdf}
}

@article{eriksson_chemometrics_2014,
	title = {A chemometrics toolbox based on projections and latent variables: {A} chemometrics toolbox based on projections and latent variables},
	volume = {28},
	issn = {08869383},
	shorttitle = {A chemometrics toolbox based on projections and latent variables},
	url = {http://doi.wiley.com/10.1002/cem.2581},
	doi = {10.1002/cem.2581},
	language = {en},
	number = {5},
	urldate = {2017-06-05},
	journal = {Journal of Chemometrics},
	author = {Eriksson, Lennart and Trygg, Johan and Wold, Svante},
	month = may,
	year = {2014},
	pages = {332--346},
	file = {Eriksson et al. - 2014 - A chemometrics toolbox based on projections and la.pdf:/Users/baumanph/Zotero/storage/QXWKN6RT/Eriksson et al. - 2014 - A chemometrics toolbox based on projections and la.pdf:application/pdf}
}

@article{kuhn_building_2008,
	title = {Building {Predictive} {Models} in {R} {Using} the caret {Package}},
	volume = {28},
	issn = {1548-7660},
	url = {https://www.jstatsoft.org/v028/i05},
	doi = {10.18637/jss.v028.i05},
	abstract = {The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The package focuses on simplifying model training and tuning across a wide variety of modeling techniques. It also includes methods for pre-processing training data, calculating variable importance, and model visualizations. An example from computational chemistry is used to illustrate the functionality on a real data set and to benchmark the benefits of parallel processing with several types of models.},
	number = {5},
	journal = {Journal of Statistical Software, Articles},
	author = {Kuhn, Max},
	year = {2008},
	pages = {1--26},
	file = {Kuhn - 2008 - Building Predictive Models in R Using the caret Pa.pdf:/Users/baumanph/Zotero/storage/56KTRWAZ/Kuhn - 2008 - Building Predictive Models in R Using the caret Pa.pdf:application/pdf}
}

@article{stevens_introduction_2014,
	title = {An introduction to the prospectr package},
	volume = {3},
	url = {ftp://200.236.31.2/CRAN/web/packages/prospectr/vignettes/prospectr-intro.pdf},
	urldate = {2017-06-05},
	journal = {R Package Vignette, Report No.: R Package Version 0.1},
	author = {Stevens, Antoine and Ramirez–Lopez, Leonardo},
	year = {2014},
	file = {Stevens and Ramirez–Lopez - 2014 - An introduction to the prospectr package.pdf:/Users/baumanph/Zotero/storage/5DIUDGK4/Stevens and Ramirez–Lopez - 2014 - An introduction to the prospectr package.pdf:application/pdf}
}

@book{friedman_elements_2001,
	title = {The elements of statistical learning},
	volume = {1},
	url = {http://statweb.stanford.edu/~tibs/book/preface.ps},
	urldate = {2017-06-05},
	publisher = {Springer series in statistics Springer, Berlin},
	author = {Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert},
	year = {2001},
	file = {ESLII_print10.pdf:/Users/baumanph/Zotero/storage/8VPPQZR6/ESLII_print10.pdf:application/pdf}
}