NIOSH Mining Program Information Circular IC 9533

Direct-on-filter Analysis for 

Respirable Crystalline Silica 

Using a Portable FTIR Instrument

Centers for Disease Control

and Prevention

National Institute for Occupational 

Safety and Health
Information Circular 9533

Direct-on-filter Analysis for Respirable Crystalline 

Silica Using a Portable FTIR Instrument 

Lauren G. Chubb and Emanuele G. Cauda

DEPARTMENT OF HEALTH AND HUMAN SERVICES 

Centers for Disease Control and Prevention 

National Institute for Occupational Safety and Health 

Office of Mine Safety and Health Research 

Pittsburgh, PA • Spokane, WA 

January 2022
This document is in the public domain and may be freely 

copied or reprinted.

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these websites. All web addresses referenced in this document were accessible as of the 

publication date. 

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Suggested Citation 

NIOSH [2022]. Direct-on-filter analysis for respirable crystalline silica using a portable 

FTIR instrument. By Chubb LG, Cauda EG. Pittsburgh PA: U.S. Department of Health 

and Human Services, Centers for Disease Control and Prevention, National Institute for 

Occupational Safety and Health, DHHS (NIOSH) Publication No. 2022–108, IC 9533. 

https://doi.org/10.26616/NIOSHPUB2022108

DOI: https://doi.org/10.26616/NIOSHPUB2022108

DHHS (NIOSH) Publication No. 2022-108

Front photo by NIOSH. Rear photo by Somchai Kong, shutterstock.com. 

January 2022
Contents 

Introduction................................................................................................................................ 1

Technical Note........................................................................................................................... 2

Overview of Field-based Monitoring........................................................................................... 3

Getting Started: Hardware and Software.................................................................................... 4

Conducting Field-based Analysis for Respirable Crystalline Silica ............................................. 6

Setting Instrument Analysis Parameters..................................................................................... 6

Verifying Instrument Integrity...................................................................................................... 8

Inserting the Sample in the Instrument....................................................................................... 8

Importance of Positioning the Sample ...........................................................................12

NIOSH-developed Accessories for FTIR Instrumentation..........................................................14

Four-piece Cassette ......................................................................................................14

Dust Sampling Cassette ................................................................................................14

Other Sampling Cassette Types ....................................................................................15

Generating Spectra...................................................................................................................16

Background Spectrum ...................................................................................................16

Quality Assurance (QA) Sample Spectra.......................................................................17

Sample Spectra.............................................................................................................19

Processing Sample Data...........................................................................................................19

Interpreting Sample Data Using FAST ......................................................................................21

Exporting Results from Agilent.......................................................................................22

Exporting Results from Bruker.......................................................................................23

Exporting Results from Perkin Elmer .............................................................................24

Exporting Results from Thermo Fisher ..........................................................................25

Exporting Results in a Generic Format ..........................................................................26

Importing Results to FAST.............................................................................................26

Supporting Field-based Analysis for Respirable Crystalline Silica .............................................29

Keeping a Logbook........................................................................................................29

Organizing Spectrum Files to Support Field-based Monitoring ......................................30

Creating a Set of Samples to be Used as Quality Assurance Samples..........................31

Generating a Site-specific Correction Factor .................................................................32

Troubleshooting: What a Sample Spectrum Should (and Should Not) Look Like ...........34

Selecting the Appropriate Sampler Type in FAST..........................................................40

“Housekeeping” Suggestions for FTIR Instruments .......................................................43

Environment for Storage and Use of FTIR Instruments..................................................44
Guidelines for Performing Standard Laboratory Analysis on Samples Analyzed in the 

Field ..............................................................................................................................45

Using Field-based Monitoring to Evaluate Efficacy of Controls ......................................46

Conclusion................................................................................................................................47

References ...............................................................................................................................49

Appendix A: Additional Resources for Field-based Monitoring ..................................................52

Appendix B: Operational Checklists for Field-based Monitoring ................................................53

Appendix C: Comparison of FTIR data obtained using different methods or parameters...........56
Figures 

Figure 1. Overview of the field-based monitoring process. ......................................................... 3

Figure 2. Examples of suitable sample cassettes for field-based analysis.................................. 9

Figure 3. Inserting the sample into the cradle or holder.............................................................10

Figure 4. Placing the cradle or holder in the FTIR instrument....................................................11

Figure 5. Illustration of the relative position of the IR beam and sample cassette......................13

Figure 6. Illustration of a spectrum baseline correction..............................................................20

Figure 7. Illustration of the quartz doublet .................................................................................21

Figure 8. Results format from the Resolutions Pro software (Agilent)........................................23

Figure 9. Results format from the Opus software (Bruker) ........................................................24

Figure 10. Results format from the Spectrum software (PerkinElmer) .......................................25

Figure 11. Results format from the Omnic software (Thermo Scientific)....................................26

Figure 12. An example of a suggested logbook entry................................................................30

Figure 13. Illustration of the deposition of material in respirable dust samples ..........................32

Figure 14. Screenshot from the “Calculate Correction Factor” function of FAST .......................34

Figure 15. Illustrations of three FTIR example spectra, background and sample.......................36

Figure 16. Illustrations of six FTIR example spectra with noise and/or distortions. ....................39

Figure 17. Illustrations of typical patterns of dust deposition from two respirable samplers .......42

Figure 18. Illustration of the cyclic process of field-based monitoring ........................................47
List of Tables 

Table 1. Hardware and software requirements and recommended materials for 

implementing field-based monitoring............................................................................... 5

Table 2. Instrument analysis parameters ........................................................................ 7

Table C1. Comparison of the two methods for background scan.................................. 59

Table C2. Comparison of three types of apodization for quartz-only samples .............. 62

Table C3. Comparison of three types of apodization for mixed-mineral samples.......... 65

Table C4. Comparison of four types of baseline correction for quartz-only samples..... 66

Table C5. Comparison of four types of baseline correction for mixed-mineral samples 69
Acronyms and Abbreviations 

CRADA cooperative research and development agreement 

FAST Field Analysis of Silica Tool 

FTIR Fourier transform infrared spectroscopy 

IR infrared 

LOD limit of detection 

LOQ limit of quantification 

MSHA Mine Safety and Health Administration 

NIOSH National Institute for Occupational Safety and Health 

OSHA Occupational Safety and Health Administration 

PVC polyvinyl chloride 

QA quality assurance 

RCS respirable crystalline silica 

SIMPEDS Safety in Mines Personal Dust Sampler 
Unit of Measure Abbreviations 

°C degrees Celsius 

cm centimeter 

°F degrees Fahrenheit 

lpm liters per minute 

mg milligram 

mm millimeter 

μg microgram 

μm micrometer 

cm-1reciprocal centimeter or inverse centimeter 
Definitions 

Amorphous silica a non-crystalline form of silica which, if present, interferes 

with the accurate quantification of crystalline forms, 

including quartz 

Apodization a mathematical process used in FTIR analysis to smooth 

the signal

Background (spectrum) a measurement taken prior to sample measurement: if a 

clean filter is used the spectral features of the filter media 

are reduced, and environmental features such as water 

vapor and carbon dioxide are largely eliminated (or 

reduced) when the sample is analyzed 

Baseline correction a mathematical adjustment to decrease a curvature or slope 

in a sample spectrum; see Figure 6 for an illustration of the 

effect of baseline correction; various correction algorithms 

can be used

Commodity a product or group of products that is mined at a particular 

operation; may also be considered a subsector within the 

mining industry 

Correction factor a mathematical adjustment made in the FAST software to 

account for mineral content of respirable dust and other 

factors that may otherwise decrease the accuracy of the 

respirable crystalline silica quantification; uses standard 

analysis results from an accredited laboratory as the 

reference method 

Cristobalite a form of crystalline silica, less commonly encountered 

than quartz 

Desiccant a material used to absorb excess moisture or humidity 

Direct-on-filter analysis that can be completed on the aerosol sampling 

filter on which the sample is collected, without altering the 

sample filter or the collected sample 

Field-based monitoring any exposure monitoring activity that can be completed 

where the sample is collected at the work site, without the 

need to send samples to an off-site laboratory for analysis

Focal point the point at which the rays of the infrared beam converge 

and are most concentrated, and where the sample should be 

positioned 
Fourier transform infrared 

spectroscopy 

an analytical method capable of collecting a high resolution 

infrared (absorption) spectrum for a sample over a broad 

spectral range and converting the raw data in a spectrum 

using Fourier transform

Integration the calculation of the area of a specific region of the sample 

spectrum; the integrated value is correlated to the amount 

of a substance that is present 

Interference a species that interferes with the analysis of quartz, possibly 

impacting the accuracy of the quantification of quartz 

Limit of detection the lowest amount of a substance that is detected with 

confidence by an analytical method; below this quantity, 

the signal may be due to random noise rather than to the 

presence of the substance 

Limit of quantification the lowest amount of a substance that is quantifiable with 

confidence by an analytical method; below this quantity, 

the amount cannot be quantified with confidence, though 

the substance can be identified with confidence if the limit 

of detection (which is always less than the limit of 

quantification) has been met 

Macro a series of software instructions that can be executed as one 

procedure; for field-based monitoring, the available macros 

(from NIOSH) first execute a baseline correction of the 

spectrum and then integrate the areas of interest; in some 

software packages, the NIOSH macro may also export 

sample results or generate a sample report 

Non-destructive any method of analysis where the sample is not altered or 

disturbed—that is, the sample is preserved in its original 

form and remains available for analysis using other 

analytical approaches 

Quality Assurance (QA) sample a sample taken for quality assurance purposes that is 

analyzed at the beginning of each session, with the results 

compared to previous data to confirm that the instrument 

hardware and software are generating consistent and 

reliable results 

Quartz (alpha quartz) the most common form of crystalline silica 

Real-time monitoring a specific type of field-based monitoring in which results 

are available after very short data collection periods (on the 

order of seconds), enabling changes in conditions (e.g., dust 

concentration) to be measured as they occur
Resolution the minimum difference in frequency that can be 

distinguished within the spectrum; increasing the resolution 

increases the level of detail that is visible 

Respirable crystalline silica particles of crystalline silica small enough to reach the 

alveolar region of the lungs when inhaled 

Respirable dust particles of dust (of any composition) small enough to 

reach the alveolar region of the lungs when inhaled 

Sample bracket a NIOSH-designed component that is installed in the FTIR 

instrument (replacing the original bracket of the 

instrument) to accept the sample cradle or the sample 

holder, ensuring the correct position of the sample for 

analysis 

Sample cradle or holder a NIOSH-designed component that is inserted in the sample 

bracket to accept either the four-piece cassette (cradle) or 

the dust sampling capsule (holder), ensuring the correct 

position of the sample for analysis 

Site-specific quantification an adjusted quantification of RCS concentration specific to 

a particular location or operation, based on previous 

comparison of the (unadjusted) quantification generated by 

the FAST software and standard laboratory analysis of the 

same samples 

Spectrum (spectra) a plot of the sample data generated by the FTIR instrument 

and software (i.e., infrared absorbance at each 

wavenumber); spectrum can refer to the visual plot of the 

data or to the collection of individual data points 

Standard analysis the laboratory-based analysis method that would typically 

be used or accepted for exposure compliance purposes in a 

particular sector or subsector; for respirable crystalline 

silica analysis in the United States, see the following 

methods: MSHA P-2 [MSHA 2013a], MSHA P-7 [MSHA 

2013b], NIOSH 7500 [NIOSH 2003], NIOSH 7603 

[NIOSH 2017b], OSHA ID-142 [OSHA 2015] 

Tridymite a form of crystalline silica, less commonly encountered 

than quartz 

Direct-on-filter Analysis for Respirable Crystalline Silica 

Using a Portable FTIR Instrument 

Lauren G. Chubb and Emanuele G. Cauda 

Introduction 

Respirable crystalline silica (RCS) exposure occurs in many industries, and the potential health 

effects of RCS have been well-documented [NIOSH 2002]. Monitoring is an essential part of 

any RCS exposure control plan, but the timeliness of results from the lab and the cumulative cost 

of analysis may limit the extent and the effectiveness of the monitoring process. 

Field-based monitoring procedures offer a supplement to sample analysis completed by an offsite 

laboratory. By eliminating the time spent transporting samples and waiting for laboratory results, 

results are available the same day or the next day to allow operators to make decisions regarding 

controls. This enhances the ability to collect data more effectively, enables better control over 

potential exposures, and protects worker health. 

This document details how to implement field-based monitoring for RCS. It is primarily intended 

for industrial hygienists and other workers with health and safety responsibilities, specifically 

within the mining industry (although workers in other industries may also find it useful). The 

document has been written for a user with experience in respirable dust or RCS exposure 

assessment but who does not necessarily have specialized training in analytical techniques. The 

following topics are covered: 

• General instructions for how to set up the equipment and the software required for fieldbased RCS monitoring. 

• Technical details of the monitoring method with explanations of why they are important. 

• Quality assurance procedures to ensure consistent data. 

• Examples and case studies on how to use different types of samplers in conjunction with 

field-based monitoring. 

• Links to additional resources for field-based monitoring (see Appendix A). 

• Checklists to guide users through the process quickly (see Appendix B). 

The NIOSH Manual of Analytical Methods contains sampling guidance for RCS in particular 

[NIOSH 2003a] and for aerosols in general [NIOSH 2016b]. 

This document contains guidance applicable to portable Fourier transform infrared spectroscopy 

(FTIR) instruments in general, though some instrument-specific guidance is also provided where 

necessary. While researchers from the National Institute for Occupational Safety and Health 

(NIOSH) have conducted extensive investigation on the use of four portable Fourier transform 

infrared spectroscopy (FTIR) instruments [Ashley et al. 2020], other models of FTIR instruments 

may also be available and compatible with field-based monitoring. 

At the time this document was published, direct-on-filter analysis with portable FTIR was not a 

stand-alone NIOSH analytical method or a standard analytical method used by MSHA or OSHA 
2

or other international bodies. The role of field-based monitoring should be to act in support of 

maintaining regulatory compliance by facilitating faster and more thorough assessment of RCS 

levels. When there is a need for a formal assessment of compliance status, the dust samples 

analyzed in the field should also be submitted to an accredited laboratory.

The field-based RCS monitoring approach presented here has been designed for respirable alpha 

quartz, which is the most common crystalline silica polymorph. The analysis is not currently 

optimized for other regulated crystalline silica polymorphs, cristobalite and tridymite, which can 

both impact the accuracy of the field-based analysis if they are present. In most mining 

environments, quartz is likely to be the only form of RCS present, but it is important to know if 

either cristobalite or tridymite has been reported in respirable samples (based on previous 

laboratory analyses). The term RCS will be used throughout this document except where more 

specificity is required.

Technical Note 

The approach to field-based monitoring described in this document and the quantification models 

used in the NIOSH-developed Field Analysis of Silica Tool (FAST) software were developed 

using 37-mm, 5-μm pore polyvinyl chloride (PVC) filters housed in 4-piece conductive cassettes 

with stainless steel support (rather than a cellulose backing pad), or 37-mm, 5-μm pore polyvinyl 

chloride (PVC) filters (within a capsule) housed in the dust sampling cassette with stainless steel 

support (see Figure 2). The models were calibrated using crystalline silica (quartz) particles 

(primarily Min-U-Sil 5, with NIST 1878a samples evaluated to confirm similarity) aerosolized in 

laboratory dust chambers. Calibration samples were analyzed using a Bruker Optics Alpha 

infrared spectrometer in transmission mode. Sixteen co-added sample scans were analyzed at a 

resolution of 4 cm-1using Blackman Harris 3-term apodization. Samples were subsequently 

processed using a concave rubberband baseline correctiona(10 iterations) with 64 baseline 

points. 

a Though a complete definition is beyond the scope of this document, a “concave rubberband correction” is a 

specific type of baseline correction that imagines a rubberband stretched between the two ends of the spectrum. The 

correction is applied iteratively, with the number of iterations determining the intensity of the correction. 

Other options for instrument analysis parameters are listed in Table 2. The spectral region 

between 816 and 767 cm-1 was integrated, and calibrated models were developed from the 

regression of the integrated area vs. gravimetric mass of crystalline silica (quartz). 

When possible, field-based monitoring for crystalline silica is best completed using the same 

filters, cassettes, and operational settings listed above. Field-based monitoring may still be 

completed using different filter media, cassettes, and settings, but this may result in error or 

uncertainty in crystalline silica quantification. 

For additional information regarding the development of field-based, direct-on-filter analysis, see 

Miller et al. [2012], Miller et al. [2013], Cauda et al. [2016], and Chubb and Cauda [2021]. This 

document is representative of all relevant findings to date, but research is ongoing in this area 

and additional findings will be published and made available to the public in the future.
3

Overview of Field-based Monitoring 

The process of field-based monitoring is depicted and described in Figure 1. Respirable dust 

samples are collected in the same way that they would be for conventional exposure monitoring 

practices. Following sample collection, the FTIR instrument is set up and its performance verified 

before respirable samples are analyzed. Sample data is processed using the FAST software, and 

the results can contribute to decision-making to decrease worker exposure to respirable crystalline 

silica. Additional samples can be collected and analyzed to confirm the efficacy of new or 

modified controls, beginning the process over again. The non-destructive analysis allows the same 

samples to be sent to an off-site laboratory following field-based analysis, if desired. 

Figure 1. Overview of the field-based monitoring process.
4

Field-based monitoring does not require laboratory experience or training but does require 

careful execution to ensure reliable data; it should be part of a well-planned RCS exposure 

assessment strategy and used in conjunction with routine laboratory analysis of respirable 

samples. Previous experience in industrial hygiene or exposure assessment is recommended for

users of field-based monitoring. Although FTIR instruments are generally designed to be userfriendly for non-expert users, NIOSH recommends contacting the manufacturer for assistance 

with initial instrument setup and with basic instrument training.

Getting Started: Hardware and Software 

Table 1 depicts and describes the hardware and software requirements. These requirements are in 

addition to the personal sampling pumps and respirable cyclone samplers that are typically used. 

The analysis of samples collected by impactor-type respirable samplers is not currently 

supported by this field-based monitoring technique.
5

Table 1. The hardware and software requirements and recommended materials for 

implementing field-based monitoring. These components are necessary to collect and 

analyze data from respirable dust samples. While it is possible to conduct field-based 

monitoring without the recommended materials, they facilitate a much faster, simpler, 

and more reliable process.b

b Links to instrument manufacturers and NIOSH products can be found at 

https://www.cdc.gov/niosh/mining/content/HandwareandSoftwareforFAST.html.

Required Hardware 

• A portable FTIR spectrometer with transmission module (KBr windows and beam 

splitter). Available from Agilent Technologies, Bruker Corporation, PerkinElmer, 

and Thermo Scientific 

• Laptop computer; see software specifications of the FTIR instrument software

and Field Analysis of Silica Tool (FAST) for system requirements 

• Sample bracket and sample cradle or sample holder (files of 3D models for 3D 

printing or traditional machining are freely available at 

https://3dprint.nih.gov/discover/3dpx013209 or by searching (by author) for 

NIOSH at https://3dprint.nih.gov/discover) 

Required Software 

• FTIR spectrometer software (available from the manufacturer of the chosen 

instrument) 

• Field Analysis of Silica Tool (FAST) 

http://www.cdc.gov/niosh/mining/www.cdc.gov/niosh/mining/works/coversheet2

056.htm

• Spreadsheet software 

Recommended Materials 

• One of the cyclone samplers listed in FAST (see FAST documentation) 

• “Analysis-ready” sample cassettes, in which the sample filter does not need to be 

removed from the cassette before analysis 

o A four-piece cassette 

o Dust sampling cassette (without check valve); in the mining industry this 

is often referred to as the coal dust sampling cassette, though it may also 

be known by different names in other industries 

• “Quality assurance” samples 

o Commercially available filter samples or standards that contain quartz 

o A “do-it-yourself” option (as described in this Information Circular) 
6

Conducting Field-based Analysis for Respirable Crystalline Silica 

After respirable dust samples have been collected, RCS is quantified by FTIR analysis. 

This section describes the analysis parameters that should be used to produce the most 

reliable results and outlines procedures for verifying that the instrument and its settings are 

appropriately configured before proceeding with sample analysis. 

Setting Instrument Analysis Parameters 

Research conducted at NIOSH has indicated that the following parameters for analysis with a 

portable FTIR instrument provide the optimal balance necessary to obtain accurate and 

reproducible data in a reasonable period of time. For example, higher resolutions result in more 

data points per sample, and increased scan accumulations decrease noise (variation) in the data, 

but both come at a cost of longer analysis times. Table 2 summarizes the relevant parameters and 

settings used by NIOSH to develop the field-based method.

These parameters are important to the integrity of the data, and therefore it is essential to ensure 

that the portable FTIR instrument is configured to these parameters before it is used for the 

application described in this document. The top (unshaded) portion of the table contains basic 

parameters that should be selected, regardless of the particular instrument or software used. 

Modifying these settings will impact analysis results and may invalidate the results. The bottom 

(shaded) portion of the table contains more advanced parameters, which may not be available for 

some instruments or software programs. While it is advisable to match these parameters, if 

available, they are less significant. Regardless, it is important that the same parameter options are 

used consistently from sample to sample and day to day. 
7

Table 2. Instrument analysis parameters 

Parameter

(including possible alternate terminology)

Value and unit

Resolution (cm-1) 4 

Sample scan time 

Scans

Accumulations (scans)

No. of scans

16 (scans) 

Background scan time

Scans 

Accumulations (scans) 

No. of scans 

16 (scans) 

Spectral rangec

Min range limit and Max range limit Start 

(cm-1) and End (cm-1)

Save data from __ cm-1to __ cm-1

4000 cm-1–400 cm-1

Result spectrum 

Y-axis units

Ordinate units

Final format

Absorbance or A 

Phase correctiond

Phase correct

Mertz 

Apodizatione Blackman Harris or Blackman Harris 3 or strong or 

NB Strong or Norton Beer Strong 

Zero-fill factord

Zero-filling factor

2 

c Some instruments may not be capable of acquiring data over the full range of 4000 cm-1–400 cm-1

; however, if the 

range of approximately 1500–500 cm-1is acquired, the quantitative region for quartz will not be impacted by this 

limitation but the ability to identify and adjust for confounding minerals may be reduced. Data from as much of the 

range of 4000 cm-1–400 cm-1as possible should be acquired.

d A software parameter of FTIR analysis that impacts how the software program processes the raw data 

generated by the instrument, although this parameter may not be available or modifiable in some software 

packages.

e The quantification models used in FAST were calibrated with data processed using Blackman Harris 3-

term apodization. More accurate results are likely to be obtained by using this same apodization, though 

using a different apodization (including using no apodization, if no option is available in the instrument 

software) is not likely to result in substantial differences in quantification of RCS, (for samples of 

respirable dust; see Appendix C). Regardless of the type of apodization selected, it should be used 

consistently for all samples analyzed. 
8

Verifying Instrument Integrity 

When the instrument is not working properly, data quality will be impacted and the acquired data 

may be less accurate. In order for the FTIR instrument to produce generally reliable data, the 

internal optical components of the instrument must be in alignment and functioning according to 

specifications. It may not be possible to easily perform a visual check of these components; the 

software associated with each portable instrument has the capability to conduct a series of tests 

to evaluate the performance of the instrument. Depending on the instrument, these tests may be 

termed Systems Checks or Instrument Checks, Instrument Verification, Performance 

Qualification (PQ), or Operation Qualification (OQ). Regardless of terminology, the tests should 

generally include a check of the instrument signal-to-noise ratio and a check of the frequency 

and alignment. 

While the instrument will function even if these tests are not performed, the acquired data may 

not be accurate. It is recommended to perform these tests on a regular basis, such as weekly, and 

in cases where the instrument may have been handled roughly (such as when shipped by a carrier 

service, or as checked baggage with an airline), or has changed environments drastically (such as 

from an arid climate to a humid climate or from a cold environment to a warm one). As the 

instrument and its internal components age, some components will need to be replaced, and 

regular performance testing can indicate when this is necessary. 

When the performance tests are carried out routinely, they will indicate potential performance 

concerns before data quality is affected; if performance tests are not carried out regularly, lowquality data may go unnoticed. See the Quality Assurance (QA) Sample Spectra section for 

additional information on verifying that the instrument is producing accurate and reproducible 

data over time. 

Inserting the Sample in the Instrument 

Once the portable FTIR is set up with the proper analysis parameters and the integrity of the 

instrument has been verified via performance tests, it is important to understand how to use the 

portable instrument for the analysis of respirable dust samples using the modality generally 

referred to as “direct-on-filter.” A few constraints and requirements need to be met to ensure that 

accurate results are obtained using direct-on-filter analysis.

When using a four-piece cassette, the top (inlet) piece and bottom (outlet) piece of the cassette 

should be removed from the two middle sections before being placed in the sample cradle. When 

using the dust sampling cassette, the foil capsule should be carefully removed from the plastic 

outer cassette before the capsule is placed in the sample holder. 

The following pictures and directions (Figures 2–4) provide guidelines on how to position the 

samples inside the portable FTIR. In general, it is good practice to position the sample with the 

sample side facing the infrared source, which is typically on the left. 
9

Figure 2. Examples of suitable sample cassettes (which allow the filter media to remain 

secured in the cassette or capsule) for field-based analysis. The holder or cassette 

containing the filter media with the sample should be open on both sides, which can be 

accomplished by removing the top and bottom cassette pieces of the four-piece cassette 

(top) or removing the capsule from the dust sampling cassette (bottom). From one side 

the dust material on the filter (or a portion of the filter, in the case of dust sampling 

capsule) should be visible, and from the other side the underside of the filter media 

should be exposed and the support ring should be visible.

Photos by NIOSH 
10

Figure 3. Inserting the sample into the cradle or holder. NIOSH has designed a cradle (top 

left) to accommodate the four-piece cassette (top right) and a filter holder (bottom left) to 

accommodate the dust sampling capsule (bottom right). The four-piece cassette should 

be inserted firmly into the cradle so that the sample is not tilted forward or backward in 

the cradle. The dust sampling capsule should be centered in the holder and secured so 

that the sample remains stationary when the holder is held vertically. Files of 3D models 

of the cradle, the filter holder, and instrument brackets (for each of the four portable 

instruments, for 3D printing or traditional machining) that fit either option are freely 

available at https://3dprint.nih.gov/discover/3dpx-013209.

Photos by NIOSH
11

Figure 4. Placing the cradle or holder in the transmission compartment of the FTIR 

instrument. The cradle (top) or the filter holder (bottom) should be inserted fully into the 

sample bracket in the instrument so that the bottom of the cradle or filter holder is tightly 

in contact with the sample bracket. 

Photos by NIOSH
12

Importance of Positioning the Sample 

The direct-on-filter analysis used for this application entails the use of transmission FTIR—i.e., 

the infrared beam needs to be free to pass through the center of the dust sample and the filter 

media and then reach the detector on the other side of the sample. Only a portion of the sample is 

evaluated directly (the center), and it is crucial that the same area of each sample is evaluated, 

since this is the area on which quantification models are based, enabling the quantity of RCS on 

the entire filter to be determined from the analysis of just a portion of the filter. As the infrared 

(IR) beam is invisible to naked eye, the correct setup of the sample (i.e. the cassette or capsule is 

correctly placed within the cradle or sample holder, which is in turn correctly placed in the 

bracket) ensures the proper conditions for the analysis. 

When positioned correctly, the sample cassette (or capsule), the cradle (or filter holder), and the 

bracket work together to ensure that the sample is perpendicular: 

1. to the transmission compartment (this relationship can be easily and quickly confirmed 

visually), and

2. to the IR beam (confirming this relationship visually is more complex and beyond the 

scope of this document). 

These two criteria are essential to ensuring that the transmission beam is unimpeded and analysis 

of the sample is accurate. If the cassette or holder is tilted, the IR beam may be partially or fully 

blocked and may not reach the detector on the other side, or it may encounter a different portion 

of the filter instead of the center. Depending on the severity of the misalignment of the sample 

relative to the IR beam, the data may be incorrect or result in an invalid or failed sample. If the 

instrument “rejects” multiple sample scans (which indicates that not enough signal is reaching 

the detector), it is useful to check the alignment and positioning of the sample—for example by 

removing and reinserting the sample if there is any uncertainty. 

The correct use of the cradle and bracket within the transmission compartment ensures two 

additional important requirements: 

1. that the vertical plane of the filter will perpendicularly intersect the IR beam at its focal 

point, and 

2. that the IR beam will be horizontally and laterally centered on the filter and dust deposit. 

The IR beam in each portable FTIR has a focal point in the transmission compartment, 

and the sample should be positioned at this focal point (see Figure 5) using the bracket. 

Modifications to this arrangement can have a negative impact on the quality of results. 

Note that in some instrument models, the bracket is easily moved, but in other models, 

the bracket is secured with a nut and bolt or a screw. Consult the instrument 

documentation to ensure the bracket is placed in the appropriate spot. 
13

Figure 5. Illustration of the relative position of the IR beam and sample cassette. 

When the sample cassette is correctly positioned in the instrument (within the 

removable cradle or filter holder), the IR beam will pass directly through the center 

of the sample at the focal point. 

Importantly, the dust sample needs to be analyzed in the center of the filter because the 

quantification performed by FAST of the RCS present on the entire filter is only possible 

by an accurate analysis of the center of the filter combined with a knowledge of the 

deposition of respirable dust and specifically RCS on the filter media. 
14

NIOSH-developed Accessories for FTIR Instrumentation 

At the focal point, the infrared beam in the four portable FTIR instruments evaluated by NIOSH 

is fixed and ranges from 6 to 9 mm [Ashley et al. 2020], each of which is larger than the inlet 

openings commonly found in the traditional three-piece sampling cassette. The material of the 

cassette absorbs much of the IR beam, leaving insufficient signal for an accurate analysis. 

Additionally, the IR beam cannot penetrate the cellulose backing pad typically used to support 

the polyvinyl chloride (PVC) filter in the cassette, inhibiting the analysis of the sample. To 

address these issues, the PVC filter can be removed from the cassette with tweezers and inserted

in a flat holder; however, this procedure requires additional handling which may induce sample 

losses. For these reasons, adjustments are needed to the cassette that is used to collect the 

respirable dust sample prior to the analysis, as described below. 

NIOSH has designed specific cradles and brackets as accessories for a variety of holders, 

cassettes, and different FTIR units. Files of 3D models for 3D printing or traditional machining 

are freely available at https://3dprint.nih.gov/discover/3dpx-013209. These designs ensure the 

correct positioning of the sample inside a portable FTIR unit. These designs are not the only 

configurations possible, and other options can be created as long as the constraints described in 

the above section (that is, the correct vertical and horizontal positioning of the sample filter in 

the transmission compartment) are met. 

Considerations for three types of samplers/cassettes are outlined below. Additional approaches 

may be explored in the future for available samplers and their related holders/cassettes.

Four-piece Cassette 

In order to minimize sample handling by keeping the filter in the cassette, NIOSH designed a 

four-piece cassette with top and bottom portions that can be removed to expose the filter to the 

IR beam while still containing the filter between two pieces of the cassette, and using a different 

support design to allow transmission FTIR analysis. The resulting cassette (see Figure 2) is 

compatible with a number of respirable samplers commonly used by industrial hygienists, many 

of which are named in the NIOSH 0600 [NIOSH 1998], NIOSH 7500 [NIOSH 2003b], NIOSH 

7602 [NIOSH 2017a], NIOSH 7603 [NIOSH 2017b], and OSHA ID-142 [OSHA 2015] methods 

for respirable dust and RCS. The cassette was designed in collaboration with Zefon International

via a Cooperative Research and Development Agreement (CRADA). The product that resulted 

from the collaboration is commercially available from Zefon International. 

Dust Sampling Cassette 

The sampling cassette used in coal mines as part of the coal mine dust personal sampler unit uses 

a slightly different process than the four-piece cassette. The aluminum capsule that encloses the 

filter media and the aluminum ring that supports the filter media allow transmission of enough of 

the infrared beam to permit a reliable analysis of the sample. The capsule simply needs to be 

removed from the plastic cassette and inserted in a filter holder (illustrated in the photo on the 

bottom of Figure 3). Note that this sampling cassette is also available for compliance sampling 

and includes a check valve, but this version of the cassette is not compatible with direct-on-filter 

analysis. 
15

While it is possible to use this sampling cassette for field-based monitoring, it is important to 

consider that the cassette was not designed for this use specifically, and other cassette options 

will likely provide more accurate results. This topic is discussed in Pampena et al. [2019]. 

Because of the potential for less accurate results, this type of cassette is most appropriate for 

relative determinations of RCS (e.g., higher-exposure potential areas or tasks vs. lower-exposure 

potential areas or tasks). 

Other Sampling Cassette Types 

The four-piece cassette described above was designed specifically for use with field-based 

monitoring and is the preferred type of cassette for field-based monitoring. At the time this 

document was published, only calibration models for the four-piece cassette and dust sampling 

cassette (and respirable samplers that use these cassettes) were available in FAST. This section

briefly describes other cassettes and samplers that are physically compatible with field-based 

analysis directly on the sample filter, but would require additional calibration models to be added 

to FAST to facilitate their use with field-based monitoring.

• A new respirable sampler has been designed and tested by NIOSH [Lee et al. 2017]. The 

sampler operates at 1.1 lpm and uses an innovative internal cassette with 25-mm PVC 

filter media. The cassette has been designed to allow direct-on-filter analysis of the dust 

sample with a portable FTIR. The performance of the sampler for respirable dust has 

been validated. The prototype sampler can be produced via 3D printing using the 

“Respirable size-selective sampler for end-of-shift quartz measurement” files available at 

https://3dprint.nih.gov/discover/3dpx011762.

• There is also an interest in direct-on-filter analysis of dust samples collected by real-time 

respirable dust monitors. The current analytical technologies used in real-time respirable 

dust monitors cannot distinguish RCS within respirable dust. Some units collect a dust 

sample on a filter after the dust sensor in order to protect the pump. The potential use of 

these collected samples is being explored by NIOSH. With the use of the proper filter 

media, the analysis of RCS is already possible (though RCS results are not available in 

real-time). NIOSH is continuing to explore ways to facilitate this additional 

implementation. Any change in the filter holder will need to be tested to confirm that the 

performance of the monitor (with respect to respirable dust) is not adversely impacted. 

• Respirable dust samples can be collected with other cassettes that could be suitable to 

direct-on-filter analysis. The use of the Safety in Mines Personal Dust Sampler 

(SIMPEDS) [Harris and Maguire 1968; Stacey et al. 2014; Stacey et al. 2013] is common 

in countries outside the United States. Although this type of sampler is not currently 

recommended for field-based monitoring, the cassette may be compatible with direct-onfilter analysis, and the SIMPEDS sampler may be an option for field-based monitoring in 

the future.
16

Generating Spectra 

Background Spectrum 

Before any sample can be scanned or analyzed, every portable FTIR instrument requires analysis 

of a background spectrum. The FTIR analysis of a sample is a relative measurement—i.e., the 

interaction of the IR beam with a sample compared to the interaction of the IR beam with a 

reference. The background spectrum provides this reference. The general process used to 

generate a background spectrum is similar for each instrument, although each instrument and 

respective software has specific procedures. Consult the instrument software documentation (or 

Help file) for more information. 

For the application described in this document, the sample consists of respirable dust on a filter, 

and the background spectrum is a clean filter (containing no respirable dust) of the same type. By 

using a clean filter for the background spectrum, the contribution of the filter media to the 

subsequent analysis of a sample (on similar filter media) will be minimized, resulting in a sample 

spectrum that is a function primarily of the respirable dust. This can be accomplished by one of 

two methods: analyzing a spectrum (with an empty transmission compartment used as the 

background spectrum) for each filter before any dust is collected and subtracting that from the 

sample spectrum later, or using a designated clean filter as the background spectrum for an entire 

set of filters. Both methods will ultimately produce results of approximately the same quality 

(see Appendix C). The designated clean filter method is preferred since it is much less 

cumbersome and much more efficient for the user, although normal differences in filter thickness 

mean that subtraction will reduce but not always eliminate the contribution of the filter.

If the clean filter method is used for the background spectrum, the following considerations 

apply: 

• The clean filter should be the same type of media used for the collection of the respirable 

dust samples. In the United States, this is generally a 37-mm, 5-μm pore size PVC filter, 

as indicated in the NIOSH methods 0600, 7500, 7602, 7603 [NIOSH 1998, 2003b, 

2017a, 2017b].

• The clean filter used for the background spectrum can be selected from the blank filters 

in the set of cassettes used for sampling and should be from the same manufacturer lot or 

batch as the filters used for sampling. A good practice is to set aside a number of blanks 

equivalent to 10% of the sampling set. An additional sample can be set aside as a clean 

filter for the background spectrum. For example, if a health and safety professional is 

planning to collect 20 respirable dust samples one day, two cassettes should be set aside 

as blanks; an additional sample should be set aside as a clean filter to be used for the 

background spectrum. (20 samples+2 blanks+1 clean filter for the background spectrum).

• The subtraction procedure described below is not required when a clean filter is used for 

the background spectrum. 

If the subtraction method is used, the following general procedure should be used: 

1. Prior to sample collection, all filters to be used for sampling should be analyzed with the 

portable FTIR instrument, using an empty transmission compartment for the background 

spectrum. Data should be saved with filenames that correspond to the sample ID and 
17

indicate that the spectrum represents a clean filter, before sample collection (e.g. 

sample1_clean). 

2. Following sample collection, all filters should be analyzed with the portable FTIR 

instrument, using the empty transmission compartment for the background spectrum. 

Data should be saved with filenames that correspond to the sample ID and indicate that 

the spectrum represents a sample filter that now contains respirable dust (e.g. 

sample1_dust). 

3. Using the instrument software, subtract the “clean filter” spectrum for the “sample filter” 

spectrum for each sample ID. Consult the software documentation for guidance on how 

to use the software to subtract spectra. Be sure to save the new spectrum files resulting 

from the subtraction procedure. It is recommended that the filename indicates that the 

spectrum is the result of subtraction (e.g. sample1_subtracted). 

4. All data processing to calculate RCS should use the subtracted spectrum files. The 

“sample filter” spectrum using an empty transmission compartment for the background 

measurement will not provide a reliable quantification of RCS. 

Regardless of which approach to the background spectrum is taken, it is crucial that the filter 

used as a background is clean (i.e., does not contain any dust and has not been touched) and is as 

similar to the sample filters as possible. For instance, a filter that is perceptibly thicker or thinner 

than other filters should not be used as the background because this could impact the quality of 

sample results, especially for small quantities of RCS. 

Quality Assurance (QA) Sample Spectra 

It is important to ensure that the instrument performance remains consistent over time. One 

element of regular performance checks should be the analysis of a set of four to seven quality 

assurance, or QA, samples, which remain with the instrument and are each analyzed every time 

the instrument is used. The calculated value of RCS should remain consistent over time for the 

set of QA samples, indicating that the instrument performance is accurate; however, note that 

these samples are not used to calibrate the instrument, as this is generally not necessary. See 

Creating a Set of Samples to be Used as Quality Assurance Samples for guidance on establishing 

a set of QA samples. 

To track the QA sample results over time, the QA Sample Tracking spreadsheet (see Appendix A 

for a complete list of resources for field-based monitoring) should be used together with this 

document. The spreadsheet can accommodate up to ten QA samples. If multiple sets of QA 

samples will be used (for example, for multiple instruments), it is recommended that a unique 

spreadsheet is used for each set of QA samples. 

To use the spreadsheet, the column headers in the “Input QA Sample Data” tab should be named 

according to the sample IDs of the QA samples. Each time the QA samples are analyzed, the 

results should be entered. The data should be typed directly into each cell rather than copied and 

pasted, as this can interrupt some of the formatting that makes the spreadsheet function. The 

spreadsheet calculates basic summary statistics for the data over time; this allows the spreadsheet 

to flag any data point that is a statistical outlier. Approximately 10–20 data points are needed 

before this function becomes a reliable indicator of true outliers; when there are only a few data 

points, the spreadsheet may flag a data point that is only slightly different from the others. This 
18

process can be accelerated by analyzing QA samples several times per day (e.g., in the morning 

and again in the afternoon) for the first several days. 

Each time the instrument is used (or daily, when the instrument is used multiple times throughout 

the day), the QA samples should be analyzed and the data entered into the spreadsheet. Any of 

the QA samples that are outliers will be highlighted automatically in the spreadsheet (unless 

there are still too few data points for a reliable indication). The incidence of one outlier (while all 

other QA samples are within normal variation) indicates that the problem is likely unique to that 

particular sample and is therefore not likely to be indicative of a problem with the instrument 

itself. Often this is a matter of a simple error during the analysis—e.g., the QA sample was not 

inserted properly in the cradle (or filter holder) or the cradle was not properly inserted in the 

bracket, the wrong QA sample was analyzed (or the spectrum was labeled incorrectly), or the 

result was incorrectly entered in the spreadsheet (e.g. transposition of digits). It is also possible 

that the QA sample was physically damaged prior to analysis, changing the result. Note that over 

time, some QA samples may begin to lose material due to repeated use; QA samples should be 

discarded when there is evidence of lost material. When one QA sample is an outlier, the 

recommended action is to re-analyze the QA sample. If the result is within the normal range for 

that sample (as indicated by previously saved results in the QA Sampling Tracking Spreadsheet), 

then sample analysis should proceed as usual. If the result is still outside of the normal range, the 

QA sample was likely damaged and should be discarded. The spreadsheet should be updated 

accordingly and then sample analysis can proceed as usual. 

The incidence of multiple outliers indicates a potential problem with the instrument. This could 

be due to either hardware or software changes. It is important not to ignore irregular QA sample 

data, because the problem will likely also impact the accuracy of subsequent sample data. 

Irregularities should be noted in a logbook. A number of issues may occur that result in irregular 

sample data, and a number of steps may help to identify the cause of the issues, including: 

• Check that all data points are entered correctly, that samples have been labeled correctly, 

and that no values have been transposed. 

• Make sure that the background was properly scanned; this will typically require scanning 

the clean filter again to produce a new background, paying particular attention to the 

positioning of the clean filter in the transmission compartment. QA samples should be 

reanalyzed after scanning the new background. 

• Check the software analysis parameters to ensure that all parameters are correct (see the 

Setting Instrument Analysis Parameters section). 

• Complete the applicable performance verification/diagnostic tests to identify components 

that may be beyond their service life or otherwise need to be replaced. 

• Examine the instrument to observe any physical abnormalities that may be present (i.e., 

cracked, cloudy, or smudged windows and mirrors); if faulty components are identified, 

replace them if possible. If faulty components are identified but cannot be immediately 

replaced, contact the manufacturer for service recommendations. 

• Replace the internal desiccant if there is reason to believe that humidity could be a factor 

(note that the desiccant is not a user-serviceable part on all instrument types). While 

excess humidity can affect the sample spectrum, it is mainly a concern due to the 
19

potential for condensation on internal instrument components, which the desiccant 

mitigates. 

• Feel the outer casing of the instrument and note if it is warmer than usual. If the

instrument seems particularly warm, turn the instrument off and allow it to come to room 

temperature before turning it on again. 

If the source of the problem cannot be identified and resolved using the steps above, it may be 

necessary to communicate directly with the manufacturer of the instrument for more advanced 

diagnostic tools or to return the instrument to the manufacturer for service. 

Sample Spectra 

After the background spectrum has been generated and the instrument performance has been 

verified by the QA sample analysis, sample spectra may be analyzed. Sample spectra should be 

given unique identifiers that can be easily linked back to the identifiers on the physical samples. 

The following section describes how sample data are processed and then interpreted to provide 

meaningful results. 

Processing Sample Data 

After the spectrum of a sample is generated, the spectrum must also be processed by two 

procedures: (1) a baseline correction, and (2) integration of areas of interest for crystalline silica 

(quartz) and other minerals that might have a confounding effect in the quantification of RCS in 

the sample. NIOSH has designed macros for the four FTIR instruments evaluated to facilitate the 

processing of the analyzed samples. Each macro serves as a combination procedure that performs 

the baseline correction, integrates the portions of the spectrum used for identification and 

quantification, and reports values for these ranges of the spectrum. These macros are available 

for users to download (https://github.com/niosh-mining/fast/) and import into their version of the 

software, and they will be updated as areas are added and/or modified based on ongoing 

research. To properly load macro and template files, see the FTIR software documentation. 

The baseline correction adjusts the spectrum to remove distortions such as slope or curvature in 

the spectrum, as shown in Figure 6. While is it important to perform a baseline correction in 

general, different software programs perform the correction differently. In some programs, only 

a generic “baseline correction” option may be available. In other cases, there may be multiple 

baseline correction options, often including an “auto baseline correction”. The most important 

practice is to use the same option consistently for both QA samples and other samples; see 

Appendix C for a comparison of several common options. In general, baseline correction 

procedures can typically be accessed through the manipulate or transform menu; search or use 

the Help file for the FTIR software if needed. Baseline correction should be performed over the 

full range of the spectrum (e.g. 4000–400 cm-1). The NIOSH macros for the Bruker, Perkin 

Elmer, and Thermo instruments include a baseline correction; spectra collected using the Agilent 

software should be baseline corrected manually.
20

Figure 6. Illustration of a spectrum baseline correction. The two spectra show the same 

sample before a baseline correction is performed (on the top) and after a baseline 

correction is performed (on the bottom). 

0.00

0.10

0.20

0.30

4000 3600 3200 2800 2400 2000 1600 1200 800 400

Absorbance

Wavenumber (cm-1)

0.00

0.10

0.20

0.30

4000 3600 3200 2800 2400 2000 1600 1200 800 400

Absorbance

Wavenumber (cm-1)

The FAST software analyzes multiple wavelength ranges of a sample spectrum when calculating 

RCS mass and concentration. The ranges each correspond to a specific mineral or group of 

minerals, and the ranges that are used depend on information about the sample that the user 

enters in FAST (such as commodity). 

The range corresponding to quartz (the Q value in FAST), which is sometimes referred to as the 

doublet area, is always used. Even if no other information about the sample is known or provided 

in FAST, an estimated mass and concentration for RCS can be determined from the result of 

integrating this area, as described below. While the other ranges used by FAST are not shown 

here, they are defined in a similar way, between a left and a right endpoint and a straight line at 

the base of the peak. Over time, as more is learned about the minerals that may be found in 
21

respirable dust and how they might impact the accuracy of field-based RCS quantification, more 

ranges for analysis may be added to FAST, or the existing ranges may be modified to improve 

overall accuracy. 

The lower boundary of each area is determined by drawing a straight line from the intersection of 

the spectrum with the left response boundary to the intersection of the spectrum with the right 

response boundary (see Figure 7). 

Figure 7. Illustration of the quartz doublet. The area between the spectrum and the lower 

boundary (defined by the left and right boundaries) is used to quantify RCS (quartz). 

Interpreting Sample Data Using FAST 

After sample spectra have been analyzed and processed in the FTIR software, the results must be 

exported to a spreadsheet, which can then be imported into FAST, which is available on the 

NIOSH Mining Program webpage at:

https://www.cdc.gov/niosh/mining/works/coversheet2056.html/. General instructions are 

included below for how to export sample results from the software programs that accompany 

four FTIR instruments, as well as a generic format that will work regardless of the instrument 

used. This information is also included in the FAST software Help file. 

For macro files, template files, and their descriptions, see https://github.com/niosh-mining/fast/.

To properly load macro and template files, see the FTIR software documentation. 
22

Exporting Results from Agilent 

In RESOLUTIONS PRO 

1. Open a new Multi-Spectral Document by selecting File >> New >> Multi-Spectral 

Document >> OK

2. Open all spectra files to be included in the report by selecting Open >> (select files) >> 

Open

3. Add each sample spectrum into the Multi-Spectral Document by dragging or copying the 

file: 

a. To drag: 

i. Click the + next to the sample name to expand. 

ii. Click on the spectrum entry and drag it to the Multi-Spectral Document. 

iii. Check the Multi-Spectral Document to confirm that the sample file was 

successfully transferred or copied. 

b. To copy: 

i. Click the + next to the sample name to expand. 

ii. In the data grid in the lower portion of the screen, highlight Row 1. iii.

Right-click and select Copy (or use Ctrl+C). iv. Click on the + next to the 

Multi-Spectral Document to expand. 

iii. In the data grid in the lower portion of the screen, place the cursor in the 

first empty row. 

iv. Right-click and select Paste (or use Ctrl+V). 

4. When all samples have been added to the Multi-Spectral Document, use the Ctrl key to 

highlight each row of the grid. 

5. Select Analysis >> Peak Options >> New Peak Template (all spectra). 

6. In the Creation Choices field (top), select Create a set from file. 

7. Select Browse and choose the Peak-area.def file. Select Open >> OK. 

8. Use the Ctrl key to select the columns for Name, K, Q, C, D, and M. 

9. Right-click and select Copy (or use Ctrl+C). 

In EXCEL 

1. Place the cursor in cell A1; right-click and select Paste (or use Ctrl+V). 

2. Save the file; the resulting file (to be imported into FAST) should have the following 

format, as shown in Figure 8. 
23

Figure 8. Results format from the Resolutions Pro software (Agilent). The results 

report exported from Resolutions Pro displays data for each sample in a row format. 

The Sample ID should appear in Column A, followed by the result areas beginning in 

Column B. 

A B C D E F

1 Name K Q C D M

2 sample1_0000(1) 0.034 0.704 -0.043 -0.036 -0.03

3 sample2_0000(1) 0.019 0.145 -0.006 -0.006 -0.02

4 sample3_0000(1) 0.015 1.722 -0.015 -0.012 -0.017

Exporting Results from Bruker 

In OPUS 

1. Select Print >> Generate Report. 

2. On the Select Files tab of the menu that appears, select Multi sample report, and load 

the template file silica_report.art. 

3. Select Load and navigate to the Processed_Spectra folder (or the location where 

processed files have been saved), then select the sample files to be included in the report. 

4. Select the Output to tab; on the menu that appears, check that the csv file option is 

selected, then specify a name for the report file. 

5. Specify a file path for the saved report, then select Generate. 

In EXCEL 

1. Open the saved report spreadsheet and highlight Column A. 

2. Select the Data >> Text to Columns. 

3. Select Delimited and click Next; select Semicolon and click Finish. 

4. Save the file; the resulting report (to be imported in FAST) should have the following 

format, as shown in Figure 9. 
24

Figure 9. Results format from the Opus software (Bruker). The results report exported 

from Opus displays data for each sample in a column format rather than a row format. 

Column B must contain the sample ID, and column C must contain the result areas for 

samples.

A B C D E

1 N o Spectrum file name Result Freq.1 Freq.2

2 1 sample1 0.704 816 767

3 0.034 930 900

4 -0.03 740 720

5 -0.036 743 718

6 -0.043 890 865

7 2 sample2 0.145 816 767

8 0.019 930 900

9 -0.02 740 720

10 -0.006 743 718

11 -0.006 890 865

12 3 sample3 1.722 816 767

13 0.015 930 900

14 -0.017 740 720

15 -0.012 743 718

16 -0.015 890 865

Exporting Results from Perkin Elmer 

In SPECTRUM 10 

1. Open all samples files to be included in the report. 

2. Select Process >> Macro >>Silica est.

3. With the Results Table open, select File >> Send To >> Excel.

In EXCEL 

1. To simplify the spreadsheet, keep the Sample Name, Q, K, M, D, and C columns 

(Columns A, E, F, G, H, I) for the processed files; all other cells can be deleted. 
25

2. Save the file; the resulting report (to be imported in FAST) should have the following 

format, as shown in Figure 10. 

Figure 10. Results format from the Spectrum software (PerkinElmer). The results report 

exported from Spectrum displays data for each sample in a row format. The Sample ID 

should appear in Column A, followed by the result areas beginning in Column B.

A B C D E F

1 Sample Name q k m d c

2 sample1.SPA_2 0.704 0.034 -0.03 -0.036 -0.043

3 sample2.SPA_2 0.145 0.019 -0.02 -0.006 -0.006

4 sample3.SPA_2 1.722 0.015 -0.017 -0.012 -0.015

Exporting Results from Thermo Fisher 

In TQ ANALYST EZ EDITION 

1. Open the TQ Analyst EZ Edition program. 

2. From the File menu, select Open Method..., then select the Silica_int.qnt method and 

click Open.

3. From the Diagnostics menu, select Multiple Summary – Select Files... Select the 

sample files to be included in the report. TQ Analyst EZ Edition will generate and open a 

spreadsheet containing the results. 

In EXCEL 

1. Depending on the version/settings of TQ Analyst EZ Edition, the format of the results 

spreadsheet may differ slightly; delete columns as needed so that the format of the 

spreadsheet is as shown below. 

2. Save the file; the resulting report (to be imported in FAST) should have the following 

format, as shown in Figure 11. 
26

Figure 11. Results format from the Omnic software (Thermo Scientific). The results report 

exported from Omnic/TQ Analyst displays data for each sample in a row format, but also 

includes a header. Sample data should begin in Row 8; Sample ID should appear in 

Column B, followed by result areas beginning in Column C. 

A B C D E F G

1 Tue Mar 07 12:10:21 2017 (GMT-05:00)

2 Method filename: C\mydocuments\omnic\Quant\Silica_int.qnt

3 Method Revision 4 Last Saved Date: Tue Feb 28 12:33:02 2017 (GMT -05:00)

4 Method title: Silica - Integration

5

6 Spectrum Spectrum q k m d c

7

8 Sample description C:\...sample1 0.704 0.034 -0.03 -0.036 -0.043

9 Sample description C:\...sample2 0.145 0.019 -0.02 -0.006 -0.006

10 Sample description C:\...sample3 1.722 0.015 -0.017 -0.012 -0.015

Exporting Results in a Generic Format 

The generic format for exporting sample results should resemble Figure 8 or Figure 10. 

Notable items about the generic import format are as follows: 

• Each row (after the header row) describes one sample. 

• Each sample is defined by 6 values: 

o the sample name;

o the Q, K, M, D, and C values. 

Importing Results to FAST 

1. Open FAST and select Data >> Import Sample Data...

2. On the Select Method screen, select Import from file. 

3. Beside the Select File field, use the Browse button to select the report file (exported from 

the instrument software) for the sample dataset. 

4. In the Select Importer field, choose the appropriate instrument option; if using an 

instrument that is not listed, select the Generic option. When the Generic option is 

selected, columns must be mapped so that FAST can identify each component of the 

dataset. Note that for any instrument, an option exists to indicate a .csv file or an .xlsx 

file. The option selected should match the format in which the report file was saved. 
27

5. Select Import. FAST will indicate the number of samples found in the report. Confirm 

that this number is correct, then select Next (at the bottom). 

6. On the Create Sample Event screen, enter the following information, which will apply 

to each sample in the report file: 

a. In the Event Name field (optional), give a short name to the sample set. 

b. In the Sample Date field (required), enter the date that the sample set was 

collected. 

c. In the Sample Analysis Date field (required), enter the date that the sample was 

analyzed by the portable method. 

d. In the Location field (optional), enter a general description of where the sample 

set was collected geographically (e.g., Tucson, or New Mexico, or the U.S. 

Southwest). To add a new location to the dropdown list, select Manage 

Locations, click Add, and type the name of the location to be added.

e. Beside the Use Correction Factor field (optional), check the box if you would 

like to use the current correction factor. Note that correction factor is tied to 

location, and the correction factor must be calculated using the correction factor 

tool within FAST. If a correction factor has not been previously calculated for a 

particular location, the Use Correction Factor option will not appear. A 

correction factor should only be applied to samples of the same commodity type. 

f. In the Commodity field (required), select either Coal or Other according to the 

type of mine where samples were collected. This information is (for Coal) or will

be (for Other) used to make mathematical adjustments to the data based on 

anticipated mineralogy. 

g. In the Instrument field (optional), select the type of FTIR instrument being used. 

To add a new instrument to the dropdown list, select Manage Instruments, click 

Add, and type the name of the instrument to be added. 

h. In the Tags field (optional), enter any descriptive words or phrases that you want 

to associate with this entire data set; use the Tab key to separate multiple tags. 

Examples of tags could be: July, longwall move, new dust suppression, etc. These 

tags will apply to the entire sample event. 

7. Select Next. On the Enter Sample Data screen, do not edit the data in the M, D, C, Q, or 

K fields; this is the FTIR data that has been exported for the sample, and changing it will 

change the estimated mass and concentration of RCS for the sample. Enter the following 

information, which will apply only to the sample indicated in the Sample ID field:

a. In the Location field (optional), enter the specific location where the sample was 

collected (e.g., primary crusher, 5 belt, mill, etc.). 

b. In the Worker field (optional), enter the worker with whom the sample is 

associated. This does not necessarily need to be the worker’s full name—first 

name only, initials, job title, or a numeric identifier are all accepted formats. This 

field can be left blank if the sample is not associated with a specific worker or if 

you do not wish to identify the worker. 
28

c. In the Sampling Duration field (required), enter the sampling time in minutes 

(e.g., 100). 

Alternatively, select the box beside Use Sampling Times to indicate the start and 

stop times for the sample; FAST will calculate the sampling duration from the 

times entered. 

d. In the Sampler Type field (required), select the type of sampler used. This 

information is used in calculations. Selecting the incorrect sampler type may 

result in less accurate determination of RCS. 

e. In the Filter Size (mm) field (required), select the size of the filter used to collect 

the sample. This is typically 37 mm. 

f. In the Respirable Dust Mass (mg) field (optional), enter the gravimetric mass of 

the sample, if known. You can add this information at a later time if you do not 

have the data yet but will have it in the future. 

g. In the Laboratory Silica (μg) field (optional), enter the RCS (quartz) mass 

reported by an accredited laboratory using the standard method, if known. You 

can add this information at a later time if you do not have the data yet but will 

have it in the future. 

h. In the Average Flow Rate (lpm) field (required), enter the pump flow rate in 

liters per minute. The entered flow rate should match the nominal flow rate for the 

selected sampler type (±5%) 

Alternatively, select the box beside Use Pre & Post Flow Rate to indicate the 

pre-sampling (calibrated) flow rate and the post-sampling flow rate; FAST will 

calculate the average flow rate from these two values. 

i. In the Spectrum File field (optional), choose the raw spectrum file associated 

with this file. 

j. In the Tags field (optional), enter any descriptive words or phrases that you want 

to associate with this specific sample; use the Tab key to separate multiple tags. 

Examples of tags could be: wearing respirator; control ON; follow-up sample, etc. 

These tags will apply to a specific sample. 

8. Use the arrows to navigate to the first/previous/next/last sample. Some fields (such as 

sampler type and filter size) are “sticky” and will not change from one sample to the next 

unless you opt to change them. FAST assumes that these fields remain the same within a 

specific sampling event, but you can change any of these fields for a particular sample if 

appropriate. 

Alternatively, switch to Grid View (from the default Sample View) to quickly enter 

data for multiple samples. Tip: before switching to Grid View, indicate the sampler 

type and filter size for the first sample. 

When all required fields have been completed, select Finish. Newly entered sample 

data can then be reviewed in either the Events or Samples tab. 
29

Supporting Field-based Analysis for Respirable Crystalline Silica 

Keeping a Logbook 

It is important to keep detailed and accurate records of the analyses that have been completed as 

well as notes indicating quality assurance measures. This is particularly helpful in situations 

where multiple people are using the instrument, or the instrument is used infrequently by the 

same person. 

To maintain an accurate record of what has been done, a logbook entry should be recorded every 

time that the instrument is used. Suggested information to include is as follows: 

• Date 

• Name or initials of the analyst 

• Whether instrument checks were performed 

• Result of instrument checks (if performed) 

• Which QA samples were analyzed 

• Results of QA sample checks 

• Any anomalies related to the QA sample check or the QA samples themselves 

• Other anomalies during analysis 

• Details identifying the sample set 

• Location of saved sample report 

A typical logbook entry might look like the example shown in Figure 12. 
30

Figure 12. An example of a suggested logbook entry. This entry shows the details related 

to analysis of QA samples, the samples analyzed, the location of the sample report, and 

notes on environmental conditions. The entry also includes the date and the initials of 

the instrument operator. 

Organizing Spectrum Files to Support Field-based Monitoring 

When generating sample spectra and naming spectrum files, it is important to give each file a 

unique name, since that name will be used to generate a report of the results from the FTIR 

software and will then be imported into FAST. For instance, the unique sample ID associated 

with the sample filter/cassette could be used, or the sample date plus the sample number (e.g., 

2019-05-05_22). 

In some cases, such as for the analysis of QA samples, it may make more sense to save data 

within distinct folders, organized by date. This allows samples with the same name (e.g., “QA 

Sample 12”) to be saved unambiguously.
31

Note that the default method of naming sample files may vary for the different FTIR software 

packages. Therefore, checking to ensure that each spectrum file is saved with a unique filename 

is also recommended. 

Creating a Set of Samples to be Used as Quality Assurance Samples 

In the Quality Assurance (QA) Sample Spectra subsection of the Generating Spectra section, the 

purpose of analyzing and tracking a set of QA samples over time is discussed. This set of QA 

samples is unique to a particular instrument and can be created by the user in the field. 

Commercially available samples of quartz on filter media (such as standard reference materials 

or similar products) may also be used as QA samples. Since the quartz mass for this kind of 

sample is known with a high level of confidence (which is not a requirement for QA samples), 

they may be expensive. 

To create a set of QA samples, four to seven respirable dust samples should be collected in the 

workplace. Each QA sample should contain a different quantity of respirable dust and a different 

quantity of RCS. It may be necessary to collect a larger set of samples (ten to twenty); after 

analyzing these samples with the FTIR instrument, the FAST software should be used to 

determine the relative amount of RCS in each sample, and a subset of four to seven samples 

should be selected so that the set as a whole satisfies the following criteria: 

• The QA sample with the lowest quantity of RCS should contain approximately 20–40 μg 

of RCS. 

• The QA sample with the highest quantity of RCS should contain approximately 120–150 

μg RCS. 

• The remaining QA samples should contain RCS quantities that are (approximately) 

evenly spread between 20 and 150 μg. 

Other considerations for selecting samples to be used as QA samples are the following: 

• If the respirable dust is visible on the sample filter, the deposition should appear radially 

symmetric. It is not uncommon to see more material at the center of the filter (which may 

cause the center to appear darker than the rest of the filter). This is sampler-dependent 

and does not present an issue for the reliability of the QA sample analysis, as long as the 

deposition is symmetric (see Figure 13). 

If the filter is heavily loaded with respirable dust, there is a higher probability that some material 

may come loose from the filter over time. When selecting QA samples, it is not recommended to 

select samples with more than approximately 5 mg of respirable material or samples where the 

deposit of respirable dust looks very thick or “cakey.” If there is visible dust loss from the QA 

sample filter (e.g. after repeated use), that sample should no longer be used. 

If the respirable dust in the specific environment is known to have a fairly low RCS content 

(<5%) that requires more heavily loaded samples to reach 120–150 μg of RCS, it is 

recommended to create a slightly larger set of QA samples (at least 6–8 samples). Over time, as 

the QA samples are used repeatedly, some may begin to lose material; a larger sample set will 

allow damaged samples to be discarded without the set of QA samples becoming too small. 
32

Figure 13. Illustration of the deposition of material in respirable dust samples. Many 

samplers result in a visible concentration of material at the center of the filter compared 

to the edge of the filter. This is not a problem, provided that the deposit is centered on 

the filter (left) rather than skewed towards the edge of the filter (right). 

Generating a Site-specific Correction Factor 

The quality of the quantification of RCS in respirable dust samples using the portable direct-onfilter FTIR approach can be affected by the presence of other minerals in the samples [Foster and 

Walker 1984; Ojima 2003; Pickard et al. 1985; Taylor et al. 1970]. Accredited laboratories have

the experience and knowledge to address this issue during the FTIR analysis and the processing 

of the data as indicated by NIOSH method 7603 [NIOSH 2017b], which adopts the FTIR 

technique for RCS analysis. In addition, the NIOSH 7603 method provides guidance for sample 

preparation procedures that can reduce the presence of certain minerals. 

Most users of the field-based method will likely not have the same level of experience as a 

laboratory infrared spectroscopy expert and, for this reason, quantification models have been 

developed to standardize the RCS quantification in the presence of confounding minerals. 

Specific quantification models have been designed for samples collected in coal mines.

For samples collected in any environment designated in the FAST software as “other” (which 

includes non-mining environments, FAST does not include tailored quantification models. 

Additional tailored quantification models may be added in future updates to FAST. Until that 

time, a different strategy—the “site-specific quantification model”—can be used to address the 

presence of mineral interferences. As the name suggests, the site-specific quantification model 

uses a correction factor that is unique to a particular site. A site can be one or several workplace 

environments (within the same operation) where it is reasonable to assume that the 

characteristics of the respirable dust (including percent quartz content) are similar. Qualitative 

information (such as the color of the respirable dust on the sample filters), quantitative 

information (such as the percent silica reported by the external laboratory for previous samples 

collected in an area), and operational information (such as knowledge of processes that use 

additives or chemical that could also be present in the dust sample) can be used to determine if 

two areas are similar enough to be considered the same site. 
33

Ultimately, a site is determined at the user’s discretion. For example, a small quarry operation, 

including the pit, the plant, and the lab, might be considered a single site if the respirable dust 

has a similar appearance and quartz content throughout. A large multi-pit metal mine might 

encompass several different sites (i.e. each pit, each processing area, and the lab) if respirable 

dust has different characteristics depending on the area of the operation. In certain situations, 

such as in industries including construction or manufacturing, correction factors that are unique 

to specific tasks could be used instead (e.g., a correction factor specific to cutting brick and a 

correction factor specific to breaking concrete). Publications describe the application of this 

method [Cauda et al. 2018; Hart et al. 2018]. 

Regardless of how big or small a site is, the correction factor for that site should be periodically 

evaluated via laboratory analysis. This evaluation should include samples from all areas of the 

facility to which the correction factor is applied; the ratio of the result of the field-based analysis 

to the results of the lab-based analysis should be approximately the same for all samples. For any 

samples where this ratio is different, a unique correction factor should be generated and used.

To generate a site-specific quantification model, the procedure outlined below should be 

followed. 

1. Collect a set of at least fifteen to twenty samples. The sample set should contain a range 

of respirable dust loadings. The final sample set should contain at least ten samples, 

ranging from 20–30 μg RCS per sample to 275–325 μg RCS per sample, which may be 

determined using either the direct-on-filter approach or the standard analysis through an 

off-site laboratory. Ideally, sample loadings will be (approximately) evenly spread 

between 20 and 325 μg of RCS. While a total respirable dust loading can be estimated 

from expected concentration and sampling time, it can be difficult to estimate the RCS 

loading accurately in this way, so additional samples are recommended. Gravimetric 

analysis (such as NIOSH 0600 [NIOSH 1998]), if available, may be helpful. These 

guidelines are summarized within the Calculate Correction Factor function of the 

FAST software.

2. Analyze the samples using the FTIR instrument and import the data into FAST. At this 

point, choose which samples will remain in the sample set and which will be excluded 

(i.e., if they are outside of the loading range; if they are duplicates or extras). When 

importing the data into FAST, be sure to indicate the location for which you plan to use 

the correction factor. 

3. Submit the sample set to an off-site laboratory for the standard RCS analysis. 

4. When the sample results are received from the laboratory, add each laboratory result to 

the appropriate sample in FAST by right clicking on a sample, selecting “edit,” and 

adding the result to the Laboratory Silica field. 

5. To calculate the correction factor, select Calculate Correction Factor from the Data 

menu in FAST. Select the appropriate event location. FAST will display samples that 

may be used to calculate the correction factor; it will not display samples for which 

laboratory results are not available, or samples outside of the range of 20–325 μg RCS. 

Click on samples to select them; at least 10 samples must be selected. Use the “Data 

Quality Evaluation Results” at the bottom of the screen (see Figure 14) to guide you as 

you select the samples to include; the correction factor generated with the currently 
34

selected samples is shown below this section. When you have selected all samples you 

wish to select, click Save Correction Factor. 

6. The saved correction factor can be selected in FAST during the Create Sample Event

step; the correction factor is applicable to any future sample events occurring in the same 

location. 

7. Once a correction factor is established, additional samples should periodically be sent to 

the off-site laboratory to verify the validity of the correction factor. This should also be 

done any time that changes in the occupational environment are perceived such that the 

existing correction factor may need to be adjusted. This may also be necessary in large 

operations, where an operator may decide to establish individual correction factors for 

specific areas of the operation. The frequency depends on how stable conditions are. For 

conditions that remain consistent over time, verification of the correction factor might 

occur once or twice a year. For conditions that are less stable (or where the stability of 

conditions is unknown), more frequent verification might be needed. 

Figure 14. Screenshot from the “Calculate Correction Factor” function of FAST. This tool 

enables RCS results calculated in FAST and RCS results from an off-site laboratory to be 

used to calculate a correction factor that can be applied to future sample events. The 

Data Quality Evaluation Results feature provides guidance for determining an 

appropriate correction factor. 

Troubleshooting: What a Sample Spectrum Should (and Should Not) Look Like 

The sample spectrum—the source of data used by FAST to calculate the amount of RCS in a 

sample—is actually the product of two spectra: the background spectrum (a clean filter) and the 

sample filter spectrum. The background spectrum is used to adjust the sample filter spectrum to 

reduce the impact of the filter material; therefore, what is ultimately used for RCS quantification 

is the primary representation of the respirable dust sample’s mineral composition. In order to 

obtain a reliable spectrum, it is important that both the background spectrum and sample 

spectrum are correct. A visual inspection can provide information on the quality of the spectrum: 

some software programs provide the option to display the background as it is scanned, or they 

provide the option to view the background component of a sample spectrum. Note that while 

some programs do enable this, they may not provide it as the default option; they may refer to it 

as the reference rather than the background; and they may not provide the background spectrum 

in the same units (generally absorbance or transmittance) as the sample spectrum. 

The exact appearance of the spectrum will depend on the composition of the sample, but in 

general, the background and sample spectra should both appear as a relatively smooth line (as 
35

opposed to a “spiky” line with many abrupt changes), especially in the right-hand side (also 

known as the fingerprint region) of the spectrum (closer to 600 cm-1). Depending on the 

instrument or the software, there may be a region of “noisy” data on the left-hand side (closer to 

4000 cm-1); as long as the noise is relatively consistent from day to day and does not occur to the 

right of approximately 3000 cm-1, this is acceptable. If noise suddenly increases or appears in an 

area that previously was not noisy, this indicates that a problem needs to be addressed before 

continuing to analyze samples. 

Figure 15 shows images of both good background and sample spectra, while Figure 16 shows 

examples of spectra with varying distortions and noise levels. Although sample spectra will all 

be slightly different (depending on how much material the sample contains and its composition), 

most will have similar qualities, and over time, many users will be able to readily distinguish a 

high quality sample spectrum from one of lower quality. These examples are intended to 

facilitate that but should not be considered comprehensive of all the spectra or issues that may 

arise. Note that spectra have not been baseline-corrected in Figures 15 and 16, except where 

noted. 
36

Figure 15. Illustrations of three FTIR example spectra, background and sample. On the 

top is the spectrum of a clean filter, which has been collected as the background 

measurement. In most FTIR software programs, the background spectrum is not shown 

by default. In the middle is the spectrum of a sample filter, after the background 

spectrum (clean filter) has been removed by the FTIR software. On the bottom is the 

same spectrum after baseline correction. In each spectrum, the fingerprint region (1500–

500 cm-1) is shown in the shaded area. 

0.00

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4000 3600 3200 2800 2400 2000 1600 1200 800 400

Absorbance 

Wavenumber (cm-1

)

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)

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37

Figure 16 illustrates various types of noise and distortions that may arise in sample spectra. 

Noise is essentially random variation in a spectrum. When the intensity of the noise is high (i.e. 

there is a lot of random variation in the signal) relative to the intensity of the quartz signal (or the 

signal of any other species of interest), the ability to accurately measure quartz (or other species 

of interest) is compromised. Noise can be present in any portion of the spectrum and may occur 

over the most or all of the spectrum (such as in 16d) or be isolated to specific regions (such as 

16a, b, and e). It is of particular concern when it is present in the fingerprint region of the 

spectrum (such as in 16d), since the quantity of quartz present in a sample is calculated from this 

region. When noise is visible in a spectrum, consider both the location of the noise (whether it 

impacts the fingerprint region) and the relative magnitude. In lightly loaded samples, noise may 

seem more pronounced relative to the low overall absorbance intensity of the sample, but might 

not indicate a problem with instrument performance. Users may need to exercise judgement on 

whether the sample is reliable; examining the fingerprint region more closely may be helpful. 

When in doubt, it is advisable to perform instrument performance verification checks and/or 

analyze the QA samples, then repeat the sample analysis. 

a. A spectrum with considerable noise outside of the fingerprint region.

-0.10

0.00

0.10

0.20

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4000 3600 3200 2800 2400 2000 1600 1200 800 400

Absorbance

Wavenumber (cm-1)

b. A spectrum with moderate noise outside of the fingerprint region. Note that the feature at 

approximately 2400 cm-1is not noise but rather is due to ambient carbon dioxide.

0.00

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0.20

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0.40

4000 3600 3200 2800 2400 2000 1600 1200 800 400

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38

c. A spectrum with minor noise outside of the fingerprint region. Note that the feature at 

approximately 3000 cm-1is not noise but rather is due to ambient humidity (water vapor).

-0.05

0.00

0.05

0.10

0.15

4000 3600 3200 2800 2400 2000 1600 1200 800 400

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d. A sample spectrum with considerable distortion, including substantial noise within the 

fingerprint region.

0.00

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39

e. A spectrum with considerable distortion that has resulted in an oscillating appearance 

throughout the spectrum, including the fingerprint region.

0.00

0.50

1.00

1.50

4000 3600 3200 2800 2400 2000 1600 1200 800 400

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f. A spectrum with a considerable distortion in the fingerprint region.

0.00

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2.00

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Figure 16. Illustrations of six FTIR example spectra with noise and/or distortions. 

Beneath each example spectrum is a brief description of key features, and in each 

spectrum, the fingerprint region (1500–500 cm-1) is shown in the shaded area. A more 

detailed discussion is provided in the text. 

A distortion—a larger change in the overall shape of a spectrum—may appear with or without 

excess noise and could be due to a variety of causes. Spectra with distortions such as those in 

16d and 16e, where virtually the entire spectrum, including the fingerprint region, is abnormal 

(see the middle and bottom panels of Figure 15 for comparison), should not be used for 

calculating quartz concentrations, as such distortions are almost certain to impact the reliability 

of the spectrum data. These types of distortions might arise as a result of an overloaded sample

filter, the instrument or sample being disturbed while the analysis is in progress (including when 

the instrument indicates that scans have been rejected), or a malfunction of the instrument. 

Reanalyzing the sample and/or analyzing the QA sample may help to identify the problem. The 

spectrum of an overloaded or heavily loaded sample may resemble 16f, where portions of the 
40

spectrum appear normal but the tops of some of the peaks in the fingerprint region are distorted. 

This happens when there is so much sample that the instrument detector is “overwhelmed” by 

the sample signal. 

Note that peaks at approximately 2400 cm-1and 3000 cm-1(see Figure 16b and 16c, respectively) 

are not noise but are normal features due to ambient carbon dioxide and water vapor, 

respectively. These peaks may be more prominent, less prominent, or even absent depending on 

sample loading and how similar the ambient conditions are when the background is scanned and 

when the sample is scanned. 

Possible Causes of Spectrum Problems 

A number of causes might be responsible for spectrum problems, as described below, followed 

by their solutions. 

Problem: Incorrect sample analysis settings. 

Solution: Check that all the sample analysis parameters match those in Table 1; correct any 

settings that do not match. 

Problem: Background spectrum is faulty. 

Solution: Re-scan; choose a different clean filter if necessary. 

Problem: Unsuitable analysis conditions (heavy vibrations, sample bracket is loose, sample is 

moving during analysis, etc.)

Solution: Make sure that the bracket and sample holder are secure so that the sample is 

stationary when analysis begins. Light vibration is typically not an issue if it is steady but may be 

problematic if it is intermittent during analysis. If vibrations are present, place the instrument on 

a sturdy surface (such as a heavy desk) or wait until vibrations decrease. 

Problem: Instrument windows are smudged, dirty, cloudy, or cracked. 

Solution: Clean or replace windows. Note that windows are very difficult to clean without 

smudging or scratching; therefore it is recommended to replace dirty windows (and store the 

instrument so that dust and humidity are minimized). 

Problem: Other hardware issue. 

Solution: Run instrument diagnostics to determine the problem; consult software help 

documentation or the instrument manufacturer to repair/replace hardware. 

Selecting the Appropriate Sampler Type in FAST 

The sampler used to collect the dust sample for direct-on-filter analysis has an important role in 

the analytical approach. The choice of sampler must ensure that the collected sample is 

representative of respirable dust in the environment monitored. The sampler also affects the 

quantification model for RCS and the appropriate sampler must be selected in the FAST software 

in order for the RCS result to be accurate. 
41

As described in the previous sections, this specific direct-on-filter approach entails the analysis 

of a small portion of the dust sample deposited on the filter. This portion is a circular area 

approximately 6–9 mm in diameter in the center of the filter. The quantification of RCS is then 

calculated by using established quantification models to correlate the analysis in the center of the 

filter with the quantity of RCS present on the entire filter. The quantification model is a function 

of how the dust sample is distributed across the filter, or the deposition pattern of respirable dust 

on the filter. 

Respirable dust particles enter a sampling cassette as an aerosol in a stream of air (see Figure 17, 

top). Once inside the cassette, the aerosol is distributed throughout the volume of the cassette 

while moving towards the filter media. Eventually, the dust particles are collected on the filter 

and the air stream exits the cassette. Several factors affect the aerodynamic behavior of the 

aerosol inside the cassette and, ultimately, the deposition pattern of the dust on the filter: the air 

velocity at the inlet of the cassette, residence time of the air inside the cassette, the shape of the 

cassette, and the sampler outlet. The combination of these factors induces different deposition 

patterns of the dust on filter. The size of the filter also has an influence on the deposition pattern: 

most samplers collect respirable dust on 37-mm-diameter filters, although some samplers use 

25mm or 47-mm diameter filters. 

Examples of deposition patterns induced by two samplers/cassettes configurations (see Figure 

17, bottom) are presented here. Both samplers use 37-mm diameter filters, which are the most 

common type used in the United States. Note that the figure refers to the nylon Dorr-Oliver 

sampler with a luer fitting; the conductive style of this sampler and the nylon version with 

coupler will result in samples with similar but not identical patterns of dust deposition. 
42

Figure 17. Illustrations of dust particles entering a respirable sampler and typical 

patterns of dust deposition from two respirable samplers. Respirable samplers separate 

particles according to size (top left and right), though samplers of different types display 

distinct patterns of respirable dust deposition on the sample filter. The nylon Dorr-Oliver 

sampler results in a high density of respirable dust deposited at the center of the filter 

relative to the perimeter of the filter (bottom left), whereas the GK2.69 cyclone sampler 

results in a more uniform density of respirable dust across the diameter of the filter 

(bottom right). 
43

The two samplers are the GK2.69 cyclone (BGI by Mesa Labs, Butler, NJ) [Stacey et al. 2014; 

Stacey et al. 2013] and the 10-mm nylon Dorr-Oliver cyclone (Zefon International, Ocala, FL) 

[Lidén and Kenny 1993; Maynard 1993; Bartley et al. 2010]. The GK2.69 sampler is operated at 

a flow rate of 4.2 lpm for respirable dust collection; the Dorr-Oliver sampler is operated at a flow 

rate of 1.7 lpm except when it is used in U.S. coal mines, where it is used at 2.0 lpm as specified 

in 30 CFR 70.205 [2019]. The GK2.69 sampler is coupled with a 37-mm diameter cassette in a 

configuration called an “open-face cassette,” in which the aerosol exits the sampler directly into 

the volume of the cassette. In contrast, the Dorr-Oliver sampler uses an enclosed cassette and the 

aerosol moves from the sampler to the cassette through a narrow coupler. Because of these 

differences and the factors presented above, the deposition pattern for respirable dust samples 

collected by the two samplers is different. The respirable dust particles collected with the 

GK2.69 sampler are spread evenly across the 37-mm diameter filter. The respirable dust particles 

collected with the DO sampler tend to be more concentrated in the center of the filter than at the 

edges. Both deposition patterns have been found to be consistent for a wide range of respirable 

dust (and RCS) loadings. 

Note that electrostatic effects can adversely affect the sample (changes to sample deposition, 

increased wall losses, etc.). Samples collected using samplers and cassettes that are composed of 

conductive or static-dissipative materials are less susceptible to these effects than samples 

collected using more insulating materials [NIOSH 2016a]. In general, the minimization of wall 

losses is preferred for direct-on-filter analysis, since only the material deposited on the filter can 

be analyzed by the instrument. 

How Deposition Pattern Affects the Quantification for Respirable Crystalline Silica Using the Direct-onfilter Analysis 

The deposition pattern influences the relationship between the area in the center of the filter that 

is analyzed directly by the FTIR and the remaining portion of the sample that is not analyzed by 

the FTIR. Different deposition patterns translate into different models for quantification of RCS 

on the entire filter. In other words, two samples may contain the same mass of RCS, but the raw 

FTIR results would be different if they were collected with different samplers. For this reason, 

the quantification model for the sampler used to collect the sample needs to be used for the 

quantification of RCS in the sample. In FAST, selecting the correct sampler type will ensure that 

the quantification model developed for that sampler is used. 

NIOSH has developed quantification models for samples collected with a number of commonly 

used samplers which have been incorporated into FAST. NIOSH is working on developing 

additional quantification models for samples collected with other samplers. Once the models for 

additional types of samplers have been developed, they will be added to the FAST software. 

“Housekeeping” Suggestions for FTIR Instruments 

Because the use of the FTIR instrument at a site might be intermittent, several housekeeping 

suggestions are recommended for optimum performance, as follows. 

Once an instrument is set up and operating, it may be left powered on for several days (or longer) 

between analysis sessions; leaving the instrument on is preferable to frequently turning it on and 

off. When the instrument will not be used for an extended period of time (several weeks or 
44

more), it is strongly recommended to store the instrument in a protective case and to store the 

case in a suitable environment. If the conditions of the storage environment (especially 

temperature and humidity) are different from the environment where the instrument will be used, 

the instrument should be allowed to equilibrate with the new environment for at least 24 hours 

before it is powered on. This helps to ensure that any condensation has time to dissipate, as this 

could harm the internal electrical components of the instrument. 

Note that the performance of an FTIR instrument relies on internal optical components. In some 

instruments, some of the optical components are partially exposed and can be affected or 

damaged by dirt and humidity if care is not taken. Best practices include keeping the lid or door 

to the transmission compartment closed when the instrument is not actively being used and 

exercising care not to touch or disturb internal components when the transmission compartment 

is opened to insert or remove a sample. 

Environment for Storage and Use of FTIR Instruments 

The environment where the FTIR instrument is stored and where it is operated should be as 

described below. Note that while FTIR instruments may be used to analyze samples collected in 

an underground environment, the instruments themselves are not intended for underground use 

and should only be used in above-ground environments. 

Low in Humidity 

Storage: If storing the instrument in a low-humidity environment is not possible, the FTIR 

instrument should be stored in an airtight container (such as a large, sealed bag) with a desiccant 

(such as a silica gel pack) to absorb excess moisture. 

Operation: Most instruments can operate in up to 80% non-condensing humidity; however, high 

humidity will degrade some components over time, negatively impacting data quality and 

eventually requiring replacement. 

Moderate in Temperature

Temperature should be between approximately 18°C and 35°C (65°F and 95°F). 

Relatively Clean and Free of Dust 

Storage: If storing the instrument in a clean environment is not possible, the FTIR instrument 

should be stored in an airtight container (such as a large, sealed bag). 

Operation: While small amounts of dust are generally not harmful, larger quantities can 

contaminate samples and may begin to accumulate in the FTIR instrument sample chamber and 

potentially in internal chambers, which could negatively impact data quality or cause equipment 

damage over time. A site office or a similar location is generally suitable; cleaning the work 

surface with a damp cloth before setting up the instrument can also help to control dust. 

Not Prone to Physical Disturbances 

Storage: Prolonged periods of physical disturbance (such as vibrations from nearby heavy 

equipment) are a concern. While short-duration vibrations and small bumps should not affect the 

instrument, prolonged movement may loosen or misalign internal components. 
45

Operation: Sudden shocks or vibrations are a concern. While occasional moderate vibrations are 

not likely to harm the instrument, they can affect the data quality. 

Guidelines for Performing Standard Laboratory Analysis on Samples Analyzed in the 

Field 

This section provides basic guidance on when it may be advantageous to conduct laboratory 

analysis on samples that have already been analyzed in the field with the portable FTIR, using 

methods such as MSHA P-2 [MSHA 2013a], MSHA P-7 [MSHA 2013b], NIOSH 7500 [NIOSH 

2003], NIOSH 7603 [NIOSH 2017b], and OSHA ID-142 [OSHA 2015]. Because the laboratory 

analysis is an additional cost (per sample) that the operator must account for, it is important to 

use this option strategically. 

Because of its nature as direct-on-filter, non-destructive analysis, field-based RCS monitoring 

does not degrade the dust samples, which allows users to analyze the same samples with the 

standard analysis as well if desired. This can be done by submitting the samples to an off-site 

accredited laboratory. The possibility of conducting laboratory analysis on the same samples 

analyzed in the field is an important feature of the monitoring approach and is an option that 

users should consider to maximize the impact of this tool. When field-based RCS monitoring is 

implemented initially, new users may find it helpful to have the same-sample comparison of 

field-based results and laboratory results for a majority of samples. This approach can be used to 

confirm that users are acquiring reliable results, as well as to determine if a single site-specific 

correction factor is appropriate for an operation, or if different correction factors would be 

appropriate for different work areas. As more experience is gained with field-based monitoring, 

laboratory analysis does not necessarily need to be conducted on all samples. Instead, laboratory 

analysis can be conducted periodically on specific sample sets to 1) confirm overall reliability of 

field-based analysis (including when new personnel become involved in field-based monitoring, 

when a new instrument is acquired or when an old instrument is serviced, or when an instrument 

has not been used for an extended period of time), 2) check site-specific correction factors, and 

3) compose reports where laboratory accredited data is required.

The FAST software has been designed to allow users to add laboratory results to existing 

samples in the FAST database. The user can edit the “laboratory mass (μg)” field to add 

laboratory RCS results for each sample at any time. In addition, the user can also edit the 

“respirable dust mass (mg)” for the same sample. These data are available from the laboratory 

results. In this way, the user can have a comprehensive set of results from the field and 

laboratory on the same sample, with the data all stored together in FAST. 

At the time this document was published, direct-on-filter analysis with portable FTIR was not a 

stand-alone NIOSH analytical method or a standard analytical method used by MSHA or OSHA 

or other international bodies. For this reason, when there is a need for a formal assessment of 

compliance status, the dust samples analyzed in the field should then be sent to an accredited 

laboratory. In this case, the role of field-based monitoring should be to act in support of 

maintaining regulatory compliance. The goal is that the faster, more comprehensive assessment 

of the RCS exposure levels that is facilitated by the use of field-based monitoring should 

increase the probability of compliance status for the operator and protection of workers. This is 

accomplished by decreasing the RCS concentration and exposure, which is the primary goal of 

the monitoring approach. 
46

The standard laboratory analysis should also be used for samples collected to generate a sitespecific quantification model, described in more detail in the Generating a Site-specific 

Correction Factor section. The laboratory analysis on selected dust samples is essential to create 

the site-specific correction factor for an individual operation. Once a correction factor is 

established (using the “FAST—Site-specific Correction Factor Tool” spreadsheet), additional 

samples should periodically be sent to the off-site laboratory to verify the validity of the 

correction factor. This should also be done anytime the user perceives changes in the 

occupational environment such that the existing correction factor may need to be adjusted. 

Finally, the standard laboratory analysis should be used for confirmation when field-based 

monitoring provides results close to an “action value” for the operator. This could be the 

permissible exposure limit or another value that would trigger an action such as modification of 

control technologies or work practices in order to decrease exposure. If the field-based method 

provides a result that is very close to an action value, the standard laboratory analysis can be 

used to confirm the result. Conversely, standard laboratory analysis is not necessary if the fieldbased method provides a result that is substantially above or below the action value, since there 

is little ambiguity in such a result. 

Using Field-based Monitoring to Evaluate Efficacy of Controls 

One of the most valuable uses of field-based monitoring is to quickly evaluate the efficacy of 

newly implemented or modified controls with respect to decreasing airborne RCS 

concentrations. When a new or modified control is introduced, adjustments to the control may 

need to be made for optimal performance. With traditional laboratory-based evaluation of RCS, 

this process may take weeks, but it can proceed much faster with the aid of field-based analysis. 

The procedure below should be followed and is depicted in Figure 18. 

1. Collect a set of samples that will serve as a baseline, before any changes have been made. 

Be sure to record the sampling times and pump flow rates so that the RCS concentrations

can be calculated. 

Note: If the results of the baseline samples are very close to the limit of detection/limit of 

quantification (LOD/LOQ, as indicated by FAST), it may be necessary to collect samples

for a longer duration, or collect samples with a high-volume sampler, so that more 

material can be collected; otherwise, it may be difficult to determine if the control has 

truly been effective. 

2. Implement the new or modified control. This could be an engineering control or a change 

to work practice. 

3. Allow enough time for the effects of the control to be fully realized, and then collect 

another set of samples (the “evaluation sample set”). Ideally, the sampling times and 

pump flow rates should be similar to the baseline sample. The same type of sampler 

should be used for both sample sets if possible. 

Note: In FAST, the same commodity option should be chosen for the baseline sample set 

and the evaluation sample set. If a correction factor is used, it should be identical for both 

sample sets. A correction factor is always advisable to lower bias due to minerals and 

other factors. Nevertheless, provided that the samples are all collected in the same 

location, under the same conditions, and in a relatively short span of time—within 
47

approximately a day of each other—a correction factor may not be necessary, because the 

samples will most likely be very similar in composition to one another, and thus can be 

directly compared to determine if RCS concentrations after the control have decreased 

relative to the baseline. 

4. Continue to modify the control and collect evaluation samples until the desired reduction 

has consistently been achieved. 

Figure 18. Illustration of the cyclic process of field-based monitoring. As shown, samples 

are collected, analyzed, and the data are interpreted in a short period of time.

Conclusion 

When used appropriately, field-based monitoring for RCS enables the timely evaluation of 

workplace exposure to crystalline silica and can be a valuable component of successful RCS 

control strategies. The accuracy of results obtained via field-based monitoring are reliant upon 

field conditions as well as upon the conscientious sampling and analysis by the user. This 

document provides users with guidance to achieve reliable and consistent results for monitoring 

purposes, while FAST can provide additional information regarding limitations of sampling data 

(such as flagging data that fall below the LOD or LOQ). Traditional laboratory analysis should 

be used for periodic evaluation of results and for circumstances where formal reporting of results 

is required. 

This document can also be used as a reference to help users procure the necessary equipment and 

software (see Getting Started: Hardware and Software), set up a portable FTIR instrument for 

field-based analysis (see Setting Instrument Analysis Parameters), and follow appropriate quality 
48

assurance procedures (see Verifying Instrument Integrity; Creating a Set of Samples to be Used 

as Quality Assurance Samples; and Keeping a Logbook). Appendix A contains links to 

additional resources that can be used in the field-based monitoring process, Appendix B contains 

checklists to guide users through the most vital field-based monitoring procedures, and Appendix 

C contains comparisons of several FTIR analysis methods and parameters discussed in this 

document. 
49

References 

Code of Federal Regulations [2019]. Sampling procedures: Approved sampling devices; 

operation; air flowrate. 30 CFR 70.205. 

Ashley E, Cauda E, Chubb LG, Tuchman DP, Rubinstein EN [2020]. Performance comparison 

of four portable FTIR instruments for direct-on-filter measurement of respirable crystalline 

silica. Ann Work Expo Health, https://doi.org/10.1093/annweh/wxaa031.

Bartley DL, Chen C-C, Song R, Fischbach TJ [2010]. Respirable aerosol sampler performance 

testing. AIHAJ 55(11):1036–1046, https://doi.org/10.1080/15428119491018303.

Cauda E, Chubb L, Reed R, Stepp R [2018]. Evaluating the use of a field-based silica monitoring 

approach with dust from copper mines. J Occup Environ Hyg 15(10):732–742, 

https://doi.org/10.1080/15459624.2018.1495333.

Cauda E, Miller A, Drake P [2016]. Promoting early exposure monitoring for respirable 

crystalline silica: Taking the laboratory to the mine site. J Occup Environ Hyg 13(3):D39–45, 

https://www.ncbi.nlm.nih.gov/pubmed/26558490.

Foster RD, Walker RF [1984]. Quantitative determination of crystalline silica in respirable-size 

dust samples by infrared spectrophotometry. Analyst 109:1117–1127, 

https://doi.org/10.1039/AN9840901117.

Harris G, Maguire B [1968]. A gravimetric dust sampling instrument (SIMPEDS): Preliminary 

results. Ann Occup Hyg 11:195–201, https://doi.org/10.1093/annhyg/11.3.195.

Hart JF, Autenrieth DA, Cauda E, Chubb L, Spear TM, Wock S, Rosenthal S [2018]. A 

comparison of respirable crystalline silica concentration measurements using a direct-on-filter 

Fourier transform infrared (FT-IR) transmission method versus a traditional laboratory X-ray 

diffraction method. J Occup Environ Hyg 15(10):743–754, 

https://doi.org/10.1080/15459624.2018.1495334.

Lee T, Lee L, Cauda E, Hummer J, Harper M [2017]. Respirable size-selective sampler for endof-shift quartz measurement: Development and performance. J Occup Environ Hyg 14(5):335–

342, https://doi.org/10.1080/15459624.2016.1252845.

Lidén G, Kenny LC [1993]. Optimization of the performance of existing respirable dust 

samplers. Appl Occup Environ Hyg 8(4):386–391, 

https://doi.org/10.1080/1047322x.1993.10389224.

Maynard AD [1993]. Respirable dust sampler characterisation: Efficiency curve reproducibility. 

J Aerosol Sci 24:S457–S458, https://doi.org/10.1016/0021-8502(93)90321-Y.

Miller AL, Drake PL, Murphy NC, Cauda E, LeBouf R, Markevicius G [2013]. Deposition 

uniformity of coal dust on filters and its effect on the accuracy of FTIR analyses for silica. 

Aerosol Sci Technol, https://doi.org/10.1080/02786826.2013.787157.

Miller AL, Drake PL, Murphy NC, Noll JD, Volkwein JC [2012]. Evaluating portable infrared 

spectrometers for measuring the silica content of coal dust. J Environ Monit 14(1):48–55, 

http://dx.doi.org/10.1039/C1EM10678C.
50

MSHA [2013a]. Method P2 X-ray diffraction determination of quartz and cristobalite in 

respirable mine dust. https://arlweb.msha.gov/Techsupp/pshtcweb/MSHA%20P2.pdf.

MSHA [2013b]. Method P7 Infrared determination of quartz in respirable coal mine dust. 

https://arlweb.msha.gov/Techsupp/pshtcweb/MSHA%20P7.pdf.

NIOSH [1998]. Method 0600 Particulates not otherwise regulated, respirable. NIOSH Manual of 

Analytical Methods, Fourth Edition, https://www.cdc.gov/niosh/docs/2003-154/pdfs/0600.pdf.

NIOSH [2002]. NIOSH hazard review: health effects of occupational exposure to respirable 

crystalline silica. Publication No. 2002-129. Cincinnati, OH: National Institute for Occupational 

Safety and Health, https://www.cdc.gov/niosh/docs/2002-

129/pdfs/2002129.pdf?id=10.26616/NIOSHPUB2002129.

NIOSH [2003a]. Chapter R: Determination of Airborne Crystalline Silica. NIOSH Manual of 

Analytical Methods, Fourth Edition, https://www.cdc.gov/niosh/docs/2003-

154/pdfs/chapterr.pdf.

NIOSH [2003b]. Method 7500 Silica, crystalline, by XRD (filter redeposition) NIOSH Manual 

of Analytical Methods, Fourth Edition, https://www.cdc.gov/niosh/docs/2003-154/pdfs/7500.pdf.

NIOSH [2016a]. Chapter AE: Factors affecting aerosol sampling. NIOSH Manual of Analytical 

Methods, Fifth Edition, https://www.cdc.gov/niosh/docs/2014-151/pdfs/chapters/chapter-ae.pdf.

NIOSH [2016b]. Chapter SA: General considerations for sampling airborne contaminants. 

NIOSH Manual of Analytical Methods, Fifth Edition, 

https://www.cdc.gov/niosh/docs/2014151/pdfs/chapters/chapter-sa.pdf.

NIOSH [2017a]. Method 7602 Silica, crystalline by IR (KBr pellet). NIOSH Manual of 

Analytical Methods, Fifth Edition, https://www.cdc.gov/niosh/nmam/pdf/7602.pdf.

NIOSH [2017b]. Method 7603 Quartz in coal mine dust, by IR (redeposition). NIOSH Manual of 

Analytical Methods, Fifth Edition, https://www.cdc.gov/niosh/nmam/pdf/7603.pdf.

Ojima J [2003]. Determining of crystalline silica in respirable dust samples by infrared 

spectrophotometry in the presence of interferences. J Occup Health 45:94–103, 

https://doi.org/10.1539/joh.45.94.

OSHA [2015]. Method ID-142 Crystalline silica, quartz and cristobalite. 

https://www.osha.gov/dts/sltc/methods/inorganic/id142/id142.pdf.

Pampena J, Cauda E, Chubb L, Meadows J [2019]. Use of the field-based silica monitoring 

technique in a coal mine: a case study. Min Metall Explor, https://doi.org/10.1007/s42461-

01900161-0.

Pickard KJ, Walker RF, West NG [1985]. A comparison of X-ray diffraction and infrared 

spectrophotometric methods for the analysis of alpha-quartz in airborne dusts. Ann Occup Hyg 

29(2):149–167, https://doi.org/10.1093/annhyg/29.2.149.

Stacey P, Lee T, Thorpe A, Roberts P, Frost G, Harper M [2014]. Collection efficiencies of high 

flow rate personal respirable samplers when measuring Arizona road dust and analysis of quartz 

by x-ray diffraction. Ann Occup Hyg 58(4):512–523, https://doi.org/10.1093/annhyg/met075.
51

Stacey P, Mecchia M, Verpaele S, Pretorius C, Key-Schwartz R, Mattenklott M, Eypert-Blaison 

C, Thorpe A, Roberts P, Frost G [2013]. Differences between samplers for respirable dust and 

the analysis of quartz—an international study, In: Silica and Associated Respirable Mineral 

Particles, STP 1565, ed. by Harper M, Lee T, West Conshohocken, PA: ASTM International, pp. 

73–102. 

Taylor DG, Nenadic CM, Crable JV [1970]. Infrared spectra for mineral identification. AIHAJ 

31(1):100–108, https://doi.org/10.1080/0002889708506215.
52

Appendix A: Additional Resources for Field-based Monitoring 

Listed below are tools that can support and facilitate field-based monitoring. Some tools are used 

in conjunction with the instrument software; several versions are available to accommodate 

different software packages. Other tools are independent of the instrument software and the same 

version can be utilized by any user. 

All of the following resources are freely available online. 

Software-specific Resources (https://github.com/niosh-mining/fast/)

• Experiment setup files (see Table 2)

• Macros for spectral processing 

• Report templates 

General resources (https://github.com/niosh-mining/fast/)

• QA Sample Tracking spreadsheet 

3D Model Files (https://3dprint.nih.gov/)

• Sample bracket (instrument-specific) 

• Sample cradle (for use with the four-piece cassette) 

• Sample holder (for use with dust sampling cassette) 

• Prototype respirable sampler (see Lee et al. 2017) 

Additional guidance for using the provided 3D Model Files: 

• The 3D Models available from the link above were designed for a fused deposition 

modeling (FDM) layer thickness of 0.005 inches when using acrylonitrile butadiene 

styrene (ABS) material. Different materials and printer formats can affect the accuracy of 

the printed model, and these specifications should be used as a benchmark. For instance: 

o An FDM layer thickness of 0.015 inches or greater will have a cross section with 

edges that appear amplified in their rounded coarseness. Printer manufacturers try 

to compensate for an amount of layer flow, but much is dependent on local 

conditions. 

o A printer utilizing a cured liquid deposition may exhibit a much finer edge section 

resulting in greater accuracy. 

• Note that the models are provided in units of inches; users who typically print in metric 

units should be mindful of the conversion. 

• Consider how the item is oriented relative to the printer head or nozzle as it is being 

printed. Generally, the accuracy of the sample cradle, either as a full or half circle, will be 

greater when oriented perpendicular to the printing head, and greater accuracy will be 

achieved when the part is printed lying flat on the print platen. 
53

Appendix B: Operational Checklists for Field-based Monitoring 

Field-based monitoring: checklist for daily operation 

❑ Set up the work area. 

❑ Area is free of excessive dust. 

❑ Work surface is level and stable. 

❑ Humidity is 80% RH or less. 

❑ Temperature is between 65°F and 95°F (18°C and 35°C). 

❑ Initiate instrument/software and check software parameters. 

❑ Background scans: 16 

❑ Sample scans: 16 

❑ Spectral range: 4000–400 cm-1

❑ Resolution: 4 cm-1

❑ Phase correction: Mertz 

❑ Zero-fill factor: 2 

❑ Apodization: Blackman Harris (preferred) or medium (second choice); if neither option is 

available, choose an option consistent with previous analyses 

❑ Results spectrum: absorbance 

❑ Begin a logbook entry for the analysis session. 

❑ Complete an instrument performance check. 

❑ Evaluate the instrument using the QA samples. 

❑ Generate a background spectrum for the QA samples. 

❑ Analyze the QA samples. 

❑ Process QA sample data and generate a report of the results using the macro(s). 

❑ Enter QA sample results in the tracking spreadsheet to verify instrument performance. 

❑ Analyze samples. 

❑ Generate a background spectrum from one of the sample set blanks. 

❑ Analyze the samples. 

❑ Process sample data and generate a report of the results using the macro(s). 

❑ Import results to FAST and enter sampling information to determine RCS concentration. 
54

Field-based monitoring: checklist for creating a quality assurance (QA) sample set 

❑ Collect 10–20 respirable dust samples. The final set should contain 4–7 samples; extra 

samples may be required if RCS content is relatively low (<5%), requiring more heavily 

loaded samples to achieve 120–150 RCS. 

❑ Analyze samples via field-based monitoring/FAST. They should meet the following 

criteria. 

❑ The QA sample with the lowest quantity of RCS contains 20–40 μg of RCS. 

❑ The QA sample with the highest quantity of RCS contains 120–150 μg of RCS. 

❑ The remaining QA samples contain RCS quantities that are (approximately) evenly 

spread between 20 and 150 μg. 

❑ The dust deposition is radially symmetric (see Figures 13 and 17). 

❑ Enter the results (from either the exported report file or from FAST) in the QA Sample 

Tracking spreadsheet. 

❑ Analyze QA samples every time the instrument is used and enter results in the 

spreadsheet to track the performance of the instrument over time. 
55

Field-based monitoring: checklist for determining a site-specific correction factor

❑ Collect 15–20 respirable dust samples. The final set should contain at least 10 samples. 

❑ Analyze samples via field-based monitoring/FAST. They should meet the following 

criteria. 

❑ The sample with the lowest quantity of RCS contains 20–30 μg (0.020–0.030 mg) of 

RCS. 

❑ The sample with the highest quantity of RCS contains 275–325 μg (0.275–0.325 mg) of 

RCS. 

❑ The remaining samples (at least 8, for a total of at least 10 in the sample set) contain RCS 

quantities that are (approximately) evenly spread between 20 and 325 μg (0.020–0.325 

mg) of RCS. 

❑ Remove any samples outside the range of 20–325 μg (0.020–0.325 mg) RCS or any 

extraneous samples within the range. 

❑ Collect and analyze additional samples to meet the above criteria, if necessary. 

❑ Send samples to off-site laboratory for the standard analysis for RCS. 

❑ Use the Calculate Correction Factor function in FAST to determine the appropriate 

correction factor (see Figure 14). 

❑ To use the correction factor, select it in FAST during the Create Sample Event step. 

❑ Periodically repeat the process above with new samples to ensure that the correction 

factor has remained consistent or to modify the correction factor as respirable dust 

composition evolves. 
56

Appendix C: Comparison of FTIR data obtained using different 

methods or parameters 

As discussed in the Generating Spectra section, there are two methods for generating a 

background spectrum, and as summarized in Table 2, there are several available options for 

instrument software settings such as apodization and baseline correction. The tables in this 

appendix illustrate the differences that could be expected in sample results for several common 

options for these settings. 

Note that differences seen in actual samples for these types of settings may be greater or less 

than the differences shown here for laboratory prepared samples. Tables C1, C2, and C4 show 

data for samples containing only quartz to illustrate a “best case scenario”, while Tables C3 and 

C5 show data for samples that are approximately 25% quartz, in addition to other mineral phases 

(such as albite/anorthite, microcline, phlogopite, and clinochlore) in order to illustrate a more 

complex sample. Since different mineral phases can have different impacts on sample spectra 

and actual samples may be even more complex, these results should not be considered 

representative of every potential composition of respirable dust that might be encountered. 

Regardless of the composition of samples or the settings chosen, it is important to use the same 

settings consistently, rather than switching between different settings for different samples. 

How the samples for Tables C1, C2, and C4 were collected and analyzed: 

The following procedures were used to collect this data with a portable FTIR instrument with the 

parameters shown in Table 2: 

1. Clean PVC filters were first analyzed by FTIR, using the empty sample compartment 

(ambient air) as the background scan. 

2. Samples of a high purity quartz material (Minusil 5) were collected on the filters using 

the Dorr-Oliver nylon cyclone, operated at a flow rate of 1.7 lpm. The mass of quartz 

collected ranged from approximately 15 μg to 300 μg of quartz crystalline silica. 

3. Sample filters were then analyzed again by FTIR, again using the empty sample 

compartment (ambient air) as the background scan. 

a. From each sample spectrum, the initial spectrum of the clean filter (from Step 1) 

was subtracted. 

b. The macro procedure was used to perform a baseline correction (concave 

rubberband correction) and calculate the peak area for quartz, and the FAST 

software was then used to estimate the silica mass. These data are reported in the 

right column of Table C1. 

4. Sample filters were analyzed once more by FTIR, using a clean filter (from the same 

lot as the rest of the sample filters) as the background scan. 

a. The macro procedure was used to perform a baseline correction (concave 

rubberband correction) and calculate the peak area for quartz, and the FAST 

software (with “other” for the selected commodity and using no correction 

factor) was then used to estimate the silica mass. These data are reported in the 

left column of Table C1, Table C2, and Table C4. 
57

5. The original sample spectra from Step 4 (before the macro procedure was applied)

were reprocessed to assess differences in results for three apodization options: 

a. Original spectra were converted from Blackman Harris 3 term apodization to 

medium apodization. The macro procedure was used on the new spectra to 

perform a baseline correction (concave rubberband correction) and calculate the 

peak area for quartz, and the FAST software was then used to estimate the silica 

mass. These data are reported in the center column of Table C2. 

b. Original spectra were converted from Blackman Harris 3 term apodization to 

boxcar apodization. The macro procedure was used on the new spectra to 

perform a baseline correction (concave rubberband correction) and calculate the 

peak area for quartz, and the FAST software was then used to estimate the silica 

mass. These data are reported in the right column of Table C2. 

6. The original sample spectra from Step 4 (before the macro procedure was applied) 

were reprocessed to assess differences in results for four baseline correction options. 

Note that while the macro procedure itself was not used here (since the macro 

procedure includes a concave rubberband baseline correction), the equivalent steps 

were performed each time (a baseline correction followed by calculating the peak 

area for quartz). 

a. Original sample spectra were baseline corrected using a scattering baseline 

correction, the peak area for quartz was calculated, and the FAST software was 

then used to estimate the silica mass. These data are reported in the second from 

the left column of Table C4. 

b. Original sample spectra were baseline corrected using a rubberband baseline 

correction, the peak area for quartz was calculated, and the FAST software was 

then used to estimate the silica mass. These data are reported in the second from 

the right column of Table C4. 

c. The peak area for quartz was calculated from the original sample spectra with no 

baseline correction, and the FAST software was then used to estimate the silica 

mass. These data are reported in the right column of Table C4. 

How the samples for Tables C3 and C5 were collected and analyzed: 

1. Samples of a material from a granite quarry (approximately 25% quartz) were 

collected on PVC filters using the Dorr-Oliver nylon cyclone, operated at a flow rate 

of 1.7 lpm. The total mass of respirable dust collected ranged from approximately 100 

μg to 1700 μg (approximately 30 to 400 μg quartz). 

2. Sample filters were analyzed by FTIR, using a clean filter as the background scan. 

a. The macro procedure was used to perform a baseline correction (concave 

rubberband correction) and calculate the peak area for quartz, and the FAST 

software was then used to estimate the silica mass. These data are reported in the 

left column of Tables C3 and C5. 
58

3. The original sample spectra from Step 2 (before the macro procedure was applied)

were reprocessed to assess differences in results for three apodization options: 

a. Original spectra were converted from Blackman Harris 3-term apodization to 

medium apodization. The macro procedure was used on the new spectra to 

perform a baseline correction (concave rubberband correction) and calculate the 

peak area for quartz, and the FAST software was then used to estimate the silica 

mass. These data are reported in the center column of Table C3. 

b. Original spectra were converted from Blackman Harris 3-term apodization to 

boxcar apodization. The macro procedure was used on the new spectra to 

perform a baseline correction (concave rubberband correction) and calculate the 

peak area for quartz, and the FAST software was then used to estimate the silica 

mass. These data are reported in the right column of Table C3. 

4. The original sample spectra from Step 2 (before the macro procedure was applied) 

were reprocessed to assess differences in results for four baseline correction options. 

Note that while the macro procedure was not used here (since the macro procedure 

includes a concave rubberband baseline correction), the equivalent steps were 

performed each time (a baseline correction followed by calculating the peak area for 

quartz). 

a. Original sample spectra were baseline corrected using a scattering baseline 

correction, the peak area for quartz was calculated, and the FAST software was 

then used to estimate the silica mass. These data are reported in the second from 

the left column of Table C5. 

b. Original sample spectra were baseline corrected using a rubberband baseline 

correction, the peak area for quartz was calculated, and the FAST software was 

then used to estimate the silica mass. These data are reported in the second from 

the right column of Table C5. 

c. The peak area for quartz was calculated from the original sample spectra with no 

baseline correction, and the FAST software was then used to estimate the silica 

mass. These data are reported in the right column of Table C5. 

Comparing the two methods for background scans 

Table C1 shows a comparison of estimated quartz mass for the same sample filter containing 

only quartz, using two different methods for the background (as described above and in the 

Generating Spectra section). For these samples, differences in results between the two methods 

range from 0–10 μg RCS, with a mean difference of 3 μg. 
59

Table C1. Comparison of the two methods for background scan 

Designated clean 

filter method result 

(μg RCS)

Subtraction 

method result 

(μg RCS)

10 11

16 12

16 14

11 15

11 15

11 15

15 19

14 19

18 19

14 19

16 19

18 20

15 20

17 21

19 21

14 22

16 22

18 22

22 23

20 23

16 23

27 23

18 24

25 26

21 26

28 27

25 28

24 28

26 29

27 30

31 30

30 30

29 30

31 30

28 31

27 31
60

Designated clean 

filter method result 

(μg RCS)

Subtraction 

method result 

(μg RCS)

29 31

29 32

34 33

32 33

37 33

31 33

33 34

34 34

33 35

39 36

33 37

31 37

37 37

38 38

30 39

39 41

38 42

42 42

38 42

43 44

43 45

41 45

46 46

44 47

48 48

51 52

53 52

60 60

67 67

85 78

83 84

84 85

85 88

93 95

111 108

134 127

141 138
61

Designated clean 

filter method result 

(μg RCS)

Subtraction 

method result 

(μg RCS)

143 143

148 148

157 158

195 190

193 193

205 195

212 212

222 221

222 228

234 232

241 235

250 251

250 253

289 290
62

Comparing three apodization options 

Table C2 shows a comparison of estimated quartz mass for the same sample filter containing 

only quartz, using three different methods for apodization (as described above and in Table 2). 

Depending on the portable instrument and its corresponding software, other apodization options 

may also be available. As most differences for these samples are less than 1 μg, one decimal 

place is shown to better demonstrate the small differences, though normally only integer values 

would be reported (i.e. 10.2 μg vs. 10 μg). For these samples, differences range from 0–15 μg 

between Blackman Harris 3-term and medium apodization, with a mean difference of less than 1 

μg, and from 0–14 μg between Blackman Harris 3-term and boxcar, with a mean difference of 

less than 1 μg. 

Table C2. Comparison of three types of apodization for quartz-only samples 

Blackman 

Harris 3-term 

result 

(μg RCS)

Medium 

result 

(μg RCS)

Boxcar 

result 

(μg RCS)

10.0 10.0 10.0

16.0 15.8 16.0

15.8 15.8 15.8

10.8 10.6 10.8

10.6 10.2 10.4

10.8 10.6 10.8

14.5 14.2 14.3

14.0 13.6 13.8

17.6 17.4 17.7

13.8 13.6 13.8

15.7 15.5 15.7

15.3 15.1 15.3

17.5 17.4 17.5

16.8 16.6 16.8

19.4 19.2 19.4

13.8 13.4 13.6

16.4 16.2 16.4

18.4 18.1 18.3

22.3 22.3 22.3

19.5 19.2 19.4

16.2 16.0 16.0

27.4 27.4 27.4

18.3 17.9 18.1

24.6 24.5 24.7

21.4 21.1 21.3

27.5 0.0 0.0
63

Blackman 

Harris 3-term 

result 

(μg RCS)

Medium 

result 

(μg RCS)

Boxcar 

result 

(μg RCS)

25.4 25.3 25.5

23.9 23.6 23.8

25.8 25.5 25.8

27.2 27.0 27.2

29.6 29.4 29.6

30.6 30.4 30.6

28.6 28.5 28.7

30.8 30.6 30.8

26.9 26.6 26.8

28.2 28.1 28.3

28.9 28.7 28.9

28.7 28.5 28.7

34.1 34.0 34.2

31.7 31.5 31.7

36.8 36.8 37.0

31.3 31.1 31.3

32.6 32.3 32.6

34.1 31.7 31.9

33.2 33.0 33.2

38.7 38.7 38.9

32.9 32.5 32.8

31.2 30.9 31.1

36.8 36.6 36.8

38.3 38.1 38.3

30.1 29.6 30.0

39.4 39.2 39.4

38.4 38.1 38.3

41.8 41.5 41.9

38.2 37.9 38.1

43.3 41.5 41.7

43.4 43.4 43.4

41.3 41.1 41.1

46.2 45.8 46.2

44.1 43.8 44.0

47.5 47.2 47.5

50.7 50.4 50.8

53.3 53.0 53.4
64

Blackman 

Harris 3-term 

result 

(μg RCS)

Medium 

result 

(μg RCS)

Boxcar 

result 

(μg RCS)

59.9 59.4 60.0

67.4 67.0 67.4

85.0 84.7 85.1

83.4 82.8 83.4

84.1 78.5 79.1

84.8 84.2 84.7

92.5 91.9 92.5

111.1 110.4 111.1

134.1 133.4 134.3

141.4 140.6 141.5

143.4 142.5 143.4

147.8 146.8 147.7

156.7 155.5 156.6

195.1 194.2 195.3

193.2 178.1 179.4

205.4 204.3 205.7

211.7 210.2 211.7

221.6 220.2 221.7

222.2 220.6 222.3

233.9 232.6 234.2

241.0 239.4 241.1

249.8 248.3 249.8

250.1 248.5 250.0

288.6 286.4 288.5

Table C3 shows a comparison of estimated quartz mass for the same sample filter containing 

quartz and other minerals, using three different methods for apodization, in samples of mixed

mineral composition. As in Table C2, differences between the three methods are often less than 1 

μg for these samples and one decimal place is shown to better demonstrate the small differences, 

though normally only integer values would be reported (i.e. 10.2 μg vs. 10 μg). For these 

samples, differences range from approximately 0.2–2.6 μg between Blackman Harris 3-term and 

medium apodization, with a mean difference of about 1.1 μg, and from approximately 0.0–0.8 

μg between Blackman Harris 3-term and boxcar (i.e. no apodization), with a mean difference of 

about 0.3 μg. 
65

Table C3. Comparison of three types of apodization for mixed-mineral samples

Blackman Harris 3-term 

result (μg RCS)

Medium result 

(μg RCS)

Boxcar result 

(μg RCS)

27.4 27.2 27.5

28.3 28.1 28.5

48.9 48.3 48.9

51.5 51.1 51.7

65.7 65.3 65.7

108.5 107.7 108.7

136.8 135.8 137.0

137.4 136.4 137.5

147.0 146.0 147.4

148.5 147.5 148.7

154.0 153.2 154.3

177.9 176.6 178.1

212.8 211.5 213.2

234.5 233.0 235.1

236.6 235.3 237.2

247.9 246.4 248.5

302.5 300.6 303.0

385.1 382.6 385.8

403.2 400.6 404.0
66

Comparing four baseline correction options

Table C4 shows a comparison of estimated quartz mass for the same sample filter containing 

only quartz, using four different methods for baseline correction (as described above and in the 

Processing Sample Data Section). As all differences for these samples are less than 1 μg, one 

decimal place is shown to better demonstrate the small differences, though normally only integer 

values would be reported (i.e. 10.2 μg vs. 10 μg). For these samples, differences range from 

approximately 0.0–0.9 μg between the concave rubberband correction and the scattering 

correction, with a mean difference of about 0.2 μg; from approximately 0.0–0.6 μg between the 

concave rubberband correction and the rubberband correction, with a mean difference of about 

0.2 μg; and from approximately 0.0–0.6 μg between the concave rubberband correction and no 

baseline correction, with a mean difference of 0.2 μg.

Table C4. Comparison of four types of baseline correction for quartz-only samples

Concave rubberband 

correction result 

(μg RCS)

Scattering 

correction result 

(μg RCS)

Rubberband 

correction result 

(μg RCS)

No baseline 

correction result 

(μg RCS)

10.0 10.0 10.0 10.0

16.0 16.2 16.2 16.2

15.8 15.8 16.0 16.0

10.8 10.6 10.6 11.1

10.6 10.6 10.6 10.6

10.8 11.5 10.9 10.9

14.5 14.9 14.7 14.7

14.0 14.9 14.2 14.2

17.6 17.7 17.7 17.7

13.8 14.5 14.0 14.0

15.7 16.2 15.8 15.8

15.3 15.1 15.5 15.5

17.5 17.7 17.7 17.7

16.8 17.0 17.0 17.0

19.4 19.4 19.4 19.4

13.8 14.2 14.2 14.2

16.4 17.4 16.8 16.8

18.4 18.5 18.5 18.5

22.3 22.3 22.5 22.5

19.5 19.8 19.8 19.8

16.2 17.2 16.4 16.4

27.4 27.5 27.7 27.5

18.3 18.9 18.5 18.5

24.6 24.7 24.7 24.7

21.4 21.5 21.5 21.5

27.5 27.5 27.5 27.5
67

Concave rubberband 

correction result 

(μg RCS)

Scattering 

correction result 

(μg RCS)

Rubberband 

correction result 

(μg RCS)

No baseline 

correction result 

(μg RCS)

25.4 25.7 25.7 25.7

23.9 24.2 24.2 24.2

25.8 25.8 26.0 26.0

27.2 27.4 27.4 27.4

29.6 29.8 29.8 29.8

30.6 30.6 30.8 30.8

28.6 28.7 28.7 28.7

30.8 30.9 30.9 30.9

26.9 27.2 27.2 27.2

28.2 28.3 28.3 28.3

28.9 29.1 29.1 29.1

28.7 28.9 28.9 28.9

34.1 34.2 34.2 34.2

31.7 31.9 31.9 31.9

36.8 36.8 37.2 37.0

31.3 31.5 31.5 31.5

32.6 32.8 32.8 32.8

34.1 34.2 34.2 34.2

33.2 33.2 33.2 33.2

38.7 38.7 39.1 38.9

32.9 33.0 33.0 33.0

31.2 31.3 31.3 31.5

36.8 36.8 36.8 36.8

38.3 38.3 38.5 38.5

30.1 30.4 30.4 30.4

39.4 39.4 39.4 39.4

38.4 38.5 38.7 38.7

41.8 41.9 41.9 41.9

38.2 38.3 38.3 38.3

43.3 43.4 43.6 43.6

43.4 43.6 43.6 43.6

41.3 41.5 41.5 41.5

46.2 46.2 46.2 46.2

44.1 44.3 44.3 44.3

47.5 47.5 47.5 47.5

50.7 50.9 50.9 50.9

53.3 53.4 53.4 53.4

59.9 60.0 60.0 60.0
68

Concave rubberband 

correction result 

(μg RCS)

Scattering 

correction result 

(μg RCS)

Rubberband 

correction result 

(μg RCS)

No baseline 

correction result 

(μg RCS)

67.4 67.5 67.5 67.5

85.0 85.1 85.5 85.5

83.4 83.6 83.6 83.6

84.1 84.2 84.2 84.2

84.8 84.9 84.9 84.9

92.5 92.6 92.6 92.6

111.1 111.1 111.3 111.3

134.1 134.2 134.5 134.5

141.4 141.5 141.7 141.7

143.4 143.6 143.6 143.6

147.8 147.9 147.9 147.9

156.7 157.0 157.0 157.0

195.1 195.3 195.7 195.7

193.2 193.4 193.6 193.4

205.4 205.5 205.8 205.8

211.7 212.1 212.1 212.1

221.6 221.9 221.9 221.9

222.2 222.6 222.6 222.6

233.9 234.2 234.5 234.3

241.0 241.3 241.3 241.3

249.8 250.2 250.2 250.2

250.1 250.6 250.6 250.6

288.6 289.1 289.2 289.2
69

Table C5 shows a comparison of estimated quartz mass for the same sample filter containing 

quartz and other minerals, using four different methods for baseline correction (as described 

above and in the Processing Sample Data Section). As in Table C3, differences between the four 

methods are often less than 1 μg for these samples and one decimal place is shown to better 

demonstrate the small differences, though normally only integer values would be reported (i.e. 

10.2 μg vs. 10 μg). For these samples, differences range from approximately 0.0–0.9 μg between 

the concave rubberband correction and the scattering correction, with a mean difference of about 

0.3 μg; from approximately 0.2–1.1 μg between the concave rubberband correction and the 

rubberband correction, with a mean difference of about 0.5 μg; and from approximately 0.2–0.9 

μg between the concave rubberband correction and no baseline correction, with a mean 

difference of 0.5 μg.

Table C5. Comparison of four types of baseline correction for mixed-mineral samples

Concave rubberband 

correction result 

(μg RCS)

Scattering 

correction result 

(μg RCS)

Rubberband 

correction result 

(μg RCS)

No baseline 

correction result 

(μg RCS)

27.4 27.5 27.7 27.7

28.3 28.3 28.5 28.5

48.9 48.9 49.1 49.1

51.5 51.5 51.7 51.7

65.7 65.7 65.8 65.8

108.5 108.5 108.7 108.7

136.8 137.0 137.2 137.2

137.4 137.5 137.7 137.7

147.0 147.4 147.4 147.4

148.5 148.7 148.9 148.9

154.0 154.2 154.5 154.5

177.9 178.5 178.5 178.5

212.8 213.4 213.4 213.4

234.5 235.1 235.3 235.3

236.6 237.0 237.4 237.4

247.9 248.5 248.7 248.7

302.5 303.2 303.2 303.2

385.1 386.0 386.0 386.0

403.2 404.0 404.3 404.2
Promoting productive workplaces through safety and health research

DHHS (NIOSH) Publication No. 2022-108

DOI: https://doi.org/10.26616/NIOSHPUB2022108