Federal Grain Inspection Service

Technology and Science Division

Overview and Updates

Timothy Norden, Acting Director 

Technology and Science Division

Grain Inspection Advisory Committee Meeting

September 5, 2018
Technology and Science Division

Overview

• FGIS – Technology and Science Division (TSD) is responsible 

for developing, improving, and supporting all official grain 

inspection methods

• Organization: Four branches plus administrative support

• Analytical Chemistry Branch

• Biotechnology and Analytical Services Branch

• Inspection Instrumentation Branch

• Board of Appeals and Review Branch

• Office of the Director

• Administrative Support Team

• Digital Media Team

• Current employees: 60 with 15 vacancies = 75 total
Analytical Chemistry Branch

(ACB)

• Develops and evaluates new or improved chemical 

and physicochemical methods for food safety and 

end-use quality factors

• Provides centralized official inspection and quality 

assurance services

• Key programs: 

• mycotoxins, pesticide residue, falling number 

and wheat varietal identification

3
Biotechnology and Analytical Services

(BASB)

• Develops and provides cutting-edge technology for 

determining genetic, nutritional, and intrinsic 

quality factors in grain and grain products

• Provides research, harmonization, and centralized 

testing services

• Key programs: 

• biotechnology and commodities testing; 

moisture, protein, and oil reference methods

4
Inspection Instrumentation Branch

(IIB)

• Develops and maintains calibrations and 

procedures for approved inspection instruments 

• Administers instrument evaluation and quality 

control programs

• Key programs: 

• Moisture meter, near infrared (NIRT) protein, oil 

and starch; and nuclear magnetic resonance 

(NMR) oil 

5
Board of Appeals and Review

(BAR)

• Directs and oversees the integrity and alignment of 

all visual inspections throughout the nation

• Provides procedures and centralized quality 

assurance services for inspection equipment

• Key programs: 

• Subjective Testing and Evaluation Process 

(STEP), training and licensing, Board Appeals, 

and equipment check testing

6
TSD Branch Updates

• Brian Adam

• Chairman, Board of Appeals and Review

• Cathleen Brenner

• Chief, Inspection Instrumentation Branch

• Tom Weber

• Chief, Analytical Chemistry Branch

• Tandace Bell

• Chief, Biotechnology and Analytical Services Branch

7
Determining HVAC in 

Durum Wheat

Brian Adam, Chief

Board of Appeals and Review Branch

Grain Inspection Advisory Committee Meeting

September 5, 2018
• HVAC (100% Hard and Vitreous of Amber Color) - Vitreous 

is the characteristic which gives the wheat kernels a hard, 

glossy appearance

• NON-HVAC – Soft durum kernels that lack the hard vitreous 

characteristic, or semi-hard kernels that contain soft chalky 

spots

• How HVAC is Determined - Visual inspection on a 15 gram 

portion cut out from the representative sample

Hard and Vitreous Kernels of Amber 

Color (HVAC)

9
SUBCLASS

Durum wheat is divided into three sub-classes based on the 

percentage of HVAC in the representative lot.

Hard Amber Durum Wheat Durum wheat with 75 percent or more of 

hard and vitreous kernels of amber color

Amber Durum Wheat Durum wheat with 60 percent or more, but 

less than 75 percent of hard and vitreous kernels of amber color

Durum Wheat Durum wheat with less than 60 percent of hard and 

vitreous kernels of amber color

Even though durum is certified by subclass, contracts may dictate 

specific limits to be met or penalties may be assessed. 10
• Durum has many uses, the most common being pasta 

which is made from durum

• Semolina is durum wheat ground into a coarse granular 

product

• Semolina content determines the end use. The amount of 

flour in the blend affects the color of the product

Whole Kernel Durum Durum Semolina

11
HVAC Non-HVAC or Soft

Vitreous and Non-Vitreous Kernels

12
Non-HVAC Kernels with Soft Spots

13
Readily Identifiable HVAC Samples

Hard Amber Durum Easy to detect soft kernels 14
Challenges with HVAC Inspection

• Time constraints lead to inconsistency between online 

inspectors, and industry keeps pushing to load faster

• If adequate time is not given to online inspection 

personnel to analyze this highly subjective factor, accuracy 

may be compromised

• Growing conditions can mask the appearance of the true 

kernel characteristics making HVAC determinations very 

difficult 

15
Difficult HVAC sample

• Weather-affected kernels

• Require more time and 

possibly cross-sectioning of 

kernels

• Samples of this appearance 

are more subjective and can 

result in more 

inconsistencies

16
Board of Appeals and Review and 

Field Office Monitoring Programs

• Monitoring programs are in place for inspectors 

and Quality Assurance Specialists (QAS) 

• Referee samples used to assess inspector and QAS 

ability 

• Conduct annual grain grading seminars for QASs

• Provide on-site training for inspectors and elevator 

personnel

• Conduct research on alternative methods for HVAC 

determination

17
QAS Alignment to the BAR

FY18 to Date

• Durum HVAC 96%

• HRS DHV 93%

Subjective Testing Evaluation Process (STEP) is a 

monitoring program used to align QASs to the BAR

18
VS

Natural vs Conditioned

Natural Bleached

w/ Potassium Hydroxide (KOH)
Potential Benefits of Bleaching

• Produces a bright vibrant kernel

• Makes difficult weather-affected kernels easier to 

separate 

• Potentially minimizes inspector variation and 

increases accuracy and consistency

• Collaborating with the Inspection Instrumentation 

Branch (IIB) to explore using the bleach method 

with imaging technology

20
Questions?

21
Inspection Instrumentation 

Branch Updates

Cathy Brenner, Chief

Inspection Instrumentation Branch

Grain Inspection Advisory Committee Meeting

September 5, 2018
LED Study

23
May 2016 Resolution –

The Advisory Committee commends FGIS for its work 

with updating inspection laboratory lighting standards; 

and recommends continued work on lighting 

advancements and testing for grain inspection. The 

committee would also like to encourage FGIS to review

how other governmental or industry groups involved 

in human-sorting-of objects are utilizing new 

technologies in LED lighting for inspection purposes.

24
FGIS Specifications

Lamp Radiation Specifications

2000 to 2499 Lumens

Color Rendering Index > 92

Color Temperature 7500 °K

Rated Average Life > 15,000 

Hours

Or

>= 2500 Lumens

Color Rendering Index > 87

Color Temperature 7500 °K

Rated Average Life >12,000 

Hours

Fluorescent Emitter Specifications

25
Lighting Research

https://www.nist.gov/image/composite-color-preferencepng

Illuminating Engineering Society Task Group

National Institute of Standards and Technology

Office of Energy Efficiency & Renewable Energy, 

Department of Energy

26
Study Design

27

• 30 Training Boxes

• 4 Test Lights

• 3 pairs of BAR Inspectors

• Number of kernels that changed determination

Soybean Mold Damage Training Box
Results – Difference between Test 

Light and approved Fluorescent Light

Test Light A Test Light B Test Light C Office Light

Inspector 1 2 5 6 3 4 1 2

Chisquare0.10 1.66 6.80 7.62 15.89 16.23 19.97 10.00

p-value 0.75 0.20 0.01 0.01 < 0.001 < 0.001 < 0.001 0.002

Chi-Square < 3.84 and p-value > 0.05 results are not significantly different 

at the 95% confidence level. 28
Light Color Composition

Approved 

Fluorescent, CRI = 93

Light A, CRI = 93

29

0

100

R1

R2

R3

R4

R5

R6

R7

R9 R8

R10

R11

R12

R13

R14

R15

0

100

R1

R2

R3

R4

R5

R6

R7

R9 R8

R10

R11

R12

R13

R14

R15
Conclusions

• LED can be used for official inspection

• Task Light only commercially available light that is not 

significantly different.

• Variations observed in the R9 (red) and R12 (blue) 

for all LEDs tested warrant further investigation for 

developing LED specifications.

30
Next Steps

• Review Training Boxes with final selection 

• Test Lights for new study

• Review commercially available

• Canadian Grain Commission approved LED

• Establish study timeline with BAR

• Completed by December 2018

31
NIR Equivalency Field 

Study

32
Previous Studies

• 2015 Cooperative Agreement with Iowa State University 

(ISU)

• 3 National Type Evaluation Program (NTEP) Approved models 

and calibrations

• Not equivalent

• FGIS field benchmark was almost twice the lab reproducibility 

for wheat protein on the approved instrument

• 2016 Cooperative Agreement with ISU

• Common sample set for wheat protein

• Updated calibrations improved reproducibility

33
October 2016 Resolution –

The Advisory Committee recommends that new 

equipment must be equal to or better than the old 

equipment in precision and repeatability in order to be 

approved as official. Performance of equivalent 

instruments should match or exceed that of the existing 

approved instrument in the same field environment. The 

Advisory Committee recommends GIPSA continue work 

with NIR Equivalence by continuing focus on improving 

performance in test instrument field studies. GIPSA should 

also consider including all NTEP approved instruments in 

the field studies to determine equivalency.

34
Study Design

• 4 NTEP Approved Models

• Barley, Corn, Soybeans, and Wheat

• 3 - 5 Specified Service Points per grain type

• Minimum 50 samples per location per grain type plus 

Standard Reference Samples

35
Status

• FOSS Infratec Nova approved as equivalent 

September 2017

• One participating manufacturer developed process to 

match raw instrument data to FOSS Infratec 1241.

• Not achievable in a field setting

• Report in review process

36
Next Step

• Final report to be completed by December 2018

37
FGIS Approval Process

For Equipment, Instruments, and 

Test Kits

38
U.S. Grain Standards Act

§ 79b – Testing of equipment

- Periodic testing of all equipment used in inspection

- Ensure accuracy and integrity of equipment

- Prohibits use of non-approved equipment for grain 

inspecting

39
Regulations under USGSA

§800.215 (c) (4) – “...proposed equipment is tested to 

determine whether the equipment will improve the 

performance of activities under the Act.”

§800.217 (d) – Testing may occur when:

(1) At request of interested party with 

concurrence of Administrator

(2) Upon determination of need by the Service

40
FGIS Current Process

• Evaluate benefit to grain inspection

• Develop performance specifications and criteria 

(as needed)

• Assign resources and evaluate

41
Questions?

42
Moisture Meter 

Performance

Request for information to assure industry that 

official moisture results are consistent and accurate.

43
Moisture Meter Models

• Do not directly measure 

moisture

• Dielectric characteristics 

closely related to moisture 

content

• Requires calibration

• Based on historical data

44
Annual Moisture Calibration Process

UGMA 

Master 

System

Perten 

AM5200A

DICKEY-john 

GAC2500-

UGMA

Air Oven 

Reference 

Method

45
Calibration Implementation 

Dates

• May 1 for Spring/Summer crops

• August 1 for Fall crops

• https://www.gipsa.usda.gov/fgis/moisture.aspx

• 5 year performance

• Explanatory Notes of impact for any updates

• Calibration bundles

46
Performance Data

47
Impact of Change (Explanatory 

Notes)

DICKEY-john

Perten

48
Quality Control – Checktest

• Twice a year

• Scale Tolerances

• Accuracy = ± 0.5 grams

• Range = 1.0 grams

• Moisture Tolerances

• Accuracy = ± 0.15 % moisture

• Range = 0.26 % moisture

49
-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

284000 289000 294000 299000 304000 Meter Moisture - Master Moisture

Record Number

Moisture Checktest - Average Moisture

January - July 2018

50
Quality Control – Sample Inspection 

& Monitoring System (SIMS)

• Tolerances by grain and/or moisture

• Selected from file samples

• Domestic Inspection Operations Office

• Quality Assurance & Compliance Division

51
52

Soybean SIMS Difference Graph
July 2014 Resolution –

The Advisory Committee recommends the GIPSA 

review and update all quality assurance tolerances 

utilized in the official system. Specifically, the 

Advisory Committee recommends that the first to be 

reviewed reflect the Unified Grain Moisture Algorithm 

(UGMA) technology for moisture measurement.

53
April 2015 Resolution –

The Advisory Committee commends FGIS for its work 

in implementing and testing of UGMA moisture 

meters; and recommends that for the Sample 

Information Monitoring System (SIMS) that FGIS 

provide on their website a listing by grains for the 

approved UGMA moisture meters the following 

information: the moisture standard deviation, ±

warning limit, ± action limit, and the moisture range 

for which these limits are applicable.

54
Commercial Rice Mill 

Study

55
October 2015 Resolution –

The Advisory Committee encourages FGIS to initiate a 

study with rough rice to determine the effectiveness 

of the Grainman Miller No. 65 for predicting 

commercial rice milling yield. Factors to consider in 

addition to milling yield are total broken kernels, 

whiteness and chalkiness. Newer rice hybrids along 

with their harvest and drying history should be 

included in the study.

56
Objective

• To evaluate the consistency of milling yield results 

between the official FGIS laboratory mill and 

commercial milling operations. 

57
Cooperative Agreement

• University of California, Davis

• California Agri Inspection Co., Ltd

• 2 Participating Mills in Arkansas

• 1 Participating Mill in California

• 1 Participating Mill in Louisiana

• Completed first year of planned 2 year study

58
Commercial mill sample collection

Mill Location Rice Types

(Variety Type)

M1 California Long

(Pure)

Medium

(Pure & Mixed)

Short

(Pure & 

Mixed)

M2 Arkansas Long

(Pure & Hybrid)

Medium

(Pure & Mixed)

M3 Louisiana Long

(Pure & Mixed)

Medium

(Pure & Mixed)

M4 Arkansas Long

(Hybrid & Mixed)

59
FGIS Milling Procedures

Type of 

rice Milling cycle Duration Brushing cycle Duration

Long-grain 2 pounds 30 sec 0 pounds 30 sec

Mediumgrain 7 pounds 30 sec 0 pounds 30 sec

Shortgrain

10 pounds for Western 

production

12 pounds for 

Southern production

30 sec

2 pounds for 

Western production

0 pounds for 

Southern production

30 sec

60
Milling Yield

Total rice yield (TRY) or Total Rice (TR) and head rice 

yield (HRY) or percent whole kernels (WK)

61
Results from First Year

Category No Significant 

Difference

FGIS Less Than 

Commercial

FGIS Greater Than 

Commercial

TRY 29% 29% 42%

HRY 35% 16% 49%

62
Results

Milling yield – Total rice yield (TRY)- Fourth quarter

A A

A

A A A A

A

B B A

A A A A

B

0

10

20

30

40

50

60

70

80

90

100

Total rice yield (%)

Rice type and variety

Commercial FGIS Lab

63
Results

Milling yield – Head rice yield (HRY)- Fourth quarter

A

A

A

A A

A A

A

B A

B

A A

B A

B

0

10

20

30

40

50

60

70

80

Head rice yield (%)

Rice type and variety

Commercial FGIS Lab

64
Conclusion

• The first year data shows no consistent trend in the

difference in the milling yield between the FGIS lab

and commercial mills.

65
Next Steps

• Final (second) year sample collection in process

• Determine if the findings from first year are seen in 

the second year

• Make conclusive statements with high confidence 

about the agreement between FGIS and commercial 

mills

• Final Report due April 2019

66
Questions?

67
Falling Number and 

Mycotoxin Testing

Tom Weber, Chief

Analytical Chemistry Branch

Grain Inspection Advisory Committee Meeting

September 5, 2018
Falling Number (FN) Method

• Indirect measurement of alpha-amylase activity

• Alpha-amylase breaks down starch

• High activity adversely effects end-use quality

• Important factor in domestic and international 

trade of wheat

• Internationally standardized and most widely 

accepted method

• FN is the time required to mix and drop rod 

through heated wheat meal / water slurry

• FN inversely proportional to alpha-amylase 

activity

69
Pacific Northwest 2016

• Low falling number values

• 44% soft white, 42% club wheat samples < 300 sec*

• High frequency of discounts applied

• Complaints that FN test too variable

• October 2016 Advisory Committee Resolution

• FGIS should continue its efforts to reduce FN testing 

variation and increase oversight and training of official 

service providers

*M. Weaver. Capital Press. 8/2/2018

70
Two Opportunities to Reduce Variation

1. Implement new correction based on barometric 

pressure

2. Require use of Shakematic

71
Correcting FN to Sea Level

• Increase elevation leads to increase in FN

• Current FGIS procedure

• Correct to sea level at locations 2000 ft. and above

• Cereal Chem. 1994,71(3), 269–271

• Correction needed below 2000 ft. to remove bias

• New FGIS procedure 

• FGIS engaged USDA Agricultural Research Service

• Correct to sea level using barometric pressure

• Cereal Chem. 2018, 00, 1–8

72
Effect of Elevation on Falling Number

Example: FN = 300 sec at sea level

29.9 29.4 28.9 28.4 27.9 27.4 26.8 26.3

290

300

310

320

330

340

350

0 500 1000 1500 2000 2500 3000 3500

Barometric Pressure (in Hg)

Predicted FN (sec)

Elevation (ft.)

Portland, OR

+12 sec

Kansas City, MO

+37 sec

Great Falls, MT

+18 sec

Devil’s Lake, ND

73
Comparison of Correction Procedures

2,545 Inspection Monitoring Results

74

-50

-40

-30

-20

-10

0

10

20

30

40

50

1174257 (50)

983149 (26)

106173 (192)

1173754 (190)1373799 (196)1473820 (118)

1375312 (21)

880116 (17)

1375309 (21)

1275827 (255)

891217 (48)911323 (15)

1375310 (12)

046148 (33)

1174227 (21)

1175219 (145)

066111 (55)

1373948 (140)

066104 (281)1573123 (32)1173466 (19)1174230 (94)

086108 (31)880107 (69)

1373950 (96)

046162 (42)

1603076 (21)1573125 (12)

088160 (38)

1174832 (16)1573539 (12)1603081 (46)1475169 (25)

770 (51)

066178 (53)972053 (52)

Relative Difference, %

Instrument Number

-50

-40

-30

-20

-10

0

10

20

30

40

50

1174257 (50)

983149 (26)

106173 (192)

1173754 (190)1373799 (196)1473820 (118)

1375312 (21)

880116 (17)

1375309 (21)

1275827 (255)

891217 (48)911323 (15)

1375310 (12)

046148 (33)

1174227 (21)

1175219 (145)

066111 (55)

1373948 (140)

066104 (281)1573123 (32)1173466 (19)1174230 (94)

086108 (31)880107 (69)

1373950 (96)

046162 (42)

1603076 (21)1573125 (12)

088160 (38)

1174832 (16)1573539 (12)1603081 (46)1475169 (25)

770 (51)

066178 (53)972053 (52)

Relative Difference, %

Instrument Number

Mean RD = 0.53% 

Standard Deviation = 8.63

Mean RD = -3.75% 

Standard Deviation = 8.21

Current Correction New Correction

Error bars represent +/- 3 standard deviations
Impact of Implementing New Correction

• Reduced variation that promotes fair trade

• Allow tighter tolerances in inspection monitoring

• Lower FN for locations below 2000 ft. 

• Higher FN for locations at 2000 ft. and above

• Magnitude of change depends on elevation and 

falling number

75
Sample Shaking Method

• Sample / water homogenization required

• FGIS allows shaking by hand or 

Shakematic

• FGIS compared the two shaking methods

• Shakematic gives 3–5% lower FN results

• FGIS recommends requiring Shakematic 

• Eliminates fatigue

• Provides more consistent mixing

• Most official service providers use Shakematic

76
Recommendations

• Implement barometric pressure correction

• Require use of Shakematic

• Implementation Plan

• Fall 2018 – notify stakeholders

• Update FGIS Program Directive 9180.38

• Target effective date of May 1, 2019

• Allow time for industry to prepare

• Ready for start of 2019 crop harvest

77
Mycotoxin Testing

Topics of Concern

• What is the expected variation between official labs?

• Why not use one test kit for all official agencies?

• Are uniform procedures established?

• Are procedures are available to industry?

78
Variation in Measurements

• Some variation is expected

• More variables lead to more variation

• Variation also dependent on concentration

79

Conditions for Smaller

Variation

Conditions for Larger 

Variation

One analyst Multiple analysts

One lab Multiple labs

One method Multiple methods

One ground sample Multiple whole grain samples

High concentration Low concentration
Prediction of Variation

• Horwitz function*

• Internationally-accepted 

method

• Between-lab variation

• Used in FGIS inspection 

monitoring to flag 

erroneous results

*Horwitz, W. et al. J. AOAC. 1984, 63, 1344 – 1354 80
Agreement between Labs

• Use predicted variation to determine acceptable 

range

• Example: Labs A and B analyze same ground sample

• A = 1.2 ppm DON; B = 2.5 ppm DON

• Mean = 1.85 ppm 

• Predicted RSD = 14.49%; SD = 0.27 ppm 

• Results should be 1.85 ± 0.54 ppm (i.e., 1.31 – 2.39 ppm) 

95% of the time

• Results from Labs A and B are outside this range

81
Test Kit Selection

• Official agencies can use any FGIS-certified test kit

• Mycotoxin Test Kit Evaluation Program

• Performance-based approval of test kits

• Test kit must meet FGIS accuracy requirements

82
Use and Availability of Procedures

• Official, written instructions issued by FGIS

• All official service providers are required to follow 

these official instructions

• Training and licensing is required

• Procedures available to industry on FGIS website

83
October 2016 Advisory Committee Resolution

“The Advisory Committee recommends looking into 

and addressing what could be the root cause of 

variances in testing results i.e., particle size, test kit 

performance, sample splitting, etc.”

• Investigated contributors to unexpected variation

• Developing a plan to minimize overall variation

84
Questions?

85