Evaluating Heat Treatment Evaluating Heat Treatment Effectiveness - - PowerPoint PPT Presentation

Evaluating Heat Treatment Evaluating Heat Treatment Effectiveness Effectiveness

Bh Bh. . Subramanyam Subramanyam ( (Subi Subi) ) Department of Grain Science and Industry Department of Grain Science and Industry Kansas State University Kansas State University Manhattan, KS 66506 Manhattan, KS 66506 Tel: 785 Tel: 785-

• 532

532-

• 4092

4092 Fax: 785 Fax: 785-

• 532

532-

• 7010

7010 E E-

• mail:

mail: bhs bhs@wheat. @wheat.ksu ksu. .edu edu May 15, 2004 Heat Treatment Workshop May 15, 2004 Heat Treatment Workshop

Insect Species Vary in Their Insect Species Vary in Their Susceptibility to Heat Susceptibility to Heat

• Fig. 1.

Temperature (oC)

50 52 54 56 58 60

LT99 (minutes)

100 200 300 400 500 Eggs Young larvae Old larvae Pupae Adults

• Red flour beetle
• Newly hatched larvae are

heat tolerant

• Fig. 2.

Temperature (oC)

46 48 50 52 54 56 58 60

LT99 (min)

50 100 150 200 250 300 350

Eggs Young larvae Old larvae Pupae Adults

• Confused flour beetle
• Old larvae are heat tolerant

Exposure Exposure time time (hours) (hours) Temp. Temp. range ( range (o

• C)

C) Hours to Hours to reach 50 reach 50o

• C

C Hours Hours above 50 above 50o

• C

C % RH % RH % % Mortality Mortality 24 24 22.1 22.1-

• 42.0

42.0 0.0 0.0 0.0 0.0 71.7 71.7 0.0 0.0 22.1 22.1-

• 42.0

42.0 0.0 0.0 0.0 0.0 58.6 58.6 0.0 0.0 22.1 22.1-

• 41.5

41.5 0.0 0.0 0.0 0.0 50.6 50.6 0.0 0.0 No No Glycerol Glycerol 22.1 22.1-

• 41.5

41.5 0.0 0.0 0.0 0.0 30.7 30.7 1.7 1.7 G. G. Chamber Chamber 27.5 27.5-

• 28.3

28.3 0.0 0.0 0.0 0.0 36.2 36.2 1.7 1.7 47 47 22.1 22.1-

• 51.8

51.8 43.0 43.0 4.0 4.0 70.5 70.5 100.0 100.0 22.1 22.1-

• 51.8

51.8 43.0 43.0 4.0 4.0 57.5 57.5 100.0 100.0 22.1 22.1-

• 50.7

50.7 44.5 44.5 2.5 2.5 49.3 49.3 100.0 100.0 No No Glycerol Glycerol 22.1 22.1-

• 51.2

51.2 44.0 44.0 3.0 3.0 26.2 26.2 100.0 100.0 G. G. Chamber Chamber 27.5 27.5-

• 28.7

28.7 0.0 0.0 0.0 0.0 37.1 37.1 0.0 0.0

Effect of Humidity on Mortality of Red Flour Beetle Adults Effect of Humidity on Mortality of Red Flour Beetle Adults

(KSU Pilot Flour Mill, Steam (KSU Pilot Flour Mill, Steam Heat

Heat Treatment, March 17

Treatment, March 17-

• 20, 2000)

20, 2000)

Effect of Humidity on Mortality of Red Flour Beetle Adults Effect of Humidity on Mortality of Red Flour Beetle Adults

(KSU Pilot Flour Mill, Steam Heat Treatment, March 17 (KSU Pilot Flour Mill, Steam Heat Treatment, March 17-

• 20, 2000)

20, 2000)

Temperature = 50.1-52.4oC; Growth chamber = 27.5oC % RH, % RH, Range Range 20 Minutes 20 Minutes 30 Minutes 30 Minutes 50 Minutes 50 Minutes 53.6 53.6-

• 63.1

63.1 0.0 0.0 29.0 b 29.0 b 100.0 100.0 46.1 46.1-

• 49.3

49.3 0.0 0.0 93.8 a 93.8 a 100.0 100.0 31.6 31.6-

• 48.1

48.1 0.0 0.0 97.5 a 97.5 a 100.0 100.0 20.4 20.4-

• 20.5

20.5 No Glycerol No Glycerol 0.0 0.0 95.0 a 95.0 a 100.0 100.0 32.4 32.4-

• 38.1

38.1 Chamber Chamber 0.0 0.0 0.0 c 0.0 c 0.0 0.0 For each time and treatment combination, n = 3.

Mortality of Red Flour Beetles Insulated by Mortality of Red Flour Beetles Insulated by Whole Wheat Kernels and Flour Whole Wheat Kernels and Flour

(KSU Pilot Flour Mill, Steam Heat Treatment, November 23 (KSU Pilot Flour Mill, Steam Heat Treatment, November 23-

• 28,

28, 1999) 1999)

Commodity Commodity Location Location Temp. Temp. range ( range (o

• C)

C) % RH, % RH, range range % % Mortality Mortality

Wheat Wheat Top, 4” Top, 4” 21.7 21.7 -

• 40.1

40.1 32.2 32.2 -

• 81.4

81.4 9.5 9.5 Middle, 14” Middle, 14” 22.9 22.9 -

• 40.1

40.1 26.2 26.2 -

• 79.5

79.5 40.0 40.0 Bottom, 23” Bottom, 23” 22.9 22.9 -

• 39.7

39.7 26.1 26.1 -

• 80.2

80.2 25.0 25.0 Flour Flour Top, 6” Top, 6” 19.4 19.4 -

• 38.8

38.8 27.9 27.9 -

• 76.4

76.4 4.8 4.8 Middle, 14” Middle, 14” 19.4 19.4 -

• 38.9

38.9 27.6 27.6 -

• 75.5

75.5 0.0 0.0 Bottom, 22” Bottom, 22” 19.8 19.8 -

• 37.9

37.9 28.3 28.3 -

• 70.6

70.6 0.0 0.0

Apply a residual pesticide such as Tempo or diatomaceous earth

Why is sanitation alone not Why is sanitation alone not enough? enough?

• Stored

Stored-

• product insects live for

product insects live for several months several months

• They can survive on very little food

They can survive on very little food

• 50% of the facility is inaccessible for

50% of the facility is inaccessible for cleaning cleaning

• Insects can seek out cool spots

Insects can seek out cool spots

Remove products and fumigate Remove products and fumigate to reduce risk of to reduce risk of reinfestation reinfestation Verify that the fumigation was Verify that the fumigation was successful successful

Should equipment be opened or closed? Should equipment be opened or closed?

Open, clean, and then close Open, clean, and then close

Monitoring Insects Monitoring Insects

• Before heat treatment, several weeks

Before heat treatment, several weeks

• After heat treatment, several weeks

After heat treatment, several weeks

• Identify species of importance

Identify species of importance

• Degree of suppression

Degree of suppression

• Duration of suppression

Duration of suppression

• Use traps or take samples of products before

Use traps or take samples of products before and after heat treatment and after heat treatment

• Results vary depending on whether traps or

Results vary depending on whether traps or products were used products were used

Monitoring Insects: Traps for Beetles Monitoring Insects: Traps for Beetles

Flit Flit-

• Trak Traps

Trak Traps

Pherocon II traps for moths

5 10 15 5 10 15 20 25 30 2 4 6 8 10

Back room

Distance from left front (m) Distance from left front (m)

A A A C B

5 10 15 5 10 15 20 25 30 2 4 6

Back room

Distance from left front (m)

A A A C B

1 2

A=Wild bird food B=Small animal food C=Cat and dog food

7/18/00 8/1/00 8/15/00 8/29/00 9/12/00

Number of adults

0.0 0.5 1.0 1.5 2.0 20 40 60 80 100 0.0 0.4 0.8 1.2 1.6 2.0 2 4 6 8 0.0 0.2 0.4 0.6 0.8 1.0 7/18/00 8/1/00 8/15/00 8/29/00 9/12/00

20 40 60 80 100 120 5 10 15

D ate (m onth/day/year)

Trogoderm a variabile Tribolium castaneum Tribolium confusum C ryptolestes ferrugineus C ryptolestes pusillus Sitophilus sp. Total adults

Changes in mean number of insects captured in traps

July 21-28, 1999 August 18-27, 1999 November 9-18, 1999

Before heat trt 1 After heat trt 1 Before heat trt 2 N KSU Pilot Flour Mill, mill floor - 1

Predictive Models Predictive Models

• Need to generate data at constant

Need to generate data at constant temperatures in the laboratory temperatures in the laboratory

• Uses temperature to determine

Uses temperature to determine mortality for any given temperature mortality for any given temperature-

• time history

time history

Nonlinear Relationship Between Survival of Nonlinear Relationship Between Survival of Old Larvae and Exposure Time at 46 Old Larvae and Exposure Time at 46-

• 60

60o

• C

C

D D( (T T) is reciprocal of the negative slope averaged over time ) is reciprocal of the negative slope averaged over time

Tim e (t), m inutes

50 100 150 200 250 300

Log survival

0.0 0.5 1.0 1.5 2.0 2.5

46 oC 48 oC 50 oC 54 oC 58 oC 60 oC

Nonlinear Relationship Between Mean D(T) and Temperature

y = 22.67 + 1.66*1014 exp(-0.56*x) n = 6; Adj R2 = 0.95

Temperature (oC)

44 46 48 50 52 54 56 58 60 62

Mean instant. D-value (minutes)

200 400 600 800 1000

The thermal death kinetic model was derived from the following equation

) ( ) ( log

10 t t dt t

T D dt N N =

−

where Nt-dt is the survival at t-dt time interval, Nt is survival at time t

∑

∆ =

t t t

T D t N N ) (

10 where Nt is number of larvae at time t; No is the original number of insects, ∆t is the incremental exposure time (0.5-min), D is the mean instantaneous D-value as a function of temperature (T), and Tt is time- dependent temperature profile

Heating rate (1.09oC/h)

Time (minutes)

200 400 600 800 1000 1200 1400 1600 1800

Number of insects

20 40 60 80 100 120

Temperature (oC)

20 40 60 80 100 120

Temperature profile Predicted population Observed population

No Tt Nt

Heating rate (1.16oC/h)

200 400 600 800 1000 1200 1400 1600

Number of insects

20 40 60 80 100 120

No Tt Nt

Heating rate (1.19oC/h)

200 400 600 800 1000 1200 1400

Temperature (oC)

20 40 60 80 100 120

Temperature profile Predicted population Observed population

No Tt Nt

Heating rate (1.22oC/h)

200 400 600 800 1000 1200 1400

Number of insects

20 40 60 80 100 120

No Tt Nt

Heating rate (1.76oC/h)

200 400 600 800 1000

Temperature (oC)

20 40 60 80 100 120

No Tt Nt

Time (minutes) Time (minutes)

Heating rate (2.12oC/h)

100 200 300 400 500 600 700 800 900

Number of insects

20 40 60 80 100 120

N o Tt N t

Heating rate (2.44oC/h)

100 200 300 400 500 600 700 800

Temperature (oC)

20 40 60 80 100 120

Temperature profile Predicted population Observed population

No Tt Nt

Heating rate (5.31oC/h)

50 100 150 200 250 300 350

Number of insects

10 20 30 40 50 60 70

No Tt Nt

Heating rate (12.02oC/h)

20 40 60 80 100 120 140 160 180 200

Temperature (oC)

10 20 30 40 50 60 70

No Tt Nt

Time (minutes) Time (minutes)

Table 3. Model Performance Table 3. Model Performance

Absolute deviation (%) in terms of Absolute deviation (%) in terms of Heating rate Heating rate (ºC/hr) (ºC/hr) Larval survival Larval survival (No. larvae/100 (No. larvae/100 larvae) larvae) Time to equal Time to equal larval survival larval survival (No. min/100 min) (No. min/100 min) 1.09 1.09 6.5 6.5 4.16 4.16 1.16 1.16 5.5 5.5 3.49 3.49 1.19 1.19 6.6 6.6 3.85 3.85 1.22 1.22 4.5 4.5 2.91 2.91 1.76 1.76 6.0 6.0 3.67 3.67 2.12 2.12 4.8 4.8 4.77 4.77 2.44 2.44 4.9 4.9 2.98 2.98 5.31 5.31 4.8 4.8 5.11 5.11 12.02 12.02 9.2 9.2 16.03 16.03

Utility of the model: Predict mortality at different locations d Utility of the model: Predict mortality at different locations during uring a heat treatment and alter heat treatment for effective insect k a heat treatment and alter heat treatment for effective insect kill ill

Conclusions Conclusions

• Heat treatments are part art, part

Heat treatments are part art, part science science

• Use heat tolerant insects of a species

Use heat tolerant insects of a species to measure effectiveness to measure effectiveness

• Monitor insects before and after heat

Monitor insects before and after heat treatment treatment

• Use predictive models

Use predictive models

• A lot of additional research data are

A lot of additional research data are needed! needed!