Cotton Dust - American Chemical Society

6 C o t t o n T r a s h and D u s t C o n t e n t s and A i r b o r n e Dust Concentration

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Feasibility of Predicting by Nondestructive Light Reflectance JOSEPH G. MONTALVO, JR., MARIE-ALICE ROUSSELLE, and ALBERT BARIL, JR. Southern Regional Research Center, New Orleans, LA 70179 TERRY A. WATKINS University of New Orleans, Lakefront, New Orleans, LA 70148 In situ content of trash and dust and airborne dust generated by mechanical processing in grade representative-randomly selected raw cottons correlated significantly with nondestructive testing by visible light reflectance. The linear correlation coefficient of particulate content (y) on In (1/reflectance) (x) was: trash (nonlint), -0.969; dust (dry assay), -0.977; dust (wet assay), -0.989; trash plus dust (dry), -0.970; trash plus dust (wet), -0.997; airborne dust, -0.930. Variation of dust-trash contents in the cottons was also investigated. Dust content increased with trash content. The ratio of dust to trash content decreased with increase in the trash value and approached a constant value at the higher trash levels. OSHA has determined that worker exposure to cotton dust presents a significant health hazard commonly referred to as byssinosis Q J . This respiratory disease is characterized by shortness of breath, cough, and chest tightness. Permissible exposure limits have been established for selected processes in the cotton industry: 200 ug/nß or less in yarn manufacturing, 750 ug/m or less in slashing and weaving, and 500 ug/nr or less elsewhere in the cotton industry. An urgent need exists for innovative approaches to monitoring and controlling cotton dust. Monitoring dust and trash concentrations in cotton by a simple, rapid, on-line process analyzer with feedback control could provide ginners an incentive to produce cleaner cotton and thus improved marketability of the commodity. Based on the dust and trash in the baled cotton, i t may also be possible to predict airborne dust levels in textile milling. Bales could be automatically blended to minimize mean dust level in the workplace. (In 3

0097-6156/82/0189-0067$06.00/0 © 1982 American Chemical Society


68

COTTON

DUST

this paper dust is defined as foreign particles with diameter generally 17 urn and trash is defined as foreign particles with diameter > 17 urn.) Several advances in instrumentation have provided tools to evaluate trash in cotton. The cotton colorimeter (2J selects cotton for grade standards and as a reference in c l a s s i f i c a t i o n . Taylor (3) measured cotton trash using near infrared reflectance. Kasdan (4J developed a prototype instrument for grading cotton according to trash and color. Lyons and Barker (5) correlated trash surface area with trash content grade. No single instrument, however, has been developed with combined capabilities for nondestructive estimation of in situ concentrations of dust and trash in cotton and the dust-release potential of cotton during mill processing. This paper presents a f e a s i b i l i t y study of visible light reflectance as a tool to predict in situ concentration of dust and trash in baled cotton and the airborne dust released in mechanical processing. Mathematical relationships between dust and trash levels in the cottons were also investigated. Selection of visible light reflectance as a candidate nondestructive test method was based on results from probing experiments. It was observed that as cotton was mechanically cleaned, its visible light reflectance increased. Conversely, addition of trace amounts of particulate (trash and dust) to extensively cleaned cotton resulted in a decrease in visible light reflectance. Finally, i t was noted that off-colored cotton was rendered whiter with repetitive mechanical cleaning. Theoretical Visible light reflectance from incident white light on raw cotton may include signal contributions from fiber, and the dust and trash on the fiber surfaces. Then the reflectance is a function depending on f i b e r , dust, and trash. Changes in reflectance due to changes in fiber might be due to fiber color, fineness, maturity or other similar aspect. We will assume, however, that the changes in reflectance due to these factors are negligible. Thus the reflectance will depend only on dust and trash. Changes in relectance values due to changes in dust and trash may depend on particle source (bract, leaf, e t c . ) , diameter, or other factors in addition to concentration. Again we make a simplifying assumption that reflectance (R) depends only on the concentrations of dust (D) and trash (T) in cotton. Thus we may write R = R(D,T)

(1)

and consider the nature of the relationship expressed in Equation 1.

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Cotton Trash and Dust Contents

If one assumes that there is a functional between dust and trash concentrations, say

relationship

D = f(T)

(2)

then i t follows, by substituting Equation 2 into 1 and solving for T, that there is some function g with


T=g(R).

(3)

Similarly, upon substituting Equation 3 into 2, one obtains D = h(R).

(4)

By similar assumptions, Equations 1 and 2 may be rewritten to show there is a AD = 1(R)

(5)

functional relationship between the concentration of airborne dust (AD) and R. Kubelka-Munh theory {§) shows that R varies non-1inearly with the concentration of an absorber, and Norris (7) has suggested that the logarithm (1/R) varies linearly with the concentration of an absorber. These results suggest that Equations 3 and 4 may be rewritten such that T = a + bi In (1/R)

(6)

D =à

+ bi In (1/R)

(7)

AD = a-j + bi In (1/R).

(8)

i

and i

where ai is the intercept and bi is the slope of the equation. Eliminating R in Equations 6 and 7 shows that i f this is a correct model then the relationship between T and D i s linear, and hence, Equation 2 may be rewritten as D = ai + bi T.

(9)

It now follows from Equations 6,7, and 9 that D + T = ai + bi In (1/R)

(10)

and D/T = a + b i / T . 1

(11)


70

COTTON

DUST

As a final comment with regard to Equations 6, 7, and 8, we observe that i f R is scaled 0-100, but values of R are in fact contained in an interval C-100, where C is not near zero, (say C>25) then there is a strong linear relationship between R and In (1/R). This suggests that the model given by Equations 6, 7, and 8 may not be a significant improvement over that obtained by assuming that T and D are each linearly related to R ( i . e . by replacing In (1/R) in Equations 6, 7, and 8 with R). We should also note that Equation 9 is valid so long as D and T are linearly related to some function of R (not necessarily In (1/R). Thus, i f In (1/R) in Equations 6 and 7, is replaced by R, Equation 9 continues to be valid. Materials, Methods, and Protocols Instrumentation. Light reflectance in the visible range was measured with a Model 610 Photovolt Reflectance Meter using white incident light. The instrumentation developed by Anderson and Baker (8J was utilized to measure dust in cotton by dry assay. In brief, compressed air is forced through a thin layer of cotton to detach the dust from the fiber and to transport the suspended material out of the fiber mass so that i t can be collected on a filter. A 100 watt ultrasonic bath was used to detach dust from the fiber by wet assay. A Shirley Analyzer (9) was used to remove trash from cotton. In brief, the analyzer contains two rotating cyclinders with saw teeth to mechanically remove the trash from cotton. The Continuous Aerosol Monitoring (CAM) analyzer developed by ppm, Inc. (10) was used to measure airborne dust. Cotton Samples. The six cottons utilized in this f e a s i b i l i t y study included one representative bale from each of the major grade divisions as determined by the United States Department of Agriculture-Agricultural Marketing Service (USDA-AMS) Classers. Designated grades are tabulated in Table I; the color group for each grade was 1(white). Each grade representative cotton was selected at random by the AMS from that produced in the following geographical regions: far west (1 sample), southwest (2 samples), southcentral (1 sample),southeast (1 sample), and unknown (1 sample). Selection of one bale per major grade reflects trash content range in cotton. Each bale was blended and a 50 lb bulk sample randomly taken from each of the blended bales. Procedures. The light reflectance instrument was turned on 30 min prior to initiating reflectance observations. The sensit i v i t y switch was set in the low position. The combination visible light emitter-reflectance detector was positioned vertically; the active end of the detector faced upward. The sample cup was a glass cylindrical cuvette with optically f l a t bottom. A constant mass of 165 g (brass slug) was placed on top

Macon, Ga.

Corpus C h r i s t i , Tex.

Low Middling(LM)

Strict Good Ordinary(SGO)

a

Run invalid

8.5

Coefficient of variation(%)

0.137 0.0115

1.345

Average Value

0.241

0.170

0.153

Pooled standard deviation(%)

3.298

Good Ordinary (GO) Fresno, C a l i f .

2.020

1.907

0.109

Unknown

Strict Low Middling(SLM)

0.407

Austin, Tex.

Middling(M)

0.0643 0.0874

0.0756

Montgomery Ala.

Strict Middling(SM) 0.365

Trash Content(%)

Source

Grade

Dust Content Dry assay

8.7

0.0272

0.314

0.649

0.368

0.314

0.226

0.215

0.106

(%) Wet assay

0.100

0.070

0.080

0.080

0.270

0.240

0.850

Dry

0.230

0.200

0.180

0.160

0.560

0.590

1.400

Wet

Pust(%)/Trash(%)

TABLE I Cotton Source and Particulate Burdens


0.116

_a

0.196

0.150

0.064

0.097

0.075

Airborne Dust(%)


72

COTTON

DUST

of the cotton tuft in the cuvette to hold the fiber tightly against the optically f l a t bottom. Cotton tufts (about 0.5 g) were plucked at random from each of the six bulk samples. A tuft was placed in the cuvette, weighted down with the brass slug, and the cuvette placed over the center of the detector. Preliminary reflectance observations indicated which two cottons gave the lowest and highest reflectance values. The scale expansion switch was turned on and the instrument calibrated at 90% and 30% reflectance using the high and low reflectance t u f t s , respect i v e l y . A tuft from one of the six cottons was (a) randomly selected, (b) reflectance recorded, (c) tuft discarded, and (d) the instrument recalibrated. The a-b-c-d protocol was repeated until three observations were taken per sample. Slight differences in glass optical reflectance were negated by using only three cups to make the measurements, two calibration and one sample cuvette. Two hundred g of cotton was randomly taken from each of the six bulk samples to measure trash content. Each was weighed to two decimal places then mechanically cleaned in a Shirley analyzer (9). After completing the f i r s t processing stage in the analyzer, the cleaned lint was recleaned seven additional times to completely remove visible trash. The trash box residue was recovered and entrained l i n t removed from it with the aid of forceps, hand cards, and sonic sieves. The remaining nonlint trash was weighed and its content in cotton computed in percentage units. The wet assay technique to measure dust in cotton was a modification of the method described by Thibodeaux (11). A 400-mg tuft of cotton, randomly selected from a bulk sample, was subjected to multiple ultrasonic washings in methanol. Clean methanol (200 ml) was used for each of three 5-min washings. The combined methanol washings were filtered through a 17 urn sizing screen (the screen was identical for both wet and dry assay procedures) and collected on a 0.5 urn f i l t e r . Increase in f i l t e r weight provided the measure of dust content (%) in cotton by wet assay. The dry assay method to measure dust in cotton was described by Anderson and Baker (8J. A fiber blending wheel was used to prepare a relatively thin rectangular shaped batt. Twenty g of cotton was randomly taken from a bulk sample. Six 3 1/3 g batts were prepared from each cotton, for a total of 20g. Each batt was placed in a specially constructed box, air at 75 psi was forced through the passive batt, then through a 17 urn sizing screen, and the dust collected on a 0.5 urn glass fiber f i l t e r . Dust collection continued for 3 min. Five additional batts were then processed. The total increase in f i l t e r weight reflected the dust concentration (%) in cotton by dry assay. Actual room conditions for dry assay were 58% relative humidity and 78°F« Airborne dust measurements were performed by Dr. F. Shofner of ppm, Inc. (10). Approximately 100 to 300 mg preweighed

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73


samples of hand-formed sliver were fed into the opening beater of a commercial open-end spinning head. Air flow transported the individualized fibers and airborne dust through a transport tube. The dust (< 15 urn) was electro-optica11y weighed during a 2 1/2 min. sampling period. Each sample was passed four times through the system and the airborne dust contents summed to provide a measure of total airborne dust(%) released by repetitive mechanical processing. Data Processing and S t a t i s t i c s . Linear, power, and exponential lines were fitted to the dust, trash, and reflectance data by standard regression methods. Results and Discussion Cotton Grade and Source. It should be noted that the six grade representative-randomly selected cottons were produced in different states (Table I). Variety planted, harvesting, ginning technique, and commodity grade are characteristic of each area. All of these factors may influence cotton particulate concentrations and visible light reflectance properties. Thus, using cottons with the natural range of variables for this f e a s i b i l i t y study should add credence to the observed trends in the data. Particulate Burdens and Refectance Data. Table I gives the particulate concentrations. Percentage trash reported in Table I reflects nonlint trash only. These trash values are not positively biased by the l i n t entrainment associated with the so-called Shirley analyzer visible trash concentrations ( 9 ) . Only one trash observation was taken per sample because of the excessive amount of time required to manually separate lint from nonlint. Coefficient of variation for dust (dry assay) and dust (wet assay) was 8.5 and 8.7%, respectively. The range of trash contents is about 50 compared to about four for dust content. As explained by Montaivo (12), differences in dust content by the dry and wet assay methods are a result of a geometry effect associated with the former technique and the variation of adhesion force of dust on cotton with environment. Only one airborne dust measurement was taken on five of the six cottons. The run was declared invalid on the remaining sample. Table II summarizes the visible light reflectance results. Results for each observation (j) on each sample (i) are included in the table. The j observations for the ith sample were averaged ( i ) ; the coefficient of variation was 4.5%. The variance in light reflectance results is about half that observed with dust content measurements. #

for

Particulate-Ref1ectance Functions. Regression parameters particulate-reflectance relationships are shown in Table III.


74

COTTON

TABLE

DUST

II

Visible Light Reflectance Results

Grade

Liqht Réflectance^) j=3 i•

j=l

3=2

SM

88.0

93.9

90.2

90.7 ± 3.0 (std. dev.)

M

82.9

81.3

86.3

83.5 + 1.6

SLM

80.0

80.1

84.0

81.4 + 2.3

LM

61.7

59.7

63.0

61.5 + 1.7

SGO

51.9

65.2

59.9

59.0 + 6.7

GO

32.3

32.1

32.2

32.2 + 0.1

Mean Reflectance

68.1

Pooled standard deviation

3.1

Coefficient of variation(%)

4.5

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TABLE

III


Linear Regression Parameters of Particulate-Reflectance Relationships

Regression (y=particulate)

Correlation Coefficient (r)

Coefficient of determination intercept (r2,%)

slope (b^

x=ln(l/reflectance) Trash

93.9

Dust(dry)

0.977

95.5

0.8237

0.1647

Dust(wet)

0.989

97.9

2.345

0.4877

Trash plus Dust(dry)

0.970

94.1

16.38

3.575

Trash plus Dust(wet)

0.997

95.4

17.77

3.867

0.930

86.5

1.260

0.2656

-0.993

98.6

5.307

-0.0582

Dust(dry)

-0.994

98.8

-.3387

-0.00296

Dust(wet)

-0.981

96.2

0.8921

-0.00851

Trash plus Dust(dry) -0.994

98.8

5.646

-0.0612

Trash plus Dust(wet) -0.997

99.4

6.198

-0.0667

84.4

0.3877

-0.00361

Airborne Dust

15.54

3.406

0.969

x=reflectance Trash

Airborne Dust

-0.919


76

COTTON

DUST

For comparison purposes, regression parameters were computed for the model defined by Equations 6, 7, 8, and 10 and the model obtained by replacing In (1/R) in those equations by R. The dependent variable (y) is particulate concentration because it is desired to predict particulate content from reflectance values. Data from Tables I and II were also fitted to exponential and power functions where the independent variable (x) was reflectance but the f i t s were found to be inferior to that of the linear relationship. Figure 1 and 2 demonstrate the linear relationship of trash and dust content (wet assay) to both In (1/reflectance) and reflectance. The coefficient of determination (r ) is the percentage of total variation explained by the regression. For example, percentage of unexplained variation ( i . e . 1.4% for y = trash content and 3.8% for y = dust content (wet assay) with x = reflectance) is indicative of a significant relationship between particulate content in cotton and In R. A small unexplained variation was observed for all of the trash and dust content functions in Table III. Airborne dust unexplained variation, however, was poorer; precision of the measurement for the five cottons investigated was not reported. Dust content (dry assay) in the cottons, and a measure of total particulate content, arbitrarily defined here as the sum of the dust (wet assay) and trash content, were computed from the regression relationships using the mean reflectance values given in Table II. Calculated particulate contents were plotted against the observed values in Table I and are shown in Figures 3 and 4. These two graphs indicate that the regression lines predict the particulate content of the six cottons very well. 2

Particulate Functions. Table IV summarizes the regression results from exploring linear relationships between dust and trash levels in cotton. Exponential and power relationships were considered but the f i t s were found inferior to the linear case. The unexplained variation ranging from 1% to 9% suggest that a model leading to Equation 7 and 9 may indeed be an appropriate choice. Dust content increased with increase in trash content. A presentation of the dust (dry assay) linear relationship is shown in Figure 5. Dust/trash content was also linearly regressed on x = reciprocal of trash content (Figure 6). The curves illustrated in Figures 5 and 6 are the f i r s t documentation of actual dust-nonlint trash trends in raw cotton. Note in Figure 6 that with increasing trash content, the dust/trash ratio approaches a constant, the intercept a^ of Equation 9. Conclusions and Recommendations. This study encompassed the particulate content range found in the major commercial grades of raw cotton. Changes in visible light reflectance between the grades exceeded within-grade variances. In situ contents of dust

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77


4.00-.

3.50

3.00

~

2.50

2

r =93.9%

LU ^ O

2.00

< °-

1.50

1.00

0.50 2

r -97.9%

_L -5

Figure 1.

-4

-3 -2 -1 In (1 / R E F L E C T A N C E , % )

Correlation of particulate with ln(l/light reflectance). Key: A , trash; O, dust, wet assay.

Figure 2. Correlation of particulate with light reflectance. Key: O, trash; dust, wet assay.



MONTALVO

ET

AL.


0.5

0.0

0.1

0.2 0.3 O B S E R V E D (%)

0.4

0.5

Figure 3. Dust content (dry), calculated vs. observed. Key: , theoretical slope (1:1); A, calculated vs. observed (from linear regression on reflectance); and O, calculated vs. observed (from linear regression on ln(l/R)).


COTTON

DUST

Figure 4. Dust plus trash content, calculated vs. observed. Key: , theoretical slope (1:1); A, calculated vs. observed (from linear regression on reflectance); and O, calculated vs. observed (from linear regression on ln(l/R)).

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TABLE IV Linear Regression Parameters of Particulate Relationships Correlation Coefficient (r)


Regression

y

Coefficient of determination

intercept (a)

slope (a)

X

Dust(dry)

Trash

0.985

97.0

0.0702

0.0499

Dust(wet)

Trash

0.954

91.0

0.1228

0.1414

Dust(dry) Trash

1 Trash

0.995

99.0

0.0678

0.0589

Dust(wet) Trash

1 Trash

0.978

95.6

0.2080

0.9033

0.4h

DUST


COTTON

Figure 6. Variation of dust to trash content with the reciprocal of trash content. Key: , dust and trash calculated from linear regression on reflectance; • , observed dust (wet) and trash; and O, observed dust (dry) and trash.


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and trash in six carefully selected cottons correlated significantly with both visible light reflectance and In (1/visible light reflectance). Airborne dust levels of five of the samples also correlated significantly with both reflectance functions. Variations of dust content between the grades exceeded within- grade variances. Dust content correlated significantly and positively with trash content. Light measurement offers the combined capability of rapidly predicting by nondestructive means dust and trash content in cotton and airborne dust l e v e l . Of course, the standard error of estimate is not a practical statistic based on only six cottons and is not reported in this f e a s i b i l i t y paper. Other statistical inferences are possible, however, about characteristics of a population of cotton samples from a study of six randomly selected from the population. We conclude that the particulate-reflectance relationship is a very strong one and that the model of Equations 6 and 7 is a "highly likely candidate" even though there is no evidence in this paper that i t is superior to the model obtained by replacing In (1/R) with R. The approach to using light to relate or predict particulate propensity of cotton in the gin and in the textile mill appears eminently feasible. Additional work is in progress with a population of over f i f t y samples in order to verify the reported findings. Vertical elutriator dust levels, and dust and trash contents in the cottons will be correlated with light measurements. Dependence of the correlation coefficient (r) on the wavelength of incidence light is being investigated. Light transmitted through a cotton sample offers several advantages over the reflectance technique and is also being studied. Acknowledqements We greatfully acknowledge trash analysis by Mrs. Cynthia Lichtenstein, dust (dry assay) by Mr. Jimmie Sandberg, and dust (wet assay ) by Mrs. Shirley Armand. Disclaimer Mention of company names or products does not constitute endorsement by the United States Department of Agriculture.

Literature Cited 1. Federal Register 1978, 43, 27350-418. 2. Amercian Society for Testing and Materials, Annu. Book ASTM Stand. 1977, Part 33, p 428-434. 3. Taylor, R. A. Proc. 1980 Beltwide Cotton Prod. Res. Conf. 1980, p 259-265. 4. Kasdan, H. L. Proceedings of the 1977 Electro Optics/Laser Conf. 1977, p 256-262.

84 5. 6. 7. 8. 9.


10. 11. 12.

COTTON DUST

Lyons, D. W.; Barker, R. L. Text. Res. J . 1976, 46, 135-9. Kubelka, P.; J . Opt. Soc Am. 1947, 38, 448-57. Norris, K. H.; personal communication. Anderson, J . D.; Baker, R. V. Trans. ASAE 1979, 22, 918-21, 925. American Society for Testing and Materials, Annu. Book ASTM Stand. 1977, Part 33, p 576-583. Shofner, F. M.; Hyde, R. E.; Duckett, K. E. Proc. 1981 Beltwide Cotton Prod. Res. Conf. 1981, p 48-52. Thibodeaux, D. P.; Baril, A. J. Text. Res. J., in press. Montalvo, J. G., Jr. Proc. 1981 Beltwide Cotton Prod. Res. Conf. 1981, p 53-54.

RECEIVED

January 20, 1982.

Cotton Dust - American Chemical Society

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