**5.3.1 Measurement system for fabric products**

94 Infrared Spectroscopy – Life and Biomedical Sciences

C80P20 C90P10 C100P0

Fig. 14. The infrared absorption spectra of the mixed yarns

C0P100 C10P90 C20P80 C30P70 C40P60 C50P50 C60P40 C70P30

4000 3500 3000 2500 2000 1500 1000 500

Wavenumber (cm-1)

And for the validated on calibration curve, the 10 samples were measured. The validation result of the calibration curve is shown in Fig.15-B. The result of SEP is ±6.2%. To view the percentage of mixed yarn in increments of 5 %, measurement error is preferably less than ± 2.5%. This result cannot accurately display the percentage of mixed yarn. As a cause, it is a small number of spectrum using in the calibration curve. Therefore, the next a problem to be solved, increased the number of spectra without lowering the accuracy of the SEC, and to create a more reliable calibration. If we have overcome the challenges, it is possible to reduce the SEP of the percentage of mixed yarn, and possible to measure accurately the

(A)The calibration curve (B) The validation result

0

0 20 40 60 80 100

Reference value of "C" (mg)

20

40

Estimated value of "C" (mg)

60

80

100

Fig. 15. The calibration curve and validation result for mixed yarn

0 20 40 60 80 100

Reference value of "C" (mg)

0

percentage of mixed yarn.

0

20

40

Estimated value of "C" (mg)

60

80

100

Absorbance

2

There are yarn products as well as a lot of fabrics products. We have to measure analysis of the composition and mixture ratio for fabric products. Measuring system for fabric products, FT-IR (IRPrestige-21) and diffuse reflection method (UP-IR) was used. Measuring system is shown in Fig.16. The sample fabric is placed to cover the hole of the top of the UP-IR, and a mirror mounted on the sample fabric.

Fig. 16. Measurement system for fabric products

As test samples, we used 38 fabrics of cotton 100%, 42 fabrics of polyester 100%, 71 mixed fabrics of cotton - polyester (CP sample). In the sample, there are woven fabric and knitted fabric, colour and thickness is different respectively. The ratio of polyester and cotton in P/C sample is also various. In measurement of spectrum, measuring range, resolution, and accumulation were 4000~700 cm-1, 4 cm-1, and 20 times. In addition,we were measured in five points on each sample to measure the entire fabric.

### **5.3.2 Analysis of the composition of cotton and polyester fabric products**

The composition of the fabric products were analysed using these absorption spectra and SIMCA. The fabric samples are classified into 100% cotton class (Class C), cotton and polyester mixed fabric class (Class CP), 100% polyester class (Class P). For the development to classification model, the absorption spectra of the 100% cotton and the 100% polyester were used each 20 samples (100 spectra), the absorption spectra of mixed fabric were used 35 sample (175 spectra). The remaining sample spectra were used to validate the classification model. We constructed four times classification models by SIMCA, and tried the classification of the spectrum. Those models changed random the combination of spectrum for class making and spectrum for verification and were constructed.

The result of constructed classification model is shown in Table 5. A high distinction rate was obtained in each model. From these results, the information on each sample was able to be extracted accurately by SIMCA. Especially, the percentage of correct answers in Class C

Introduction of Non-Invasive Measurement Method by Infrared Application 97

The measurement result is shown in Fig.18-(A). In this Fig.18-(A), this result has a highly significant correlation. And, SEC is 2.9 %. To display in increments of 5 % to the product mixture ratio, SEC needs to be less than 2.5 %. If you check the Fig.18-(A), the sample near the reference mixture ratio 40~50 % is distant from Y = X. The absorption spectra of this sample (Sample-A) and sample of close to the Y = X (Sample-B) are shown in Fig.19. In Fig.19, the mixture ratio of two samples is very similar, but, the shape of the absorption spectra is different. This was the impact of textile design different of each sample. Ratio of the kind of strings that appear on the surface is different by two sides when the composition

is different because of warp yarn and weft yarn. It influences the spectrum shape.

(A) Calibration curve by normal spectra (B) Calibration curve by AVE spectra Fig. 18. Prediction result of mixture ratio of polyester in normal spectra and average spectra

0

20

40

Estimated value of "P" (mg)

60

Sample-B

80

100

Sample-A Sample-B

0 20 40 60 80 100

Sample-A

Reference value of "P" (mg)

4000 3500 3000 2500 2000 1500 1000 500

Consequently, the diffuse reflectance spectrum was acquired on both sides of these samples. The top surface and bottom surface in each sample was measured five times, averaged of obtained 10 spectra. Result of calibration curve developed by using average spectra and PLSR is shown in Fig.18-(B). The sample near the reference mixture ratio 40~50 % is close

Wavenumber (cm-1)

Fig. 19. The absorption spectra of sample A and B

0 20 40 60 80 100

Sample-A

Sample-B

Reference value of "P" (mg)

1

3

0

20

40

Estimated value of "P" (mg)

60

80

100

Absorbance


Table 5. The result of constructed classification model

and Class CP is all over 90 %. Meanwhile, the percentage of correct answers in Class P is lower than other classes. As this cause, it is considered the polyester included different components as the polyethylene terephthalate (PET) and poly trimethylene terephthalate (PTT). The first differential spectra of cotton and polyester and discrimination power are shown in Fig.17. Discrimination power indicates the magnitude of the effect of each wavelength, when the samples are classified into each class. By Fig.17, it was confirmed that it had been classified by composition information because main peak of discrimination power was corresponding to peak of spectra. Therefore, the validity of classification model was shown.

Fig. 17. The first differential spectra of cotton and polyester, and discrimination power

#### **5.3.3 Measurement of mixture ratio of fabric products**

We must seek a mixture ratio of the sample, when the sample is classified as mixed fabric by SIMCA method. Therefore, mixed ratio determined using the PLSR. The samples and measurement method are same to section 5-3-1. However, the average spectrum of five spectra obtained from one sample was used as a measured absorption spectrum of this sample. We developed the calibration curve by using absorption spectra and known mixture ratio. In this time, the known mixture ratio is used the mixture ratio of the polyester.

Class C 97% 93% 93% 97% Class CP 95% 94% 96% 97% Class P 81% 81% 91% 88%

and Class CP is all over 90 %. Meanwhile, the percentage of correct answers in Class P is lower than other classes. As this cause, it is considered the polyester included different components as the polyethylene terephthalate (PET) and poly trimethylene terephthalate (PTT). The first differential spectra of cotton and polyester and discrimination power are shown in Fig.17. Discrimination power indicates the magnitude of the effect of each wavelength, when the samples are classified into each class. By Fig.17, it was confirmed that it had been classified by composition information because main peak of discrimination power was corresponding to peak of spectra. Therefore, the validity of classification model

Cotton Polyester

Discrimination Power

Fig. 17. The first differential spectra of cotton and polyester, and discrimination power

ratio. In this time, the known mixture ratio is used the mixture ratio of the polyester.

We must seek a mixture ratio of the sample, when the sample is classified as mixed fabric by SIMCA method. Therefore, mixed ratio determined using the PLSR. The samples and measurement method are same to section 5-3-1. However, the average spectrum of five spectra obtained from one sample was used as a measured absorption spectrum of this sample. We developed the calibration curve by using absorption spectra and known mixture

4000 3500 3000 2500 2000 1500 1000 500

Wavenumber (cm-1)

**5.3.3 Measurement of mixture ratio of fabric products** 

Table 5. The result of constructed classification model

was shown.

d(ABS)/dv


600

0

200

Absorbance

400

0

50

1st 2nd 3rd 4th

The measurement result is shown in Fig.18-(A). In this Fig.18-(A), this result has a highly significant correlation. And, SEC is 2.9 %. To display in increments of 5 % to the product mixture ratio, SEC needs to be less than 2.5 %. If you check the Fig.18-(A), the sample near the reference mixture ratio 40~50 % is distant from Y = X. The absorption spectra of this sample (Sample-A) and sample of close to the Y = X (Sample-B) are shown in Fig.19. In Fig.19, the mixture ratio of two samples is very similar, but, the shape of the absorption spectra is different. This was the impact of textile design different of each sample. Ratio of the kind of strings that appear on the surface is different by two sides when the composition is different because of warp yarn and weft yarn. It influences the spectrum shape.

(A) Calibration curve by normal spectra (B) Calibration curve by AVE spectra

Fig. 18. Prediction result of mixture ratio of polyester in normal spectra and average spectra

Fig. 19. The absorption spectra of sample A and B

Consequently, the diffuse reflectance spectrum was acquired on both sides of these samples.

The top surface and bottom surface in each sample was measured five times, averaged of obtained 10 spectra. Result of calibration curve developed by using average spectra and PLSR is shown in Fig.18-(B). The sample near the reference mixture ratio 40~50 % is close

Introduction of Non-Invasive Measurement Method by Infrared Application 99

By the measurement method of remaining dirt on inner surface of narrow tubule, devised a system that combines the cone-shaped mirror and IR fiber probe, as shown in Fig.20. This system is inserted into the narrow tubule. Infrared light passing through the fiber probe is reflected by the cone-shaped mirror, and to irradiate the inner surface of the narrow tubule. If the dirt remains on inner surface of tubule, the infrared light is absorbed by the amount of dirt. Absorbed infrared light is reflected by the cone-shaped mirror, and is detected after passing through the fiber probe. We measured four times for one measured value. The coneshaped mirror (Bottom diameter and Height: 4mm) placed inside the aluminium pipe. The fiber probe was arranged on 1 mm distance from the top of the aluminium pipe. We measured in this arrangement (1st time), and aluminium pipe was rotated 180 degrees (2nd times), and turn aluminium pipe upside down (3rd times), and aluminium pipe was rotated 180 degrees again (4th times). We averaged the four IR spectra detected in the measurements. The resolution, the accumulation, the measuring wavenumber range set at 4

**6.2 Measurement system by IR fiber probe method** 

cm-1, 100 times, 4000~900 cm-1 measurement condition.

**6.3 Qualitative analysis of lard** 

triolein present in the human body.

used as the remaining amount of the lard (measured value).

Fig. 20. Measurement system of remaining dirt on inner surface of narrow tubule

The remaining dirt of endoscope is excreted from the human body. For example, blood, protein and lipid dirt. We used the lard as a model of triolein contained in the lipid dirt. The absorption spectra of lard and triolein are shown in Fig.21. Many peaks by lard can be seen in spectra (2920 cm-1, 2850 cm-1 : stretching vibrations by CH2 groups, 1470 cm-1 : scissoring vibration by CH2 group, 750 cm-1 : bending vibration by CH, 1740 cm-1 : stretching vibrations by C=O, 1160 cm-1 : antisymmetric stretching vibrations by CO). From this absorption spectrum, lard is the ideal remaining dirt model because is very similar to

In the sample preparation, lard and aluminum pipe (ID: 5 mm, OD: 10mm, height: 8 mm) were used as the model substance of the human soil and narrow tubule. Model sample were prepared by the following process. Lard is dissolved in the hexane solution. The aluminum pipe has been kept in the solution for 1 minute. The sample pipe is left in the laboratory for five minutes until the residual hexane is fully evaporated. The dirt on the outer surface of aluminum pipe is wiped off. The weight of aluminum pipe before and after these processes was measured by using the electronic balance. The weight difference of aluminum pipe was

in Y = X, and SEC has also improved. Furthermore, this system can be measured mixture ratio of mixed fabric products, because we could be obtained the good result which SEC is 1.8%.

#### **5.4 Conclusion of non-destructive analysis of the composition and mixture ratio for textile product**

In this paper, qualitative analysis of a single composition yarn could determine the exact composition of all. And, the measurement of percentage of mixed yarn can build a good calibration curve, which can be mixed with non-destructive measurement. IR spectrum with each feature of polyester and cotton and CP fabric sample quickly was able to be acquired by using the diffused reflection method of FT-IR. It turned out that our method was able to be applied from this outcome of an experiment to textile goods.

In the future, we need to build calibration curves for mixed yarn of other materials. If measurement method will be developed, could be proposed as a new method having better characteristics for measuring of textile products. The measurement time is about 10 minutes in this system. Since this system has the characteristics of these, bring significant benefits to such the samples of indestructible material determine the historical artefact and cultural heritage. From these results, we propose a new evaluation method of textile products not previously exist.

#### **6. Measurement system of remaining dirt on inner surface of a narrow tubule based on infrared spectroscopy**

#### **6.1 Background**

There is surely remaining dirt on the used products. In order to use the product for long periods, you need to wash the dirt. Therefore, many studies have been done for the dirty washing techniques and cleaning agents, analysis of remaining dirt. It has been researched in so many fields as the industrial, medical, food, yarn sector. The study of wash technology is so many, but washed cleanliness assessment has been neglected. As one of the reasons, we are under the impression that convinced that the product which satisfactory results were obtained by visual evaluation has not remaining dirt.

Especially, in the medical institutions, must be careful to hospital acquired infections. To prevent hospital acquired infections, cleanness is essential for all medical equipment. In particular, the instruments used in the human body must be aware of the cleanness. If you cannot detect small amounts of remaining dirt by visual evaluation, might be robbed a life. Therefore, the nurse must evaluate the cleanliness of the equipment used after cleaning and sterilizing. So far, the cleanness of medical equipment has been evaluated by visual inspection, test soil method or ATP method. But, these measurement methods have several disadvantages (individual differences, re-cleaning, numerical evaluation is difficult et al). This paper examines the application of qualitative and quantitative evaluation to residual contamination on the medical devices by infrared spectroscopy, and discussed the possibility of a new cleanness evaluation method. It is also difficult to evaluate the cleanness of a narrow tubule which is used by endoscope or drip tube. In this study, we developed a measurement system of the remaining dirt on the inner surface of narrow tubule.

#### **6.2 Measurement system by IR fiber probe method**

98 Infrared Spectroscopy – Life and Biomedical Sciences

in Y = X, and SEC has also improved. Furthermore, this system can be measured mixture ratio of mixed fabric products, because we could be obtained the good result which SEC is

**5.4 Conclusion of non-destructive analysis of the composition and mixture ratio for** 

be applied from this outcome of an experiment to textile goods.

In this paper, qualitative analysis of a single composition yarn could determine the exact composition of all. And, the measurement of percentage of mixed yarn can build a good calibration curve, which can be mixed with non-destructive measurement. IR spectrum with each feature of polyester and cotton and CP fabric sample quickly was able to be acquired by using the diffused reflection method of FT-IR. It turned out that our method was able to

In the future, we need to build calibration curves for mixed yarn of other materials. If measurement method will be developed, could be proposed as a new method having better characteristics for measuring of textile products. The measurement time is about 10 minutes in this system. Since this system has the characteristics of these, bring significant benefits to such the samples of indestructible material determine the historical artefact and cultural heritage. From these results, we propose a new evaluation method of textile products not

**6. Measurement system of remaining dirt on inner surface of a narrow tubule** 

There is surely remaining dirt on the used products. In order to use the product for long periods, you need to wash the dirt. Therefore, many studies have been done for the dirty washing techniques and cleaning agents, analysis of remaining dirt. It has been researched in so many fields as the industrial, medical, food, yarn sector. The study of wash technology is so many, but washed cleanliness assessment has been neglected. As one of the reasons, we are under the impression that convinced that the product which satisfactory results were

Especially, in the medical institutions, must be careful to hospital acquired infections. To prevent hospital acquired infections, cleanness is essential for all medical equipment. In particular, the instruments used in the human body must be aware of the cleanness. If you cannot detect small amounts of remaining dirt by visual evaluation, might be robbed a life. Therefore, the nurse must evaluate the cleanliness of the equipment used after cleaning and sterilizing. So far, the cleanness of medical equipment has been evaluated by visual inspection, test soil method or ATP method. But, these measurement methods have several disadvantages (individual differences, re-cleaning, numerical evaluation is difficult et al). This paper examines the application of qualitative and quantitative evaluation to residual contamination on the medical devices by infrared spectroscopy, and discussed the possibility of a new cleanness evaluation method. It is also difficult to evaluate the cleanness of a narrow tubule which is used by endoscope or drip tube. In this study, we developed a measurement system of the remaining dirt on the inner surface of

1.8%.

**textile product** 

previously exist.

**6.1 Background** 

narrow tubule.

**based on infrared spectroscopy** 

obtained by visual evaluation has not remaining dirt.

By the measurement method of remaining dirt on inner surface of narrow tubule, devised a system that combines the cone-shaped mirror and IR fiber probe, as shown in Fig.20. This system is inserted into the narrow tubule. Infrared light passing through the fiber probe is reflected by the cone-shaped mirror, and to irradiate the inner surface of the narrow tubule. If the dirt remains on inner surface of tubule, the infrared light is absorbed by the amount of dirt. Absorbed infrared light is reflected by the cone-shaped mirror, and is detected after passing through the fiber probe. We measured four times for one measured value. The coneshaped mirror (Bottom diameter and Height: 4mm) placed inside the aluminium pipe. The fiber probe was arranged on 1 mm distance from the top of the aluminium pipe. We measured in this arrangement (1st time), and aluminium pipe was rotated 180 degrees (2nd times), and turn aluminium pipe upside down (3rd times), and aluminium pipe was rotated 180 degrees again (4th times). We averaged the four IR spectra detected in the measurements. The resolution, the accumulation, the measuring wavenumber range set at 4 cm-1, 100 times, 4000~900 cm-1 measurement condition.

Fig. 20. Measurement system of remaining dirt on inner surface of narrow tubule

#### **6.3 Qualitative analysis of lard**

The remaining dirt of endoscope is excreted from the human body. For example, blood, protein and lipid dirt. We used the lard as a model of triolein contained in the lipid dirt. The absorption spectra of lard and triolein are shown in Fig.21. Many peaks by lard can be seen in spectra (2920 cm-1, 2850 cm-1 : stretching vibrations by CH2 groups, 1470 cm-1 : scissoring vibration by CH2 group, 750 cm-1 : bending vibration by CH, 1740 cm-1 : stretching vibrations by C=O, 1160 cm-1 : antisymmetric stretching vibrations by CO). From this absorption spectrum, lard is the ideal remaining dirt model because is very similar to triolein present in the human body.

In the sample preparation, lard and aluminum pipe (ID: 5 mm, OD: 10mm, height: 8 mm) were used as the model substance of the human soil and narrow tubule. Model sample were prepared by the following process. Lard is dissolved in the hexane solution. The aluminum pipe has been kept in the solution for 1 minute. The sample pipe is left in the laboratory for five minutes until the residual hexane is fully evaporated. The dirt on the outer surface of aluminum pipe is wiped off. The weight of aluminum pipe before and after these processes was measured by using the electronic balance. The weight difference of aluminum pipe was used as the remaining amount of the lard (measured value).

Introduction of Non-Invasive Measurement Method by Infrared Application 101

Estimated value (mg)

(A) The calibration curve for cleanness (B) The validation result of calibration curve

The results obtained in this study, this measurement system was only possible to evaluate

0

0 0.5 1 1.5 2

Measured value (mg)

0.5

1

1.5

2

The measurement time is about 2 minutes, which is shorter than the other measurement methods. In addition, this system is possible to measure in non-contact and non-destructive, because only the infrared light irradiation. And, we can detect the multiple dirt by using FT-IR. Therefore, even if protein and blood are remained together with lipid contamination,

The next challenge is to integrate the fiber and the cone-shaped mirror. When we overcome the next challenges, this measurement system also can be used to long narrow tubules. If there is no dirt, the absorption peak is not detected, only if the dirt is remaining, to detect infrared spectra. From the position of the fiber probe when dirt is detected, the remaining dirt position can be identified. The type of remaining dirt can be identified from the shape of infrared absorption spectrum, and the amount of remaining dirt is detected by using a

By the characteristics of more than, this measurement system has so many advantages, we report as a new method of measuring remaining dirt. Thus, this system is possible to evaluate the cleanliness of the inner surface in narrow tubules. And can be reduce the risk of

As has been mentioned above, a non-destructive measurement can perform in many areas by using FT-IR. And, Infrared spectroscopy can be measured in quickly, non-contact, nondestructive. Therefore, various sensors will be able to develop by devise of measurement method and analysis method. Infrared spectroscopy can measure liquids and gases. In

Fig. 22. The calibration curve and the validation result

0 0.2 0.4 0.6 0.8

Reference value (mg)

the cleanness of the inner surface of a narrow tubule.

calibration curve.

0

0.2

0.4

Calibration curve value (mg)

0.6

0.8

**7. Conclusion** 

nosocomial infections in clinical practice.

**6.5 Conclusion of cleanness evaluation by IR spectroscopy** 

respectively dirt can be detected by this measurement method.

Fig. 21. The absorption spectra of lard and triolein

#### **6.4 Cleanness evaluation of inner surface of the narrow tubule by PLSR**

This study uses the PLSR analysis to obtain more accurate information contained in the infrared absorption spectrum of contaminants. To measure the amount of remaining dirt on the inner surface of the narrow tubules, first, we measured the infrared absorption spectrum of a known amount of remaining dirt, and the calibration curve was made using the measured infrared spectra by PLSR. Then, measuring the infrared absorption spectrum of an unknown amount of remaining dirt samples, we calculated the amount of deposited from the calibration curve was constructed. The number of sample are 50 kinds, the average amount of remaining lard is 0.65mg. As a cleanness evaluation of inner surface of the tubule, calibration curves were constructed with the PLSR. The analysis wavenumber range is 1800~1000 cm-1.

The correlation scatter diagram of the calibration curve is shown in Fig.22-(A). The absorption spectrum was subjected to spectral correction and differential for the measurement S/N ratio in order to standardize. The horizontal axis represents the amount of lard, the vertical axis is the estimated weight obtained from the PLSR. The results of the correlation coefficient and the SEC were 0.843 and ± 0.19 mg/cm2. This result indicates a highly significant correlation. This calibration curve is considered to be reasonable. The main causes of this, many lard information was considered obtained more than a single absorption peak because we analyzed a wide wavenumber band including a plurality of absorption peaks on the infrared spectra.

And for the validated on calibration curve, the 10 samples were measured. The estimated remaining dirt value is calculated from assign the measured spectra to a calibration curve. And, we weighed the samples, obtained this weight as the reference value. The validation result of the calibration curve is shown in Fig.22-(B). The results of the correlation coefficient is 0.957, and the SEP is ± 0.10 mg. EI (Evaluation index) method was applied for validation of the calibration curve, the EI value was shown to 21.5%. This value is classified into highly practical rank B. Thus, the amount of remaining dirt on the inner surface of narrow tubule could be measured quantitatively. And shown in Fig.22-(B), substituting the infrared absorption spectrum of minimum coating weight was calculated 0.08 mg by the calibration curve, the detection limit of this method is considered to be less than 0.08mg, as well as other measurement method. These results lead us to the conclusion that the cleanness on the inner surface of narrow tubule can be measured by this measurement system.

Lard Triolein

4000 3500 3000 2500 2000 1500 1000 500

Wavenumber (cm-1)

This study uses the PLSR analysis to obtain more accurate information contained in the infrared absorption spectrum of contaminants. To measure the amount of remaining dirt on the inner surface of the narrow tubules, first, we measured the infrared absorption spectrum of a known amount of remaining dirt, and the calibration curve was made using the measured infrared spectra by PLSR. Then, measuring the infrared absorption spectrum of an unknown amount of remaining dirt samples, we calculated the amount of deposited from the calibration curve was constructed. The number of sample are 50 kinds, the average amount of remaining lard is 0.65mg. As a cleanness evaluation of inner surface of the tubule, calibration curves were

The correlation scatter diagram of the calibration curve is shown in Fig.22-(A). The absorption spectrum was subjected to spectral correction and differential for the measurement S/N ratio in order to standardize. The horizontal axis represents the amount of lard, the vertical axis is the estimated weight obtained from the PLSR. The results of the correlation coefficient and the SEC were 0.843 and ± 0.19 mg/cm2. This result indicates a highly significant correlation. This calibration curve is considered to be reasonable. The main causes of this, many lard information was considered obtained more than a single absorption peak because we analyzed a wide wavenumber band including a plurality of

And for the validated on calibration curve, the 10 samples were measured. The estimated remaining dirt value is calculated from assign the measured spectra to a calibration curve. And, we weighed the samples, obtained this weight as the reference value. The validation result of the calibration curve is shown in Fig.22-(B). The results of the correlation coefficient is 0.957, and the SEP is ± 0.10 mg. EI (Evaluation index) method was applied for validation of the calibration curve, the EI value was shown to 21.5%. This value is classified into highly practical rank B. Thus, the amount of remaining dirt on the inner surface of narrow tubule could be measured quantitatively. And shown in Fig.22-(B), substituting the infrared absorption spectrum of minimum coating weight was calculated 0.08 mg by the calibration curve, the detection limit of this method is considered to be less than 0.08mg, as well as other measurement method. These results lead us to the conclusion that the cleanness on the

**6.4 Cleanness evaluation of inner surface of the narrow tubule by PLSR** 

constructed with the PLSR. The analysis wavenumber range is 1800~1000 cm-1.

inner surface of narrow tubule can be measured by this measurement system.

Fig. 21. The absorption spectra of lard and triolein

absorption peaks on the infrared spectra.

Absorbance

(A) The calibration curve for cleanness (B) The validation result of calibration curve

Fig. 22. The calibration curve and the validation result

#### **6.5 Conclusion of cleanness evaluation by IR spectroscopy**

The results obtained in this study, this measurement system was only possible to evaluate the cleanness of the inner surface of a narrow tubule.

The measurement time is about 2 minutes, which is shorter than the other measurement methods. In addition, this system is possible to measure in non-contact and non-destructive, because only the infrared light irradiation. And, we can detect the multiple dirt by using FT-IR. Therefore, even if protein and blood are remained together with lipid contamination, respectively dirt can be detected by this measurement method.

The next challenge is to integrate the fiber and the cone-shaped mirror. When we overcome the next challenges, this measurement system also can be used to long narrow tubules. If there is no dirt, the absorption peak is not detected, only if the dirt is remaining, to detect infrared spectra. From the position of the fiber probe when dirt is detected, the remaining dirt position can be identified. The type of remaining dirt can be identified from the shape of infrared absorption spectrum, and the amount of remaining dirt is detected by using a calibration curve.

By the characteristics of more than, this measurement system has so many advantages, we report as a new method of measuring remaining dirt. Thus, this system is possible to evaluate the cleanliness of the inner surface in narrow tubules. And can be reduce the risk of nosocomial infections in clinical practice.
