1 # 2 # 3 # 4

fitting, because the PLS factor number is 5.

Fig. 11. The Clark grid result of each subject

Estimated value (mg/dl)

D

This section describes a Non-destructive analysis of the composition and mixture ratio for textile products. In Japan, the composition display of textile goods is obligated by the "Household Goods Labeling law". Therefore, analysis of the composition and mixture ratio for textile products in accordance with JIS method (L 1030-1,-2) are executed. But, existing methods is the destructive inspection, and a lot of time (six hours or more) and proficiency is necessary for the analysis. Also has concern about the safety and the environmental impact of such tests to use chemicals such as organic solvents. In addition, if the amount of remaining dirt on the sample is very small, if the remaining dirt color is same color of sample, the dirt cannot be verified visually.

In here, it proposes the application of the infrared spectroscopy for improve those problems. The method that we propose is non-destructive measurement, quickly and easy. We measured an infrared absorption spectrum of a textile by using a FT-IR. And we examined the possibility of a composition classification and a mixture ratio calculation by chemometrics. These experiments showed that the measurement system was effective.

#### **5.2 Analysis of the composition and mixture ratio for yarn products by ATR**

#### **5.2.1 Measurement system for yarn products**

Measuring system in this study, FT-IR (IRPrestige-21) and ATR method (DuraSampler-IR) was used. Measuring system is shown in Fig.12. The measuring yarn is folded, and set to cover on the prism. After, the measurement part of the prism and yarns were contacted by

Introduction of Non-Invasive Measurement Method by Infrared Application 93

Cotton Hemp

4000 3500 3000 2500 2000 1500 1000 500

intensity difference is the difference between the percentage of cellulose in cotton and hemp. And, the cotton and hemp has slightly different components (such as pectin) present, but those absorption peaks are very small. In this method to accurately measure this subtle

In textile products, the mixed yarn including multiple compositions is present. In the mixed yarn should seek the composition and percentage of each composition. Measurement of composition percentage of mixed yarn, we used cotton and polyester mixed yarn samples 45 kinds. These yarns were measured 5 times each spectrum, the average of the measured spectra. Developing a calibration curve by PLSR, focusing on the absorbance wavenumber specific absorption peaks in the components of the cotton and polyester in average spectra. Substituting the spectrum of the unknown sample to the calibration curve, calculated the

The absorption spectra of the yarn are shown in Fig.14. In the Fig.14, "C" shows for the percentage of cotton, and "P" shows for the percentage of polyester (C100P0 : 100% cotton yarn, C90P10 : 90% cotton and 10% polyester mixed yarn,... , C0P100 : 100% polyester yarn). These absorption spectra are changing the percentage of mixed of the cotton and polyester in increments of 10 %. There are several wavenumber ranges of absorbance changing just like the mixture ratio. The absorbance of 3392 cm-1, 1624 cm-1, 1226~1039 cm-1 peaks are proportional to the percentage of cotton. And, the absorbance of1738 cm-1, 1409 cm-1, 1298 cm-1 peaks are proportional to the percentage of polyester. Therefore, by analysis in the wavenumber range including these characteristic absorption peaks, we can determine the mixture ratio. Using the spectra of cotton and polyester mixed yarn, the results of PLSR in wavenumber range in 3000~700 cm-1 is shown in Fig.15-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 composition percentage of cotton, and the vertical axis is the estimated value of cotton from the PLSR. The results of the correlation coefficient and the measurement accuracy (SEC) were 0.988 and ±3.4%. This result indicates a highly significant correlation. This calibration curve is considered to be reasonable. Thus, this calibration curve is

good result, we used this calibration curve of cotton and polyester mixed yarn.

Wavenumber (cm-1)

Fig. 13. Infrared absorption spectra of cotton and hemp

difference, qualitative analysis for each yarn was possible.

**5.2.3 Measurement of mixture ratio of mixed yarn** 

composition percentage of cotton.

0

0.2

Absorbance

0.4

Fig. 12. Measurement system for yarn products

pressing parts from above. IR spectrum is measured with this device. It is quickly because the measurement time is about two minute. The resolution, the accumulation, the measuring wavenumber range set at 4 cm-1, 50 times, 4000~500cm-1 (wavelength 2.5~20 μm) measurement condition.

#### **5.2.2 Analysis of the composition of yarn products**

We used 7 samples, Cotton, Silk, Hemp, Wool, Polyester, Rayon, and PET. We used the KNN method to determine the composition of textile products. Building a KNN space was divided into seven classes by type for each spectrum. KNN space was built by a total 140 spectra by 20 spectra for each sample. 10 spectra of each sample as an unknown sample are put into space. We tried each spectrum to be divided into classes. KNN space was constructed in wavenumber range in 1800-650 cm-1. Because, there were characteristic peaks of each samples in this wavenumber range.

The result of qualitative analysis for each sample is shown in Table 4. Results showed that all samples are classified correctly from Table 4. Therefore, it is possible to the qualitative evaluation in a single composition yarn by this measurement method. We were able to determine the cellulose yarns of cotton and hemp, is very good results.

Infrared absorption spectra of cotton and hemp are shown in Fig.13. These spectra in Fig.13 are very similar, because the main component of cotton and hemp is cellulose. Very different point is the absorbance intensity of 1180 cm-1 (Arrow in Fig.13). This absorbance


Table 4. The result of qualitative analysis for each sample by KNN

pressing parts from above. IR spectrum is measured with this device. It is quickly because the measurement time is about two minute. The resolution, the accumulation, the measuring wavenumber range set at 4 cm-1, 50 times, 4000~500cm-1 (wavelength 2.5~20 μm)

We used 7 samples, Cotton, Silk, Hemp, Wool, Polyester, Rayon, and PET. We used the KNN method to determine the composition of textile products. Building a KNN space was divided into seven classes by type for each spectrum. KNN space was built by a total 140 spectra by 20 spectra for each sample. 10 spectra of each sample as an unknown sample are put into space. We tried each spectrum to be divided into classes. KNN space was constructed in wavenumber range in 1800-650 cm-1. Because, there were characteristic peaks

The result of qualitative analysis for each sample is shown in Table 4. Results showed that all samples are classified correctly from Table 4. Therefore, it is possible to the qualitative evaluation in a single composition yarn by this measurement method. We were able to

Infrared absorption spectra of cotton and hemp are shown in Fig.13. These spectra in Fig.13 are very similar, because the main component of cotton and hemp is cellulose. Very different point is the absorbance intensity of 1180 cm-1 (Arrow in Fig.13). This absorbance

Cotton 10/10 0/10 0/10 0/10 0/10 0/10 0/10 Silk 0/10 10/10 0/10 0/10 0/10 0/10 0/10 Wool 0/10 0/10 10/10 0/10 0/10 0/10 0/10 Hemp 0/10 0/10 0/10 10/10 0/10 0/10 0/10 Polyester 0/10 0/10 0/10 0/10 10/10 0/10 0/10 Rayon 0/10 0/10 0/10 0/10 0/10 10/10 0/10 PET 0/10 0/10 0/10 0/10 0/10 0/10 10/10

Cotton Silk Wool Hemp Polyester Rayon PET

determine the cellulose yarns of cotton and hemp, is very good results.

Table 4. The result of qualitative analysis for each sample by KNN

Fig. 12. Measurement system for yarn products

**5.2.2 Analysis of the composition of yarn products** 

of each samples in this wavenumber range.

measurement condition.

Fig. 13. Infrared absorption spectra of cotton and hemp

intensity difference is the difference between the percentage of cellulose in cotton and hemp. And, the cotton and hemp has slightly different components (such as pectin) present, but those absorption peaks are very small. In this method to accurately measure this subtle difference, qualitative analysis for each yarn was possible.

#### **5.2.3 Measurement of mixture ratio of mixed yarn**

In textile products, the mixed yarn including multiple compositions is present. In the mixed yarn should seek the composition and percentage of each composition. Measurement of composition percentage of mixed yarn, we used cotton and polyester mixed yarn samples 45 kinds. These yarns were measured 5 times each spectrum, the average of the measured spectra. Developing a calibration curve by PLSR, focusing on the absorbance wavenumber specific absorption peaks in the components of the cotton and polyester in average spectra. Substituting the spectrum of the unknown sample to the calibration curve, calculated the composition percentage of cotton.

The absorption spectra of the yarn are shown in Fig.14. In the Fig.14, "C" shows for the percentage of cotton, and "P" shows for the percentage of polyester (C100P0 : 100% cotton yarn, C90P10 : 90% cotton and 10% polyester mixed yarn,... , C0P100 : 100% polyester yarn). These absorption spectra are changing the percentage of mixed of the cotton and polyester in increments of 10 %. There are several wavenumber ranges of absorbance changing just like the mixture ratio. The absorbance of 3392 cm-1, 1624 cm-1, 1226~1039 cm-1 peaks are proportional to the percentage of cotton. And, the absorbance of1738 cm-1, 1409 cm-1, 1298 cm-1 peaks are proportional to the percentage of polyester. Therefore, by analysis in the wavenumber range including these characteristic absorption peaks, we can determine the mixture ratio. Using the spectra of cotton and polyester mixed yarn, the results of PLSR in wavenumber range in 3000~700 cm-1 is shown in Fig.15-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 composition percentage of cotton, and the vertical axis is the estimated value of cotton from the PLSR. The results of the correlation coefficient and the measurement accuracy (SEC) were 0.988 and ±3.4%. This result indicates a highly significant correlation. This calibration curve is considered to be reasonable. Thus, this calibration curve is good result, we used this calibration curve of cotton and polyester mixed yarn.

Introduction of Non-Invasive Measurement Method by Infrared Application 95

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-

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

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

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

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

spectrum for class making and spectrum for verification and were constructed.

**5.3 Analysis of the composition and mixture ratio for fabric products by diffuse** 

**reflection method** 

**5.3.1 Measurement system for fabric products** 

IR, and a mirror mounted on the sample fabric.

Fig. 16. Measurement system for fabric products

five points on each sample to measure the entire fabric.

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

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 percentage of mixed yarn.

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

#### **5.3 Analysis of the composition and mixture ratio for fabric products by diffuse reflection method**
