WO2016027792A1

WO2016027792A1 - Fiber identification method

Info

Publication number: WO2016027792A1
Application number: PCT/JP2015/073066
Authority: WO
Inventors: 高柳　正夫; 季織吉村; 暢山形; 健安藤; 麻奈美菅野
Original assignee: 一般財団法人ニッセンケン品質評価センター
Priority date: 2014-08-22
Filing date: 2015-08-18
Publication date: 2016-02-25
Also published as: JPWO2016027792A1; JP2020034565A; JP6798885B2

Abstract

Provided is a fiber identification method exhibiting objectivity, using a comparatively simple identification operation, and capable of identifying fibers of the same category but different type, without relying on the experience or know-how of an inspector. The method involves: obtaining absorption spectra by selecting a plurality of single fibers, the fiber type of which is known, and mixed fibers obtained by mixing the known single fibers in a series of mixing ratios as fibers for comparison, and irradiating each of the fibers for comparison with infrared rays in a wavenumber range of 4,000cm^-1 to 500cm^-1, excluding near-infrared rays; extracting spectrum data in a prescribed wavenumber range from these absorption spectra, and accumulating analysis data groups obtained by multivariate analysis as a database; and next, selecting a fiber, the fiber type of which is unknown, as a fiber to be tested, obtaining analysis data from the absorption spectrum of this fiber to be tested, collating with the data groups in the database, and identifying the mixing ratio and type of the fiber to be tested.

Description

Fiber identification method

The present invention relates to a fiber discrimination method for discriminating the type and mixed rate of fibers used in textile products or woven or knitted fabrics. In particular, the present invention relates to a fiber discrimination method for differentiating different types of similar fibers such as cellulosic fibers and animal hair fibers.

In the market, many textile products are distributed for wide use. In addition, today, where textile products are produced and consumed globally, in order to ensure the safety and trust of transactions when importing and exporting textile products, textile inspection is conducted by textile-related inspection organizations in each country. Has been done.

In these inspection organizations, for example, in Japan, JIS L-1030-1 (Fiber product mix rate test method-Part 1: Fiber discrimination) and JISL 1030-2 (Fiber product mix rate test method-2 Part: fiber mixed rate).

For example, the discrimination method in JIS L1030-1 (Fiber product mixed rate test method-Part 1: Fiber discrimination) includes a combustion test, a chlorine check test in a fiber, a nitrogen check test in a fiber, a microscopic test, There are coloring test with iodine-potassium iodide solution, xanthoprotein reaction test, infrared absorption spectrum measurement test and so on.

These test methods are effective, and many fibers can be identified by combining them. However, fibers having the same chemical composition (hereinafter referred to as “similar heterogeneous fibers”), for example, cellulose fibers such as cotton, various hemps, various rayons, copper ammonia rayon, solvent-spun cellulose fibers, cashmere, wool, yak, mohair Animal fibers such as Angola, Alpaca, Vicuna, Camel, and Llama are distinguished from the same or similar fibers by the chemical test method among the above test methods. Therefore, these chemical test methods cannot clearly distinguish homologous heterogeneous fibers.

Also, the above test method includes an infrared absorption spectrum measurement test. However, this measurement test is intended only to distinguish fibers having different chemical compositions (hereinafter referred to as “heterogeneous fibers”), and it is said that homogenous heterogeneous fibers cannot be clearly distinguished.

Therefore, a microscopic test mainly using the difference in appearance characteristics as an index is effective and widely used for the discrimination of similar fibers such as cellulosic fibers and animal hair fibers. In order to perform fiber discrimination by this microscopic test, an inspector visually compares the fiber to be inspected with a standard photograph sample using an optical microscope. In addition, in this discrimination method, in order to obtain the mixed fiber mixture rate, an inspector visually determines the number and diameter of different types of fibers contained in the mixed fiber to be inspected using an optical microscope, or separates and determines each of the mixed fibers. This is done by measuring the weight.

Therefore, in these methods, there is a problem that the discrimination results vary due to differences in the experience and know-how of the inspectors of each inspection organization. In addition, when the mixed rate is obtained, there is a problem that the inspector is very complicated and requires a long time. Further, expensive animal hair fibers and the like are often subjected to elaborate disguise, and there is a problem that accurate discrimination cannot be performed only by the above method.

Further, among cellulose fibers, solvent-spun cellulose fibers such as Tencel (registered trademark) and Lyocell (registered trademark) (hereinafter also referred to as “lyocell”) have an appearance characteristic of copper ammonia rayon (hereinafter “cupra”). It has the same circular cross-section as (also called). In addition, the fiber products using cashmere, which is considered to be a particularly high-class animal hair fiber, are mixed with yak hair that is difficult to distinguish from cashmere, or the wool scale is removed (referred to as “descaling”). Elaborate camouflage such as mixing is performed. In such a case, it is difficult for an inspector who has experience in a microscopic examination to make an accurate judgment.

On the other hand, the following Patent Document 1 proposes a method for discriminating between cellulose fiber (same as solvent-spun cellulose fiber) and cupra, which are cellulosic heterogeneous fibers. In this discrimination method, the dissolved state when both fibers are immersed in 61% or more of sulfuric acid is observed under a microscope to discriminate cellulosic homologous different fibers.

Further, in Patent Document 2 below, a fiber discrimination method and a fiber discrimination device for animal hair similar and different fibers are proposed. This fiber discrimination method uses the terahertz spectroscopy that uses electromagnetic waves with longer wavelengths than general infrared rays to analyze the cell structure (primary structure) and cell aggregation (higher order structure) of animal hair fibers. The system distinguishes between similar and dissimilar fibers.

Furthermore, in the following non-patent document 1, the inventor of the present invention proposes discrimination of a fabric material using near infrared spectroscopy having a wavelength shorter than that of general infrared rays. This discriminating method applies a technique of spectrum fluctuation analysis by Fourier transform to near-infrared spectroscopy.

Further, in the following Non-Patent Document 2, the inventor of the present invention proposes fiber discrimination and mixed ratio measurement of a blended fiber fabric by near infrared spectroscopy. This discrimination method is an application of an analysis method using near infrared spectroscopy and chemometrics (a coined word consisting of chemistry indicating chemistry and metrics indicating metric).

JP-A-10-332684 JP 2011-203138 A

The discrimination method of the above-mentioned Patent Document 1 is to observe the dissolved state of the fiber under a microscope. In this case as well, there is a problem that the discrimination results vary due to differences in inspector experience and know-how. In addition, when the fibers are dyed or processed with resin, there is a problem in that the state of dissolution changes and accurate discrimination cannot be performed.

Moreover, in the fiber discrimination method of the above-mentioned Patent Document 2, if the fiber to be identified is not sufficiently smaller in size (about 10 μm) than the wavelength of the terahertz electromagnetic wave, the incident terahertz electromagnetic wave is scattered and incident on the detector. The intensity of is attenuated. Therefore, there is a demand for a method of freeze pulverization while preventing a temperature rise associated with pulverization. These operations are complicated, and there is a problem that the higher-order structure of the fiber is destroyed by pulverization, and the amount of information is reduced.

On the other hand, the discrimination method of Non-Patent Document 1 suggests the possibility of discrimination, but has not yet made an accurate discrimination. Further, in the discrimination method of Non-Patent Document 2 above, it suggests the possibility of obtaining the blending rate of cotton-polyester blended fabrics that are mainly different fibers. It is not a suggestion.

Therefore, the present invention addresses the above-described problems, and provides a fiber discrimination method that is relatively easy to differentiate and has objectivity, and that can distinguish between different types of similar fibers without relying on the experience and know-how of the inspector. The purpose is to provide.

In solving the above-mentioned problems, the present inventors, as a result of earnest research, adopted general infrared spectroscopy, which has abundant amount of information of absorption spectrum compared to terahertz spectroscopy and near infrared spectroscopy, and obtained absorption It was found that spectral data of a specific wavelength range was extracted from the spectrum, and this type of information was statistically processed to distinguish homologous heterogeneous fibers. Thus, the present invention was completed.

That is, according to the description of claim 1, the fiber discrimination method according to the present invention is as follows.
A fiber discrimination method for differentiating different types of similar fibers classified as the same, such as cellulosic fibers and animal hair fibers,
And providing a plurality of comparison fibers types of fibers to originate from a plurality of known single fibers, and irradiating infrared rays in the wave number range of 4000 cm -1 ^~ 500 cm ^-1, excluding the near infrared for each comparative fibers Find each absorption spectrum,
A database creation step of extracting spectrum data in a predetermined wavenumber region from these absorption spectra, and accumulating as a database an analysis data group obtained by multivariate analysis of the spectrum data;
The test fiber is an unknown fiber type, and the analysis data is obtained from the absorption spectrum of the test fiber in the same manner as the database creation step.
A verification step of comparing the analysis data of the test fiber with the data group of the database, and using the consistency between the analysis data and the data group of the database as an index, ing.

Moreover, according to the description of Claim 2, this invention is the fiber identification method of Claim 1,
In the cellulosic fiber, in the database creation step, two types of comparative fibers according to the following combinations,
(1) Natural fiber, pair, regenerated fiber,
(2) Cotton, pair, hemp,
(3) Flax, VS, Hemp,
(4) Viscose rayon, pair, copper ammonia rayon or solvent-spun cellulose fiber,
(5) Copper ammonia rayon, pair, solvent-spun cellulose fiber,
Each analysis data obtained by multivariate analysis of the spectrum data of is accumulated as a database of differential models related to each combination,
The analysis data of the test fiber is collated with a discrimination model according to each combination of the databases, and the type of the test fiber is discriminated.

According to the description of claim 3, the present invention is the fiber discrimination method according to claim 1,
In addition to the plurality of comparative fibers originating from the plurality of single fibers, a series of comparative fibers composed of mixed fibers prepared by mixing the same type of different kinds of fibers prepared in advance at a series of mixing ratios are prepared. A series of analysis data groups obtained in the same way as the database creation process is accumulated as a database,
The analysis data of the test fiber is collated with a series of analysis data groups of the database, the type of the test fiber, the test fiber is a mixture of at least two kinds of similar dissimilar fibers, It is characterized by discriminating the kind of the same kind of different fibers mixed in the test fiber and / or the mixed rate of the test fiber.

According to the description of claim 4, the present invention is the fiber discrimination method according to claim 3,
In the cellulosic fiber, in the database creation step, in addition to the spectral data of the plurality of comparison fibers originating from the plurality of single fibers, two types of comparison fibers according to the following combinations,
(1) Natural fiber, pair, regenerated fiber,
(2) Cotton, pair, hemp,
(3) Flax, VS, Hemp,
(4) Viscose rayon, pair, copper ammonia rayon or solvent-spun cellulose fiber,
(5) Copper ammonia rayon, pair, solvent-spun cellulose fiber,
Each analysis data obtained by multivariate analysis of the spectrum data of the mixed fiber mixed with a series of mixed rate is accumulated as a database of differential models of mixed rate related to each combination,
The analysis data of the test fiber is collated with a discrimination model according to each combination of the databases, and the mixed use rate of the test fiber is discriminated.

According to the description of claim 5, the present invention is the fiber discrimination method according to any one of claims 1 to 4,
In the cellulosic fiber, for distinguishing between natural fiber and regenerated fiber, one or two or more spectral data including mainly in the range of wave number 1200 to 850 cm ⁻¹ or in the vicinity thereof are used for the analysis. To do.

Further, according to the description of claim 6, the present invention is the fiber discrimination method according to any one of claims 1 to 4,
For cellulosic fibers, one or more spectral data including mainly in the range of wave numbers 1400 to 900 cm ⁻¹ or in the vicinity thereof are used for the analysis in order to distinguish cotton from hemp. .

Further, according to the description of claim 7, the present invention is the fiber discrimination method according to any one of claims 1 to 4,
Cellulosic fibers are characterized in that one or two or more spectral data including mainly in the range of wave numbers 1400 to 800 cm ⁻¹ or in the vicinity thereof are used for the analysis in order to distinguish between regenerated fibers.

Further, according to the description of claim 8, the present invention is the fiber discrimination method according to any one of claims 1 to 4,
For cellulosic fibers, for distinguishing between copper ammonia rayon and solvent-spun cellulose fibers, one or more spectral data including mainly in the range of wave number 1250 to 900 cm ⁻¹ or in the vicinity thereof is used for analysis. It is characterized by.

According to the description of claim 9, the present invention is the fiber identification method according to any one of claims 1 to 4,
Cellulose fibers are characterized in that, for differentiation between flax and urn, one or more spectral data including mainly in the range of wave numbers 3500 to 3000 cm ⁻¹ or in the vicinity thereof are used for the analysis.

Moreover, according to the description of Claim 10, this invention is the fiber identification method of

Claim

5 or 6,
The cellulosic fiber is characterized in that an absorption spectrum is obtained after pretreatment with an alkaline substance is performed on the comparative fiber and the test fiber.

According to the description of claim 11, the present invention is the fiber discrimination method according to any one of claims 1 to 10,
The multivariate analysis is principal component analysis, or multiple regression analysis such as principal component regression or PLS regression.

According to the description of claim 12, the present invention is the fiber discrimination method according to any one of claims 1 to 11,
The method for obtaining the absorption spectra of the comparative fiber and the test fiber is an ATR method (total reflection measurement method).

According to the description of claim 13, the present invention is the fiber identification method according to any one of claims 1 to 12,
The same type of heterogeneous fibers classified as cellulose fibers include cotton, flax, linseed, jute, cannabis, viscose rayon, high wet modulus rayon, polynosic rayon, copper ammonia rayon, and solvent-spun cellulose fiber. It is characterized by.

According to the said structure, this invention is a fiber discrimination method which discriminate | determines the same type | system | group dissimilar fiber classified into the same type, such as a cellulosic fiber and an animal hair type | system | group fiber. Fibers having the same chemical composition but different origins can be distinguished by infrared absorption spectra. According to the above configuration, first, in the database creation step, a plurality of types of similar and different types of fibers originating from a plurality of single fibers whose fiber types are known are prepared as comparative fibers. Next, infrared absorption spectra for these comparative fibers are obtained. Infrared used uses a range of wave number ^{4000 cm} -1 ~ 500 cm ^-1, excluding the near-infrared.

Next, spectrum data in a predetermined wave number range is extracted from the obtained absorption spectrum. Next, an analysis data group obtained by multivariate analysis of the extracted spectrum data is accumulated as a database. As these databases, a plurality of sets of databases in which two types of single fibers are combined may be created. As described above, by analyzing a plurality of spectral data obtained from the comparative sample and creating a database of the obtained data group, accurate discrimination is made possible. As a result, more accurate fiber discrimination can be performed comparatively easily and objectively.

Further, according to the above configuration, in the subsequent discrimination process, a fiber whose type of fiber to be identified is unknown is prepared as a test fiber. Next, spectrum data in a predetermined wave number region is extracted from the absorption spectrum of the test fiber in the same manner as the database creation step, and analysis data is obtained. The analysis data of the test fiber is collated with the data group of the database. This makes it possible to objectively identify the type of test fiber.

Further, according to the above configuration, in the database creation process, in addition to a database for a plurality of comparative fibers originating from a single fiber, a plurality of types of similar heterogeneous fibers are mixed in advance with a series of mixed ratios. You may make it produce the database with respect to a series of comparison fibers which consist of the mixed fiber which were made. As these databases, a plurality of sets of databases in which two types of single fibers are combined may be created. Next, in the discrimination step, the analysis data of the test fiber is collated with a series of analysis data groups in the database. Thus, even when the test fiber is a mixture of at least two types of fibers, the type of the mixed fibers and the mixture ratio can be objectively differentiated.

Therefore, according to the above configuration, objective fiber discrimination can be performed, and the type of the test fiber can be accurately discriminated. Since these operations are relatively simple and are instrumental analysis, there is no variation in discrimination results due to differences in inspector experience and know-how.

Further, according to the above configuration, when obtaining the analysis data of the comparative fiber and the test fiber, the spectrum data in a predetermined wave number region extracted by a combination of similar and different fibers to be differentiated may be changed. In this way, the discrimination accuracy can be further improved by selecting the wave number region used for the analysis.

In addition, according to the above configuration, the absorption spectrum may be obtained after the comparison fiber and the test fiber are pretreated with an alkaline substance. Thus, the accuracy of discrimination can be further improved by performing pre-processing.

Therefore, according to the present invention, it is possible to provide a fiber discrimination method that has a relatively simple discrimination operation, has objectivity, and can discriminate between different types of fibers without depending on the experience and know-how of an inspector. .

It is a figure which shows the absorption spectrum (average spectrum) of various cellulosic fibers. It is a figure which shows the spectrum (differential spectrum) which carried out the primary differentiation of each spectrum of FIG. It is a discrimination flow figure showing the analysis procedure which discriminates test fiber in a 1st embodiment. It is the partial flowchart which extracted a part of identification flowchart of FIG. 2 is a scatter diagram of principal component scores of “natural fibers” and “regenerated fibers” obtained in Example 1. FIG. 2 is a scatter diagram of principal component scores of “cotton” and “hemp” obtained in Example 1. FIG. 4 is a scatter diagram of principal component scores of “linen” and “ramie” obtained in Example 1. FIG. 4 is a scatter diagram of principal component scores of “rayon” and “cupra and lyocell” obtained in Example 1. FIG. 4 is a scatter diagram of principal component scores of “cupra” and “lyocell” obtained in Example 1. FIG. It is a discrimination flow figure showing the analysis procedure which discriminates test fiber in a 2nd embodiment. FIG. 6 is a scatter diagram of principal component scores of “natural fibers” and “regenerated fibers” in which principal component scores of test fibers are plotted in Example 2. FIG. 6 is a scatter diagram of principal component scores of “cotton” and “hemp” plotting principal component scores of test fibers in Example 2. FIG. 6 is a scatter diagram of principal component scores of “rayon” and “cupra and lyocell” in which principal component scores of test fibers are plotted in Example 2. It is the figure which compared the mixed use rate of the test fiber calculated | required in Example 2 with the conventional method. FIG. 6 is a scatter diagram of principal component scores of “natural fibers” and “regenerated fibers” in which principal component scores of test fibers are plotted in Example 3. FIG. 6 is a scatter diagram of principal component scores of “cotton” and “hemp” plotting principal component scores of test fibers in Example 3. FIG. 6 is a scatter diagram of principal component scores of “rayon” and “cupra and lyocell” in which principal component scores of test fibers are plotted in Example 3. FIG. 6 is a scatter diagram of principal component scores of “cupra” and “lyocell” in which principal component scores of test fibers are plotted in Example 3. It is the figure which compared the mixing ratio of the test fiber calculated | required in Example 3 with the conventional method.

In the present invention, the fiber generally means all fibers used in various textile products such as clothing and industrial materials. For example, examples of synthetic fibers include polyester, nylon, and acrylic. Examples of semi-synthetic fibers include acetate. Examples of natural cellulose fibers include cotton, and hemp such as flax (linen), ramie (ramie), jute, and hemp. Examples of the regenerated cellulose fiber include viscose rayon, high wet modulus rayon (also referred to as “HWM rayon”), polynosic rayon, copper ammonia rayon (cupra), solvent-spun cellulose fiber (tensel and lyocell), and the like. Hereinafter, viscose rayon, high wet modulus rayon (HWM rayon) and polynosic rayon, which are fibers regenerated by the viscose method among these regenerated cellulose fibers, are also referred to as “viscose rayon”.

In addition to silk, protein fibers include wool (sheep wool), cashmere (cashmere goat hair), yak (cow yak hair), mohair (Angola goat hair), and angora Hair), alpaca (small humpless camel alpaca hair), vicuna (small humpless camel vicuna hair), camel (camel hair), llama (small humpless camel llama hair), fox (fox's And hair fibers such as mink (weasel type mink hair), chinchilla (mouse type chinchilla hair), rabbit (rabbit hair) and the like.

Among these fibers, in the present invention, fibers having the same chemical composition are defined as “similar heterogeneous fibers”. As this similar dissimilar fiber, the fiber group contained in the above-mentioned natural cellulose fiber and regenerated cellulose fiber is made into a cellulose fiber, for example. Moreover, let the fiber group contained in protein fibers other than the above-mentioned silk be an animal hair fiber.

These similar and different fibers have the same chemical composition and are not easily distinguished. In particular, optical measurement methods such as infrared absorption spectrum, which is an easy-to-use and objective differentiation method, are effective in differentiating different fibers with different chemical compositions. It has been. On the other hand, the present invention is effective in differentiating cellulosic fibers or animal hair fibers, which are similar and different fibers.

For example, in the case of cellulosic fibers, it is possible to distinguish between natural cellulose fibers and regenerated cellulose fibers. In addition, it is possible to distinguish between cotton and hemp, which are the same natural cellulose fibers. In addition, it becomes possible to distinguish flax (linen) and ramie (ramie), which are the same hemp. Furthermore, it becomes possible to distinguish viscose rayon, which is the same regenerated cellulose fiber, from “cupra and lyocell”. In addition, it is possible to distinguish between cupra and lyocell, which are the same regenerated cellulose fibers and have a circular fiber cross-sectional shape and are difficult to determine by microscopic observation. Furthermore, in animal hair fibers, it is possible to distinguish high-quality cashmere from yak that resembles this, and descaled wool.

Here, when the database is created in the present invention, infrared absorption spectra are obtained from fibers having known fiber types (hereinafter referred to as “comparison fibers”) and fibers to be differentiated (hereinafter referred to as “test fibers”). The method of obtaining is described. This method is generally referred to as infrared spectroscopy (hereinafter referred to as “IR spectroscopy”). Irradiation of a material to be measured with infrared rays and spectrum of transmitted light or reflected light is obtained to obtain characteristics of the object. It is a way to know. This IR spectroscopy is used to know the molecular structure and state of an object, and is a very general method for analyzing organic substances having different chemical compositions.

In general, spectroscopy is a method for measuring the emission or absorption of electromagnetic waves widely from radio waves to gamma rays. Among them, IR spectroscopy is widely used not only in research departments but also in manufacturing departments and quality control departments. is doing. In general, infrared rays are distinguished as near infrared rays, middle infrared rays, and far infrared rays, but they may be further finely distinguished, and the definition of the wavelength range is not clear. Therefore, in the present invention, the wavelength range used in IR spectroscopy is 2500 nm to 20000 nm excluding near infrared rays. Further, the IR spectroscopy, it is often (in the present invention also referred to as "WN") also wavenumber than the wavelength using the wave number range used in the present invention, the ^{4000 cm} -1 ~ 500 cm ^-1, excluding the near infrared Within range.

Here, differences from near-infrared spectroscopy (hereinafter referred to as “NIR spectroscopy”) proposed for use in various industries in recent years will be described. Generally, the wavelength range used in the NIR spectroscopy is ^{800nm ~ 2500nm (12500cm -1 ~ 4000cm} -1 in wavenumber ranges). NIR spectroscopy has extremely small absorption compared to general IR spectroscopy, and therefore, non-destructive and non-contact measurement is possible. On the other hand, direct association with chemical composition is difficult, and the amount of information is extremely small compared to general IR spectroscopy. However, the development of chemometrics by multivariate analysis has made it possible to apply to quantitative analysis in recent years.

Here, the boundary of the wavenumber region used in IR spectroscopy and NIR spectroscopy is set to 4000 cm ⁻¹ . Therefore, there may be doubts whether 4000 cm ⁻¹ itself is near infrared or mid infrared. Therefore, in the present invention, when it is necessary to strictly interpret the meaning of ^"4000cm range of -1 ~ 500 cm ^-1, excluding the near-infrared", the "wave number (WN) is ^4000cm ^-1> WN ≧ 500cm -1 Within the range of ".

As described above, NIR spectroscopy has been proposed for use in fiber discrimination (

Patent Documents

1 and 2 above). However, due to the small amount of information, practical discrimination between homologous and dissimilar fibers and accurate discrimination of the mixed rate have not been achieved. Therefore, the present invention is generally used only for differentiating different fibers having different chemical compositions in spite of a large amount of information obtained from the fiber, and has not been used for differentiating similar different fibers. It uses the law. In addition, the infrared spectrophotometer used in the present invention is inexpensive because it is used in many industries, and the cost of equipment investment and maintenance for identification can be suppressed.

In the present invention, a commonly used Fourier transform infrared spectrophotometer (hereinafter referred to as “FT / IR spectrophotometer”) can be used. In order to obtain the absorption spectrum of the comparative fiber and the test fiber, the fiber may be finely pulverized to form a tablet together with potassium bromide (KBr) powder, and measurement may be performed by the KBr tablet method in which the transmitted light is measured. Or you may make it measure by ATR method (total reflection measuring method) which can measure reflected light as it is, without destroying a woven or knitted fabric. In addition, the obtained absorption spectrum is preferably subjected to atmospheric correction, baseline correction, smoothing correction, submersion depth correction, and the like by a normal method as necessary.

In addition, when pulverizing fibers in the KBr tablet method, cutting with scissors, pulverization with a mill, freeze pulverization, or the like can be used. Further, the length and diameter of the fiber after pulverization may be arbitrary, but pulverization is preferable. Moreover, it is preferable to control the moisture absorption state of the fiber regardless of whether the KBr tablet method or the ATR method is employed. In particular, in the case of cellulosic fibers and animal hair fibers having high hygroscopicity, the hygroscopicity is different among the same type of different fibers, and the absorption spectrum may be affected by free water in the fibers.

Here, the absorption spectrum of each fiber of cotton, hemp (linen and ramie), viscose rayon (hereinafter, simply referred to as “rayon”), cupra, and lyocell contained in the cellulose fiber is compared. . FIG. 1 is a diagram showing absorption spectra (average spectra) of various cellulosic fibers. Each absorption spectrum of FIG. 1 (1-6) was measured in a wave number range of ^{4000 cm} -1 ~ 500 cm ^-1 in the ATR method of FT / IR spectrophotometer. In FIG. 1, Avicel (registered trademark, Asahi Kasei Kogyo), which is crystalline cellulose (6), was compared with each fiber (1-5) as a standard sample. These absorption spectra are average spectra normalized by standard deviation.

In FIG. 1, the absorption spectra of rayon (3), cupra (4), and lyocell (5) that are regenerated fibers from 3600 cm ⁻¹ to 3100 cm ⁻¹ are the natural fibers of cotton (1) and hemp (2). Compared to, a wider peak width is shown. In addition, a sharp peak appears at 3330 cm ⁻¹ in natural fibers such as cotton (1) and hemp (2), but in regenerated fibers such as rayon (3), cupra (4), and lyocell (5). Absent. ^Furthermore, the main peak appearing over the 1200 cm -1 ~ 800 cm ^-1, cotton (1) is a natural fiber, two peaks appear in the vertex hemp compound (2), a regenerated fiber Rayon (3), cupra (4) Only one peak appears in lyocell (5).

Thus, there is a possibility that natural fibers and regenerated fibers can be differentiated by observing in detail the absorption spectrum in the range of wave numbers from 4000 cm ⁻¹ to 500 cm ⁻¹ . However, by simply comparing these absorption spectra, it is not easy to clearly distinguish natural fibers from regenerated fibers in cellulosic fibers. In addition, it is difficult to distinguish between cotton and hemp, which are the same natural fibers, to distinguish between linen and ramie, which are the same hemp, between regenerated fibers, and between cupra and lyocell having similar shapes.

In the present invention, the reason why natural fibers and regenerated fibers, which are cellulosic fibers having the same chemical composition, can be distinguished from the absorption spectrum obtained by IR spectroscopy is that the crystalline state of cellulose constituting these fibers is different. It seems to be. In general, regarding the crystal structure of cellulosic fibers, natural fibers are type I crystals and regenerated fibers constitute type II crystals. As for the orientation state of cellulose molecules, natural fibers have a parallel structure in which the reducing ends of the molecules are directed in the same direction, and regenerated fibers have an antiparallel structure in which the reducing ends of the molecules are alternately changed in position.

These differences in crystal state are thought to cause shifts in absorption positions and changes in absorption intensity in the absorption spectra of natural fibers and regenerated fibers. Furthermore, the present inventors have also found that in the same cellulosic natural fiber, cotton and hemp, or in the same cellulosic regenerated fiber, rayon, cupra, lyocell, crystallinity, crystal size, crystal distribution. Therefore, it was predicted that some information was included in the absorption spectrum of IR spectroscopy.

Hereinafter, the fiber identification method according to the present invention will be described in detail by each embodiment. The present invention is not limited to the following embodiments. In the present invention, spectrum data of each type of comparative fiber is subjected to multivariate analysis, and the obtained analysis data group is accumulated as a database (also referred to as “differentiation model”). This process is called “database creation process”. Next, the analysis data obtained from the spectrum data of the test fiber is collated with the analysis data group in the database. In this collation, the type and mixed rate of the test fiber are discriminated using the consistency between the analysis data of the test fiber and the data group of the database of the comparison fiber as an index. This process is called “discrimination process”.

<< First Embodiment >>
In this 1st Embodiment, discrimination between each single fiber is performed with respect to multiple types of single fiber. For example, in the case of cellulosic fibers, the differentiation between natural fibers and regenerated fibers, the differentiation between cotton and hemp, the differentiation between linen and ramie, the differentiation between regenerated fibers, and the differentiation between cupra and lyocell will be explained. .

(1) Database creation process In this 1st embodiment, when distinguishing the combination of the fiber which is going to be discriminated, for example, cotton and hemp, absorption spectrum is calculated for these fibers as comparative fibers. In addition, you may make it perform various correction | amendment with respect to these absorption spectra by a predetermined method. Examples of these corrections include correction of the depth of penetration of infrared light due to changes in wave number, or multiplicative scattering correction (MSC).

Here, when obtaining the absorption spectrum of cotton, a plurality of samples made of cotton are taken as one group, and each absorption spectrum is obtained. Moreover, also when calculating | requiring the absorption spectrum of hemp, each absorption spectrum is calculated | required by making the some sample which consists of hemp into one group. Here, the absorption spectrum may be obtained with linen, ramie, jute, hemp and the like as hemp as one hemp group. Further, for example, an absorption spectrum may be obtained with linen and ramie as separate groups, and each group may be distinguished from a cotton group.

In the case of cellulosic fiber products, fiber processing with an alkaline substance may be performed in order to improve dyeability and physical properties at the stage of becoming a woven or knitted fabric. As this fiber processing, generally an immersion treatment with an alkaline aqueous solution or liquid ammonia is performed. In particular, the soaking treatment of cotton with an aqueous sodium hydroxide solution is called “mercerizing” and is widely performed. In addition, an infrared absorption spectrum may change a little depending on whether the fiber processing by an alkaline substance is given to the cellulosic fiber which is a discrimination target. Therefore, when discrimination is performed using a sample group in which the presence or absence of fiber processing using an alkaline substance is mixed, the discrimination accuracy may be reduced.

In addition, cellulosic fiber products that have been subjected to fiber processing with an alkaline substance and cellulosic fiber products that have not been subjected to fiber processing with an alkaline substance are on the market. Accordingly, the present inventors have found that the discrimination accuracy is improved by obtaining an infrared absorption spectrum after pre-treating the comparative fiber and the test fiber with an alkaline substance having a predetermined concentration. In other words, by performing pretreatment with an alkaline substance on comparative fibers and test fibers mixed with or without fiber processing with an alkaline substance, these are unified and absorbed as cellulosic fibers subjected to fiber processing with an alkaline substance. The spectrum approximates and the discrimination accuracy is improved.

The conditions for the pretreatment with the alkaline substance are not particularly limited, and may be appropriately selected depending on the kind and concentration of the alkaline substance to be used, the kind of fiber to be treated, and the like. For example, when the cellulosic fiber is cotton, it is 8 to 24% by weight as an alkaline substance, preferably 10 to 20% by weight, more preferably 15 to By using a 20 wt% sodium hydroxide aqueous solution and soaking at room temperature, the discrimination accuracy is improved. *

In addition, for both the comparison fiber when creating the database, and the infrared absorption spectrum that has not been subjected to the pretreatment with the alkaline substance and the infrared absorption spectrum that has not been applied to the test fiber to be identified, The discrimination accuracy is further improved by using these in combination.

Furthermore, each fiber has distinction between woven and knitted fabrics, differences in yarn thickness, presence or absence of dyeing, presence or absence of fiber processing agents, and the like. Therefore, the absorption spectrum of each group is obtained in consideration of the relationship between the influence of these pieces of information on the absorption spectrum and the wave number range (described later) used for analysis.

In the first embodiment, an FT / IR spectrophotometer is used when obtaining the absorption spectrum of the comparative fiber. This is because the FT / IR spectrophotometer is inexpensive because it is used in many industries, and the cost of equipment investment and maintenance for identification can be suppressed. In the first embodiment, the ATR method is used for measurement. This is because, in many cases, the fibers to be identified are already in a woven or knitted state as a fiber product, and there is an advantage that the fibers can be identified in a non-destructive state in the woven or knitted state. In addition, although the kind of prism used for ATR method is not specifically limited, In the case of a fiber sample, it is generally preferable to use a ZnSe prism.

In the first embodiment, it obtains the respective absorption spectrum by irradiating infrared range of wave number ^{4000 cm} -1 ~ 500 cm ^-1, excluding the near-infrared. Here, “excluding near infrared rays” does not mean that “the absorption spectrum of the near infrared portion is not measured” when measuring the absorption spectrum. One in which construed as "range of the absorption spectrum to be used for discrimination is intended to be within the range of the wave number ^4000cm ^-1 ~ 500cm -1".

Further, in this first embodiment, it is not intended to analyze the absorption spectra of all regions in the wave number range of ^4000cm ^-1 ~ 500cm -1. In the first embodiment, spectrum data in a predetermined wave number range is used for analysis. This is because by limiting the wave number range used for the analysis, infrared absorption necessary for discrimination is emphasized and noise is eliminated to improve discrimination accuracy.

Here, for extraction of spectrum data in a predetermined wave number range, a measurement region may be determined in advance, and an absorption spectrum may be obtained only within that range. However, in general, it is preferable to extract spectral data in a predetermined wave number range required for analysis from the absorption spectrum obtained in the entire measurement region of the spectrophotometer used.

In addition, it is preferable to select the predetermined wave number range here as appropriate depending on the type and combination of fibers to be identified. For example, in cellulosic fibers, natural fiber and regenerated fiber, cotton and linen, linen and ramie, regenerated fibers, cupra and lyocell, etc. There is an appropriate wavenumber range. In addition, about the predetermined wave number range used for discrimination, you may make it analyze only in one wave number range, or you may make it analyze combining two or more several wave number ranges.

Here, in analyzing the spectral data of the comparative fiber, it is preferable to first preprocess the spectral data. For the pre-processing, for example, first-order differentiation, second-order differentiation, or higher-order differentiation processing is preferably performed. This is because spectral information can be enhanced by such differential processing, such as sharpening a peak buried in the spectrum or eliminating the influence of the background. The method and dimension used in the differentiation process are not particularly limited and are preferably selected as appropriate depending on the type and combination of fibers to be identified. As an example, FIG. 2 shows the spectra (1 to 5) obtained by first-order differentiation of the spectra (1 to 5) in FIG. 1 by the Savitzky-Golay method.

Next, spectrum data in a predetermined wavenumber region effective for analysis is extracted from the obtained differential spectrum. The method for extracting the spectral data is not particularly limited. You may make it select the wave number range by the specific functional group considered to be required for an analysis with respect to the kind and combination of the fiber to identify. Further, spectrum data in a wave number range considered to have noise for analysis may be positively excluded. For example, in the case of cellulosic fibers, spectral data in the range of wave numbers from 2750 to 1850 cm ⁻¹ may not be used for analysis. This is because there is little information effective for analysis from this wave number range, and conversely, absorption of CO _{2 or} the like may appear as noise.

For example, in the differentiation of cellulosic fibers, spectral data in the range of wave numbers 3500 cm ⁻¹ to 3000 cm ⁻¹ (O—H) and in the range of wave numbers 1200 cm ⁻¹ to 1000 cm ⁻¹ (C—OH, C— Since spectrum data of O-C, C-C) and the like are considered important, they may be analyzed in combination. However, in distinguishing highly hygroscopic cellulosic fibers, spectral data within a wave number range of 3500 cm ⁻¹ to 3000 cm ⁻¹ is easily affected by the hygroscopic state, and it is conceivable to distinguish them by eliminating them.

On the other hand, as another method of extracting spectrum data, wave number range selection may be performed using various analysis software such as PCR Moving Window. Here, in PCR Moving Window, first, a spectrum of a predetermined width is cut out from the end, and principal component regression (PCR) is performed on that portion to create a calibration model. The predicted value derived from this calibration model is compared with the actual measurement value, and the residual sum of squares is recorded. Next, the same operation is performed by moving the region to be cut out to the region adjacent to one spectrum point. By performing this over the entire wave number range, the part where the difference between the predicted value and the actual measurement value becomes small is selected as the wave number range necessary for the analysis.

For example, the following spectral data could be extracted using PCR Moving Window. For example, in the discrimination between natural fibers and regenerated fibers, spectral data within a wave number range of 1200 to 850 cm ⁻¹ (C—OH, C—O—C, C—C) was extracted. Further, in the discrimination between cotton and linen, spectral data in the range of wave numbers 1400 to 900 cm ⁻¹ (O—H, C—OH, C—O—C, C—C) were extracted. Further, in the discrimination between linen and ramie, spectral data in the range of wave numbers 3500 to 3000 cm ⁻¹ (O—H) was extracted. Further, in the discrimination between regenerated fibers, that is, the discrimination between rayon and “cupra and lyocell”, the spectrum in the range of wave numbers 1400 to 800 cm ⁻¹ (OH, C—OH, C—O—C, C—C) Data was extracted. Further, in the discrimination between cupra and lyocell, spectral data in the range of wave numbers 1250 to 900 cm ⁻¹ (C—OH, C—O—C, C—C) were extracted.

In addition, about the spectrum data extracted using PCR Moving Window, the extracted range may be used for analysis as it is, or the range in the vicinity of the extracted range is analyzed in order to further improve the analysis accuracy. You may make it use for. That is, for the range extracted by PCR Moving Window (for example, wave number 1250 to 900 cm ⁻¹ ), the analysis has a slightly wider range (for example, wave number 1300 to 850 cm ⁻¹ ), or within a slightly narrow range (for example, wave number 1250 to 900 cm ⁻¹ ). A wave number of 1200 to 1000 cm ⁻¹ ) may be used.

Next, multivariate analysis is performed on the extracted spectrum data to obtain an analysis data group. The analysis method used for the multivariate analysis is not particularly limited, and any method may be adopted as long as it is an analysis method used for chemometrics. In general, multivariate analysis methods include principal component analysis, multiple regression analysis, independent component analysis, factor analysis, discriminant analysis, quantification theory, cluster analysis, and multidimensional scaling method. In the present invention, it is preferable to use principal component analysis (PCA) or multiple regression analysis such as principal component regression (PCR) or PLS regression among these. In particular, when performing discrimination between each single fiber in the first embodiment, it is preferable to use principal component analysis (PCA). The software used for each analysis is not particularly limited.

In the first embodiment, discrimination using principal component analysis (PCA) will be described. Specifically, a principal component analysis is performed on one or two or more wave number spectrum data groups extracted from absorption spectra of two fiber groups to be distinguished (for example, cotton and hemp), and each principal component score is analyzed. Ask for. The analysis software used for the principal component analysis is not particularly limited. In the first embodiment, analysis was performed using a program constructed by the inventor himself using commercially available program creation software. Moreover, after classifying two fiber groups (for example, cotton and hemp) which should be distinguished beforehand, you may make it perform a layered analysis by a principal component analysis. In this case, the principal component analysis is performed after setting a dummy variable in which one fiber (for example, cotton) to be identified is set to “1” and the other fiber (for example, hemp) is set to “0”. It may be.

The analysis data group when the principal component analysis is performed on the spectrum data of each fiber group combination thus obtained is stored as a database (differentiation model). These differentiation models will be described in detail in Example 1 below.

(2) Identification process In the identification process of the first embodiment, first, an absorption spectrum of a test fiber to be identified is obtained. A method for obtaining an absorption spectrum, a method for performing various corrections on the absorption spectrum, and a method for performing a pretreatment such as a differentiation process on the obtained absorption spectrum are the same as those for the above-described comparative fiber. Next, spectral data in the same wave number region as that of the comparative fiber is extracted from the obtained differential spectrum, and each principal component score (described later) is obtained from the spectral data in the same manner as the comparative fiber. Next, the principal component score of the obtained test fiber is compared with the principal component score of the database (discrimination model) to discriminate which fiber group the test fiber belongs to. Alternatively, the principal component analysis may be performed by combining the extracted spectral data of the test fiber with the spectral data of the comparative fiber.

In this discrimination step, the type of test fiber is unknown, but it has been found that the test fiber is a cellulosic fiber by relatively simple microscopy. However, it is not clear which of the cellulosic fibers the test fiber is. Therefore, in the first embodiment, it is preferable to perform fiber discrimination according to the following procedure.

FIG. 3 is a discrimination flowchart showing an analysis procedure for discriminating the test fiber in the first embodiment. In FIG. 3, first, it is discriminated whether the test fiber is a natural fiber or a regenerated fiber. At this time, a discrimination model obtained from natural fibers and regenerated fibers is used. Here, when it is determined that the test fiber is a natural fiber, it is subsequently determined whether the test fiber is cotton or hemp. At this time, a discrimination model obtained from cotton and hemp is used. Further, when it is determined that the test fiber is hemp, it is subsequently determined what hemp is the test fiber. In FIG. 3, whether linen or ramie is the most common linen is discriminated. At this time, a discrimination model obtained from linen and ramie is used.

Similarly, in FIG. 3, when it is determined that the test fiber is a regenerated fiber, it is subsequently determined whether the test fiber is rayon or “cupra or lyocell”. At this time, a differential model obtained from rayon and “cupra and lyocell” is used. Here, when it is determined that the test fiber is “cupra or lyocell”, it is subsequently determined whether the test fiber is cupra or lyocell. At this time, a discrimination model obtained from cupra and lyocell is used.

FIG. 4 is a partial flow diagram in which a part of the discrimination flow diagram of FIG. 3 is extracted. In FIG. 4, when a test fiber is identified as a natural fiber or a regenerated fiber, there is a model that cannot be handled by a differential model obtained from a natural fiber and a regenerated fiber. In this case, the test fiber indistinguishable in FIG. 4 may be a mixed fiber of natural fibers and regenerated fibers, or fibers other than cellulosic fibers may be mixed. In the case of a mixed fiber of natural fiber and regenerated fiber, it is necessary to identify the mixed rate according to the second embodiment described later.

Thus, by performing discrimination according to the discrimination flow diagram of FIG. 3, the type of the test fiber can be objectively discriminated. Next, the identification method of the first embodiment will be described in detail with reference to Example 1.

This Example 1 performs discrimination between each single fiber according to the first embodiment, and performs discrimination according to a discrimination flow diagram (see FIG. 3) with respect to a plurality of test fibers. Each of the test fibers has been found to be a cellulosic fiber in preliminary identification such as microscopy.

(1) Database creation step In Example 1, first, an absorption spectrum of 45 points in total, which is 9 points of cotton, 6 points of linen, 3 points of ramie, 9 points of rayon, 9 points of cupra, 9 points of lyocell, is obtained. It was. The cotton was pretreated with an aqueous sodium hydroxide solution at room temperature. Measurement of absorption spectrum, using FT / IR spectrophotometer VIR-9550 a (JASCO Corporation), in the ATR method by ZnSe prism, the absorption spectrum was measured at a wavenumber of ^4000cm ^-1 ~ 500cm -1. Next, each absorption spectrum after multiplicative scattering correction (MSC) was first-order differentiated by the Savitzky-Golay method to obtain a differential spectrum.

Next, these absorption spectra (differential spectra) were classified into the following groups.
・ First group (A1): Natural fibers (cotton, linen and ramie)
-Second group (A2): Regenerated fibers (rayon, cupra and lyocell)
・ 3rd group (B1): Cotton ・ 4th group (B2): Hemp (linen and ramie)
・ 5th Group (C1): Linen ・ 6th Group (C2): Ramie ・ 7th Group (D1): Rayon ・ 8th Group (D2): Cupra and Lyocell ・ 9th Group (E1): Cupra ・ 10th Group (E2): Lyocell Two of the first group (A1) to tenth group (E2) were combined to form five combinations. Next, spectrum data of a predetermined wavenumber range characterizing each combination was extracted from these differential spectra using PCR Moving Window. Principal component analysis was performed in the extracted wavenumber region, and differential models A to E were obtained. In the first embodiment, the differential models A to E and the extracted wave number ranges are shown below.
・ Differential model A: “Natural fiber” and “Regenerated fiber”
First group (A1) and second group (A2): wave number 1200 to 850 cm ⁻¹
・ Differential Model B: “Cotton” and “Hemp”
3rd group (B1) and 4th group (B2): Wave number 1400-900cm ^-1
・ Model C: “Linen” and “Rummy”
5th group (C1) and 6th group (C2): Wave number 3500-3000cm ^-1
・ Differential Model D: “Rayon” and “Cupra and Lyocell”
7th group (D1) and 8th group (D2): Wave number 1400-800cm ^-1
・ Difference model E: “Cupra” and “Lyocell”
Ninth group (E1) and tenth group (E2): wave number 1250 to 900 cm ⁻¹
FIGS. 5 to 9 are scatter diagrams of principal component scores for the differentiating models A to E obtained in the first embodiment. FIG. 5 is a scatter diagram of the principal component scores of the discrimination model A: “natural fiber” and “regenerated fiber”, with the first principal component (PC1) as the horizontal axis and the second principal component (PC2) as the vertical axis, Two fiber groups, “natural fibers” and “regenerated fibers”, were clearly stratified. The linear discriminant function (L1) for distinguishing between “natural fibers” and “regenerated fibers” is shown in FIG. FIG. 6 is a scatter diagram of the principal component scores of the discrimination model B: “cotton” and “hemp”, with the first principal component (PC1) as the horizontal axis and the second principal component (PC2) as the vertical axis. Two fiber groups, cotton and hemp, were clearly stratified. A linear discriminant function (L2) for distinguishing between “cotton” and “hemp” is shown in FIG. FIG. 7 is a scatter diagram of principal component scores of the discrimination model C: “linen” and “ramie”, with the second principal component (PC2) as the horizontal axis and the third principal component (PC3) as the vertical axis. "And" Rummy "are clearly stratified. The linear discriminant function (L3) for distinguishing between “linen” and “ramie” is shown in FIG. FIG. 8 is a scatter diagram of principal component scores of differential model D: “rayon” and “cupra and lyocell”, with the second principal component (PC2) as the horizontal axis and the third principal component (PC3) as the vertical axis. Two fiber groups, “rayon” and “cupra and lyocell” were clearly stratified. A linear discriminant function (L4) for distinguishing between “rayon” and “cupra and lyocell” is shown in FIG. FIG. 9 is a scatter diagram of principal component scores of the discrimination model E: “cupra” and “lyocell”, with the second principal component (PC2) as the horizontal axis and the third principal component (PC3) as the vertical axis. "And" Lyocell "were clearly stratified. A linear discriminant function (L5) for distinguishing between “cupra” and “lyocell” is shown in FIG. The analysis data group obtained in this way was accumulated as a database of Example 1 as a differential model for each combination.

(2) Identification process In the identification process of Example 1, five test fibers X1 to X5 made of a single fiber of cellulosic fibers were prepared. First, in the same manner as in the database creation step, absorption spectra of the test fibers X1 to X5 to be identified were obtained, and a differential spectrum was obtained.

First, it was discriminated whether the test fibers X1 to X5 belong to the group of “natural fibers” or “regenerated fibers”. Specifically, spectrum data in the same wave number region (wave number 1200 to 850 cm ⁻¹ ) as the database was extracted from the obtained differential spectrum, and each principal component score was obtained. The principal component scores of the test fibers X1 to X5 thus obtained were collated with a database (differentiation model A) of “natural fibers” and “regenerated fibers”. Further, the principal component scores of the test fibers X1 to X5 were plotted in the scatter diagram of the principal component scores in FIG. 5 (X1 to X5 in FIG. 5). 5, it can be seen that among the test fibers X1 to X5 of Example 1, the test fibers X1 and X2 are fibers belonging to the first group (A1) of “natural fibers”. On the other hand, the test fibers X3 to X5 are fibers belonging to the second group (A2) of “regenerated fibers”.

Next, it was discriminated whether the test fibers X1, X2 identified as “natural fibers” belong to “cotton” or “linen”. Specifically, spectrum data in the same wave number region (wave number 1400 to 900 cm ⁻¹ ) as the database was extracted from the obtained differential spectrum, and each principal component score was obtained. The principal component scores of the test fibers X1 and X2 thus obtained were collated with the “cotton” and “hemp” database (differentiation model B). Moreover, the principal component scores of the test fibers X1 and X2 were plotted in the scatter diagram of the principal component scores in FIG. 6 (X1 and X2 in FIG. 6). In FIG. 6, it can be seen that among the test fibers X1 and X2 of Example 1, the test fiber X1 is a fiber belonging to the third group (B1) of “cotton”. On the other hand, it is understood that the test fiber X2 is a fiber belonging to the fourth group (B2) of “Hemp”.

Next, it was discriminated whether the test fiber X2 identified as “Hemp” belongs to “linen” or “ramie”. Specifically, spectrum data in the same wave number region (wave number 3500 to 3000 cm ⁻¹ ) as that in the database was extracted from the obtained differential spectrum to obtain a principal component score. The principal component score of the test fiber X2 thus obtained was collated with the “linen” and “ramie” databases (differentiation model C). Further, the principal component score of the test fiber X2 was plotted in the scatter diagram of the principal component score in FIG. 7 (X2 in FIG. 7). In FIG. 7, it can be seen that the test fiber X2 of Example 1 is a fiber belonging to the fifth group (C1) of “linen”.

On the other hand, whether the test fibers X3 to X5 identified as “regenerated fibers” belong to “rayon” or “cupra and lyocell” groups was identified. Specifically, spectrum data in the same wave number region (wave number 1400 to 800 cm ⁻¹ ) as the database was extracted from the obtained differential spectrum, and each principal component score was obtained. The principal component scores of the test fibers X3 to X5 thus obtained were collated with a database (differentiation model D) of “rayon” and “cupra and lyocell”. Further, the principal component scores of the test fibers X3 to X5 were plotted in the scatter diagram of the principal component scores in FIG. 8 (X3 to X5 in FIG. 8). In FIG. 8, it can be seen that among the test fibers X3 to X5 of Example 1, the test fiber X3 is a fiber belonging to the seventh group (D1) of “rayon”. On the other hand, it can be seen that the test fibers X4 and X5 are fibers belonging to the eighth group (D2) of “cupra and lyocell”.

Next, it was discriminated whether the test fibers X4 and X5 identified as “cupra and lyocell” belong to “cupra” and “lyocell”. Specifically, spectrum data in the same wave number region (wave number 1250 to 900 cm ⁻¹ ) as the database was extracted from the obtained differential spectrum, and each principal component score was obtained. The principal component scores of the test fibers X4 and X5 thus obtained were collated with the “cupra” and “Lyocell” databases (differentiation model E). Further, the principal component scores of the test fibers X4 and X5 were plotted in the scatter diagram of the principal component scores in FIG. 9 (X4 and X5 in FIG. 9). 9, it can be seen that among the test fibers X4 and X5 of Example 1, the test fiber X4 is a fiber belonging to the ninth group (E1) of “cupra”. On the other hand, it can be seen that the test fiber X5 is a fiber belonging to the tenth group (E2) of “Lyocell”.

As described above, in the first embodiment, the type of fiber could be easily and accurately distinguished from the test fiber consisting of a single fiber. Therefore, in the present invention, it is possible to provide a fiber discrimination method in which discrimination operation is relatively simple and objective, and can discriminate between different types of different fibers without depending on the experience and know-how of the inspector.

<< Second Embodiment >>
In this 2nd Embodiment, it does not distinguish between each single fiber as a test fiber, but makes the case where two or more types of fibers are mixed. That is, although it is a cellulose fiber in preliminary appraisal such as a microscopic method, it is adopted when it is found that two or more kinds of fibers are mixed. Further, in the discrimination process of the first embodiment, the data obtained by analyzing the spectrum data of the test fiber does not match any single fiber region whose fiber type is known, or matches a plurality of single fibers. This is used when the fiber type cannot be clearly identified, such as when it is identified.

(1) Database creation step In the second embodiment, for example, two types of single fibers of cotton and rayon and mixed fibers obtained by mixing these fibers at a series of mixed ratios are prepared as a series of comparative fibers. . Next, each absorption spectrum is obtained for each of these comparative fibers. The method for obtaining the absorption spectrum, the method for performing various corrections on the absorption spectrum, and the method for performing preprocessing such as differentiation on the obtained absorption spectrum are the same as the database creation step of the first embodiment. Further, in the same manner as in the database creation process of the first embodiment, spectrum data of one or more wave numbers is extracted from each absorption spectrum.

Next, multivariate analysis is performed on the extracted spectrum data to obtain an analysis data group. The analysis method used for multivariate analysis is not particularly limited, and uses multiple regression analysis such as principal component analysis (PCA), principal component regression (PCR), or PLS regression as in the first embodiment. It is preferable to do. In particular, when performing discrimination of mixed fibers, it is preferable to use PLS regression analysis instead of the principal component analysis (PCA) of the first embodiment. The software used for each analysis is not particularly limited.

In the second embodiment, the discrimination using PLS regression analysis will be described. Specifically, with respect to a spectrum data group of one or more wavenumber regions extracted from an absorption spectrum of a series of comparative fibers obtained by mixing two fiber groups (for example, cotton and rayon) to be distinguished at a series of mixed ratios. PLS regression analysis is performed to obtain a discrimination model (also referred to as “quantitative model”). The analysis software used for the PLS regression analysis is not particularly limited. In the second embodiment, analysis was performed using a program constructed by the inventor himself with commercially available program creation software.

The analysis data group when the spectrum data of the series of comparison fibers (each mixed fiber) thus obtained is subjected to PLS regression analysis is accumulated as a database (differentiation model). These differential models will be described in detail in Example 2 and Example 3 below.

(2) Identification process In the identification process of the second embodiment, first, an absorption spectrum of a test fiber to be identified is obtained. A method for obtaining an absorption spectrum, a method for performing various corrections on the absorption spectrum, and a method for performing a pretreatment such as a differentiation process on the obtained absorption spectrum are the same as those for the above-described comparative fiber. Next, spectrum data in the same wave number region as that of the comparative fiber is extracted from the obtained differential spectrum. In the PLS regression analysis, the extracted spectrum data of the test fiber is used as analysis data, and the analysis data is applied (multiplied) to the discrimination model (quantitative model) to obtain the mixed ratio of the test fiber.

In this discrimination process, the mixing ratio is unknown as well as the type of fiber mixed with the test fiber, but it became clear that the test fiber was a cellulosic fiber by relatively simple microscopy. ing. Therefore, in the second embodiment, it is preferable to perform fiber discrimination by the following procedure.

FIG. 10 is a discrimination flowchart showing an analysis procedure for discriminating the test fiber in the second embodiment. In FIG. 10, first, the single fiber discrimination method described in the first embodiment is performed on the test fiber. As a result, it is determined that the fiber is not a single fiber and the type of fiber being mixed. If the type of fiber being mixed is known, an optimum discrimination model (quantitative model) is selected for this test fiber. Next, as described above, the spectrum data of the test fiber is used as analysis data, and the analysis data is applied (multiplied) to the selected discrimination model (quantitative model), so that the mixed rate of the test fiber is discriminated. To do.

Thus, by performing discrimination according to the discrimination flow diagram of FIG. 10, it is possible to objectively discriminate the types of fibers mixed in the test fibers and their mixed rate. Next, the discrimination method of the second embodiment will be described in detail with reference to Example 2 and Example 3.

The present Example 2 performs discrimination of mixed fibers in which two or more kinds of fibers according to the second embodiment are mixed, and according to a discrimination flow diagram (see FIG. 10) for a plurality of test fibers. It is to make a discrimination. In the second embodiment, a knitted or knitted fabric (mixed knitted fabric) in which cotton and rayon are mixed will be described as an example.

(1) Database creation process First, a series of mixed fibers in which two types of cellulose fibers are mixed at a predetermined ratio are prepared. In Example 2, 46 woven or knitted fabrics in which cotton and rayon were blended at 100: 0 to 0: 100 were prepared, and absorption spectra were obtained for these. Measurement of absorption spectrum, using FT / IR spectrophotometer VIR-9550 a (JASCO Corporation), in the ATR method by ZnSe prism, the absorption spectrum was measured at a wavenumber of ^4000cm ^-1 ~ 500cm -1. Next, each absorption spectrum after multiplicative scattering correction (MSC) was first-order differentiated by the Savitzky-Golay method to obtain a differential spectrum.

Next, with respect to these absorption spectra (differential spectra), spectral data in the wavenumber region considered to be optimal for distinguishing the mixture ratio of cotton and rayon were extracted using the PLSR Moving Window. In order to distinguish the mixing ratio of cotton and rayon, a wave number range of wave number 1200 to 850 cm ⁻¹ was selected. PLS regression analysis was performed in this wave number region, and the obtained analysis data group was accumulated as a database of Example 2 as a differential model (quantitative model) of mixed fibers of cotton and rayon.

(2) Identification process In the identification process of Example 2, six test fibers Y1 to Y6 in which cotton and rayon were mixed at various ratios were prepared. These mixed rates were unknown at the discrimination stage. First, in the same manner as the database creation step, the absorption spectra of the test fibers Y1 to Y6 to be identified were obtained, and the differential spectra were obtained.

First, the test fibers Y1 to Y6 were differentiated according to the combination of each single fiber (differentiation models A to E in Example 1 above) according to the differentiation flow of the first embodiment (see FIG. 3). As a result, among the above-mentioned five differentiation models, a mixture of cotton and rayon is obtained from the discrimination results of differentiation model A (natural fibers and regenerated fibers), differentiation model B (cotton and hemp), and differentiation model D (regenerated fibers). Judged to be fiber.

FIG. 11 is a scatter diagram (differentiation model A) of principal component scores of “natural fibers” and “regenerated fibers” in which principal component scores of test fibers Y1 to Y6 are plotted. FIG. 12 is a scatter diagram (differentiation model B) of the principal component scores of “cotton” and “hemp” plotting the principal component scores of the test fibers Y1 to Y6. FIG. 13 is a scatter diagram (differentiation model D) of principal component scores of “rayon” and “cupra and lyocell” in which principal component scores of test fibers Y1 to Y6 are plotted.

The test fibers Y1 to Y6 were plotted in the vicinity of the middle of “natural fiber” and “regenerated fiber” across the linear discriminant function L1 in FIG. Further, although plotted on the “cotton” side of the linear discriminant function L2 in FIG. 12, it is distributed in a wide range of “rayon” and “cupra and lyocell” across the linear discriminant function L4 in FIG. From these, it was determined that the test fibers Y1 to Y6 were mixed with cotton and rayon.

Therefore, each spectral data of the test fibers Y1 to Y6 is used as analysis data, and the analysis data is applied (multiplied) to the selected model (quantitative model) of the mixed fiber of cotton and rayon. The mixed ratio of the fibers Y1 to Y6 was identified. In parallel, the mixed use rate was identified by the conventional method of JISL 1030-2. Table 1 compares the results of Example 2 with the results of the conventional method.

As can be seen from Table 1, the mixing ratio of the test fibers Y1 to Y6 showed almost the same value even when compared with the conventional method in which the operation was complicated. Moreover, the graph which plotted the result of Table 1 in FIG. 14 is shown. In FIG. 14, the result of the present Example 2 shows a good correlation as compared with the conventional method.

The present Example 3 performs the discrimination of the mixed fiber in which two or more kinds of fibers are mixed similarly to the above Example 2, and according to the discrimination flow diagram (see FIG. 10) for a plurality of test fibers. It is to make a discrimination. In Example 3, a woven or knitted fabric (mixed woven fabric) in which cotton and lyocell are mixed will be described as an example.

(1) Database creation process First, a series of mixed fibers in which two types of cellulose fibers are mixed at a predetermined ratio are prepared. In Example 3, 41 woven or knitted fabrics in which cotton and lyocell were blended at 100: 0 to 0: 100 were prepared, and absorption spectra were obtained for these. Measurement of absorption spectrum, using FT / IR spectrophotometer VIR-9550 a (JASCO Corporation), in the ATR method by ZnSe prism, the absorption spectrum was measured at a wavenumber of ^4000cm ^-1 ~ 500cm -1. Next, each absorption spectrum after multiplicative scattering correction (MSC) was first-order differentiated by the Savitzky-Golay method to obtain a differential spectrum.

Next, with respect to these absorption spectra (differential spectra), spectral data in the wavenumber region considered to be optimal for distinguishing the mixture ratio of cotton and lyocell were extracted using the PLSR Moving Window. In order to distinguish the mixed rate of cotton and lyocell, a wave number range of wave numbers 1300 to 800 cm ⁻¹ was selected. A PLS regression analysis was performed in this wavenumber region, and the obtained analysis data group was accumulated as a database of Example 3 as a differentiation model (quantitative model) of mixed fibers of cotton and lyocell.

(2) Identification process In the identification process of Example 3, six test fibers Z1 to Z6 in which cotton and lyocell were mixed at various ratios were prepared. These mixed rates were unknown at the discrimination stage. First, in the same manner as the database creation step, the absorption spectra of the test fibers Z1 to Z6 to be identified were obtained, and the differential spectra were obtained.

First, the test fibers Z1 to Z6 were differentiated according to the combination of each single fiber (differentiation models A to E of Example 1 above) according to the differentiation flow of the first embodiment (see FIG. 3). As a result, among the above five differentiation models, differentiation model A (natural fibers and regenerated fibers), differentiation model B (cotton and hemp), differentiation model D (regenerated fibers), differentiation model E (cupra and lyocell) From each discrimination result, it was judged to be a mixed fiber of cotton and lyocell.

FIG. 15 is a scatter diagram (differentiation model A) of the principal component scores of “natural fibers” and “regenerated fibers” in which the principal component scores of the test fibers Z1 to Z6 are plotted. FIG. 16 is a scatter diagram (differentiation model B) of principal component scores of “cotton” and “hemp” in which principal component scores of test fibers Z1 to Z6 are plotted. FIG. 17 is a scatter diagram (differentiation model D) of principal component scores of “rayon” and “cupra and lyocell” in which principal component scores of test fibers Z1 to Z6 are plotted. FIG. 18 is a scatter diagram (differentiation model E) of principal component scores of “cupra” and “lyocell” in which principal component scores of test fibers Z1 to Z6 are plotted.

The test fibers Z1 to Z6 are plotted in the vicinity of the middle of “natural fibers” and “regenerated fibers” across the linear discriminant function L1 in FIG. Further, although plotted on the “cotton” side of the linear discriminant function L2 in FIG. 16, in FIG. 17, it is distributed over a wide range of “rayon” and “cupra and lyocell” across the linear discriminant function L4. Furthermore, although plotted on the “lyocell” side of the linear discriminant function L5 in FIG. 18, it is distributed over a fairly wide range. From these, it was determined that the test fibers Z1 to Z6 were mixed with cotton and lyocell.

Therefore, each spectral data of the test fibers Z1 to Z6 is used as analysis data, and the analysis data is applied (multiplied) to the selected model (quantitative model) of the mixed fiber of cotton and lyocell. The mixed ratio of the fibers Z1 to Z6 was identified. In parallel, the mixed use rate was identified by the conventional method of JISL 1030-2. The results of Example 3 and the results of the conventional method are compared and shown in Table 2.

As can be seen from Table 2, the mixed ratio of the test fibers Z1 to Z6 showed almost the same value even when compared with the conventional method in which the operation was complicated. Moreover, the graph which plotted the result of Table 2 in FIG. 19 is shown. In FIG. 19, the result of Example 3 shows a better correlation than the conventional method.

As described above, in the second embodiment, it was possible to easily and accurately distinguish the mixed rate of each mixed fiber with respect to the test fiber in which two types of single fibers were mixed. Therefore, in the present invention, it is possible to provide a fiber discrimination method in which discrimination operation is relatively simple and objective, and can discriminate between different types of different fibers without depending on the experience and know-how of the inspector.

In carrying out the present invention, the following various modifications are not limited to the above embodiments.
(1) In each of the embodiments described above, cellulose fibers are used as examples for distinguishing fibers having the same chemical composition. However, the present invention is not limited to this, and various animal hair fibers as protein fibers are used. You may make it perform discrimination and the discrimination between fibers with the same chemical composition.
(2) In each of the above-described embodiments, multivariate analysis is performed between two fiber groups. However, the present invention is not limited to this, and multivariate analysis is performed simultaneously between three or more fiber groups. You may make it do.
(3) In each of the embodiments described above, discrimination is performed based on the absorption spectrum obtained by IR spectroscopic analysis. However, the present invention is not limited to this, and discrimination may be performed based on the transmission spectrum obtained by IR spectroscopic analysis. Good.
(4) In each of the above embodiments, an absorption spectrum within a wave number range of 4000 to 500 cm ⁻¹ is measured, and spectrum data in a predetermined wave number range used for analysis is extracted from the absorption spectrum. Instead, only the absorption spectrum in one or two or more predetermined wavenumber regions used for analysis may be measured.
(5) In the first embodiment, spectrum data is analyzed by principal component analysis. In the second embodiment, spectrum data is analyzed by PLS regression analysis. However, the present invention is not limited to this. Also, the spectral data may be analyzed by multiple regression analysis such as principal component regression (PCR) or PLS regression, or other multivariate analysis.
(6) In each of the above embodiments, the absorption spectrum is obtained by the ATR method. However, the present invention is not limited to this, and other methods such as the KBr tablet method after pulverizing the comparative fiber or the test fiber are used. For example, an absorption spectrum may be obtained.
(7) In each of the above embodiments, an FT / IR spectrophotometer is used. However, the present invention is not limited to this, and a dispersive infrared spectrophotometer may be used.
(8) In Examples 1 to 3 above, spectral data in a specific wave number range extracted by PCR Moving Window or PLSR Moving Window is used. However, the present invention is not limited to this, and other analysis software is used. The wave number range may be extracted by using a specific functional group that is considered necessary for the analysis.
(9) In Example 1, the combination of the first principal component and the second principal component or the combination of the second principal component and the third principal component was used for the analysis. However, the combination of principal components used for the analysis is not limited to these, and any combination of principal components may be used. Moreover, you may make it analyze in three dimensions or more using three or more main components.
(10) In Example 1 described above, only spectral data within a wave number range of 1200 to 850 cm ⁻¹ is used for discrimination between natural fibers and regenerated fibers, but the present invention is not limited to this. Spectral data in the range of 3500 to 3000 cm ⁻¹ or in the vicinity thereof may be used in combination.
(11) In the first embodiment, only the spectral data in the range of wave numbers 1400 to 900 cm ⁻¹ is used for discrimination between cotton and hemp, but the present invention is not limited to this. For example, the wave number 3500 Spectral data in the range of up to 3000 cm ⁻¹ or in the vicinity thereof may be used in combination.
(12) In the first embodiment, only spectral data within a wave number range of 3500 to 3000 cm ⁻¹ is used for discrimination between linen and ramie, but the present invention is not limited to this. Spectral data in the range of 850 cm ⁻¹ or in the vicinity thereof may be used in combination.
(13) In Example 1 described above, only the spectral data within the range of wave numbers 1400 to 800 cm ⁻¹ is used to distinguish between regenerated fibers, but the present invention is not limited to this. For example, wave numbers of 3500 to 3000 cm are used. The spectral data in the range of ⁻¹ or in the vicinity thereof may be used in combination.
(14) In the first embodiment, only spectral data within a wave number range of 1250 to 900 cm ⁻¹ is used for discrimination between cupra and lyocell. However, the present invention is not limited to this. For example, a wave number of 3500 to Spectral data within the range of 3000 cm ⁻¹ or in the vicinity thereof may be used in combination.

In the market, many textile products are distributed for wide use. Also, today, where textile products are produced and consumed globally, in order to ensure the safety and reliability of transactions when importing and exporting textile products, importing and exporting is quick and accurate. A discrimination method is desired. In particular, there is a demand for accurate differentiation between cellulosic fibers having the same chemical composition and high-grade animal hair fibers such as cashmere having the same chemical composition and other cheap animal hair fibers.

The present invention provides an accurate discrimination means for such market demands and does not rely on the experience and know-how of the inspector unlike the conventional method. In particular, the fact that it is possible to objectively distinguish between types of fibers that have the same chemical composition, and that accurate discrimination results can be obtained even if they are mistakenly mixed or camouflaged, has never been possible. It becomes.

Therefore, the present invention provides an effective discrimination means for market stability and international fair trade, and is simply a conventional method JISL 1030-1 (Fiber product mixture rate test method-Part 1). : Fiber discrimination), and to provide a discrimination means that can be used as an international standard, as well as a discrimination means that complements JISL 1030-2 (Fiber mixed rate test method-Part 2: Fiber mixed rate) it can.

1 ... cotton, 2 ... linen, 3 ... rayon, 4 ... cupra, 5 ... lyocell, 6 ... crystalline cellulose,
A ~ E ... Differential model,
A1 ... natural fiber group, A2 ... regenerated fiber group,
B1 ... cotton group, B2 ... hemp group,
C1 ... Linen group, C2 ... Rummy group,
D1 ... group of rayon, D2 ... group of cupra and lyocell,
E1 ... Cupra group, E2 ... Lyocell group,
L1 to L5: linear discriminant function,
X1 to X5, Y1 to Y6, Z1 to Z6 ... test fibers.

Claims

A fiber discrimination method for differentiating different types of similar fibers classified as the same, such as cellulosic fibers and animal hair fibers,
And providing a plurality of comparison fibers types of fibers to originate from a plurality of known single fibers, and irradiating infrared rays in the wave number range of 4000 cm -1 ~ 500 cm -1, excluding the near infrared for each comparative fibers Find each absorption spectrum,
A database creation step of extracting spectrum data in a predetermined wavenumber region from these absorption spectra, and accumulating as a database an analysis data group obtained by multivariate analysis of the spectrum data;
The test fiber is an unknown fiber type, and the analysis data is obtained from the absorption spectrum of the test fiber in the same manner as the database creation step.
A fiber having a discrimination step of comparing the analysis data of the test fiber with the data group of the database and discriminating the type of the test fiber using the consistency between the analysis data and the data group of the database as an index Identification method.
In the cellulosic fiber, in the database creation step, two types of comparative fibers according to the following combinations,
(1) Natural fiber, pair, regenerated fiber,
(2) Cotton, pair, hemp,
(3) Flax, VS, Hemp,
(4) Viscose rayon, pair, copper ammonia rayon or solvent-spun cellulose fiber,
(5) Copper ammonia rayon, pair, solvent-spun cellulose fiber,
Each analysis data obtained by multivariate analysis of the spectrum data of is accumulated as a database of differential models related to each combination,
2. The fiber discrimination method according to claim 1, wherein the analysis data of the test fiber is collated with a discrimination model according to each combination of the databases, and the type of the test fiber is discriminated.
In addition to the plurality of comparative fibers originating from the plurality of single fibers, a series of comparative fibers composed of mixed fibers prepared by mixing the same type of different kinds of fibers prepared in advance at a series of mixing ratios are prepared. A series of analysis data groups obtained in the same way as the database creation process is accumulated as a database,
The analysis data of the test fiber is collated with a series of analysis data groups of the database, the type of the test fiber, the test fiber is a mixture of at least two kinds of similar dissimilar fibers, The fiber discrimination method according to claim 1, wherein the type of the same kind of different fibers mixed in the test fiber and / or the mixed rate of the test fiber is discriminated.
In the cellulosic fiber, in the database creation step, in addition to the spectral data of the plurality of comparison fibers originating from the plurality of single fibers, two types of comparison fibers according to the following combinations,
(1) Natural fiber, pair, regenerated fiber,
(2) Cotton, pair, hemp,
(3) Flax, VS, Hemp,
(4) Viscose rayon, pair, copper ammonia rayon or solvent-spun cellulose fiber,
(5) Copper ammonia rayon, pair, solvent-spun cellulose fiber,
Each analysis data obtained by multivariate analysis of the spectrum data of the mixed fiber mixed with a series of mixed rate is accumulated as a database of differential models of mixed rate related to each combination,
4. The fiber discrimination method according to claim 3, wherein the analysis data of the test fiber is collated with a discrimination model according to each combination of the databases to discriminate the mixed rate of the test fiber.
In the cellulosic fiber, for distinguishing between natural fiber and regenerated fiber, one or two or more spectral data including mainly in the range of wave number 1200 to 850 cm −1 or in the vicinity thereof are used for the analysis. The fiber discrimination method according to any one of claims 1 to 4.
For cellulosic fibers, one or more spectral data including mainly in the range of wave numbers 1400 to 900 cm −1 or in the vicinity thereof are used for the analysis in order to distinguish cotton from hemp. The fiber discrimination method according to any one of claims 1 to 4.
The cellulosic fiber is characterized in that one or two or more spectral data including mainly in the range of the wave number of 1400 to 800 cm -1 or in the vicinity thereof are used for the analysis for discrimination between the regenerated fibers. 5. The fiber discrimination method according to any one of 1 to 4.
For cellulosic fibers, for distinguishing between copper ammonia rayon and solvent-spun cellulose fibers, one or more spectral data including mainly in the range of wave number 1250 to 900 cm −1 or in the vicinity thereof is used for analysis. The fiber discrimination method according to any one of claims 1 to 4, wherein:
In the cellulosic fiber, one or more spectral data including mainly in the range of wave number 3500 to 3000 cm −1 or in the vicinity thereof are used for the analysis in order to distinguish flax from linseed. Item 5. The fiber discrimination method according to any one of Items 1 to 4.
The fiber discrimination method according to claim 5 or 6, wherein in the cellulose fiber, the absorption spectrum is obtained after pretreatment with an alkaline substance is performed on the comparative fiber and the test fiber.
The fiber discrimination method according to any one of claims 1 to 10, wherein the multivariate analysis is principal component analysis or multiple regression analysis such as principal component regression or PLS regression.
The fiber identification method according to any one of claims 1 to 11, wherein a method for obtaining an absorption spectrum of the comparative fiber and the test fiber is an ATR method (total reflection measurement method).
The same type of heterogeneous fibers classified as cellulose fibers include cotton, flax, linseed, jute, cannabis, viscose rayon, high wet modulus rayon, polynosic rayon, copper ammonia rayon, and solvent-spun cellulose fiber. The fiber discrimination method according to any one of claims 1 to 12, wherein: