Discernment of textile fibers by polarization-sensitive Digital Holographic microscope and machine learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F24%3A73627318" target="_blank" >RIV/61989592:15310/24:73627318 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0143816624003737" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0143816624003737</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.optlaseng.2024.108395" target="_blank" >10.1016/j.optlaseng.2024.108395</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Discernment of textile fibers by polarization-sensitive Digital Holographic microscope and machine learning

Popis výsledku v původním jazyce

Garment quality and preciousness depend on the type of textile fiber used in the manufacturing. The softer and rarer the animal fiber, the more expensive the textile garment. The cheapest clothes are made by mixing precious fibers such as cashmere with common ones such as sheep wool. To stop clothing counterfeit and quality forgery, checking the type of animal fibers used in textile industries is pivotal. More in general, law regulations require that the declared composition of a tissue meet some standards of quality that have to be assayed carefully by expert operators. Microscopy techniques such as Scanning Electron Microscopy (SEM) and Light Microscopy (LM) are commonly used to discriminate between textile animal fibers. However, analysis by SEM and LM depends on skilled experts called to judge, one-by-one, each fiber. This process is slow, cumbersome, and may be inaccurate, especially if the textile fibers share similar morphologies. Furthermore, the chemical treatments required by some textile processes can heavily modify the morphology of the fibers making more difficult to get correct results. In this work, the textile animal fibers are characterized by a polarization-sensitive, stain-free, Digital Holographic Microscopy (DHM) technique. In particular, we show how cashmere and wool fibers differ according to their anisotropy properties, e.g., birefringence. The optical characterization of textile fibers through the Jones matrix formalism allowed us extracting polarization-dependent DH features capable of accurately classifying three types of animal microfibers using a machine learning approach. Such promising results smooth the path towards an automatic, rapid, and objective identification process for textile industry and standardization purposes.

Název v anglickém jazyce

Discernment of textile fibers by polarization-sensitive Digital Holographic microscope and machine learning

Popis výsledku anglicky

Garment quality and preciousness depend on the type of textile fiber used in the manufacturing. The softer and rarer the animal fiber, the more expensive the textile garment. The cheapest clothes are made by mixing precious fibers such as cashmere with common ones such as sheep wool. To stop clothing counterfeit and quality forgery, checking the type of animal fibers used in textile industries is pivotal. More in general, law regulations require that the declared composition of a tissue meet some standards of quality that have to be assayed carefully by expert operators. Microscopy techniques such as Scanning Electron Microscopy (SEM) and Light Microscopy (LM) are commonly used to discriminate between textile animal fibers. However, analysis by SEM and LM depends on skilled experts called to judge, one-by-one, each fiber. This process is slow, cumbersome, and may be inaccurate, especially if the textile fibers share similar morphologies. Furthermore, the chemical treatments required by some textile processes can heavily modify the morphology of the fibers making more difficult to get correct results. In this work, the textile animal fibers are characterized by a polarization-sensitive, stain-free, Digital Holographic Microscopy (DHM) technique. In particular, we show how cashmere and wool fibers differ according to their anisotropy properties, e.g., birefringence. The optical characterization of textile fibers through the Jones matrix formalism allowed us extracting polarization-dependent DH features capable of accurately classifying three types of animal microfibers using a machine learning approach. Such promising results smooth the path towards an automatic, rapid, and objective identification process for textile industry and standardization purposes.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10306 - Optics (including laser optics and quantum optics)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

OPTICS AND LASERS IN ENGINEERING

ISSN

0143-8166

e-ISSN

1873-0302

Svazek periodika

181

Číslo periodika v rámci svazku

OCT

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

"108395-1"-"108395-10"

Kód UT WoS článku

001264417100001

EID výsledku v databázi Scopus

2-s2.0-85197079318