Application of near infrared spectroscopy in cotton fiber micronaire measurement

Liu, Yongliang; Delhom, Christopher D.; Campbell, B. Todd; Martin, Vikki

doi:10.1016/j.inpa.2016.01.001

Cited by 9 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Near-infrared spectroscopy has been widely studied in the qualitative analysis of textile fibers, traceability, impurity content of raw cotton, qualitative analysis of blended fabrics, etc. The accuracy of using near-infrared spectroscopy to estimate micronaire value exceeds 97% [41], which is consistent with the results of the correlation analysis of this study. All spectral indexes that have significant influence on the estimation results are calculated according to the NIR (Figure 10).…”

Section: Correlation Between Reflectivity and Quality Parameters Of C...supporting

confidence: 90%

Cotton Fiber Quality Estimation Based on Machine Learning Using Time Series UAV Remote Sensing Data

Yang

Chen

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

As an important factor determining the competitiveness of raw cotton, cotton fiber quality has received more and more attention. The results of traditional detection methods are accurate, but the sampling cost is high and has a hysteresis, which makes it difficult to measure cotton fiber quality parameters in real time and at a large scale. The purpose of this study is to use time-series UAV (Unmanned Aerial Vehicle) multispectral and RGB remote sensing images combined with machine learning to model four main quality indicators of cotton fibers. A deep learning algorithm is used to identify and extract cotton boll pixels in remote sensing images and improve the accuracy of quantitative extraction of spectral features. In order to simplify the input parameters of the model, the stepwise sensitivity analysis method is used to eliminate redundant variables and obtain the optimal input feature set. The results of this study show that the R2 of the prediction model established by a neural network is improved by 29.67% compared with the model established by linear regression. When the spectral index is calculated after removing the soil pixels used for prediction, R2 is improved by 4.01% compared with the ordinary method. The prediction model can well predict the average length, uniformity index, and micronaire value of the upper half. R2 is 0.8250, 0.8014, and 0.7722, respectively. This study provides a method to predict the cotton fiber quality in a large area without manual sampling, which provides a new idea for variety breeding and commercial decision-making in the cotton industry.

show abstract

Section: Correlation Between Reflectivity and Quality Parameters Of C...supporting

confidence: 90%

Cotton Fiber Quality Estimation Based on Machine Learning Using Time Series UAV Remote Sensing Data

Yang

Chen

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Thereby are not only moisture content but also micronaire monitored. 6,7 Experiments Materials. A flax fiber woven fabric (Biotex Flax 400 g/m 2 2×2 twill, Composites evolution, United Kingdom) is used.…”

Section: Introductionmentioning

confidence: 99%

Controlling Moisture Content of Natural Fibres in RTM-Process

Salzmann

Schledjewski

2019

KEM

View full text Add to dashboard Cite

The quality of composite materials based on natural fibres is highly influence by humidity content of the fibres. For a high product quality in the resin-transfer-moulding (RTM) process a constant humidity content has to be achieved. As the humidity content of the fibres can change relative quickly depending on the humidity, measuring humidity content in the mould is beneficial. Near-Infrared-Spectroscopy (NIR) is a widely used tool for humidity content measurement allowing determination of the moisture content within seconds. To do so a calibration model with good accuracy is required. To generate the calibration model a dry flax woven fabric is placed in a climate chamber and weight change is recorded as well as NIR-Spectra. By correlating the spectra with the weight increase a model can be developed allowing to assign the spectra with unknown weight. This allows not only monitoring the moisture content of natural fibres with in the mould. Also can the moisture content reduced to an aspired value by applying vacuum to the preheated mould, before starting the resin infusion.

show abstract

“…SI1), we used 10 PCs for both PCA-LDA (error rate 0%) model of the single fibres dataset and 20 PCs for the model including the [43] 6658 1502 O-H from adsorbed water and aminoacids, 1st overtone of N-H [30,44] 6831 1464 1st overtone of N-H, 3rd overtone of C=O [30] 7133 1402 1stovertone of O-H stretching vibration of water [45] 8410 1189 3rd overtone C-H [45] Silk 6345 1576 Amide groups in β-sheet, O-H and N-H overtones and combination bands [8,26] 6506 1537 1st overtone amide A stretching N-H amorphous [23,27] 6658 1502 N-H stretching from Amide A crystalline and 1st overtone stretching Amide II. Amide groups in β-sheet [26,27] 7153 1398 1stovertone of O-H stretching vibration of water [45] 8292 1206 3rd overtone C-H [45] Cotton 8190 1221 2nd overtone C-H stretching CH and CH 2 from cellulose [46,47] 7962 1256 2nd overtone C-H stretching CH and CH 2 from cellulose [46,47] 6729 1486 1st overtone OH stretching from semi-crystalline cellulose [46,47] fibre blends (error rate 5.29%). The SIMCA model showed an average lower error rate for both datasets; however, the advantage of SIMCA technique is the possibility to calculate the model based on a target class, and therefore, the error rate varied according to the target and the number of PCs selected (Table SI3).…”

Section: Evaluation Of Classification Techniquesmentioning

confidence: 99%

Non-invasive identification of textile fibres using near-infrared fibre optics reflectance spectroscopy and multivariate classification techniques

et al. 2022

View full text Add to dashboard Cite

The identification of textile fibres from cultural property provides information about the object's technology. Today, microscopic examination remains the preferred method, and molecular spectroscopies (e.g. Fourier transform infrared (FTIR) and Raman spectroscopies) can complement it but may present some limitations. To avoid sampling, non-invasive fibre optics reflectance spectroscopy (FORS) in the near-infrared (NIR) range showed promising results for identifying textile fibres; but examining and interpreting numerous spectra with features that are not well defined is highly time-consuming. Multivariate classification techniques may overcome this problem and have already shown promising results for classifying textile fibres for the textile industry but have been seldom used in the heritage science field. In this work, we compare the performance of two classification techniques, principal component analysis–linear discrimination analysis (PCA-LDA) and soft independent modelling of class analogy (SIMCA), to identify cotton, wool, and silk fibres, and their mixtures in historical textiles using FORS in the NIR range (1000–1700 nm). We built our models analysing reference samples of single fibres and their mixtures, and after the model calculation and evaluation, we studied four historical textiles: three Persian carpets from the nineteenth and twentieth centuries and an Italian seventeenth-century tapestry. We cross-checked the results with Raman spectroscopy. The results highlight the advantages and disadvantages of both techniques for the non-invasive identification of the three fibre types in historical textiles and the influence their vicinity can have in the classification.

show abstract

Application of near infrared spectroscopy in cotton fiber micronaire measurement

Cited by 9 publications

References 9 publications

Cotton Fiber Quality Estimation Based on Machine Learning Using Time Series UAV Remote Sensing Data

Cotton Fiber Quality Estimation Based on Machine Learning Using Time Series UAV Remote Sensing Data

Controlling Moisture Content of Natural Fibres in RTM-Process

Non-invasive identification of textile fibres using near-infrared fibre optics reflectance spectroscopy and multivariate classification techniques

Contact Info

Product

Resources

About