Fiber-Content Measurement of Wool–Cashmere Blends Using Near-Infrared Spectroscopy

2017 ◽  
Vol 71 (10) ◽  
pp. 2367-2376 ◽  
Author(s):  
Jinfeng Zhou ◽  
Rongwu Wang ◽  
Xiongying Wu ◽  
Bugao Xu
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Chemical Bonds ◽  
Multilinear Regression ◽  
Textile Production ◽  
Protein Fibers ◽  
Characteristic Wavelength ◽  
Product Classification ◽  
Band Assignment ◽  
Blend Ratios

Cashmere and wool are two protein fibers with analogous geometrical attributes, but distinct physical properties. Due to its scarcity and unique features, cashmere is a much more expensive fiber than wool. In the textile production, cashmere is often intentionally blended with fine wool in order to reduce the material cost. To identify the fiber contents of a wool–cashmere blend is important to quality control and product classification. The goal of this study is to develop a reliable method for estimating fiber contents in wool–cashmere blends based on near-infrared (NIR) spectroscopy. In this study, we prepared two sets of cashmere–wool blends by using either whole fibers or fiber snippets in 11 different blend ratios of the two fibers and collected the NIR spectra of all the 22 samples. Of the 11 samples in each set, six were used as a subset for calibration and five as a subset for validation. By referencing the NIR band assignment to chemical bonds in protein, we identified six characteristic wavelength bands where the NIR absorbance powers of the two fibers were significantly different. We then performed the chemometric analysis with two multilinear regression (MLR) equations to predict the cashmere content (CC) in a blended sample. The experiment with these samples demonstrated that the predicted CCs from the MLR models were consistent with the CCs given in the preparations of the two sample sets (whole fiber or snippet), and the errors of the predicted CCs could be limited to 0.5% if the testing was performed over at least 25 locations. The MLR models seem to be reliable and accurate enough for estimating the cashmere content in a wool–cashmere blend and have potential to be used for tackling the cashmere adulteration problem.

Estimating the Composition of Tomato Juice Products by near Infrared Spectroscopy

2003 ◽  
Vol 11 (2) ◽  
pp. 123-136 ◽  
Author(s):  
Athanasia M. Goula ◽  
Konstantinos G. Adamopoulos
Keyword(s):  
Near Infrared ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Tomato Juice ◽  
Calibration Data ◽  
Multilinear Regression ◽  
Statistical Parameters ◽  
Data Set ◽  
Prediction Ability ◽  
Specific Product

The use of near infrared (NIR) reflectance spectroscopy for the rapid and accurate measurement of moisture, sugar, acid, protein and salt was explored in a diverse group of tomato juice products. Partial and overall calibrations were performed on four different tomato juice products. Partial calibrations for each product included samples of the specific product, whereas overall calibration used samples of all the products. Samples were analysed employing traditional chemical methods and scanned using an Instalab 600-Dickey-John NIR apparatus to obtain NIR spectra. Calibrations were achieved with the use of multilinear regression between chemical and spectral data from each calibration data set. A separate set of samples was used to validate the calibrations. Linear regression was applied to compare the results obtained by NIR spectroscopy for all constituents of the validation set with those obtained by the reference methods. In addition, the root mean square error of prediction ( RMSEP), the bias and the correlation coefficients ( r and r′) were calculated. All of the statistical parameters were better with overall than with partial calibrations. Prediction ability of overall calibration was very good for all the constituents. r and r′ values were higher than 0.9488 and 0.9453, respectively, RMSEP values were smaller than 0.1067, whereas bias varied from −0.020 to 0.016. The partial calibrations are considerable less variable with the correlation coefficients r and r′ ranged from 0.8890 to 0.9477 and from 0.7202 to 0.8518, respectively, RMSEP varied from 0.0647 to 0.4942 and bias from −0.365 to 0.071. NIR measurement as performed by the Dickey-John Analyser was proved a rapid and accurate method for analysis of tomato juice samples and may be used as a replacement for conventional expensive and time-consuming wet chemistry methods.

scholarly journals Fiber content determination of linen/viscose blends using NIR spectroscopy

BioResources ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 3006-3016
Author(s):  
Jing Huang ◽  
Chongwen Yu
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Spectral Data ◽  
Near Infrared ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Chemical Bonds ◽  
Specific Component ◽  
Content Determination

Linen/viscose blended yarns provide unique properties, but the quality and cost of the fabric composed of the blended yarns are affected by the amount of linen fibers. The existing methods of detecting blending ratio are microscopy or specific component dissolution, which is time-consuming and inconvenient. This study considers the possibility of rapid and simple determination of linen content by the near infrared (NIR) method. A set of linen/viscose powdered blends with 11 different ratios was fabricated. For each sample, 10 sets of spectra were collected by Fourier transform (FT)-NIR spectrometer. A total of 110 spectra sets were generated, in which 60 were used for calibration and 50 for validation. There were verified differences in NIR peaks assigning to representative chemical bonds in cellulose. With the chemometric analysis, a partial least squares (PLS) model was established to predict the linen content in a blended sample. With a combination of smoothing, baseline offset, and multiplicative scatter correction processing of the spectral data, the established PLS model was further improved to achieve a standard validation error of only 1.182% and SD value of the predicted linen content less than 0.2, which indicated the accuracy of the developed method.

Multi-Site Cr3+ Occupation Related Broadband NIR Luminescence in Cr3+-doped Li3Mg2NbO6

CrystEngComm ◽  
2021 ◽  
Author(s):  
Fen Xiao ◽  
Chengning Xie ◽  
Shikun Xie ◽  
Rongxi Yi ◽  
Huiling Yuan ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Luminescent Materials ◽  
Optical Source ◽  
Nir Emission ◽  
Nir Luminescence

Broadband near infrared (NIR) luminescent materials have attracted great attention recently for the advance smart optical source of NIR spectroscopy. In this work, a broadband NIR emission from 650 nm...

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

Near infrared spectroscopy of Eucalyptus pellita for foliar nutrients and the potential for real-time monitoring of trees in fertiliser trial plots

2021 ◽  
pp. 096703352110079
Author(s):  
Agustan Alwi ◽  
Roger Meder ◽  
Yani Japarudin ◽  
Hazandy A Hamid ◽  
Ruzana Sanusi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Acacia Mangium ◽  
Nir Spectroscopy ◽  
Nutrient Status ◽  
Significant Loss ◽  
Eucalyptus Pellita ◽  
Operational Decision Making ◽  
Fertiliser Application ◽  
Planting Distance

Eucalyptus pellita F. Muell. has become an important tree species in the forest plantations of SE Asia, and in Malaysian Borneo in particular, to replace thousands of hectares of Acacia mangium Willd. which has suffered significant loss caused by Ceratocystis manginecans infection in Sabah, Malaysia. Since its first introduction at a commercial scale in 2012, E. pellita has been planted in many areas in the region. The species replacement requires new silvicultural practices to induce the adaptability of E. pellita to grow in the region and this includes relevant research to optimise such regimes as planting distance, pruning, weeding practices and nutrition regimes. In this present study, the nutritional status of the foliage was investigated with the aim to develop near infrared spectroscopic calibrations that can be used to monitor and quantify nutrient status, particularly total foliar nitrogen (N) and phosphorus (P) in the field. Spectra acquired on fresh foliage in situ on the tree could be used to predict N and P with accuracy suitable for operational decision-making regards fertiliser application. If greater accuracy is required, spectra acquired on dry, milled foliage could be used to predict N and P within a relative error of 10% (R2c, r2CV, RMSEP, RPD = 0.77, 0.71, 0.02 g 100/g, 1.9 for foliar P and = 0.90, 0.88, 0.21 g 100/g, 3.0 for foliar N on dry, milled foliage). The ultimate application of this is in situ nutrient monitoring, particularly to aid longitudinal studies in fertiliser trial plots and forest operations, as the non-destructive nature of NIR spectroscopy would enable regular monitoring of individual leaves over time without the need to destructively sample them. This would aid the temporal and spatial analysis of field data.

scholarly journals Textile Recognition and Sorting for Recycling at an Automated Line Using Near Infrared Spectroscopy

Recycling ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Kirsti Cura ◽  
Niko Rintala ◽  
Taina Kamppuri ◽  
Eetta Saarimäki ◽  
Pirjo Heikkilä
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Automated Recognition ◽  
Textile Materials ◽  
Input Material ◽  
Adequate Quality ◽  
Automated Line

In order to add value to recycled textile material and to guarantee that the input material for recycling processes is of adequate quality, it is essential to be able to accurately recognise and sort items according to their material content. Therefore, there is a need for an economically viable and effective way to recognise and sort textile materials. Automated recognition and sorting lines provide a method for ensuring better quality of the fractions being recycled and thus enhance the availability of such fractions for recycling. The aim of this study was to deepen the understanding of NIR spectroscopy technology in the recognition of textile materials by studying the effects of structural fabric properties on the recognition. The identified properties of fabrics that led non-matching recognition were coating and finishing that lead different recognition of the material depending on the side facing the NIR analyser. In addition, very thin fabrics allowed NIRS to penetrate through the fabric and resulted in the non-matching recognition. Additionally, ageing was found to cause such chemical changes, especially in the spectra of cotton, that hampered the recognition.

Evaluation of swelling properties and drug release from mechanochemical pre-gelatinized glutinous rice starch matrix tablets by near infrared spectroscopy

2021 ◽  
pp. 096703352098235
Author(s):  
Tomomi Takaku ◽  
Yusuke Hattori ◽  
Tetsuo Sasaki ◽  
Tomoaki Sakamoto ◽  
Makoto Otsuka
Keyword(s):  
Drug Release ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Matrix Tablets ◽  
Rice Starch ◽  
Release Time ◽  
Swelling Properties ◽  
X Ray ◽  
Glutinous Rice ◽  
Pharmaceutical Properties

The effect of grinding on the pharmaceutical properties of matrix tablets consisting of ground glutinous rice starch (GRS) and theophylline (TH) was predicted by near infrared (NIR) spectroscopy. Ground GRS samples were prepared by grinding GRS in a planetary ball mill for 0-120 min, measured by X-ray diffractometry (XRD) and NIR, and then evaluated for crystallinity (%XRD) based on XRD profiles. Tablets containing TH (5 w/w%), ground GRS (94 w/w%), and magnesium stearate (1 w/w%) were formed by compression. Gel-forming and drug-release processes of the tablets were measured using a dissolution instrument with X-ray computed tomography (XCT). Swelling ratio (SWE) and mean drug-release time (MDT) were evaluated based on XCT and drug-release profiles, respectively. Calibration models for predicting percent %XRD, MDT, and SWE were constructed based on the NIR of ground GRS using partial least-squares. The results indicated the possibility of controlling the pharmaceutical properties of matrix tablets by altering the pre-gelatinization of GRS based on changes in their NIR spectra during the milling process.

scholarly journals Assessment of chicken breast shelf life based on bench-top and portable NIR spectroscopy tools coupled with chemometrics

2020 ◽  
Author(s):  
Ilaria Lanza ◽  
Daniele Conficoni ◽  
Stefania Balzan ◽  
Marco Cullere ◽  
Luca Fasolato ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Shelf Life ◽  
Meat Quality ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Life Assessment ◽  
Chicken Breast ◽  
Portable Devices ◽  
Physicochemical Processes ◽  
Insight Into

Abstract Near-infrared (NIR) spectroscopy is a rapid technique able to assess meat quality even if its capability to determine the shelf life of chicken fresh cuts is still debated, especially for portable devices. The aim of the study was to compare bench-top and portable NIR instruments in discriminating between four chicken breast refrigeration times (RT), coupled with multivariate classifier models. Ninety-six samples were analysed by both NIR tools at 2, 6, 10 and 14 days post-mortem. NIR data were subsequently submitted to partial least squares discriminant analysis (PLS-DA) and canonical discriminant analysis (CDA). The latter was preceded by double feature selection based on Boruta and Stepwise procedures. PLS-DA sorted moderate separation of RT theses, while shelf life assessment was more accurate on application of Stepwise-CDA. Bench-top tool had better performance than portable one, probably because it captured more informative spectral data as shown by the variable importance in projection (VIP) and restricted pool of Stepwise-CDA predictive scores (SPS). NIR tools coupled with a multivariate model provide deep insight into the physicochemical processes occurring during storage. Spectroscopy showed reliable effectiveness to recognise a 7-day shelf life threshold of breasts, suitable for routine at-line application for screening of meat quality.

Sign in / Sign up

Export Citation Format

Share Document