scholarly journals Estimation of Acacia melanoxylon unbleached Kraft pulp brightness by NIR spectroscopy

2015 ◽  
Vol 24 (2) ◽  
pp. eRC03 ◽  
Author(s):  
António J.A. Santos ◽  
Ofélia Anjos ◽  
Helena Pereira
Keyword(s):  
Kraft Pulp ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Present Model ◽  
Nir Spectroscopy ◽  
Kraft Pulps ◽  
Least Squares Regression ◽  
Acacia Melanoxylon ◽  
Pls Analysis ◽  
Plsr Model

<p><em>Aim of the study:</em> The ability of NIR spectroscopy for predicting the ISO brightness was studied on unbleached Kraft pulps of <em>Acacia melanoxylon</em> R. Br.</p><p><em>Area of study: </em>Sites covering littoral north, mid interior north and centre interior of Portugal.</p><p><em>Materials and methods:</em> The samples were Kraft pulped in standard identical conditions targeted to a kappa number of 15. A Near Infrared (NIR) partial least squares regression (PLSR) model was developed for the ISO brightness prediction using 75 pulp samples with a variation range of 18.9 to 47.9 %.</p><p><em>Main results:</em> Very good correlations between NIR spectra and ISO brightness were obtained. Ten methods were used for PLS analysis (cross validation with 48 samples), and a test set validation was made with 27 samples. The 1stDer pre-processed spectra coupling two wavenumber ranges from 9404 to 7498 cm<sup>-1</sup> and 4605 to 4243 cm<sup>-1</sup> allowed the best model with a root mean square error of ISO brightness prediction of 0.5 % (RMSEP), a r<sup>2</sup> of 99.5 % with a RPD of 14.7.</p><p><em>Research highlights:</em> According to AACC Method 39-00, the present model is sufficiently accurate to be used for process control (RPD ≥ 8).</p><p class="BioresourcesKeywords"><strong>Key words:</strong>  Acacia melanoxylon;<em> unbleached Kraft pulps; ISO Brightness; NIR; RPD.</em></p>

scholarly journals Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging

2019 ◽  
Vol 9 (18) ◽  
pp. 3926 ◽  
Author(s):  
Yue Zhang ◽  
Hongzhe Jiang ◽  
Wei Wang
Keyword(s):  
Hyperspectral Imaging ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Chicken Meat ◽  
Regions Of Interest ◽  
Regression Coefficients ◽  
Least Squares Regression ◽  
Distribution Maps ◽  
Plsr Model ◽  
Volume Range

The detection of carrageenan adulteration in chicken meat using a hyperspectral imaging (HSI) technique associated with three spectroscopic transforms was investigated. Minced chicken was adulterated with carrageenan solution (2% w/v) in the volume range of 0–5 mL at an increment of 1 mL. Hyperspectral images of prepared samples were captured in a reflectance mode in a Visible/Near-Infrared (Vis/NIR, 400–1000 nm) region. The reflectance (R) spectra were first extracted from regions of interest (ROIs) by applying a mask that was built using band math combined with thresholding and were then transformed into two other spectral units, absorbance (A) and Kubelka-Munck (KM). Partial least squares regression (PLSR) models based on full raw and preprocessed spectra in the three profiles were established and A spectra were found to perform best with Rp2 = 0.92, root mean square error of prediction set (RMSEP) = 0.48, and residual predictive deviation (RPD) = 6.18. To simplify the models, several wavelengths were selected using regression coefficients (RC) based on all three spectral units, and 10 wavelengths selected from A spectra (409, 425, 444, 521, 582, 621, 763, 840, 893, and 939 nm) still performed best with the Rp2, RMSEP, and RPD of 0.85, 0.93, and 3.20, respectively. Thus, the preferred simplified RC-A-PLSR model was selected and transferred into each pixel to obtain the distribution maps and finally, the general different adulteration levels of different samples were readily discernible. The overall results ascertained that the HSI technique demonstrated to be an effective tool for detecting and visualizing carrageenan adulteration in authentic chicken meat, especially in the absorbance mode.

NIR Spectroscopy and Partial Least Squares Regression for the Determination of Phosphate Content and Viscosity Behaviour of Potato Starch

2001 ◽  
Vol 9 (2) ◽  
pp. 133-139 ◽  
Author(s):  
L.G. Thygesen ◽  
S.B. Engelsen ◽  
M.H. Madsen ◽  
O.B. Sørensen
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Potato Starch ◽  
Nir Spectroscopy ◽  
Peak Time ◽  
Phosphate Content ◽  
Least Squares Regression ◽  
Data Prediction

A set of 97 potato starch samples with a phosphate content corresponding to a phosphorus content between 0.029 and 0.11 g per 100 g dry matter was analysed using a Rapid Visco Analyzer (RVA) and near infrared (NIR) spectroscopy, (700–2498 nm). NIR-based prediction of phosphate content was possible with a root mean square error of cross-validation ( RMSECV) of 0.006% using PLSR (partial least squares regression). However, the NIR/PLSR model relied on weak spectral signals, and was highly sensitive to sample preparation. The best prediction of phosphate content from the RVA viscograms was a linear regression model based on the RVA variable Breakdown, which gave a RMSECV of 0.008%. NIR/PLSR prediction of the RVA variables Peak viscosity and Breakdown was successful, probably because they were highly related to phosphate content in the present data. Prediction of the other RVA variables from NIR/PLSR was mediocre (Through, Final Viscosity) or not possible (Setback, Peak time, Pasting temperature).

scholarly journals The application of NIR spectroscopy in moisture determining of vegetable seeds

2020 ◽  
Vol 38 (No. 2) ◽  
pp. 131-136
Author(s):  
Wojciech Poćwiardowski ◽  
Joanna Szulc ◽  
Grażyna Gozdecka
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Moisture Analysis ◽  
Non Destructive

The aim of the study was to elaborate a universal calibration for the near infrared (NIR) spectrophotometer to determine the moisture of various kinds of vegetable seeds. The research was conducted on the seeds of 5 types of vegetables – carrot, parsley, lettuce, radish and beetroot. For the spectra correlation with moisture values, the method of partial least squares regression (PLS) was used. The resulting qualitative indicators of a calibration model (R = 0.9968, Q = 0.8904) confirmed an excellent fit of the obtained calibration to the experimental data. As a result of the study, the possibilities of creating a calibration model for NIR spectrophotometer for non-destructive moisture analysis of various kinds of vegetable seeds was confirmed.<br /><br />

scholarly journals Feasibility of Determination of Foodborne Microbe Contamination of Fresh-Cut Shredded Cabbage Using SW-NIR

2019 ◽  
Vol 1 (2) ◽  
pp. 246-256
Author(s):  
Benjamaporn Matulaprungsan ◽  
Chalermchai Wongs-Aree ◽  
Pathompong Penchaiya ◽  
Phonkrit Maniwara ◽  
Sirichai Kanlayanarat ◽  
...  
Keyword(s):  
Quantitative Measurement ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Sample Type ◽  
Least Squares Regression ◽  
Calibration Equation ◽  
E Coli ◽  
Total Bacteria

Shredded cabbage is widely used in much ready-to-eat food. Therefore, rapid methods for detecting and monitoring the contamination of foodborne microbes is essential. Short wavelength near infrared (SW-NIR) spectroscopy was applied on two types of solutions, a drained solution from the outer surface of the shredded cabbage (SC) and a ground solution of shredded cabbage (GC) which were inoculated with a mixture of two bacterial suspensions, Escherichia coli and Salmonella typhimurium. NIR spectra of around 700 to 1100 nm were collected from the samples after 0, 4, and 8 h at 37 °C incubation, along with the growth of total bacteria, E. coli and S. typhimurium. The raw spectra were obtained from both sample types, clearly separated with the increase of incubation time. The first derivative, a Savitzky–Golay pretreatment, was applied on the GC spectra, while the second derivative was applied on the SC spectra before developing the calibration equation, using partial least squares regression (PLS). The obtained correlation (r) of the SC spectra was higher than the GC spectra, while the standard error of cross-validation (SECV) was lower. The ratio of prediction of deviation (RPD) of the SC spectra was higher than the GC spectra, especially in total bacteria, quite normal for the E. coli but relatively low for the S. typhimurium. The prediction results of microbial spoilage were more reliable on the SC than on the GC spectra. Total bacterial detection was best for quantitative measurement, as E. coli contamination could only be distinguished between high and low values. Conversely, S. typhimurium predictions were not optimal for either sample type. The SW-NIR shows the feasibility for detecting the existence of microbes in the solution obtained from SC, but for a more specific application for discrimination or quantitation is needed, proving further research in still required.

Variable Screening for Near Infrared (NIR) Spectroscopy Data Based on Ridge Partial Least Squares Regression

2020 ◽  
Vol 23 (8) ◽  
pp. 740-756
Author(s):  
Naifei Zhao ◽  
Qingsong Xu ◽  
Man-lai Tang ◽  
Hong Wang
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Screening Methods ◽  
Spectroscopy Data ◽  
Least Squares Regression ◽  
Variable Screening ◽  
Highly Correlated

Aim and Objective: Near Infrared (NIR) spectroscopy data are featured by few dozen to many thousands of samples and highly correlated variables. Quantitative analysis of such data usually requires a combination of analytical methods with variable selection or screening methods. Commonly-used variable screening methods fail to recover the true model when (i) some of the variables are highly correlated, and (ii) the sample size is less than the number of relevant variables. In these cases, Partial Least Squares (PLS) regression based approaches can be useful alternatives. Materials and Methods : In this research, a fast variable screening strategy, namely the preconditioned screening for ridge partial least squares regression (PSRPLS), is proposed for modelling NIR spectroscopy data with high-dimensional and highly correlated covariates. Under rather mild assumptions, we prove that using Puffer transformation, the proposed approach successfully transforms the problem of variable screening with highly correlated predictor variables to that of weakly correlated covariates with less extra computational effort. Results: We show that our proposed method leads to theoretically consistent model selection results. Four simulation studies and two real examples are then analyzed to illustrate the effectiveness of the proposed approach. Conclusion: By introducing Puffer transformation, high correlation problem can be mitigated using the PSRPLS procedure we construct. By employing RPLS regression to our approach, it can be made more simple and computational efficient to cope with the situation where model size is larger than the sample size while maintaining a high precision prediction.

Estimation of Crystallinity of Poplar Cellulose Using Near Infrared Spectroscopy

2012 ◽  
Vol 622-623 ◽  
pp. 1532-1535
Author(s):  
Zhen Bo Liu ◽  
Wen Yang Kong ◽  
Yi Xing Liu ◽  
Zhan Chuan Xue ◽  
Xiao Yan Shen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Wood Properties ◽  
Least Squares Regression ◽  
Cellulose Crystallinity ◽  
Test Models

Many studies have successfully applied near infrared (NIR) spectroscopy to estimate important wood properties. In this paper, the use of NIR (350–2500 nm) spectroscopy to predict the cellulose crystallinity of Poplar (Populus nigra var.) was investigated. The calibration and test models were constructed using partial least squares regression (PLS). The correlations were significant both the calibration and the test samples using six factors, and the correlation coefficients (R2) were 0.9367, 0.9472 respectively. The results suggest that NIR spectroscope may provide a useful tool for rapid and accurate prediction of the cellulose crystallinity of Poplar.

scholarly journals Near-Infrared Spectroscopy as an Analytical Process Technology for the On-Line Quantification of Water Precipitation Processes during Danhong Injection

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xuesong Liu ◽  
Chunyan Wu ◽  
Shu Geng ◽  
Ye Jin ◽  
Lianjun Luan ◽  
...  
Keyword(s):  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Process Technology ◽  
Danhong Injection ◽  
Fiber Optic Probes ◽  
Quantitative Monitoring ◽  
On Line ◽  
Predicted Values

This paper used near-infrared (NIR) spectroscopy for the on-line quantitative monitoring of water precipitation during Danhong injection. For these NIR measurements, two fiber optic probes designed to transmit NIR radiation through a 2 mm flow cell were used to collect spectra in real-time. Partial least squares regression (PLSR) was developed as the preferred chemometrics quantitative analysis of the critical intermediate qualities: the danshensu (DSS, (R)-3, 4-dihydroxyphenyllactic acid), protocatechuic aldehyde (PA), rosmarinic acid (RA), and salvianolic acid B (SAB) concentrations. Optimized PLSR models were successfully built and used for on-line detecting of the concentrations of DSS, PA, RA, and SAB of water precipitation during Danhong injection. Besides, the information of DSS, PA, RA, and SAB concentrations would be instantly fed back to site technical personnel for control and adjustment timely. The verification experiments determined that the predicted values agreed with the actual homologic value.

Vis–NIR spectroscopy for the on-site prediction of wood properties

2013 ◽  
Vol 89 (05) ◽  
pp. 631-638 ◽  
Author(s):  
Hikaru Kobori ◽  
Miho Kojima ◽  
Hiroyuki Yamamoto ◽  
Yasutoshi Sasaki ◽  
Fabio Minoru Yamaji ◽  
...  
Keyword(s):  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Eucalyptus Grandis ◽  
Nir Spectroscopy ◽  
Wood Properties ◽  
Stress Waves ◽  
Least Squares Regression ◽  
Key Factors ◽  
Breast Height ◽  
Lateral Growth Rate

We investigated the feasibility of visible–near-infrared (Vis–NIR) spectroscopy for estimation of wood qualities of fast-growing Eucalyptus grandis. Partial least squares regression (PLSR) models are applied to predict the diameter at the breast height (DBH), lateral growth rate (LGR) and propagation velocity of stress waves (PVSW). It was possible to estimate LGR and PVSW with appropriate accuracy. This suggested that perhaps information in terms of maturation is included in Vis–NIR spectra. The key factors in the validation of PVSW and LGR were the water and cellulose condition in wood.

scholarly journals Size Estimation of Tomato Fruits Based on Spectroscopic Analysis

2011 ◽  
Vol 225-226 ◽  
pp. 1254-1257 ◽  
Author(s):  
Hai Qing Yang ◽  
Bo Yan Kuang ◽  
Abdul M. Mouazen
Keyword(s):  
Latent Variables ◽  
Near Infrared ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Size Estimation ◽  
Tomato Fruits ◽  
Leave One Out ◽  
Plsr Model

This study used visible and near-infrared (VIS-NIR) spectroscopy for size estimation of tomato fruits of three cultivars. A mobile, fibre-type, VIS-NIR spectrophotometer (AgroSpec, Tec 5, Germany) with spectral range of 350-2200 nm, was used to measure reflectance spectra of on-vine tomatoes growing from July to September 2010. Spectra were divided into a calibration set (75%) and an independent validation set (25%). A partial least squares regression (PLSR) with leave-one-out cross validation was adopted to establish calibration models between fruit diameter and spectra. Furthermore, the latent variables (LVs) obtained from PLS regression was used as input to back-propagation artificial neural network (BPANN) analysis. Result shows that the prediction of PLSR model can produce good performance with coefficient of determination (R2) of 0.82, root-mean-square error of prediction (RMSEP) of 4.87 mm and residual prediction deviation (RPD) of 2.35. Compared to the PLSR model, the PLS-BPANN model provides considerably higher prediction performance withR2of 0.88, RMSEP of 3.98 mm and RPD of 2.89. It is concluded that VIS-NIR spectroscopy coupled with PLS-BPANN can be adopted successfully for size estimation of tomato fruits.

scholarly journals Construction and Validation of Quantification Methods for Determining the Cannabidiol Content in Liquid Pharma-Grade Formulations by Means of Near-Infrared Spectroscopy and Partial Least Squares Regression

2019 ◽  
Vol 2 (1) ◽  
pp. 43-55
Author(s):  
Joan Espel Grekopoulos
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Developed Countries ◽  
Pharmaceutical Products ◽  
European Medicines Agency ◽  
Least Squares Regression ◽  
Technical Requirements

There is an increasing interest in cannabinoids as they are being proved to effectively treat the symptoms of a variety of medical conditions. Commercialization of cannabinoid-based pharmaceutical products is expected to grow in the near future, favored by the recent changes in medical regulations in many developed countries. Hence, robust and reliable analytical methods for determining the content of the active pharmaceutical ingredient will be needed, as this is one of the most relevant parameters for the decision to release the final pharmaceutical product into the market. The aim of this work was to demonstrate that near-infrared (NIR) spectroscopy fulfills the needed requirements for this purpose, as well as to provide a methodology to be applied to other cannabinoid-based products. We present two validated methods for the quantification of different liquid pharma-grade cannabidiol (CBD) formulations based on NIR spectroscopy and partial least squares regression modelling. The methods were constructed and validated with spectra belonging both to production samples and to laboratory samples specifically made for this purpose, and they fulfill European Medicines Agency and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use guideline requirements. These methods allow determining the CBD content with results comparable to the usual method of choice while saving reagent- as well as time-related costs.

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