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.

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 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>

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 Real-time and on-line parallel detection of key fermentation process parameters by near-infrared spectroscopy in different environments

2021 ◽  
Author(s):  
Yang Chen ◽  
Lingli Chen ◽  
Meijin Guo ◽  
Xu Li ◽  
Jinsong Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Fermentation Process ◽  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Environmental Parameters ◽  
Inhomogeneous System ◽  
Least Squares Regression ◽  
Experiment Platform ◽  
On Line

Abstract The fermentation process is dynamically changing, and the metabolic status can be grasped through real-time monitoring of environmental parameters. In this study, a real-time and on-line monitoring experiment platform for substrates and products detection was developed based on non-contact type near-infrared (NIR) spectroscopy technology. The prediction models for monitoring the fermentation process of lactic acid, sophorolipids and sodium gluconate were established based on partial least-squares regression and internal cross-validation methods. Through fermentation verification, the accuracy and precision of the NIR model for the complex fermentation environments, different rheological properties (uniform system and multi-phase inhomogeneous system) and different parameter types (substrate, product and nutrients) have good applicability, and R2 is greater than 0.90, exhibiting a good linear relationship. The root mean square error shows that the model has high credibility. This research provides a basis for the application of NIR spectroscopy in complex fermentation systems.

Predicting the lignin H/G ratio of Pinus sylvestris L. wood samples by PLS-R models based on near-infrared spectroscopy

Holzforschung ◽  
2020 ◽  
Vol 74 (7) ◽  
pp. 655-662 ◽  
Author(s):  
Ana Alves ◽  
Rita Simões ◽  
José Luís Lousada ◽  
José Lima-Brito ◽  
José Rodrigues
Keyword(s):  
Pinus Sylvestris ◽  
Near Infrared ◽  
External Validation ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Predicted Values ◽  
G Ratio

AbstractSoftwood lignin consists mainly of guaiacyl (G) units and low amounts of hydroxyphenyl (H) units. Even in a small percentage, the ratio of H to G (H/G) and the intraspecific variation are crucial wood lignin properties. Analytical pyrolysis (Py) was already successfully used as a reference method to develop a model based on near-infrared (NIR) spectroscopy for the determination of the H/G ratio on Pinus pinaster (Pnb) wood samples. The predicted values of the Pinus sylvestris (Psyl) samples by this model were well correlated (R = 0.91) with the reference data (Py), but with a bias that increased with increasing H/G ratio. Partial least squares regression (PLS-R) models were developed for the prediction of the H/G ratio, dedicated models for Psyl wood samples and common models based on both species (Pnb and Psyl). All the calibration models showed a high coefficient of determination and low errors. The coefficient of determination of the external validation of the dedicated models ranged from 0.92 to 0.96 and for the common models ranged from 0.83 to 0.93. However, the comparison of the predictive ability of the dedicated and common models using the Psyl external validation set showed almost identical predicted values.

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