scholarly journals An efficient method to reduce grain angle influence on NIR spectra for predicting extractives content from heartwood stem cores of Toona. sinensis

2020 ◽  
Author(s):  
Yanjie Li ◽  
Xin Dong ◽  
Yang Sun ◽  
Jun Liu ◽  
Jingmin Jiang
Keyword(s):  
Near Infrared ◽  
Genetic Effect ◽  
Nir Spectroscopy ◽  
Solid Wood ◽  
Grain Angle ◽  
Toona Sinensis ◽  
Multivariate Correlation ◽  
Standard Normal ◽  
Predicted Model ◽  
Non Destructive

Abstract Background: A fast, reliable and non-destructive method is needed to qualify the extractives content (EC) in heartwood of T. sinensis cores in the breeding program for studying the genetic effect on EC. However, the influence of grain angle on near infrared (NIR) spectra prediction model for EC is unclear. In this study, NIR spectra were collected from both cross section and radial section of wood core samples in order to predict the EC in heartwood. Results: The effect of grain angle on calibration EC model was studied. Several different spectra pre-processing methods were implemented for calibration. It was found that standard normal variation (SNV) followed by 1st derivative yielded the best calibration result for T. sinensis EC. Grain angle had a significant influence on the predicted model for EC when using the whole NIR spectra. However, after testing a certain point of the prior variables for EC that were selected by the significant multivariate correlation (sMC), the influence of grain angle was significantly eliminated. Conclusions: It is suggested that NIR spectroscopy is a promising method to predict EC in the solid wood without effecting grain angle.

scholarly journals An efficient method to reduce grain angle influence on NIR spectra for predicting extractives content from heartwood stem cores of Toona. sinensis

2020 ◽  
Author(s):  
Yanjie Li ◽  
Xin Dong ◽  
Yang Sun ◽  
Jun Liu ◽  
Jingmin Jiang
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Solid Wood ◽  
Grain Angle ◽  
Toona Sinensis ◽  
Multivariate Correlation ◽  
Standard Normal ◽  
Predict Model ◽  
Non Destructive ◽  
Selection Of

Abstract Background A fast and reliable non-destructive method for qualifying the content of extracts content (EC) in heartwood of T. sinensis cores is needed in the breeding program for studying the genetically infect on EC. However, the affecting of grain angle on near infrared (NIR) spectra prediction model for EC is unclear. In this study, NIR spectra were collected both from cross section and radial section of wood core samples in order to predict the EC in heartwood. Results The effect of grain angle on calibration EC model was studied. Several different spectra pre-processing methods were tested for calibration. It was found that standard normal variation (SNV) followed by 1 st derivative yield the best calibration for EC. Grain angle has a significant influence on the predict model for EC when use the whole NIR spectra. However, after using the significant multivariate correlation (sMC) selection of the prior of wavenumbers for EC, the influence of grain angle have been significantly reduced. Conclusions It was suggested that NIR spectroscopy could be a promising methods to predict EC in solid wood without the infection of grain angle.

An efficient method to reduce grain angle influence on NIR spectra for predicting extractives content from heartwood stem cores of T. sinensis

2019 ◽  
Author(s):  
Yanjie Li ◽  
Xin Dong ◽  
Yang Sun ◽  
Jun Liu ◽  
Jingmin Jiang
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Solid Wood ◽  
Normal Variation ◽  
Grain Angle ◽  
Multivariate Correlation ◽  
Standard Normal ◽  
Predict Model ◽  
Non Destructive ◽  
Selection Of

Abstract BackgroundA fast and reliable non-destructive method for qualifying the content of extracts content (EC) in heartwood of T. sinensis cores is needed in the breeding program for studying the genetically infect on EC. However, the affecting of grain angle on near infrared (NIR) spectra prediction model for EC is unclear. In this study, NIR spectra were collected both from cross section and radial section of wood core samples in order to predict the EC in heartwood.ResultsThe effect of grain angle on calibration EC model was studied. Several different spectra pre-processing methods were tested for calibration. It was found that standard normal variation (SNV) followed by 1st derivative yield the best calibration for EC. Grain angle has a significant influence on the predict model for EC when use the whole NIR spectra. However, after using the significant multivariate correlation (sMC) selection of the prior of wavenumbers for EC, the influence of grain angle have been significantly reduced. ConclusionsIt was suggested that NIR spectroscopy could be a promising methods to predict EC in solid wood without the infection of grain angle.

scholarly journals Development of a Non-Destructive Method for Detection of the Juiciness of Pear via VIS/NIR Spectroscopy Combined with Chemometric Methods

Foods ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 1778
Author(s):  
Fan Wang ◽  
Chunjiang Zhao ◽  
Guijun Yang
Keyword(s):  
Near Infrared ◽  
External Validation ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Spectral Ratio ◽  
Optical Path Length ◽  
Standard Normal Variate ◽  
Length Estimation ◽  
Standard Normal ◽  
Non Destructive

Juiciness is a primary index of pear quality and freshness, which is also considered as important as sweetness for the consumers. Development of a non-destructive detection method for pear juiciness is meaningful for producers and sellers. In this study, visible−near-infrared (VIS/NIR) spectroscopy combined with different spectral preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR), was used for comparison in detection of pear juiciness. Partial least squares (PLS) regression was used to establish the calibration models between the preprocessing spectra (650–1100 nm) and juiciness measured by the texture analyzer. In addition, competitive adaptive reweighted sampling (CARS) was used to identify the characteristic wavelengths and simplify the PLS models. All obtained models were evaluated via Monte Carlo cross-validation (MCCV) and external validation. The PLS model established by 19 characteristic variables after LRC-SR preprocessing displayed the best prediction performance with external verification determination coefficient (R2v) of 0.93 and root mean square error (RMSEv) of 0.97%. The results demonstrate that VIS/NIR coupled with LRC-SR method can be a suitable strategy for the quick assessment of juiciness for pears.

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

Rapid Determination of Heartwood Extractives in Larix sp. by Means of Fourier Transform near Infrared Spectroscopy

2002 ◽  
Vol 10 (3) ◽  
pp. 203-214 ◽  
Author(s):  
N. Gierlinger ◽  
M. Schwanninger ◽  
B. Hinterstoisser ◽  
R. Wimmer
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Solid Wood ◽  
Hot Water ◽  
Extractive Content ◽  
Fast Method ◽  
Wood Powder ◽  
Test Set

The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy to rapidly determine extractive and phenolic content in heartwood of larch trees ( Larix decidua MILL., L. leptolepis (LAMB.) CARR. and the hybrid L. x eurolepis) was investigated. FT-NIR spectra were collected from wood powder and solid wood using a fibre-optic probe. Partial Least Squares (PLS) regression analyses were carried out describing relationships between the data sets of wet laboratory chemical data and the FT-NIR spectra. Besides cross and test set validation the established models were subjected to a further evaluation step by means of additional wood samples with unknown extractive content. Extractive and phenol contents of these additional samples were predicted and outliers detected through Mahalanobis distance calculations. Models based on the whole spectral range and without data pre-processing performed well in cross-validation and test set validation, but failed in the evaluation test, which is based on spectral outlier detection. But selection of data pre-processing methods and manual as well as automatic restriction of wavenumber ranges considerably improved the model predictability. High coefficients of determination ( R2) and low root mean square errors of cross-validation ( RMSECV) were obtained for hot water extractives ( R2 = 0.96, RMSECV = 0.86%, range = 4.9–20.4%), acetone extractives ( R2 = 0.86, RMSECV = 0.32%, range = 0.8–3.6%) and phenolic substances ( R2 = 0.98, RMSECV = 0.21%, range = 0.7–4.9%) from wood powder. The models derived from wood powder spectra were more precise than those obtained from solid wood strips. Overall, NIR spectroscopy has proven to be an easy to facilitate, reliable, accurate and fast method for non-destructive wood extractive determination.

Prediction of Stilbene Content from Heartwood Increment Cores of Scots Pine Using near Infrared Spectroscopy Methodology

2016 ◽  
Vol 24 (6) ◽  
pp. 517-528 ◽  
Author(s):  
Susanna Pulkka ◽  
Vincent Segura ◽  
Anni Harju ◽  
Tarja Tapanila ◽  
Johanna Tanner ◽  
...  
Keyword(s):  
Scots Pine ◽  
High Throughput ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Tree Breeding ◽  
Decay Resistance ◽  
Increment Core ◽  
Automatic Selection ◽  
Non Destructive ◽  
Selection Of

High-throughput and non-destructive methods for quantifying the content of the stilbene compounds of Scots pine ( Pinus sylvestris L.) heartwood are needed in the breeding for decay resistance of heartwood timber. In this study, near infrared (NIR) spectroscopy calibrations were developed for a large collection of solid heartwood increment core samples in order to predict the amount of the stilbene pinosylvin (PS), its monomethyl ether (PSM) and their sum (STB). The resulting models presented quite accurate predictions in an independent validation set with R2V values ranging between 0.79 and 0.91. The accuracy of the models strongly depended on the chemical being calibrated, with the lowest accuracy for PS, intermediate accuracy for PSM and highest accuracy for STB. The effect of collecting one, two or more (up to five) spectra per sample on the calibration models was studied and it was found that averaging multiple spectra yielded better accuracy as it may account for the heterogeneity of wood along the increment core within and between rings. Several statistical pretreatments of the spectra were tested and an automatic selection of wavenumbers prior to calibration. Without the automatic selection of wavenumbers, a first derivative of normalised spectra yielded the best accuracies, whereas after the automatic selection of wavenumbers, no particular statistical pretreatment appeared to yield better results than any other. Finally, the automatic selection of wavenumbers slightly improved the accuracy of the models for all traits. These results demonstrate the potential of NIR spectroscopy as a high-throughput and non-destructive phenotyping technique in tree breeding for the improvement of decay resistance in heartwood timber.

scholarly journals Differentiation of South African Game Meat Using Near-Infrared (NIR) Spectroscopy and Hierarchical Modelling

Molecules ◽  
2020 ◽  
Vol 25 (8) ◽  
pp. 1845 ◽  
Author(s):  
Kiah Edwards ◽  
Marena Manley ◽  
Louwrens C. Hoffman ◽  
Anel Beganovic ◽  
Christian G. Kirchler ◽  
...  
Keyword(s):  
South African ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Muscle Type ◽  
Game Species ◽  
Analysis Techniques ◽  
Hierarchical Modelling ◽  
Analysis Models ◽  
Non Destructive

Near-infrared (NIR) spectroscopy, combined with multivariate data analysis techniques, was used to rapidly differentiate between South African game species, irrespective of the treatment (fresh or previously frozen) or the muscle type. These individual classes (fresh; previously frozen; muscle type) were also determined per species, using hierarchical modelling. Spectra were collected with a portable handheld spectrophotometer in the 908–1676-nm range. With partial least squares discriminant analysis models, we could differentiate between the species with accuracies ranging from 89.8%–93.2%. It was also possible to distinguish between fresh and previously frozen meat (90%–100% accuracy). In addition, it was possible to distinguish between ostrich muscles (100%), as well as the forequarters and hindquarters of the zebra (90.3%) and springbok (97.9%) muscles. The results confirm NIR spectroscopy’s potential as a rapid and non-destructive method for species identification, fresh and previously frozen meat differentiation, and muscle type determination.

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