Foliar Damage Assessment and Selection of Cold Resistant Genotypes Using near Infrared Spectra of Eucalyptus Globulus Leaves

2009 ◽  
Vol 17 (4) ◽  
pp. 223-231 ◽  
Author(s):  
Rosario del P. Castillo ◽  
David Contreras ◽  
Matthias Otto ◽  
Jaime Baeza ◽  
Juanita Freer
Keyword(s):  
Eucalyptus Globulus ◽  
Near Infrared ◽  
Cold Resistance ◽  
External Validation ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Near Infrared Spectra ◽  
Orthogonal Signal Correction ◽  
Foliar Damage ◽  
Reference Parameters

Near infrared (NIR) spectroscopy was used to predict cold resistance in Eucalyptus globulus genotypes after acclimatation treatments at low temperatures. Branches of the genotypes were maintained during 31 days in three cold chambers and the NIR spectra of milled leaves were obtained. The samples were subsequently exposed to artificial freezing (with some branches exposed to −2°C) and the foliar damage was assessed by visually estimating the necrotic area of each leaf. These values were used as reference parameters to evaluate cold resistance in the genotypes. A partial least squares (PLS) method was performed using the foliar damage and the NIR spectra of leaves. Spectra were treated with multiplicative scatter correction (MSC) and orthogonal signal correction (OSC). An excellent model was achieved which predicted foliar damage in the genotypes with a low standard error of prediction (3.5%), a high regression coefficient in cross-validation and external validation ( r > 0.9) and a high percentage of the variance explained by the spectra (95.4%). Furthermore, a pattern recognition method, using a regularised discriminant analysis (RDA) of the scores matrix obtained in PLS, in a denominated PLS/RDA on scores strategy, was applied directly to the spectra to classify each genotype as tolerant or sensitive; 100% of the genotypes were correctly assigned. These results demonstrate the advantages of using the NIR spectra of leaves as a rapid, nondestructive tool to evaluate cold resistance in genotypes.

Application of Support Vector Machine in Identifying inside Moldy Chestnut Using Near Infrared Spectroscopy

2014 ◽  
Vol 615 ◽  
pp. 169-172
Author(s):  
Jie Liu ◽  
Xiao Yu Li ◽  
Wei Wang ◽  
Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Near Infrared ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Training Set ◽  
Near Infrared Spectra ◽  
Standard Normal ◽  
Validation Set ◽  
Normal Transformation

NIR spectroscopy has been applied in detecting inside quality of chestnut successfully. In this work, Support Vector Machine Discriminant Analysis was utilized to identify the qualified chestnuts, the serious moldy chestnuts and the slight moldy chestnuts using their Near infrared spectra region from 833 nm to 2500 nm. 109 chestnut samples were involved and four different preprocessing methods were compared. The results showed that for all the models, the average correct rates of training set and validation set were higher than 90%. The performance of model based on raw spectra was not as good as other models, which indicated the necessity of preprocessing. The models based on the spectra preprocessed by first derivative and multiplicative scatter correction had the same performances, with 97% and 85% as the correct rate of training set and validation set. The models based on the spectra preprocessed by Standard normal transformation has 100% correct rate of training set while 88% of validation set. The second derivative model had the best result with 100% and 90% as the correct rate of training set and validation set. These results demonstrated that the NIR spectroscopy had capability to detect interior mildew of intact chestnut nondestructively.

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 Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Sylvio Barbon ◽  
Ana Paula Ayub da Costa Barbon ◽  
Rafael Gomes Mantovani ◽  
Douglas Fernandes Barbin
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Chicken Meat ◽  
Poultry Meat ◽  
Spectral Information ◽  
Support Vector ◽  
Near Infrared Spectra ◽  
Meat Samples

Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIEL∗a∗b∗, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with 77.2% precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.

On the Scales Associated with Near-Infrared Reflectance Difference Spectra

1995 ◽  
Vol 49 (6) ◽  
pp. 765-772 ◽  
Author(s):  
M. S. Dhanoa ◽  
S. J. Lister ◽  
R. J. Barnes
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
The Other ◽  
Difference Spectra ◽  
Near Infrared Reflectance ◽  
Standard Normal Variate ◽  
Near Infrared Spectra ◽  
Standard Normal ◽  
Relationship Of ◽  
The Relationship

Scale differences of individual near-infrared spectra are identified when set-independent standard normal variate (SNV) and de-trend (DT) transformations are applied in either SNV followed by DT or DT then SNV order. The relationship of set-dependent multiplicative scatter correction (MSC) to SNV is also referred to. A simple correction factor is proposed to convert derived spectra from one order to the other. It is suggested that the suitable order for the study of changes using difference spectra (when removing baselines) should be DT followed by SNV, which leads to all derived spectra on the scale of mean zero and variance equal to one. If baselines are identical, then SNV scale spectra can be used to calculate differences.

The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

1988 ◽  
Vol 42 (7) ◽  
pp. 1273-1284 ◽  
Author(s):  
Tomas Isaksson ◽  
Tormod Næs
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Data Sets ◽  
Prediction Errors ◽  
Regression Methods ◽  
Multiplicative Scatter Correction ◽  
Linearity Improvement

Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7–68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.

Fault Detection Based on Near-Infrared Spectra for the Oil Desalting Process

2018 ◽  
Vol 72 (8) ◽  
pp. 1199-1204 ◽  
Author(s):  
Xiaoli Luan ◽  
Minjun Jin ◽  
Fei Liu
Keyword(s):  
Fault Detection ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Molecular Spectra ◽  
Process Variables ◽  
Vibrational Signal ◽  
Near Infrared Spectra ◽  
Detection Approach ◽  
Squared Prediction Error

The fault detection problem of the oil desalting process is investigated in this paper. Different from the traditional fault detection approaches based on measurable process variables, near-infrared (NIR) spectroscopy is applied to acquire the process fault information from the molecular vibrational signal. With the molecular spectra data, principal component analysis was explored to calculate the Hotelling T2 and squared prediction error, which act as fault indicators. Compared with the traditional fault detection approach based on measurable process variables, NIR spectra-based fault detection illustrates more sensitivity to early failure because of the fact that the changes in the molecular level can be identified earlier than the physical appearances on the process. The application results show that the detection time of the proposed method is earlier than the traditional method by about 200 min.

Detection of the Contents of the Ingredient of Food by Using the NIR Spectroscopy and the Backward Interval Partial Least-Squares

2013 ◽  
Vol 726-731 ◽  
pp. 4337-4341
Author(s):  
Yong Fu Liu ◽  
Xi Chen ◽  
Bin Zheng ◽  
Ze Yu Xu ◽  
Guo Tian He
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
High Speed ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Set Partitioning ◽  
New Approach ◽  
Orthogonal Signal Correction ◽  
The Relationship ◽  
Sample Set

Near-infrared spectroscopy (NIRS), with the characteristics of high speed, non-destructiveness, high precision and reliable detection data etc., is a pollution-free, rapid, quantitative and qualitative analysis method. A new approach for the discrimination of the ingredients of corn (moisture, oil, protein, starch) by means of NIR spectroscopy (1100-2498 nm) was developed in this work. The relationship between the reflectance spectra and the ingredients of corn was established. The data were spilt into training and testing subsets by sample set partitioning based on join x-y distance (SPXY),the spectral data was compressed by orthogonal signal correction (OSC), wavelength was selected by backward interval partial least-squares (biPLS),the 60 samples to build PLS mode, the model was used to predict the varieties of 20 unknown samples. The standard error of prediction (SEP) was 0.173; the relative error of prediction (PRE) was 0.55%; the correlation coefficient (R) was 0.98. The way to detect the ingredient of food is simply, reliable.

scholarly journals The Combined Optimization of Savitzky-Golay Smoothing and Multiplicative Scatter Correction for FT-NIR PLS Models

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Huazhou Chen ◽  
Qiqing Song ◽  
Guoqiang Tang ◽  
Quanxi Feng ◽  
Liang Lin
Keyword(s):  
Latent Variables ◽  
Near Infrared ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Spectroscopy Analysis ◽  
Multiplicative Scatter Correction ◽  
Pls Modeling ◽  
Smoothing Parameters ◽  
Better Than

The combined optimization of Savitzky-Golay (SG) smoothing and multiplicative scatter correction (MSC) were discussed based on the partial least squares (PLS) models in Fourier transform near-infrared (FT-NIR) spectroscopy analysis. A total of 5 cases of separately (or combined) using SG smoothing and MSC were designed and compared for optimization. For every case, the SG smoothing parameters were optimized with the number of PLS latent variables (F), with an expanded number of smoothing points. Taking the FT-NIR analysis of soil organic matter (SOM) as an example, the joint optimization of SG smoothing and MSC was achieved based on PLS modeling. The results showed that the optimal pretreatment was successively using SG smoothing and MSC, in which the SG smoothing parameters were 4th degree of polynomial, 2nd-order derivative, and 67 smoothing points, the best corresponding F, RMSEP, and RP were 7, 0.3982 (%), and 0.8862, respectively. This result was far better than those without any pretreatment. The combined optimization of SG smoothing and MSC could obviously improve the modeling result for NIR analysis of SOM. In addition, a new method for the classification of calibration and prediction was proposed by normalization principle. The optimizations were done on this basis of this classification.

Sawmill Trial of At-Line Prediction of Recovered Lumber Stiffness by NIR Spectroscopy of Pinus Radiata Cants

2003 ◽  
Vol 11 (2) ◽  
pp. 137-143 ◽  
Author(s):  
Roger Meder ◽  
Armin Thumm ◽  
David Marston
Keyword(s):  
Infrared Spectra ◽  
Pinus Radiata ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Bending Test ◽  
Long Span ◽  
Four Point Bending ◽  
Centre Line ◽  
Near Infrared Spectra ◽  
Four Point Bending Test

Pinus radiata D. Don cants (100 or 200 mm thick × 4.8 m) from a commercial sawmill operation were assessed in the green state using near infrared (NIR) spectroscopy. Near infrared spectra were acquired along the centre line of one cant face and at 50 mm offsets to one side of the centre line. The cants were ripped to produce either 50 × 100 or 50 × 200 mm rough sawn boards, which were then kiln-dried and gauged to final dimensions. The long-span modulus of elasticity ( L MoE) on each board was determined using a four-point bending test and the corresponding NIR spectra of each board (the 50 mm edge from the cant) were regressed against the long-span MoE value using partial least squares modeling. The results are explained in terms of the potential for NIR to predict the potential upgrade to higher value products for timber recovered from the corewood zone of logs.

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