Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

This study aimed to measure the carbon content of tree species rapidly and accurately using visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods. Currently, the carbon content of trees used for calculating the carbon storage of forest trees in the study of carbon sequestration is obtained by two methods. One involves measuring carbon content in the laboratory (K2CrO7-H2SO4 oxidation method or elemental analyzer), and another involves directly using the IPCC (Intergovernmental Panel on Climate Change) default carbon content of 0.45 or 0.5. The former method is destructive, time-consuming, and expensive, while the latter is subjective. However, Vis-NIR detection technology can avoid these shortcomings and rapidly determine carbon content. In this study, 96 increment core samples were collected from six tree species in the Heilongjiang province of China for analysis. The spectral data were preprocessed using seven methods, including extended multiplicative scatter correction (EMSC), first derivative (1D), second derivative (2D), baseline correction, de-trend, orthogonal signal correction (OSC), and normalization to eliminate baseline drifting and noise, as well as to enhance the model quality. Linear models were established from the spectra using partial least squares regression (PLS). At the same time, we also compared the effects of full-spectrum and reduced spectrum on the model’s performance. The results showed that the spectral data processed by 1D with the full spectrum could obtain a better prediction model. The 1D method yielded the highest R2c of 0.92, an RMSEC (root-mean-square error of calibration) of 0.0056, an R2p of 0.99, an RMSEP (root-mean-square error of prediction) of 0.0020, and the highest RPD (residual prediction deviation) value of 8.9. The results demonstrate the feasibility of Vis-NIR spectroscopy coupled with chemometric methods in determining the carbon content of tree species as a simple, rapid, and non-destructive method.

Download Full-text

Determination of Magnesium Oxide Content in Mineral Medicine Talcum Using Near-Infrared Spectroscopy Integrated with Support Vector Machine

Applied Spectroscopy ◽

10.1177/0003702817727016 ◽

2017 ◽

Vol 71 (11) ◽

pp. 2427-2436 ◽

Cited By ~ 5

Author(s):

Mi Lei ◽

Long Chen ◽

Bisheng Huang ◽

Keli Chen

Keyword(s):

Root Mean Square Error ◽

Least Squares ◽

Partial Least Squares ◽

Mean Square Error ◽

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Support Vector ◽

Mean Square ◽

Characteristic Spectrum

In this research paper, a fast, quantitative, analytical model for magnesium oxide (MgO) content in medicinal mineral talcum was explored based on near-infrared (NIR) spectroscopy. MgO content in each sample was determined by ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy, and then a variety of processing methods of spectra data were compared to establish a good NIR spectroscopy model. To start, 50 batches of talcum samples were categorized into training set and test set using the Kennard–Stone (K-S) algorithm. In a partial least squares regression (PLSR) model, both leave-one-out cross-validation (LOOCV) and training set validation (TSV) were used to screen spectrum preprocessing methods from multiplicative scatter correction (MSC), and finally the standard normal variate transformation (SNV) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLSR method, and the characteristic spectrum ranges were determined as 11995–10664, 7991–6661, and 4326–3999 cm−1, with four optimal ranks. In the support vector machine (SVM) model, the radical basis function (RBF) kernel function was used. Moreover, the full spectrum data of samples pretreated with SNV, the characteristic spectrum data screened using synergy interval partial least squares (SiPLS), and the scoring data of the first four ranks obtained by a partial least squares (PLS) dimension reduction of characteristic spectrum were taken as input variables of SVM, and the MgO content reference values of various sample were taken as output values. In addition, the SVM model internal parameters were optimized using the grid optimization method (GRID), particle swarm optimization (PSO), and genetic algorithm (GA) so that the optimal C and g-values were determined and the validation model was established. By comprehensively comparing the validation effects of different models, it can be concluded that the scoring data of the first four ranks obtained by PLS dimension reduction of characteristic spectrum were taken as input variables of SVM, and the PLS-SVM regression model established using GRID was the optimal NIR spectroscopy quantitative model of talc. This PLS-SVM regression model (rank = 4) measured that the MgO content of talcum was in the range of 17.42–33.22%, with root mean square error of cross validation (RMSECV) of 2.2127%, root mean square error of calibration (RMSEC) of 0.6057%, and root mean square error of prediction (RMSEP) of 1.2901%. This model showed high accuracy and strong prediction capacity, which can be used for rapid prediction of MgO content in talcum.

Download Full-text

Detection of deltamethrin in cabbages using visible shortwave near-infrared spectroscopy

Food Research ◽

10.26656/fr.2017.5(3).742 ◽

2021 ◽

Vol 5 (3) ◽

pp. 273-280

Author(s):

C.D.M. Ishkandar ◽

N.M. Nawi ◽

R. Janius ◽

N. Mazlan ◽

T.T. Lin

Keyword(s):

Infrared Spectroscopy ◽

Root Mean Square Error ◽

Spectral Data ◽

Mean Square Error ◽

Root Mean Square ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Partial Least Square ◽

Calibration Model ◽

Mean Square

Pesticides have long been used in the cabbage industry to control pest infestation. This study investigated the potential application of low-cost and portable visible shortwave near-infrared spectroscopy for the detection of deltamethrin residue in cabbages. A total of sixty organic cabbage samples were used. The sample was divided into four batches, three batches were sprayed with deltamethrin pesticide whereas the remaining batch was not sprayed (control sample). The first three batches of the cabbages were sprayed with the pesticide at three different concentrations, namely low, medium and high with the values of 0.08, 0.11 and 0.14% volume/volume (v/v), respectively. Spectral data of the cabbage samples were collected using visible shortwave near-infrared (VSNIR) spectrometer with wavelengths range between 200 and 1100 nm. Gas chromatography-electron capture detector (GC-ECD) was used to determine the concentration of deltamethrin residues in the cabbages. Partial least square (PLS) regression method was adopted to investigate the relationship between the spectral data and deltamethrin concentration values. The calibration model produced the values of coefficient of determination (R2 ) and the root mean square error of calibration (RMSEC) of 0.98 and 0.02, respectively. For the prediction model, the values of R2 and the root mean square error of prediction (RMSEP) were 0.94 and 0.04, respectively. These results demonstrated that the proposed spectroscopic measurement is a promising technique for the detection of pesticide at different concentrations in cabbage samples.

Download Full-text

Determination of Adulteration Content in Extra Virgin Olive Oil Using FT-NIR Spectroscopy Combined with the BOSS–PLS Algorithm

Molecules ◽

10.3390/molecules24112134 ◽

2019 ◽

Vol 24 (11) ◽

pp. 2134 ◽

Cited By ~ 6

Author(s):

Hui Jiang ◽

Quansheng Chen

Keyword(s):

Root Mean Square Error ◽

Olive Oil ◽

Mean Square Error ◽

Virgin Olive Oil ◽

Nir Spectroscopy ◽

Extra Virgin Olive Oil ◽

Mean Square ◽

Spectroscopy Technique ◽

Full Spectrum ◽

Variable Subset

This work applied the FT-NIR spectroscopy technique with the aid of chemometrics algorithms to determine the adulteration content of extra virgin olive oil (EVOO). Informative spectral wavenumbers were obtained by the use of a novel variable selection algorithm of bootstrapping soft shrinkage (BOSS) during partial least-squares (PLS) modeling. Then, a PLS model was finally constructed using the best variable subset obtained by the BOSS algorithm to quantitative determine doping concentrations in EVOO. The results showed that the optimal variable subset including 15 wavenumbers was selected by the BOSS algorithm in the full-spectrum region according to the first local lowest value of the root-mean-square error of cross validation (RMSECV), which was 1.4487 % v/v. Compared with the optimal models of full-spectrum PLS, competitive adaptive reweighted sampling PLS (CARS–PLS), Monte Carlo uninformative variable elimination PLS (MCUVE–PLS), and iteratively retaining informative variables PLS (IRIV–PLS), the BOSS–PLS model achieved better results, with the coefficient of determination (R2) of prediction being 0.9922, and the root-mean-square error of prediction (RMSEP) being 1.4889 % v/v in the prediction process. The results obtained indicated that the FT-NIR spectroscopy technique has the potential to perform a rapid quantitative analysis of the adulteration content of EVOO, and the BOSS algorithm showed its superiority in informative wavenumbers selection.

Download Full-text

Determination of Moisture Content in Ginger Using PSO Combined with Vis/NIR

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.320.563 ◽

2011 ◽

Vol 320 ◽

pp. 563-568 ◽

Cited By ~ 1

Author(s):

Jing Li ◽

Long Xue ◽

Mu Hua Liu ◽

Ping Lv ◽

Lin Yuan Yan

Keyword(s):

Moisture Content ◽

Root Mean Square Error ◽

Spectral Data ◽

Nir Spectroscopy ◽

Mean Square ◽

Full Spectrum ◽

Validation Set ◽

Prediction Capability ◽

Spectrum Model

Vis/NIR spectroscopy was used to measure the moisture content of ginger. 330 samples were separated into two groups, as training and validation. Vis/NIR reflection spectral data from 350 to 1800 nm were collected using ginger within the training and validation sets. PSO was used to establish the PLS model. In comparison to the full spectrum model (contained 1451 variables), the prediction capability was improved after using PSO for PLS models. The number of selected variables and LVs were 300 and 6, respectively. The correlation of determination in validation set (), root mean square error of prediction (RMSEP), and bias by PSO-PLS were 0.9881, 4.7827, and 0.1751.

Download Full-text

Near Infrared Spectroscopic Evaluation of Starch Properties of Diverse Sorghum Populations

Processes ◽

10.3390/pr9111942 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1942

Author(s):

Kamaranga H. S. Peiris ◽

Xiaorong Wu ◽

Scott R. Bean ◽

Mayra Perez-Fajardo ◽

Chad Hayes ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Grain Sorghum ◽

Calibration Model ◽

Mean Square ◽

Starch Properties ◽

End Uses

Starch, mainly composed of amylose and amylopectin, is the major nutrient in grain sorghum. Amylose and amylopectin composition affects the starch properties of sorghum flour which in turn determine the suitability of sorghum grains for various end uses. Partial least squares regression models on near infrared (NIR) spectra were developed to estimate starch and amylose contents in intact grain sorghum samples. Sorghum starch calibration model with a coefficient of determination (R2) = 0.87, root mean square error of cross validation (RMSECV) = 1.57% and slope = 0.89 predicted the starch content of validation set with R2 = 0.76, root mean square error of prediction (RMSEP) = 2.13%, slope = 0.93 and bias = 0.20%. Amylose calibration model with R2 = 0.84, RMSECV = 2.96% and slope = 0.86 predicted the amylose content in validation samples with R2 = 0.76, RMSEP = 2.60%, slope = 0.98 and bias = −0.44%. Final starch and amylose cross validated calibration models were constructed combining respective calibration and validation sets and used to predict starch and amylose contents in 1337 grain samples from two diverse sorghum populations. Protein and moisture contents of the samples were determined using previously tested NIR spectroscopy models. The distribution of starch and protein contents in the samples of low amylose (<5%) and normal amylose (>15%) and the overall relationship between starch and protein contents of the sorghum populations were investigated. Percent starch and protein were negatively correlated, low amylose lines tended to have lower starch and higher protein contents than lines with high amylose. The results showed that NIR spectroscopy of whole grain can be used as a high throughput pre-screening method to identify sorghum germplasm with specific starch quality traits to develop hybrids for various end uses.

Download Full-text

Quantitative Analysis ofPanax ginsengby FT-NIR Spectroscopy

Journal of Analytical Methods in Chemistry ◽

10.1155/2014/741571 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Xin-fang Xu ◽

Li-xing Nie ◽

Li-li Pan ◽

Bian Hao ◽

Shao-xiong Yuan ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Multivariate Regression ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Mean Square ◽

Least Squares Regression ◽

Regression Methods

Near-infrared spectroscopy (NIRS), a rapid and efficient tool, was used to determine the total amount of nine ginsenosides inPanax ginseng. In the study, the regression models were established using multivariate regression methods with the results from conventional chemical analytical methods as reference values. The multivariate regression methods, partial least squares regression (PLSR) and principal component regression (PCR), were discussed and the PLSR was more suitable. Multiplicative scatter correction (MSC), second derivative, and Savitzky-Golay smoothing were utilized together for the spectral preprocessing. When evaluating the final model, factors such as correlation coefficient (R2) and the root mean square error of prediction (RMSEP) were considered. The final optimal results of PLSR model showed that root mean square error of prediction (RMSEP) and correlation coefficients (R2) in the calibration set were 0.159 and 0.963, respectively. The results demonstrated that the NIRS as a new method can be applied to the quality control ofGinseng Radix et Rhizoma.

Download Full-text

Rapid and Simultaneous Measurement of Praeruptorin A, Praeruptorin B, Praeruptorin E, and Moisture Contents in Peucedani Radix Using Near-Infrared Spectroscopy and Chemometrics

Journal of AOAC International ◽

10.5740/jaoacint.19-0126 ◽

2020 ◽

Vol 103 (2) ◽

pp. 504-512

Author(s):

Yijuan Hu ◽

Hongjian Zhang ◽

Weiqing Liang ◽

Pan Xu ◽

Kelang Lou ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Mean Square ◽

Determination Coefficient ◽

Moisture Contents ◽

Calibration Models

Abstract Background: Peucedani Radix is a popular traditional Chinese medicine herb with a long history in China. Praeruptorin A (PA), praeruptorin B (PB), and praeruptorin E (PE) are usually taken as important quality indexes of Peucedani Radix. Objective: To establish a rapid method for simultaneous determination of PA, PB, PE, and moisture contents in Peucedani Radix using near-infrared (NIR) spectroscopy and chemometrics. Methods: One hundred twenty Peucedani Radix samples were analyzed with HPLC as a reference method. The NIR spectral scanning range was from 12000 cm−1 to 4000 cm−1. Partial least squares (PLS) regression algorithm was used to establish calibration models. Three variable selection methods were investigated, including variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and Monte Carlo uninformative variable elimination (MCUVE). The performances of the established models were evaluated by root-mean-square error (RMSEC) and determination coefficient (Rc2) of calibration set, root-mean-square error (RMSEP) and determination coefficient (Rp2) of prediction set, and residual predictive deviation (RPD). Results: A clear ranking of the performance of the calibration models could be as follows: CARS-PLS > MCUVE-PLS > VIP-PLS > Full-PLS. For CARS-PLS, Rp2, RMSEP, and RPD of the prediction set are as follows: 0.9204, 0.0860%, and 3.5850 for PA; 0.8011, 0.0431%, and 2.0868 for PB; 0.8043, 0.0367%, and 2.1569 for PE; and 0.9249, 0.3350%, and 3.6551 for moisture, respectively. Conclusions: The NIR spectroscopy combined with CARS-PLS calibration models could be used for rapid and accurate determination of PA, PB, PE, and moisture contents in Peucedani Radix samples.

Download Full-text

Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics

Energies ◽

10.3390/en14051460 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1460

Author(s):

Jinming Liu ◽

Changhao Zeng ◽

Na Wang ◽

Jianfei Shi ◽

Bo Zhang ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Variable Selection ◽

Root Mean Square Error ◽

Least Squares ◽

Mean Square Error ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Mean Square ◽

Biochemical Methane Potential ◽

Methane Potential

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.

Download Full-text

Quantitative Analysis of Forsythiae fructus by Near Infrared Spectroscopy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.807-809.1967 ◽

2013 ◽

Vol 807-809 ◽

pp. 1967-1971

Author(s):

Yan Bai ◽

Xiao Yan Duan ◽

Hai Yan Gong ◽

Cai Xia Xie ◽

Zhi Hong Chen ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Cross Validation ◽

Ethanol Extract ◽

Mean Square ◽

Validation Set

In this paper, the content of forsythoside A and ethanol-extract were rapidly determinated by near-infrared reflectance spectroscopy (NIRS). 85 samples of Forsythiae Fructus harvested in Luoyang from July to September in 2012 were divided into a calibration set (75 samples) and a validation set (10 samples). In combination with the partical least square (PLS), the quantitative calibration models of forsythoside A and ethanol-extract were established. The correlation coefficient of cross-validation (R2) was 0.98247 and 0.97214 for forsythoside A and ethanol-extract, the root-mean-square error of calibration (RMSEC) was 0.184 and 0.570, the root-mean-square error of cross-validation (RMSECV) was 0.81736 and 0.36656. The validation set were used to evaluate the performance of the models, the root-mean-square error of prediction (RMSEP) was 0.221 and 0.518. The results indicated that it was feasible to determine the content of forsythoside A and ethanol-extract in Forsythiae Fructus by near-infrared spectroscopy.

Download Full-text