scholarly journals Mechanical properties of wood materials using near-infrared spectroscopy based on correlation local embedding and partial least-squares

2019 ◽  
Vol 31 (3) ◽  
pp. 1053-1060 ◽  
Author(s):  
Lei Yu ◽  
Yuliang Liang ◽  
Yizhuo Zhang ◽  
Jun Cao
Keyword(s):  
Mechanical Properties ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Tangential Surface ◽  
Prediction Coefficient ◽  
Validation Set ◽  
Mongolian Oak

Abstract This study used near-infrared (NIR) spectroscopy to predict mechanical properties of wood. NIR spectra were collected in wavelengths 900–1700 nm, and spectra averaged by radial and tangential surface spectra were used to establish a partial least square (PLS) model based on correlation local embedding (CLE). Mongolian oak (Quercus mongolica Fisch. ex Ledeb.) was used to test the effectiveness of the model. The cross-validation method was used to verify the robustness of the CLE–PLS model. Ninety samples were tested as the calibration set and forty-five as the validation set. The results show that the prediction coefficient of determination ($$R_{p}^{2}$$Rp2) is 0.80 for MOR, and 0.78 for MOE. The ratio of performance to deviation is 2.23 for MOR and 2.15 for MOE.

scholarly journals Determination of the Mechanical Properties of Rubber by FT-NIR

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Rattapol Pornprasit ◽  
Philaiwan Pornprasit ◽  
Pruet Boonma ◽  
Juggapong Natwichai
Keyword(s):  
Mechanical Properties ◽  
Tensile Properties ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Mechanical Tests ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Styrene Butadiene Rubber ◽  
Butadiene Rubber

Mechanical tests, for example, tensile and hardness tests, are usually used to evaluate the properties of rubber materials. In this work, mechanical properties of selected rubber materials, that is, natural rubber (NR), styrene butadiene rubber (SBR), nitrile butadiene rubber (NBR), and ethylene propylene diene monomer (EPDM), were evaluated using a near infrared (NIR) spectroscopy technique. Here, NR/NBR and NR/EPDM blends were first prepared. All of the samples were then scanned using a FT-NIR spectrometer and fitted with an integration sphere working in a diffused reflectance mode. The spectra were correlated with hardness and tensile properties. Partial least square (PLS) calibration models were built from the spectra datasets with preprocessing techniques, that is, smoothing and second derivative. This indicated that reasonably accurate models, that is, with a coefficient of determination [R2] of the validation greater than 0.9, could be achieved for the hardness and tensile properties of rubber materials. This study demonstrated that FT-NIR analysis can be applied to determine hardness and tensile values in rubbers and rubber blends effectively.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

Application of Near Infrared Spectroscopy in the Analysis of Printing Color Detection

2012 ◽  
Vol 262 ◽  
pp. 59-64
Author(s):  
Hong Wei Lu ◽  
Hong He ◽  
Jun Ji ◽  
Guo Qiang Liu ◽  
Ying Hu
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Color Parameters ◽  
Detection Model ◽  
Color Detection ◽  
Validation Set

For the fast and exact detection of printing color, we combine the near infrared (NIR) spectroscopy technique with partial least square (PLS) to build the detection model of printing color. Applying the 144 samples of spectral curve which obtained by the near infrared spectroscopy randomly separated into calibration set and validation set, and base on the 120 calibration set data to establish the prediction model of printing color by PLS, then this model is employed for predicting the color of the 24 validation set. The RMSEC of the 24 blocks’ color parameters L*, a*, b*, E are 0.73, 2.26, 3.03 and 1.09 respectively; The RMSEP are 0.97, 2.77, 3.57 and 1.34 respectively. Those results tell that the NIR spectrum and blocks’ color parameters L*, a*, b*, E could accurately establish a quantitative regression model, applying such model also can accurately predict unknown samples, and the results approximate to the original reference data. The use of near infrared spectroscopy to detect the printed matter nondestructively is feasible, and lays the foundation for the further analysis and establishment of printing chroma model.

Near-Infrared Spectroscopic Measurement of Organic Matter in Soil

2012 ◽  
Vol 499 ◽  
pp. 414-418
Author(s):  
Tao Pan ◽  
Zhen Tao Wu ◽  
Jie Mei Chen
Keyword(s):  
Organic Matter ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Modeling Prediction ◽  
Validation Set ◽  
Selection Of

Near-infrared (NIR) spectroscopy was successfully applied to chemical free and rapid determination of the organic matter in soil, and moving window partial least square (MWPLS) combining with Savitzky-Golay (SG) smoothing was used to the selection of NIR waveband. Thirty-five samples were randomly selected from all 97 collected soil samples as the validation set. The remaining 62 samples were divided into similar modeling calibration set (37 samples) and modeling prediction set (25 samples) based on partial least square cross-validation predictive bias (PLSPB). The selected waveband was 1896 nm to 2138 nm; the SG smoothing parameters and PLS factor OD, DP, NSP and F were 2, 6, 71 and 15, respectively; the modeling effect M-SEP and M-RPwere 0.219% and 0.944, respectively; the validating effect V-SEP and V-RPwere 0.243% and 0.878, respectively. The result provided a reliable NIR model and valuable references for designing specialized NIR instruments.

Prediction of internal compositions change in potato during storage using visible/near-infrared (Vis/NIR) spectroscopy

2020 ◽  
Vol 16 (4) ◽  
Author(s):  
Roya Farhadi ◽  
Amir H. Afkari-Sayyah ◽  
Bahareh Jamshidi ◽  
Ahmad Mousapour Gorji
Keyword(s):  
Moisture Content ◽  
Cold Storage ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Reducing Sugar ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Reliable Technique

AbstractVisible/Near-infrared (Vis/NIR) spectroscopy at a range of 450–1000 nm was used to predict the values of three qualitative variables (starch, reducing sugar, and moisture content) on 200 potato tubers from 2 potato genotypes (‘Agria’ and ‘Clone 397009–8’) stored in both traditional and cold storages. After spectroscopy measurements, these variables were measured using reference methods. Then, Partial Least Square (PLS) models were developed. To evaluate developed models, Root Mean Square Error of calibration and cross validation (RMSEC and RMSECV), as well as coefficient of determination for calibration and cross validation (R2C and R2CV), and Residual Predictive Deviation (RPD) were used. The best prediction belonged to reducing sugar with statistical values of R2C = 0.99, R2CV = 0.98, RMSEC = 0.029, RMSECV = 0.037, and RPD = 7.57 in ‘Clone’ genotype stored under cold storage. The weakest prediction was related to moisture content with statistical values of R2C = 0.93, R2CV = 0.92, RMSEC = 0.268, RMSECV = 0.279, and RPD = 6.45 in stored ‘Clone’ genotypes under cold storage. Results of the study showed that, Vis/NIR spectroscopy as a non-destructive, fast, and reliable technique can be used for prediction of inner compositions of stored potatoes.

Quantitative Evaluation of Glycyrrhizic Acid That Affects the Product Quality of Kakkonto Extract, a Traditional Herbal Medicine, by a Chemometric near Infrared Spectroscopic Method

2009 ◽  
Vol 17 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Yoshifumi Mohri ◽  
Yukoh Sakata ◽  
Makoto Otsuka
Keyword(s):  
Acid Content ◽  
Glycyrrhizic Acid ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Wave Number ◽  
Coefficient Of Determination ◽  
Calibration Model

The purpose of this study was to construct a calibration model for the prediction of glycyrrhizic acid content in Kakkonto extracts using near infrared (NIR) spectroscopy. The NIR spectra of the Kakkonto extracts were obtained using a Fourier transform NIR spectrometer in transmission mode and chemometric analysis was performed using partial least-square (PLS) regression. The calibration model was constructed by the selection of wave number regions and by the first derivative pre-treatment of NIR spectra. The glycyrrhizic acid content could be predicted using a calibration model comprising three principal components (PCs) obtained by the PLS method. The calibration model was theoretically analysed by investigating the standard error of prediction values, the loading vectors of each PC and the regression vector. The relationship between the actual and predicted glycyrrhizic acid contents in the Kakkonto extract exhibited a straight line with a coefficient of determination of 0.966 (calibration) and 0.945 (validation), respectively. The predicted glycyrrhizic acid content in the Kakkonto extract was within the 95% predictive intervals.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

Determination of Fat, Protein, and Total Solids in Cheese by Near-Infrared Reflectance Spectroscopy

1995 ◽  
Vol 78 (3) ◽  
pp. 802-806 ◽  
Author(s):  
José Louis Rodriguez-Otero ◽  
Maria Hermida ◽  
Alberto Cepeda
Keyword(s):  
Near Infrared ◽  
Independent Set ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sample Treatment ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Total Solids

Abstract Near-infrared reflectance (NIR) spectroscopy was used to analyze fat, protein, and total solids in cheese without any sample treatment. A set of 92 samples of cow’s milk cheese was used for instrument calibration by principal components analysis and modified partial least-square regression. The following statistical values were obtained: standard error of calibration (SEC) = 0.388 and squared correlation coefficient (R2) = 0.99 for fat, SEC = 0.397 and R2 = 0.98 for protein, and SEC = 0.412 and R2 = 0.99 for total solids. To validate the calibration, an independent set of 25 cheese samples of the same type was used. Standard errors of validation were 0.47,0.50, and 0.61 for fat, protein, and total solids, respectively, and hf for the regression of measurements by reference methods versus measurements by NIR spectroscopy was 0.98 for the 3 components.

scholarly journals Prediction of macro- and microelements content in Croatian common bean landraces (Phaseolus vulgaris L.) by NIR spectroscopy

Poljoprivreda ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 48-55
Author(s):  
Marina Vranić ◽  
Marko Petek ◽  
Krešimir Bošnjak ◽  
Boris Lazarević ◽  
Klaudija Carović Stanko
Keyword(s):  
Phaseolus Vulgaris ◽  
Common Bean ◽  
Near Infrared ◽  
Model Development ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Potassium Oxide ◽  
Phaseolus Vulgaris L

In this study, near-infrared spectroscopy (NIRS) was used to predict the contents of essential macro- and microelements in common bean (Phaseolus vulgaris L.) accessions of most widespread Croatian landraces. Total of 175 samples were used for the model development by modified partial least square (MPLS), principal component regression (PCR) and partial least square (PLS) techniques. Based on the coefficients of determination (R2), standard error of calibration (SEC) and error of prediction (SEP) the models developed were (i) nearly applicable for nitrogen (N) (0.89, 0.12 and 0.45 respectively), (ii) poor for iron (Fe), cinc (Zn), potassium oxide (K2O) and potassium (K), (iii) usable for phosphorus pentoxide (P2O5), phosphorus (P), phytic acid (PA) and manganese (Mn). The MPLS regression statistics suggested the most accurate models developed comparing with PLS and PCR. It was concluded that a wider set of common bean samples needs to be used for macro- and microelements prediction by NIRS.

scholarly journals Analysis of protein glycation in human fingernail clippings with near-infrared (NIR) spectroscopy as an alternative technique for the diagnosis of diabetes mellitus

2018 ◽  
Vol 56 (9) ◽  
pp. 1551-1558 ◽  
Author(s):  
Tinne Monteyne ◽  
Renaat Coopman ◽  
Antoine S. Kishabongo ◽  
Jonas Himpe ◽  
Bruno Lapauw ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Healthy Subjects ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Protein Glycation ◽  
Patients With Diabetes ◽  
Diagnosis Of Diabetes Mellitus

Abstract Background: Glycated keratin allows the monitoring of average tissue glucose exposure over previous weeks. In the present study, we wanted to explore if near-infrared (NIR) spectroscopy could be used as a non-invasive diagnostic tool for assessing glycation in diabetes mellitus. Methods: A total of 52 patients with diabetes mellitus and 107 healthy subjects were enrolled in this study. A limited number (n=21) of nails of healthy subjects were glycated in vitro with 0.278 mol/L, 0.556 mol/L and 0.833 mol/L glucose solution to study the effect of glucose on the nail spectrum. Consequently, the nail clippings of the patients were analyzed using a Thermo Fisher Antaris II Near-IR Analyzer Spectrometer and near infrared (NIR) chemical imaging. Spectral classification (patients with diabetes mellitus vs. healthy subjects) was performed using partial least square discriminant analysis (PLS-DA). Results: In vitro glycation resulted in peak sharpening between 4300 and 4400 cm−1 and spectral variations at 5270 cm−1 and between 6600 and 7500 cm−1. Similar regions encountered spectral deviations during analysis of the patients’ nails. Optimization of the spectral collection parameters was necessary in order to distinguish a large dataset. Spectra had to be collected at 16 cm−1, 128 scans, region 4000–7500 cm−1. Using standard normal variate, Savitsky-Golay smoothing (7 points) and first derivative preprocessing allowed for the prediction of the test set with 100% correct assignments utilizing a PLS-DA model. Conclusions: Analysis of protein glycation in human fingernail clippings with NIR spectroscopy could be an alternative affordable technique for the diagnosis of diabetes mellitus.

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