Geographical origin discrimination and polysaccharides quantitative analysis of Radix codonopsis with micro near-infrared spectrometer engine

At present, Tradition Chinese Medicine (TCM) industry in China is in the stage from the empirical development to industrial production. Near infrared (NIR) spectroscopy has been widely used in the quality control of TCM’s modernization with its characteristics including rapidness, nondestruction, simplicity, economy, and so on. In this study, as one type of a portable micro NIR spectrometer, Micro NIR 1700 was used to establish the qualitative models for identification of geographical region and authenticity of Radix codonopsis based on discriminant analysis (DA) method. Both of the DA models had better predictive ability with 100% accuracy. In addition, a method for rapid quantitative analysis of polysaccharide in Radix codonopsis was also developed based on Micro NIR 1700 spectrometer with partial least-squares (PLS) algorithm. In the PLS calibration model, the NIR spectra of samples were pretreated with different preprocessing methods and the spectral region was selected with different variable selection methods as well. The performance of the final PLS model was evaluated according to correlation coefficient of calibration ([Formula: see text]), correlation coefficient of prediction ([Formula: see text]), root mean squared error of cross validation (RMSECV), and root mean squared of prediction (RMSEP). The values of [Formula: see text], [Formula: see text], RMSECV, and RMSEP were 0.9775, 0.9602, 2.496, and 2.734[Formula: see text]g/mL, respectively. This work demonstrated that micro infrared spectrometer could be more convenient and rapid for quality control of Radix codonopsis, and the presented models would be a useful reference for quality control of other similar raw materials of TCM.

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Optimizing Rice Near-Infrared Models Using Fractional Order Savitzky–Golay Derivation (FOSGD) Combined with Competitive Adaptive Reweighted Sampling (CARS)

Applied Spectroscopy ◽

10.1177/0003702819895799 ◽

2020 ◽

Vol 74 (4) ◽

pp. 417-426 ◽

Cited By ~ 2

Author(s):

Zhenzhen Xia ◽

Jie Yang ◽

Jing Wang ◽

Shengpeng Wang ◽

Yan Liu

Keyword(s):

Fractional Order ◽

Near Infrared ◽

Mean Squared Error ◽

Nir Spectroscopy ◽

Predictive Ability ◽

Selection Method ◽

Wavelength Selection ◽

Root Mean Squared Error ◽

Squared Error ◽

Accuracy And Stability

Developing a rapid and stable method for analyzing the quality parameters of rice is important. Near-infrared (NIR) spectroscopy combined with chemometric techniques have been used to predict the critical contents of rice and shown its accuracy and stability. To further improve the predictive ability, we combine the derivative method of fractional order Savitzky–Golay derivation (FOSGD) with the wavelength selection method of competitive adaptive reweighted sampling (CARS). Compared with the traditional integer order Savitzky–Golay derivation (IOSGD), the FOSGD could improve the resolution ratio of the raw spectra more effectively. The wavelength selection method, CARS, could further extract the informative variables from the processed spectra. Four key contents of rice samples, including moisture, amylose, chalkiness degree, and gel consistency, were utilized to validate this method. The prediction results indicated that partial least squares (PLS) models optimized with FOSGD-CARS own higher accuracy and stability with smaller the root mean squared error of cross validations (RMSECVs) and root mean squared error of predictions (RMSEPs). The proposed method is convenient and provides a practical alternative for rice analysis.

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Identification of geographical origin of TPFD based on handheld NIR spectroscopy and PLSDA

NIR news ◽

10.1177/0960336020944007 ◽

2020 ◽

Vol 31 (5-6) ◽

pp. 25-29

Author(s):

Rita-Cindy Aye-Ayire Sedjoah ◽

Bangxing Han ◽

Hui Yan

Keyword(s):

Quality Control ◽

Near Infrared ◽

Geographical Origin ◽

Nir Spectroscopy ◽

Analysis Model ◽

First Order ◽

Standard Normal Variate ◽

Standard Normal ◽

Infrared Spectrometer ◽

Near Infrared Spectrometer

The present study is focused on the identification of geographical origin (Zhejiang, Yunnan and Anhui, China) of Dendrobium officinale’s dried stem called Tiepi fengdou by mean of the handheld near-infrared spectrometer. Raw data were preprocessed to reduce unwanted spectral variations by the first-order derivative followed by standard normal variate transformation, and partial least squares discriminant analysis model was developed for calibration. The results showed that more than 90% of the origins were identified. Therefore, it is possible to classify the geographical origin of Tiepi fengdou by the use of the handheld near-infrared spectrometer for effective quality control.

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Rapid Classification of Softwood and Hardwood by Near Infrared Spectroscopy of Wood Tangential Section

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.157-158.1624 ◽

2012 ◽

Vol 157-158 ◽

pp. 1624-1627

Author(s):

Zhong Yang ◽

Ya Na Liu ◽

Bin Lv ◽

Xu Qin Xie

Keyword(s):

Quality Control ◽

Infrared Spectroscopy ◽

Correlation Coefficient ◽

Near Infrared Spectroscopy ◽

Classification Accuracy ◽

Near Infrared ◽

Raw Materials ◽

Tangential Section ◽

Wood Resource

Due to the shortage of wood resource, manufacturers often use the wood raw materials mixed with softwood and hardwood. Rapid classification and ratio prediction of softwood and hardwood during processing is important to quality control. The feasibility on rapid classification of softwood and hardwood by near infrared spectroscopy (NIR) was investigated in this paper. The results showed that: 1) the classification accuracy of the calibration set samples was 100%. The correlation coefficient (r) between the NIR predicted and the true category variable value was 0.98～0.99 with low SEC of 0.06～0.11; 2) the classification accuracy to the unknown samples was 100%. It was suggested that near infrared spectroscopy can be used to rapidly and accurately classify softwood and hardwood samples.

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Scatter Correction of Transmission Near-Infrared Spectra by Photon Migration Data: Quantitative Analysis of Solids

Applied Spectroscopy ◽

10.1366/000370205774783269 ◽

2005 ◽

Vol 59 (11) ◽

pp. 1381-1387 ◽

Cited By ~ 25

Author(s):

Christoffer Abrahamsson ◽

Alexandra Löwgren ◽

Birgitta Strömdahl ◽

Tomas Svensson ◽

Stefan Andersson-Engels ◽

...

Keyword(s):

Absorption Coefficient ◽

Path Length ◽

Near Infrared ◽

Diffusion Theory ◽

Scatter Correction ◽

Nir Spectroscopy ◽

Predictive Ability ◽

Light Transport ◽

Calibration Model ◽

Scattering Effects

The scope of this work is a new methodology to correct conventional near-infrared (NIR) data for scattering effects. The technique aims at measuring the absorption coefficient of the samples rather than the total attenuation measured in conventional NIR spectroscopy. The main advantage of this is that the absorption coefficient is independent of the path length of the light inside the sample and therefore independent of the scattering effects. The method is based on time-resolved spectroscopy and modeling of light transport by diffusion theory. This provides an independent measure of the scattering properties of the samples and therefore of the path length of light. This yields a clear advantage over other preprocessing techniques, where scattering effects are estimated and corrected for by using the shape of the measured spectrum only. Partial least squares (PLS) calibration models show that, by using the proposed evaluation scheme, the predictive ability is improved by 50% as compared to a model based on conventional NIR data alone. The method also makes it possible to predict the concentration of active substance in samples with other physical properties than the samples included in the calibration model.

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Potential Use of Near-Infrared Spectroscopy to Predict Fatty Acid Profile of Meat from Different European Autochthonous Pig Breeds

Applied Sciences ◽

10.3390/app10175801 ◽

2020 ◽

Vol 10 (17) ◽

pp. 5801

Author(s):

Alberto Ortiz ◽

Silvia Parrini ◽

David Tejerina ◽

José Pedro Pinto de Araújo ◽

Marjeta Čandek-Potokar ◽

...

Keyword(s):

Quality Control ◽

Fatty Acid ◽

Lipid Content ◽

Near Infrared ◽

External Validation ◽

Nir Spectroscopy ◽

Predictive Ability ◽

Meat Industry ◽

Pig Breeds ◽

Mean Square Errors

Autochthonous pig breeds provide products of differentiated quality, among which quality control is difficult to perform and insufficient for current market requirements. The present research evaluates the predictive ability of near-infrared (NIR) spectroscopy, combined with chemometric methods as a rapid and affordable tool to assure traceability and quality control. Thus, NIR technology was assessed for intact and minced muscle Longissimus thoracis et lumborum samples collected from 12 European autochthonous pig breeds for the quantification of lipid content and fatty acid composition. Different tests were performed using different numbers of samples for calibration and validation. The best predictive ability was found using minced presentation and set with 80% of the samples for the calibration and the remaining 20% for the external validation test for the following traits: lipid content and saturated and polyunsaturated fatty acids, which attained both the highest determination coefficients (0.89, 0.61, and 0.65, respectively) and the lowest root mean square errors in external validation (0.62, 1.82, and 1.36, respectively). Lower predictive ability was observed for intact muscles. These results could contribute to improve the management of autochthonous breeds and to ensure quality of their products by traditional meat industry chains.

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

Journal of Near Infrared Spectroscopy ◽

10.1177/0967033520987315 ◽

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.

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Measurement of the Ratio of Tea Polyphenols to Amino Acids in Green Tea Infusion Based on near Infrared Spectroscopy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.301-303.1093 ◽

2011 ◽

Vol 301-303 ◽

pp. 1093-1097 ◽

Cited By ~ 2

Author(s):

Shi Rong Ai ◽

Rui Mei Wu ◽

Lin Yuan Yan ◽

Yan Hong Wu

Keyword(s):

Amino Acids ◽

Infrared Spectroscopy ◽

Correlation Coefficient ◽

Green Tea ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Nir Spectroscopy ◽

Tea Polyphenols ◽

Strong Absorption ◽

Tea Infusion

This study attempted the feasibility to determine the ratio of tea polyphenols to amino acids in green tea infusion using near infrared (NIR) spectroscopy combined with synergy interval PLS (siPLS) algorithms. First, SNV was used to preprocess the original spectra of tea infusion; then, siPLS was used to select the efficient spectra regions from the preprocessed spectra. Experimental results showed that the spectra regions [7 8 18] were selected, which were out of the strong absorption of H2O. The optimal PLS model was developed with the selected regions when 6 PCs components were contained. The RMSEP value was equal to 0.316 and the correlation coefficient (R) was equal to 0.8727 in prediction set. The results demonstrated that NIR can be successfully used to determinate the ration of tea polyphenols to amino acids in green tea infusion.

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Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

10.5194/egusphere-egu21-3896 ◽

2021 ◽

Author(s):

Iva Hrelja ◽

Ivana Šestak ◽

Igor Bogunović

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Spectral Data ◽

Near Infrared ◽

Mean Squared Error ◽

Nir Spectroscopy ◽

Soil Samples ◽

Least Squares Regression ◽

Macro Scale ◽

Sentinel 2

Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.The study is performed in Mediterranean Croatia (44&#176; 05&#8217; N; 15&#176; 22&#8217; E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed Quercus ssp. and Juniperus ssp. forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22nd 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31st and September 5th 2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSEp) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSEp (Spectroscopy dataset RMSEp = 1.91; Sentinel-2 dataset RMSEp = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.Keywords: remote sensing, soil spectroscopy, wildfires, soil organic matterAcknowledgment: This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Per&#269;in is acknowledged for her cooperation during the laboratory work.

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Measuring The Tensile Strain of Wood By Visible And Near-Infrared Spatially Resolved Spectroscopy

10.21203/rs.3.rs-570550/v1 ◽

2021 ◽

Author(s):

Ma Te ◽

Tetsuya Inagaki ◽

Masato Yoshida ◽

Mayumi Ichino ◽

Satoru Tsuchikawa

Keyword(s):

Light Scattering ◽

Prediction Accuracy ◽

Tensile Strain ◽

Near Infrared ◽

Mean Squared Error ◽

Calibration Model ◽

Spatially Resolved ◽

Squared Error ◽

Stiffness Evaluation ◽

Spatially Resolved Spectroscopy

Abstract Wood has various mechanical properties, so stiffness evaluation is critical for quality management. Using conventional strain gauges constantly is high cost, also challenging to measure precious wood materials due to the use of strong adhesive. This study demonstrates the correlation between light scattering changes inside the wood cell walls and tensile strain. A multifiber-based visible-near-infrared (Vis–NIR) spatially resolved spectroscopy (SRS) system was designed to rapidly and conventiently acquire such light scattering changes. For the preliminary experiment, samples with different thicknesses were measured to evaluate the influence of thickness. The differences in Vis–NIR SRS spectral data diminish with an increase in sample thickness, which suggests that the SRS method can successfully measure the whole strain (i.e., surface and inside) of wood samples. Then, for the primary experiment, 18 wood samples with the same thickness (2 mm) were tested to construct a strain calibration model. The prediction accuracy was characterized by a determination coefficient (R2) of 0.86 with a root mean squared error (RMSE) of 297.89 με for five-fold cross-validation; for test validation, The prediction accuracy was characterized by an R2 of 0.82 and an RMSE of 345.44 με.

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Prediction of Douglas-Fir Lumber Properties: Comparison between a Benchtop Near-Infrared Spectrometer and Hyperspectral Imaging System

Applied Sciences ◽

10.3390/app8122602 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2602 ◽

Cited By ~ 4

Author(s):

Laurence Schimleck ◽

Joseph Dahlen ◽

Seung-Chul Yoon ◽

Kurt Lawrence ◽

Paul Jones

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Imaging System ◽

Nir Spectroscopy ◽

Physical And Mechanical Properties ◽

Douglas Fir ◽

Modulus Of Rupture ◽

Visual Grade ◽

Fit Statistics ◽

Infrared Spectrometer

Near-infrared (NIR) spectroscopy and NIR hyperspectral imaging (NIR-HSI) were compared for the rapid estimation of physical and mechanical properties of No. 2 visual grade 2 × 4 (38.1 mm by 88.9 mm) Douglas-fir structural lumber. In total, 390 lumber samples were acquired from four mills in North America and destructively tested through bending. From each piece of lumber, a 25-mm length block was cut to collect diffuse reflectance NIR spectra and hyperspectral images. Calibrations for the specific gravity (SG) of both the lumber (SGlumber) and 25-mm block (SGblock) and the lumber modulus of elasticity (MOE) and modulus of rupture (MOR) were created using partial least squares (PLS) regression and their performance checked with a prediction set. The strongest calibrations were based on NIR spectra; however, the NIR-HSI data provided stronger predictions for all properties. In terms of fit statistics, SGblock gave the best results, followed by SGlumber, MOE, and MOR. The NIR-HSI SGlumber, MOE, and MOR calibrations were used to predict these properties for each pixel across the transverse surface of the scanned samples, allowing SG, MOE, and MOR variation within and among rings to be observed.

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