Application of near-infrared spectroscopy for the detection of internal insect infestation inPiceaabiesseed lots

2004 ◽  
Vol 34 (1) ◽  
pp. 76-84 ◽  
Author(s):  
Mulualem Tigabu ◽  
Per Christer Odén ◽  
Tong Yun Shen
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Absorption Bands ◽  
Classification Rate ◽  
Data Set ◽  
Storage Lipids ◽  
Nir Absorption ◽  
Seed Origin ◽  
Single Seeds

The use of near-infrared (NIR) spectroscopy to discriminate between uninfested seeds of Picea abies (L.) Karst and seeds infested with Plemeliella abietina Seitn (Hymenoptera, Torymidae) larva is sensitive to seed origin and year of collection. Five seed lots collected during different years from Sweden, Finland, and Belarus were used in this study. Initially, seeds were classified as infested or uninfested with X-radiography, and then, NIR spectra from single seeds were collected with a NIR spectrometer from 1100 to 2498 nm with a resolution of 2 nm. Discriminant models were derived by partial least squares regression using raw and orthogonal signal corrected spectra (OSC). The resulting OSC model developed on a pooled data set was more robust than the raw model and resulted in 100% classification accuracy. Once irrelevant spectral variations were removed by using OSC pretreatment, single-lot calibration models resulted in similar classification rates for the new samples irrespective of origin and year of collection. Dis criminant analyses performed with selected NIR absorption bands also gave nearly 100% classification rate for new samples. The origin of spectral differences between infested and uninfested seeds was attributed to storage lipids and proteins that were completely depleted in the former by the feeding larva.

Ability of Vis-PIR spectroscopy to monitor changes in organic carbon of loamy soils at two depths

2021 ◽  
Author(s):  
Hayfa Zayani ◽  
Youssef Fouad ◽  
Didier Michot ◽  
Zeineb Kassouk ◽  
Zohra Lili-Chabaane ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Near Infrared ◽  
Full Range ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Calibration Data ◽  
Least Squares Regression ◽  
Data Set ◽  
Soil Carbon Stocks ◽  
The Mean

<p>Visible-Near Infrared (Vis-NIR) spectroscopy has proven its efficiency in predicting several soil properties such as soil organic carbon (SOC) content. In this preliminary study, we explored the ability of Vis-NIR to assess the temporal evolution of SOC content. Soil samples were collected in a watershed (ORE AgrHys), located in Brittany (Western France). Two sampling campaigns were carried out 5 years apart: in 2013, 198 soil samples were collected respectively at two depths (0-15 and 15-25 cm) over an area of 1200 ha including different land use and land cover; in 2018, 111 sampling points out of 198 of 2013 were selected and soil samples were collected from the same two depths. Whole samples were analyzed for their SOC content and were scanned for their reflectance spectrum. Spectral information was acquired from samples sieved at 2 mm fraction and oven dried at 40°C, 24h prior to spectra acquisition, with a full range Vis-NIR spectroradiometer ASD Fieldspec®3. Data set of 2013 was used to calibrate the SOC content prediction model by the mean of Partial Least Squares Regression (PLSR). Data set of 2018 was therefore used as test set. Our results showed that the variation ∆SOC<sub>obs</sub><sub></sub>obtained from observed values in 2013 and 2018 (∆SOC<sub>obs</sub> = Observed SOC (2018) - Observed SOC (2013)) is ranging from 0.1 to 25.9 g/kg. Moreover, our results showed that the prediction performance of the calibrated model was improved by including 11 spectra of 2018 in the 2013 calibration data set (R²= 0.87, RMSE = 5.1 g/kg and RPD = 1.92). Furthermore, the comparison of predicted and observed ∆SOC between 2018 and 2013 showed that 69% of the variations were of the same sign, either positive or negative. For the remaining 31%, the variations were of opposite signs but concerned mainly samples for which ∆SOCobs is less than 1,5 g/kg. These results reveal that Vis-NIR spectroscopy was potentially appropriate to detect variations of SOC content and are encouraging to further explore Vis-NIR spectroscopy to detect changes in soil carbon stocks.</p>

Discrimination of Storage Duration of Apples Stored in a Cooled Room and Shelf-Life by Visible-Near Infrared Spectroscopy

2007 ◽  
Vol 15 (3) ◽  
pp. 169-177 ◽  
Author(s):  
C. Camps ◽  
P. Guillermin ◽  
J.C. Mauget ◽  
D. Bertrand
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Absorption Bands ◽  
Storage Duration ◽  
Data Set ◽  
Global Models ◽  
Duration Of Storage ◽  
Apple Cultivars

Improved non-destructive instrumental approaches for grading fruit during post-harvest could be an efficient way to monitor stock in the apple industry. The objective of this study was to evaluate the ability of visible-near infrared (vis-NIR) spectroscopy in reflectance mode for classifying apples left on the shelf or stored in a cooled room. The ability of NIR spectroscopy to classify the duration of storage of three apple cultivars in two storage modalities was evaluated. A total of 450 fruit, sampled after 7, 14, 28, 60, 90 and 120 days of storage in a cooled room (CR) and 7, 14 and 28 days in shelflife (SL), has been studied. The classification of these modalities was analysed by factorial discriminant analysis (FDA) pooling the spectral data of all cultivars (global models) into a common data set. Then, the cultivar effect on the classification of the same modalities was analysed by processing data from each cultivar in separate factorial descriminant analyses. A preliminary analysis showed the genetic variability of spectral data due to the three apple cultivars. We show that vis-NIR spectroscopy allowed the correct classification of the fruits of each cultivar by more than 95%. The classification relied on both vis and NIR absorption bands: 500, 680, 1400 to 1700, 1850, 1950, 2200 and 2300 nm. We show that storage modalities of global models can be classified by more than 75% and 83% for fruits stored in a cooled room and shelf, respectively. Classification of the same storage modalities was improved by cultivar models with percentage of individuals correctly classified of 86% (Gala), 89% (Elstar) and 85% (Smoothee) for fruits stored in a cooled room and 95% (Gala), 98% (Elstar) and 95% (Smoothee) for fruits left in shelflife. We conclude that despite the slight increase of efficiency of the models when we considered each apple cultivar separately, global models applicable to a set of different cultivars presents a correct level of classification and could be usefull for some commercial applications.

scholarly journals Evaluating basic density calibrations based on NIR spectra recorded on the three wood faces and subject to different mathematical treatments

2021 ◽  
Vol 51 ◽  
Author(s):  
Evelize A. Amaral ◽  
Luana M. Dos Santos ◽  
Paulo R.G. Hein ◽  
Emylle V.S. Costa ◽  
Sebastião Carlos S. Rosado ◽  
...  
Keyword(s):  
Near Infrared ◽  
Eucalyptus Grandis ◽  
Basic Density ◽  
Nir Spectroscopy ◽  
Physical And Mechanical Properties ◽  
Integrating Sphere ◽  
Least Squares Regression ◽  
Immersion Method ◽  
Data Set ◽  
Eucalyptus Wood

Background: Near infrared (NIR) spectroscopy has been successfully applied to estimate the chemical, physical and mechanical properties of various biological materials, including wood. This study aimed to evaluate basic density calibrations based on NIR spectra collected from three wood faces and subject to different mathematical treatments. Methods: Diffuse reflectance NIR spectra were recorded using an integrating sphere on the transverse, radial and tangential surfaces of 278 wood specimens of Eucalyptus urophylla x Eucalyptus grandis. Basic density of the wood specimens was determined in the laboratory by the immersion method and correlated with NIR spectra by Partial Least Squares regression. Different statistical treatments were then applied to the data, including Standard Normal Variate, Multiplicative Scatter Correction, First and Second Derivatives, Normalization, Autoscale and MeanCenter transformations. Results: The predictive model based on NIR spectra measured on the transverse surface performed the best (R²cv = 0.85 and RMSE = 25.5 kg/m³) while the model developed from the NIR spectra measured on the tangential surface had the poorest performance (R²cv = 0.53 and RMSE = 46.8 kg/m³). The difference in performance between models based on original (untreated) and mathematically-treated spectra was minimal. Conclusions: Multivariate models fitted to NIR spectra were found to be efficient for predicting the basic density of Eucalyptus wood, especially when based on spectra measured on the transversal surface. For this data set, models based on the original spectra and mathematically treated spectra had similar performance. The reported findings show that mathematical transformations are not always able to extract more information from the spectra in the NIR.

scholarly journals Visible + Near Infrared Spectroscopy as taxonomic tool for identifying birch species

Silva Fennica ◽  
2018 ◽  
Vol 52 (4) ◽  
Author(s):  
Mulualem Tigabu ◽  
Mostafa Farhadi ◽  
Lars-Göran Stener ◽  
Per Odén
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Absorption Bands ◽  
Orthogonal Projections ◽  
Projections To Latent Structures ◽  
Discriminant Models ◽  
Single Seeds ◽  
Latent Structures ◽  
Multivariate Discriminant

The genus L. is composed of several species, which are difficult to distinguish in the field on the basis of morphological traits. The aim of this study was to evaluate the taxonomic importance of using visible + near infrared (Vis + NIR) spectra of single seeds for differentiating Roth and Ehrh. Seeds from several families (controlled crossings of known parent trees) of each species were used and Vis + NIR reflectance spectra were obtained from single seeds. Multivariate discriminant models were developed by Orthogonal Projections to Latent Structures – Discriminant Analysis (OPLS-DA). The OPLS-DA model fitted on Vis + NIR spectra recognized with 100% classification accuracy while the prediction accuracy of class membership for was 99%. However, the discriminant models fitted on NIR spectra alone resulted in 100% classification accuracies for both species. Absorption bands accounted for distinguishing between birch species were attributed to differences in color and chemical composition, presumably polysaccharides, proteins and fatty acids, of the seeds. In conclusion, the results demonstrate the feasibility of NIR spectroscopy as taxonomic tool for classification of species that have morphological resemblance.BetulaBetula pendulaBetula pubescensB. pubescensB. pendula

Multivariate Classification of Sound and Insect-Infested Seeds of a Tropical Multipurpose Tree, Cordia Africana, with near Infrared Reflectance Spectroscopy

2002 ◽  
Vol 10 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Mulualem Tigabu ◽  
Per Christer Odén
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Multipurpose Tree ◽  
Cordia Africana

Near infrared (NIR) spectroscopy was used to classify insect-infested and sound seeds of a tropical multipurpose tree, Cordia africana Lam. A calibration model derived by partial least squares regression of orthogonal signal corrected spectra resulted in a 100% classification rate. Difference spectrum and partial least squares weight indicated that absorbance differences between insect-infested and sound seeds might have been due to differences in composition of chitin and cuticular lipid components as well as moisture content. The result shows the possibility of using NIR spectroscopy in the seed cleaning process in the future provided that appropriate sorting instruments are developed.

Structure and microstructure of near infrared-absorbing Au–Au2S nanoparticles

2007 ◽  
Vol 22 (9) ◽  
pp. 2531-2538 ◽  
Author(s):  
Mei Chee Tan ◽  
Jackie Y. Ying ◽  
Gan Moog Chow
Keyword(s):  
Optical Properties ◽  
Surface Segregation ◽  
Near Infrared ◽  
Small Degree ◽  
Core Shell ◽  
Shell Microstructure ◽  
Absorption Bands ◽  
Interfacial Effects ◽  
Nir Absorption ◽  
Structure And Microstructure

Near infrared (NIR) absorbing nanoparticles synthesized by the reduction of HAuCl4 with Na2S exhibited absorption bands at ∼530 nm, and in the NIR region of 650–1100 nm. The NIR optical properties were not found to be related to the earlier proposed Au2S–Au core-shell microstructure in previous studies. From a detailed study of the structure and microstructure of as-synthesized particles in this work, S-containing, Au-rich, multiply-twinned nanoparticles were found to exhibit NIR absorption. They consisted of amorphous AuxS (where x = 2), mostly well mixed within crystalline Au, with a small degree of surface segregation of S. Therefore, NIR absorption was likely due to interfacial effects on particle polarization from the introduction of AuxS into Au particles, and not the dielectric confinement of plasmons associated with a core-shell microstructure.

Composition, particle size, and near-infrared irradiation effects on optical properties of Au–Au2S nanoparticles

2008 ◽  
Vol 23 (1) ◽  
pp. 281-293 ◽  
Author(s):  
Mei Chee Tan ◽  
Jackie Y. Ying ◽  
Gan Moog Chow
Keyword(s):  
Particle Size ◽  
Optical Properties ◽  
Thermal Effects ◽  
Near Infrared ◽  
Sodium Sulfide ◽  
Irradiation Effects ◽  
Absorption Bands ◽  
Molar Ratios ◽  
Nir Absorption ◽  
Structure And Microstructure

Near-infrared (NIR)-absorbing nanoparticles synthesized by the reduction of tetrachloroauric acid (HAuCl4) using sodium sulfide (Na2S) exhibited absorption bands at ∼530 nm and at the NIR region of 650−1100 nm. A detailed study on the structure and microstructure of as-synthesized nanoparticles was reported previously. The as-synthesized nanoparticles were found to consist of amorphous AuxS (x = ∼2), mostly well mixed within crystalline Au. In this work, the optical properties were tailored by varying the precursor molar ratios of HAuCl4 and Na2S. In addition, a detailed study of composition and particle-size effects on the optical properties was discussed. The change of polarizability by the introduction of S in the form of AuxS (x = ∼2) had a significant effect on NIR absorption. Also, it was found in this work that exposure of these particles to NIR irradiation using a Nd:YAG laser resulted in loss of the NIR absorption band. Thermal effects generated during NIR irradiation had led to microstructural changes that modified the optical properties of particles.

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

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

<p>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.</p><p>The study is performed in Mediterranean Croatia (44° 05’ N; 15° 22’ E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed <em>Quercus ssp.</em> and <em>Juniperus ssp.</em> forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22<sup>nd</sup> 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 31<sup>st</sup> and September 5<sup>th </sup>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 (RMSE<sub>p</sub>) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.</p><p>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 RMSE<sub>p </sub>(Spectroscopy dataset RMSE<sub>p</sub> = 1.91; Sentinel-2 dataset RMSE<sub>p</sub> = 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.</p><p><strong>Keywords:</strong> remote sensing, soil spectroscopy, wildfires, soil organic matter</p><p><strong>Acknowledgment: </strong>This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Perčin is acknowledged for her cooperation during the laboratory work.</p>

Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease

2014 ◽  
Vol 2 (1) ◽  
pp. 1-9
Author(s):  
Nawaf Abu-Khalaf ◽  
Mazen Salman
Keyword(s):  
Leaf Spot ◽  
Near Infrared ◽  
Latent Infection ◽  
Linear Method ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Olive Leaf ◽  
Classification Rate ◽  
Infection Period ◽  
Linear Svm

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease

2014 ◽  
Vol 2 (1) ◽  
pp. 1-9
Author(s):  
Nawaf Abu-Khalaf ◽  
Mazen Salman
Keyword(s):  
Leaf Spot ◽  
Near Infrared ◽  
Latent Infection ◽  
Linear Method ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Olive Leaf ◽  
Classification Rate ◽  
Infection Period ◽  
Linear Svm

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

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