Near Infrared Spectroscopy of Fuel Contaminated Sand and Soil. I. Preliminary Results and Calibration Study

1998 ◽  
Vol 6 (1) ◽  
pp. 189-197 ◽  
Author(s):  
Heinz W. Zwanziger ◽  
Heidrun Förster
Keyword(s):  
Near Infrared ◽  
Direct Detection ◽  
Scatter Correction ◽  
Reflectance Spectroscopy ◽  
Calibration Error ◽  
Particle Size Fractions ◽  
Cultivated Soil ◽  
Synthetic Hydrocarbon ◽  
Calibration Models ◽  
Nir Reflectance Spectroscopy

Regional areas may be contaminated by the past activities of the chemical and oil industries and of the military. Therefore, the present study was undertaken to test the possibilities of near infrared (NIR) reflectance spectroscopy for direct detection and determination of oil and fuel contaminations. If reliable results are obtained NIR reflectance spectroscopy could be a valuable part of land remediation processes. Preliminary investigations showed that it is possible to distinguish samples of stone chippings, sand, cultivated soil, humus and potting soil by multivariate data analysis. After spiking with gasoline, diesel, motor oil and synthetic hydrocarbon mixtures (BTEX) sand rather than cultivated soil shows obvious spectral absorptions due to contaminations higher than 1% (w/w). The influence of particle size fractions has been investigated in detail using dry sand sieved to < 500 μm (fine), 500–800 μm (medium) and >800 μm (coarse). Contaminations in fine and medium fractions often can be modelled with only one intensity at sufficiently low calibration error, SEC. With coarse fractions SEC is three times higher. Models based on derivative spectra have no significant advantage. In general, mean centring results in more pronounced error minima than multiplicative scatter correction (MSC). Partial least squares (PLS) models can be fitted to obtain any wanted SEC even by cross-validation. For comparable SEC, PLS models in general do not need more factors if samples become more inhomogeneous. Data pre-processing techniques such as Kubelka–Munk transformation, Saunderson correction, MSC and combinations thereof have been tested. Adequate sample variation of the diffuse reflectance fraction of detected light according to the Saunderson model could improve the performance of calibration models. The best values for standard error of prediction, SEP, are obtained if calibration models are derived from sets of spectra of sieved samples and used for contamination prediction of natural samples, and not vice versa. Spectra of contaminated soil and humus need cleverer spectral selection and pre-processing for better performance of calibration models.

The use of visible (VIS) and near infrared (NIR) reflectance spectroscopy to predict fibre diameter in both clean and greasy wool samples

2005 ◽  
Vol 80 (3) ◽  
pp. 333-337 ◽  
Author(s):  
D. Cozzolino ◽  
F. Montossi ◽  
R. San Julian
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Model Development ◽  
Fibre Diameter ◽  
Reflectance Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Calibration Models ◽  
Nir Reflectance Spectroscopy

AbstractAbstract Visible (VIS) and near infrared (NIR) reflectance spectroscopy combined with multivariate data analysis were explored to predict fibre diameter in both clean and greasy Merino wool samples. Fifty clean and 400 greasy wool samples were analysed. Samples were scanned in a large cuvette using a NIRSystems 6500 monochromator instrument by reflectance in the VIS and NIR regions (400 to 2500 nm). Partial least square (PLS) regression was used to develop a number of calibration models between the spectral and reference data. Different mathematical treatments were used during model development. Cross validation was used to assess the performance and avoid overfitting of the models. The NIR calibration models gave a coefficient of determination in calibration (R2) > 0·90 for clean wool samples and a R2 < 0·50 for greasy wool samples. The values for the residual predictive value, RPD (ratio of standard deviation (s. d.) to the root mean square of the standard error of cross validation (RMSECV)) were 3 for clean and 0·6 for greasy wool samples, respectively. The results indicated that fibre diameter in greasy wool samples was poorly predicted with NIR, while clean wool showed good relationships.More research is required to improve the calibration on greasy wool samples if the technology is to be used for rapid analysis to assist in the selection of animals in breeding programmes.

Exploring the Use of near Infrared Reflectance Spectroscopy to Study Physical Properties and Microelements in Soils

2003 ◽  
Vol 11 (2) ◽  
pp. 145-154 ◽  
Author(s):  
A. Moron ◽  
D. Cozzolino
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Agricultural Soils ◽  
Low Cost ◽  
Scatter Correction ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Second Derivative ◽  
Near Infrared Reflectance ◽  
Nir Reflectance Spectroscopy

Near infrared (NIR) reflectance spectroscopy was used to predict the content of silt, sand, clay, iron (Fe), copper (Cu), manganese (Mn) and zinc (Zn) in soil. A total of 332 samples from agricultural soils (0–15 cm depth) in Uruguay (South America) were used. The samples were scanned in a monochromator instrument (NIRSystems 6500, Silver Spring, MD, USA). Two mathematical treatments (first and second derivative) with SNVD (scatter normal variate and detrend) and without scatter correction were studied. Modified partial least squares (mPLS) was used to develop the calibration models. The coefficient of determination in calibration ( R2cal) and the standard error in calibration ( SEC) using the second derivative were 0.81 ( SEC: 5.1), 0.83 ( SEC: 5.3), 0.92 ( SEC: 2.6) for percent sand, silt and clay, respectively. The R2cal and standard error of cross-validation ( SECV) were for Cu 0.87 ( SEC: 0.7), for Fe 0.92 ( SEC: 21.7), for Mn 0.72 ( SEC: 83.0) and for Zn 0.72 ( SEC: 1.2) on mg kg−1 dry matter. It was concluded that NIR reflectance spectroscopy has a great potential as an analytical method for routine analysis of soil texture, Fe, Zn and Cu due the speed and low cost of analysis.

Near Infrared Assessment of Vegetable Quality

1998 ◽  
Vol 6 (A) ◽  
pp. A107-A110 ◽  
Author(s):  
Christopher N.G. Scotter
Keyword(s):  
Product Quality ◽  
Industrial Application ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Quality Factors ◽  
Sensory Assessment ◽  
Research Consortium ◽  
The Uk ◽  
Nir Reflectance Spectroscopy ◽  
Vegetable Quality

NIR reflectance spectroscopy has been applied to the assessment of product quality factors for vining peas and sweetcorn. A CCFRA Industrial Research Club and an EU research consortium led by CCFRA, have been conducting the work since 1994. Sample sets of peas have been collected which are broadly representative of commercial material in the UK, Hungary, and Bulgaria All sweetcorn samples were from Hungary. The calibration results, at this stage, for alcohol insoluble solids (AIS) in peas and sweetcorn, and Tenderometer measurement and sensory assessment for both vegetables indicate that the work will find industrial application.

scholarly journals Noninvasive Prediction of Glucose by Near-Infrared Diffuse Reflectance Spectroscopy

1999 ◽  
Vol 45 (9) ◽  
pp. 1651-1658 ◽  
Author(s):  
Stephen F Malin ◽  
Timothy L Ruchti ◽  
Thomas B Blank ◽  
Suresh N Thennadil ◽  
Stephen L Monfre
Keyword(s):  
Blood Glucose ◽  
Wavelength Range ◽  
Standard Error ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Calibration Model ◽  
Calibration Models ◽  
Blood Glucose Concentrations

Abstract Background: Self-monitoring of blood glucose by diabetics is crucial in the reduction of complications related to diabetes. Current monitoring techniques are invasive and painful, and discourage regular use. The aim of this study was to demonstrate the use of near-infrared (NIR) diffuse reflectance over the 1050–2450 nm wavelength range for noninvasive monitoring of blood glucose. Methods: Two approaches were used to develop calibration models for predicting the concentration of blood glucose. In the first approach, seven diabetic subjects were studied over a 35-day period with random collection of NIR spectra. Corresponding blood samples were collected for analyte analysis during the collection of each NIR spectrum. The second approach involved three nondiabetic subjects and the use of oral glucose tolerance tests (OGTTs) over multiple days to cause fluctuations in blood glucose concentrations. Twenty NIR spectra were collected over the 3.5-h test, with 16 corresponding blood specimens taken for analyte analysis. Results: Statistically valid calibration models were developed on three of the seven diabetic subjects. The mean standard error of prediction through cross-validation was 1.41 mmol/L (25 mg/dL). The results from the OGTT testing of three nondiabetic subjects yielded a mean standard error of calibration of 1.1 mmol/L (20 mg/dL). Validation of the calibration model with an independent test set produced a mean standard error of prediction equivalent to 1.03 mmol/L (19 mg/dL). Conclusions: These data provide preliminary evidence and allow cautious optimism that NIR diffuse reflectance spectroscopy using the 1050–2450 nm wavelength range can be used to predict blood glucose concentrations noninvasively. Substantial research is still required to validate whether this technology is a viable tool for long-term home diagnostic use by diabetics.

Authentication of Whole and Ground Coffee Beans by near Infrared Reflectance Spectroscopy

1994 ◽  
Vol 2 (2) ◽  
pp. 85-92 ◽  
Author(s):  
Gerard Downey ◽  
Jerôme Boussion ◽  
Dominique Beauchêne
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Correct Identification ◽  
Correct Classification Rate ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Identification Rate ◽  
Coffee Beans ◽  
Correct Identification Rate ◽  
Nir Reflectance Spectroscopy

The potential of NIR reflectance spectroscopy for discriminating between pure Arabica and pure Robusta coffees and blends of these two was investigated. Studies were performed on whole and ground beans using a factorial discriminant procedure. For whole beans, in the absence of blended samples, a correct classification rate of 96.2% was achieved. Inclusion of blended samples reduced this figure to between 82.9 and 87.6%. In the case of ground samples, including blends, a correct identification rate of 83.02% was achieved. The molecular basis for discrimination is discussed.

Studies to Measure Cotton Fibre Length, Strength, Micronaire and Colour by Vis/NIR Reflectance Spectroscopy. Part I: Descriptive Statistics of Fibre Properties and Reflectance Spectra

1993 ◽  
Vol 1 (3) ◽  
pp. 153-173 ◽  
Author(s):  
Joseph G. Montalvo ◽  
Sherman E. Faught ◽  
Harmon H. Ramey ◽  
Steven E. Buco
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
A Priori ◽  
Fibre Length ◽  
High Volume ◽  
Reflectance Spectroscopy ◽  
Reflectance Spectra ◽  
Fibre Property ◽  
Property Data ◽  
Nir Reflectance Spectroscopy

Fibre property data representing the 1989 and 1990 crop years and its reflectance spectra are analysed using standard error, regression and correlation analysis. The six properties of interest are upper-half mean length, uniformity index, strength and micronaire measured on two high volume instrument systems placed side-by-side, and colour (Rd and +b) measured by the traditional lab system. Visible (vis) and near infrared (NIR) reflectance spectra are observed on a scanning spectrophotometer, and span the 400–2500 nm range. Three findings highlight the research. One, a diagnostic test is presented to decide, a priori of reflectance spectroscopy, the degree to which the mean property values have reduced random error. Two, the standard error of replicate spectra provides a way to probe the fibre mass in the diffuse reflectance optical path. The spectral error is strongly influenced by both how the cotton is packed into the spectrophotometric cell and the non-homogeneity of the sample. And three, correlations between the spectra confirm that some visible and NIR wavelength regions contain mutually exclusive information about the properties of this natural staple.

scholarly journals Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Lin Zhang ◽  
Baohua Zhang ◽  
Jun Zhou ◽  
Baoxing Gu ◽  
Guangzhao Tian
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Near Infrared ◽  
Scatter Correction ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Biological Variability ◽  
Wavelength Selection ◽  
Calibration Models ◽  
Solids Content

Uninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and mean normalization, as well as their combinations were conducted on raw Fourier transform near-infrared spectra to eliminate the uninformative biological variability. Subsequently, robust calibration models were established by using partial least squares regression analysis and wavelength selection algorithms. Results indicated that the partial least squares calibration models with characteristic variables selected by CARS method coupled with preprocessing of Savitzky-Golay smoothing and multiplicative scatter correction had a considerable potential for predicting apple soluble solids content regardless of the biological variability.

scholarly journals One leaf for all: Chemical traits of single leaves measured at the leaf surface using Near infrared-reflectance spectroscopy (NIRS)

2020 ◽  
Author(s):  
Matteo Petit Bon ◽  
Hanna Böhner ◽  
Sissel Kaino ◽  
Torunn Moe ◽  
Kari Anne Bråthen
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
The Arctic ◽  
Infrared Reflectance ◽  
Growth Forms ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Calibration Models ◽  
Chemical Traits

AbstractThe leaf is an essential unit for measures of plant ecological traits. Yet, measures of plant chemical traits are often achieved by merging several leaves, masking potential foliar variation within and among plant individuals. This is also the case with cost-effective measures derived using Near-infrared reflectance spectroscopy (NIRS). The calibration models developed for converting NIRS spectral information to chemical traits are typically based on spectra from merged and milled leaves. In this study we ask if such calibration models can be applied to spectra derived from whole leaves, providing measures of chemical traits of single leaves.We sampled cohorts of single leaves from different biogeographic regions, growth forms, species and phenological stages in order to include variation in leaf and chemical traits. For each cohort we first sampled NIRS-spectra from each whole, single leaf, including leaf sizes down to Ø 4 mm (the minimum area of our NIRS application). Next, we merged, milled and tableted the leaves and sampled spectra from the cohort as a tablet. We applied arctic-alpine calibration models to all spectra and derived chemical traits. Finally, we evaluated the performance of the models in predicting chemical traits of whole, single leaves by comparing the traits derived at the level of leaves to that of the tablets.We found that the arctic-alpine calibration models can successfully be applied to single, whole leaves for measures of Nitrogen (R2=0.88, RMSE=0.824), Phosphorus (R2=0.65, RMSE=0.081), and Carbon (R2=0.78, RMSE=2.199) content. For Silicon content we found the method acceptable when applied to Silicon-rich growth forms (R2=0.67, RMSE=0.677). We found a considerable variation in chemical trait values among leaves within the cohorts.This time- and cost-efficient NIRS-application provides non-destructive measures of a set of chemical traits in single, whole leaves, including leaves of small sizes. The application can facilitate research into the scales of variability of chemical traits and include intraindividual variation. Potential trade-offs among chemical traits and other traits within the leaf unit can be identified and be related to ecological processes. In sum this NIRS-application can facilitate further ecological understanding of the role of leaf chemical traits.

scholarly journals Pengembangan Metode Pengujian Keaslian Beras Aceh Menggunakan Nirs Dengan Metode PCA

2019 ◽  
Vol 4 (1) ◽  
pp. 578-587
Author(s):  
Masyitah Masyitah ◽  
Syahrul Syahrul ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Principal Component Analysis ◽  
Protein Content ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Ft Ir

Abstrak. Tujuan dari penelitian ini adalah membangun model pendugaan untuk menilai keaslian beras Aceh berdasarkan spektrum NIRS yang dihasilkan. Pendeteksian keaslian beras Aceh secara cepat dan efesien dapat diwujudkan melalui pengembangan teknologi Near Infrared Reflectance Spectroscopy (NIRS). Penelitian ini menggunakan beras varietas Sigupai (Aceh Barat Daya), varietas  Sanbay (Simeulue) dan varietas Ciherang. Jumlah sampel yang digunakan pada penelitian ini adalah 45 sampel. Pengukuran spektrum beras menggunakan Self developed FT-IR IPTEK T-1516. Klasifikasi data spektrum beras menggunakan Principal Component Analysis (PCA) dengan dua  pretreatment yaitu De-trending dan Multiplicative Scatter Correction. Hasil penelitian ini diperoleh yaitu: Spektrum NIRS beras menunjukkan keberadaan kandungan lemak pada panjang gelombang 2355 nm - 2462 nm. Kandungan karbohidrat pada panjang gelombang 2256 nm - 2321 nm.  Kandungan protein pada panjang gelombang 2056 nm - 2166 nm. Kandungan kadar air pada panjang gelombang 1910 nm-1980 nm dan panjag gelombang 1411 nm - 1492 nm menunjukkan kandungan protein dan kadar air. NIRS dengan metode PCA mampu membedakan pencampuran beras Sigupai dengan beras Ciherang dimana pembedaan terbaik terjadi dalam bentuk dua macam pengelompokan yaitu beras  Sigupai ≥ 75 dan beras Sigupai ≤50 dan pretreatment de-trending merupakan pretreatment terbaik dalam mengklasifikasi beras Aceh (Sigupai dan Sanbay) dengan beras Nasional (Ciherang).Development of Methods for Testing the Authenticity of Aceh Rice Using NIRS with the PCA MethodAbstract. The purpose of this study is to develop a prediction model to assess the authenticity of Aceh rice based on the NIRS spectrum produced. The detection of the authenticity of Aceh rice quickly and efficiently can be realized through technological development Near Infrared Reflectance Spectroscopy (NIRS). This study uses Sigupai rice varieties (Aceh Barat Daya), Sanbay (Simeulue) and Ciherang. The number of samples used in this study was 45 samples. Measurement of rice spectrum  using Self developed FT-IR IPTEK T-1516. Rice spectrum data classification uses the Principal Component Analysis (PCA) with two pretreatments, namely De-trending and Multiplicative Scatter Correction. The results of this study were obtained: NIRS spectrum of rice showed the presence of fat content at a wavelength of 2355 nm - 2462 nm. Carbohydrate content at wavelength 2256 nm - 2321 nm. Protein content at wavelength 2056 nm - 2166 nm. The content of water content at a wavelength of 1910 nm-1980 nm and wave length of 1411 nm - 1492 nm shows the protein content and water content. NIRS with the PCA method was able to distinguish the mixing of Sigupai rice from Ciherang rice where the best differentiation occurred in the form of two types of grouping namely Sigupai rice ≥ 75 and Sigupai rice ≤ 50 and de-trending pretreatment was the best pretreatment in classifying Aceh rice (Sigupai and Sanbay) with National rice (Ciherang).

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