Determination of Particle Size in Powders by Scatter Correction in Diffuse Near-Infrared Reflectance

1988 ◽  
Vol 42 (5) ◽  
pp. 722-728 ◽  
Author(s):  
J. L. Ilari ◽  
H. Martens ◽  
T. Isaksson
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Scatter Correction ◽  
Large Degree ◽  
Chemical Compositions ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Analytical Technique ◽  
Scattering Effects ◽  
Reflectance Data

Diffuse near-infrared reflectance spectroscopy has traditionally been an analytical technique for determining chemical compositions in a sample. We will, in this paper, focus on light scattering effects and their ability to determine the mean particle sizes of powders. The reflectance data of NaCl, broken glass, and sorbitol powders are linearized and submitted to the Multiplicative Scatter Correction (MSC), and the ensuing parameters are used in subsequent multivariate calibrations. The results indicate that particle size can, to a large degree, be determined from NIR reflectance data for a given type of powder. Up to 99% of the partical size variance was explained by the regression.

Factors affecting the prediction of organic matter Digestibility of grass silage by near infrared reflectance spectroscopy

1994 ◽  
Vol 1994 ◽  
pp. 73-73
Author(s):  
E. R. Deaville ◽  
D. L Givens
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Scatter Correction ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Correction Procedure ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Grass Silage

Earlier studies (Barber et al., 1990) showed the superiority of near infrared reflectance spectroscopy (NIRS) for predicting the organic matter digestibility (OMD) in vivo of grass silage over fibre and in vitro procedures. However, during routine application occasional erroneous values were predicted for which there were no obvious reasons. Baker and Barnes (1990) reported that the likely sources of the problems contributing to the errors were instrumental and environmental noise, sample particle size effects and variable moisture content of the samples. These authors also reported that standard normal variate - detrend (SNV-D) scatter correction procedure of Barnes et al. (1989) could be used to reduce the effects of particle size variation and they also emphasised the need to test NIRS calibrations for repeatability. The purpose of the present work was to evaluate the use of the SNV-D scatter correction procedure, the techniques for reducing the sensitivity of calibrations to residual moisture and methods to improve the repeatability of the predicted OMD in vivo values of grass silage. In addition, a further objective was to compare three calibration methods, namely modified stepwise regression (MSR), modified partial least squares (MPLS) and principal component analysis (PCA).

Effects of Particle Size on the Near-Infrared Reflectance Spectra of Wheat and Rape Seed Meal Mixtures

1995 ◽  
Vol 49 (1) ◽  
pp. 84-91 ◽  
Author(s):  
Marie-Françoise Devaux ◽  
Nathalie Nathier-Dufour ◽  
Paul Robert ◽  
Dominique Bertrand
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Reflectance Spectra ◽  
Seed Meal ◽  
Infrared Reflectance ◽  
Rape Seed ◽  
Near Infrared Reflectance

A comparison of sample preparation and calibration techniques for the estimation of nitrogen, oil and glucosinolate content of rapeseed by near infrared spectroscopy

1985 ◽  
Vol 104 (2) ◽  
pp. 317-323 ◽  
Author(s):  
Carol Starr ◽  
Janet Suttle ◽  
A. G. Morgan ◽  
D. B. Smith
Keyword(s):  
Stepwise Regression ◽  
Near Infrared ◽  
Glucosinolate Content ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Reflectance Analysis ◽  
Glucosinolate Concentration ◽  
Forward Stepwise ◽  
Calibration Techniques ◽  
Reflectance Data

SummaryPredictions of nitrogen, oil and glucosinolate concentration in rapeseed samples were made by near infrared reflectance analysis after various grinding treatments. Also examined were the effects of normalizing reflectance data and the possible advantage of using all combinations of two and three wavelengths in the calibration regression analysis over forward stepwise regression. The main conclusion was that drying the samples prior to a controlled grinding treatment gave the best results, although acceptable results for selection purposes could be obtained using whole seeds to predict nitrogen and oil. None of the treatments of the seed or reflectance data allowed acceptable prediction of glucosinolate content.

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).

scholarly journals The Quality Control of Tea by Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ming-Zhi Zhu ◽  
Beibei Wen ◽  
Hao Wu ◽  
Juan Li ◽  
Haiyan Lin ◽  
...  
Keyword(s):  
Quality Control ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Chemical Compositions ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Control Quality ◽  
Tea Quality ◽  
Instrumental Technique

Tea is known to be one of the most popular beverages enjoyed by two-thirds of the world’s population. Concern of variability in tea quality is increasing among consumers. It is of great significance to control quality for commercialized tea products. As a rapid, noninvasive, and nondestructive instrumental technique with simplicity in sample preparation, near-infrared reflectance (NIR) spectroscopy has been proved to be one of the most advanced and efficient tools for the control quality of tea products in recent years. In this article, we review the most recent advances and applications of NIR spectroscopy and chemometrics for the quality control of tea, including the measurement of chemical compositions, the evaluation of sensory attributes, the identification of categories and varieties, and the discrimination of geographical origins. Besides, challenges and future trends of tea quality control by NIR spectroscopy are also presented.

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