On the Scales Associated with Near-Infrared Reflectance Difference Spectra

1995 ◽  
Vol 49 (6) ◽  
pp. 765-772 ◽  
Author(s):  
M. S. Dhanoa ◽  
S. J. Lister ◽  
R. J. Barnes
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
The Other ◽  
Difference Spectra ◽  
Near Infrared Reflectance ◽  
Standard Normal Variate ◽  
Near Infrared Spectra ◽  
Standard Normal ◽  
Relationship Of ◽  
The Relationship

Scale differences of individual near-infrared spectra are identified when set-independent standard normal variate (SNV) and de-trend (DT) transformations are applied in either SNV followed by DT or DT then SNV order. The relationship of set-dependent multiplicative scatter correction (MSC) to SNV is also referred to. A simple correction factor is proposed to convert derived spectra from one order to the other. It is suggested that the suitable order for the study of changes using difference spectra (when removing baselines) should be DT followed by SNV, which leads to all derived spectra on the scale of mean zero and variance equal to one. If baselines are identical, then SNV scale spectra can be used to calculate differences.

Use of Difference near Infrared Reflectance Spectra to Extract Relevant Information from the Spectra of Agro–Food Products

1998 ◽  
Vol 6 (1) ◽  
pp. 89-95 ◽  
Author(s):  
Ana Garrido-Varo ◽  
Ronald Carrete ◽  
Víctor Fernández-Cabanás
Keyword(s):  
Near Infrared ◽  
Food Products ◽  
Relevant Information ◽  
Near Infrared Reflectance ◽  
Standard Normal Variate ◽  
Corn Gluten ◽  
Corn Gluten Feed ◽  
Standard Normal ◽  
Gluten Feed ◽  
By Products

This paper compares the use of log 1/ R versus standard normal variate (SNV) and Detrending (DT) transformations calculated either of two forms, SNV followed by DT (SNV+DT) or DT then SNV (DT+SNV) for their abilities to enhance interpretation of spectra and to detect areas of maximum differences in composition of two agro–food products (sunflower seed and corn) and their corresponding by-products (sunflower meal and corn gluten feed). The results obtained show that the SNV+DT and the DT+SNV transformations of the raw data make the existing chemical differences between scattering agro–food products more easily interpretable.

Perbandingan Metode Koreksi Pencaran pada Data Hasil Alat Pemantau Kadar Glukosa Darah Non-Invasif

2019 ◽  
Vol 7 (3) ◽  
Author(s):  
Siti Raudlah ◽  
Mohammad Masjkur ◽  
Kusman Sadik ◽  
. Erfiani
Keyword(s):  
Scatter Correction ◽  
The Other ◽  
Measuring Instrument ◽  
Simple Linear Regression ◽  
Average Value ◽  
Non Invasive ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
The Time Domain ◽  
Correction Result

Scatter correction is one of the methods in data preprocessing that aim at eliminating the physical properties of the spectrum and reducing the variance between samples. The most commonly methods of scatter correction used are the Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) methods. The MSC method corrects the spectrum by utilizing the results of simple linear regression parameter estimation. The SNV method performs spectral correction with the median and standard deviation. Another alternative method of scatter correction is the Orthogonal Scatter Correction (OSC) applying the principle of orthogonality. The methods  used in this research were MSC, SNV, and OSC methods in order to correct the result data of non-invasive blood glucose measuring instrument. The result of this research showed that the time domain spectrum data and intensity had different amount so that the summarized data was needed. Furthermore, this research found that the OSC method with the five series of statistics gained a good correction result compared to the other methods. The OSC method produced a smaller average value of the variance than the other methods.

scholarly journals Model Prediksi Kadar Air Buah Cabai Rawit Domba (Capsicum frutescens L.) Menggunakan Spektroskopi Ultraviolet Visible Near Infrared

2021 ◽  
Vol 4 (1) ◽  
pp. 15-22
Author(s):  
Kusumiyati Kusumiyati ◽  
Ine Elisa Putri ◽  
Agus Arip Munawar
Keyword(s):  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Scatter Correction ◽  
Capsicum Frutescens ◽  
Least Squares Regression ◽  
Orthogonal Signal ◽  
Standard Normal Variate ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Standard Normal

Penelitian ini bertujuan untuk menduga kadar air buah cabai rawit domba (Capsicum frutescens L.) menggunakan spektroskopi UV-Vis-NIR. Total sampel yang digunakan yaitu 45 buah. Analisis dilakukan di Laboratorium Hortikultura, Fakultas Pertanian, Universitas Padjadjaran. Akuisisi data spektra dengan rentang panjang gelombang 300 – 1050 nm (Nirvana AG410). Spektra diperbaiki dengan metode multiplicative scatter correction (MSC), standard normal variate transformation (SNV), orthogonal signal correction (OSC), first derivative (dg1) dan second derivative (dg2). Analisis data dilakukan dengan menggunakan partial least squares regression (PLSR). Berdasarkan penelitian ini menunjukkan bahwa metode koreksi OSC menghasilkan model kalibrasi tertinggi dengan Rkal, RMSEC, Rval, RMSECV, RPD dan faktornya masing-masing yaitu 0.99, 0.31, 0.98, 0.68, 6.62 dan 4. Hal ini menunjukkan bahwa spektroskopi UV-Vis-NIR dapat digunakan untuk memprediksi kadar air pada buah cabai rawit domba.

Baseline Correction of Diffuse Reflection Near-Infrared Spectra Using Searching Region Standard Normal Variate (SRSNV)

2015 ◽  
Vol 69 (12) ◽  
pp. 1432-1441 ◽  
Author(s):  
Takuma Genkawa ◽  
Hideyuki Shinzawa ◽  
Hideaki Kato ◽  
Daitaro Ishikawa ◽  
Kodai Murayama ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Diffuse Reflection ◽  
Baseline Correction ◽  
Standard Normal Variate ◽  
Near Infrared Spectra ◽  
Standard Normal

Application of Support Vector Machine in Identifying inside Moldy Chestnut Using Near Infrared Spectroscopy

2014 ◽  
Vol 615 ◽  
pp. 169-172
Author(s):  
Jie Liu ◽  
Xiao Yu Li ◽  
Wei Wang ◽  
Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Near Infrared ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Training Set ◽  
Near Infrared Spectra ◽  
Standard Normal ◽  
Validation Set ◽  
Normal Transformation

NIR spectroscopy has been applied in detecting inside quality of chestnut successfully. In this work, Support Vector Machine Discriminant Analysis was utilized to identify the qualified chestnuts, the serious moldy chestnuts and the slight moldy chestnuts using their Near infrared spectra region from 833 nm to 2500 nm. 109 chestnut samples were involved and four different preprocessing methods were compared. The results showed that for all the models, the average correct rates of training set and validation set were higher than 90%. The performance of model based on raw spectra was not as good as other models, which indicated the necessity of preprocessing. The models based on the spectra preprocessed by first derivative and multiplicative scatter correction had the same performances, with 97% and 85% as the correct rate of training set and validation set. The models based on the spectra preprocessed by Standard normal transformation has 100% correct rate of training set while 88% of validation set. The second derivative model had the best result with 100% and 90% as the correct rate of training set and validation set. These results demonstrated that the NIR spectroscopy had capability to detect interior mildew of intact chestnut nondestructively.

scholarly journals Development of a Non-Destructive Method for Detection of the Juiciness of Pear via VIS/NIR Spectroscopy Combined with Chemometric Methods

Foods ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 1778
Author(s):  
Fan Wang ◽  
Chunjiang Zhao ◽  
Guijun Yang
Keyword(s):  
Near Infrared ◽  
External Validation ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Spectral Ratio ◽  
Optical Path Length ◽  
Standard Normal Variate ◽  
Length Estimation ◽  
Standard Normal ◽  
Non Destructive

Juiciness is a primary index of pear quality and freshness, which is also considered as important as sweetness for the consumers. Development of a non-destructive detection method for pear juiciness is meaningful for producers and sellers. In this study, visible−near-infrared (VIS/NIR) spectroscopy combined with different spectral preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR), was used for comparison in detection of pear juiciness. Partial least squares (PLS) regression was used to establish the calibration models between the preprocessing spectra (650–1100 nm) and juiciness measured by the texture analyzer. In addition, competitive adaptive reweighted sampling (CARS) was used to identify the characteristic wavelengths and simplify the PLS models. All obtained models were evaluated via Monte Carlo cross-validation (MCCV) and external validation. The PLS model established by 19 characteristic variables after LRC-SR preprocessing displayed the best prediction performance with external verification determination coefficient (R2v) of 0.93 and root mean square error (RMSEv) of 0.97%. The results demonstrate that VIS/NIR coupled with LRC-SR method can be a suitable strategy for the quick assessment of juiciness for pears.

scholarly journals 11 The effect of tissue depth and day of healing on effectiveness of near infrared spectroscopy to predict quantitative differences in histological imagery of superficial burn scars in cattle

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 14-15
Author(s):  
Douglas R Tolleson ◽  
Laura Bryan ◽  
Canaan Whitfield ◽  
Thomas Hairgrove
Keyword(s):  
Near Infrared ◽  
Bos Taurus ◽  
Healing Process ◽  
Scatter Correction ◽  
Diagnostic Technique ◽  
Predictive Ability ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Burn Scar ◽  
Near Infrared Spectra

Abstract Hot-iron branding is a common method of cattle identification. The branding procedure results in a burn scar that once healed, provides a permanent mark. Near infrared reflectance spectroscopy (NIRS) has been applied to monitor healing and determine age in cattle burn scars. The ability of NIRS to quantify histological characteristics of cattle burn scars has not been reported. The objective of this study was to evaluate the effectiveness of portable NIRS as a non-invasive chute-side diagnostic tool to detect histological changes in hot-iron brand burn scars during the healing process. Near infrared spectra (1100–1700nm) were obtained from burn scars on 15 Bos taurus cross steers (~ 270 d old; 238 ± 7 kg) at 21, 32, and 51 d post-branding. Spectra were obtained on branded (n = 3) and unbranded (n = 3) skin from each animal, each date. Skin punch biopsies (8 mm, n = 3 per animal) were obtained from a unique subset (n = 5) of animals at each date (n = 44 total). Spectra were analyzed as log 1/reflectance with 1st derivative and scatter correction applied. Skin tissue was preserved in ethanol and subjected to standard histological staining techniques at cutaneous, transitional, and sub-cutaneous layers. Relationships between spectra and histological values were determined by partial least squares regression. NIRS successfully predicted (P < 0.001) Gomori values for the total calibration (RSQ = 0.80; RMSE = 0.20). Success of Gomori calibrations varied by day of healing (d 21, NS; d 32, P < 0.001; d 51 NS). Other staining calibrations were also significant (P < 0.05) but not deemed useful for predicting quantitative histological values (RSQ < 0.5). Predictive ability generally declined with depth. Portable NIRS may prove useful as a chute-side histological diagnostic technique but requires further evaluation.

scholarly journals Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared-Hyperspectral Image

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 316
Author(s):  
Lakkana Pitak ◽  
Kittipong Laloon ◽  
Seree Wongpichet ◽  
Panmanas Sirisomboon ◽  
Jetsada Posom
Keyword(s):  
Near Infrared ◽  
Hyperspectral Image ◽  
Thermal Conversion ◽  
High Energy ◽  
Volatile Matter ◽  
Successive Projections Algorithm ◽  
Color Distribution ◽  
Standard Normal Variate ◽  
Distribution Mapping ◽  
Standard Normal

Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.

scholarly journals On Newcomb's method of investigating periodicities and its application to Brückner's weather cycle

1914 ◽  
Vol 90 (619) ◽  
pp. 349-355
Keyword(s):  
Thermal Radiation ◽  
The Other ◽  
New Method ◽  
Critical Examination ◽  
Numerical Work ◽  
Average Value ◽  
Other Hand ◽  
Relationship Of ◽  
The Relationship ◽  
Great Simplicity

During the last few years of his life Prof. Simon Newcomb was keenly interested in the problem of periodicities, and devised a new method for their investigation. This method is explained, and to some extent applied, in a paper entitled "A Search for Fluctuations in the Sun's Thermal Radiation through their Influence on Terrestrial Temperature." The importance of the question justifies a critical examination of the relationship of the older methods to that of Newcomb, and though I do not agree with his contention that his process gives us more than can be obtained from Fourier's analysis, it has the advantage of great simplicity in its numerical work, and should prove useful in a certain, though I am afraid, very limited field. Let f ( t ) represent a function of a variable which we may take to be the time, and let the average value of the function be zero. Newcomb examines the sum of the series f ( t 1 ) f ( t 1 + τ) + f ( t 2 ) f ( t 2 + τ) + f ( t 3 ) f ( t 3 + τ) + ..., where t 1 , t 2 , etc., are definite values of the variable which are taken to lie at equal distances from each other. If the function be periodic so as to repeat itself after an interval τ, the products are all squares and each term is positive. If, on the other hand, the periodic time be 2τ, each product will be negative and the sum itself therefore negative. It is easy to see that if τ be varied continuously the sum of the series passes through maxima and minima, and the maxima will indicated the periodic time, or any of its multiples.

Sign in / Sign up

Export Citation Format

Share Document