Depth-Resolved Near-Infrared Spectroscopy

1996 ◽  
Vol 50 (2) ◽  
pp. 285-291 ◽  
Author(s):  
Nadhamuni G. Nerella ◽  
James K. Drennen
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Principal Component ◽  
Depth Resolution ◽  
New Drugs ◽  
Penetration Enhancers ◽  
Calibration Model ◽  
Near Ir ◽  
Hydrogel Matrix

While there is substantial evidence proving the success of transdermal drug delivery, there have been few accomplishments in the area of depth-resolved prediction of drug concentration during diffusion through a matrix. Such a method for noninvasive quantification of a diffusing species could assist in the development of new drugs, dosage forms, and penetration enhancers. Near-infrared depth-resolved measurements were accomplished by strategically controlling the amount of reflected light reaching the detectors using a combination of diaphragms with different-diameter apertures. Near-IR spectra were collected from a set of cellulose and Silastic® membranes to prove the possibility of depth-resolved near-IR measurements. Principal component regression was used to estimate the depth resolution of this method, yielding an average resolution of 31 μm. Further, to demonstrate depth-resolved near-IR spectroscopy in a practical in vitro system, we determined concentrations of salicylic acid (SA) in a hydrogel matrix during diffusion experiments carried out for up to three hours. An artificial-neural-network-based calibration model was developed which predicted SA concentrations accurately ( R2 = 0.993, SEP = 123 μg/mL).

Rapid identification of plant- and chemical-dyed cotton fabrics using the near-infrared technique

2020 ◽  
Vol 90 (19-20) ◽  
pp. 2275-2283
Author(s):  
Mingxia Li ◽  
Guangting Han ◽  
Wei Jiang ◽  
Chengfeng Zhou ◽  
Yuanming Zhang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Recognition Rate ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Rapid Identification ◽  
Cotton Fabrics ◽  
Calibration Model ◽  
Pattern Recognition Methods ◽  
Validation Set

Plant dye is a promising dyestuff to be used in textiles due to its unique environmental compatibility. However, currently there is no effective method for the identification of plant-dyed and chemical-dyed textiles. In this study, near-infrared (NIR) spectroscopy combined with three kinds of pattern recognition methods, namely soft independent modeling of class analogy (SIMCA), partial least squares (PLS) regression and principal component regression (PCR), were applied to identify cotton fabrics dyed with plant and chemical dyes. A total of 336 plant dye and chemical dye dyed cotton fabrics were prepared and the NIR spectra were collected; 267 samples were used as the calibration set, while the remaining 69 samples were used as the validation set. After pretreatment with the Savitzky–Golay first derivative, the calibration model was constructed. In the SIMCA model, the correct recognition rate values of the calibration and prediction sets were 100% and 98.55%, respectively. The PLS model showed that the number of principal components (PCs) and the correlation coefficient ( R2) were 8 and 0.9978, respectively, and the results of PCR were PC = 10, R2 = 0.9937. Both methods were satisfactory for the predicted results. The overall results indicated that NIR spectroscopy could be used for rapid and nondestructive identification of plant-dyed cotton fabrics and chemical-dyed cotton fabrics.

scholarly journals A Quantitative Analysis of Glucose from Enhanced NIR Spectra through Linear Regression Model Coupled with Optimized Bandpass Filtering

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 1010
Author(s):  
Mahbubur Rahman Mishal ◽  
Tanvir Tazul Islam ◽  
Shahadat Hossain Antor ◽  
Tanzilur Rahman
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Near Infrared ◽  
Phosphate Buffer Solution ◽  
Principal Component Regression ◽  
Principal Component ◽  
Calibration Model ◽  
Linear Regression Models ◽  
Buffer Solution ◽  
Correction Technique

This study proposes a new preprocessing technique that combines Chebyshev filtering with baseline correction technique Asymmetric Least Squares (ALS) and Savitzky-Golay transformation (SGT) to improve the prediction of Glucose from near Infrared (NIR) spectra through linear regression models Partial Least Squares (PLS) and Principal Component Regression (PCR). To investigate the performance of the proposed technique, a calibration model was first developed and then validated through prediction of Glucose from NIR spectra of a mixture of glucose, urea, and triacetin in a phosphate buffer solution where the component concentrations are within their physiological range in blood. Results indicate that the proposed technique improves the performance of both PLS and PCR and achieves standard error of prediction (SEP) as low as 12.76 mg/dL which is in the clinically acceptable level and comparable to the existing literature.

Nondestructive NIR and NIT Determination of Protein, Fat, and Water in Plastic-Wrapped, Homogenized Meat

1992 ◽  
Vol 46 (11) ◽  
pp. 1685-1694 ◽  
Author(s):  
Tomas Isaksson ◽  
Charles E. Miller ◽  
Tormod Næs
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Prediction Errors ◽  
Optimal Test ◽  
Meat Samples ◽  
Water Contents

In this work, the abilities of near-infrared diffuse reflectance (NIR) and transmittance (NIT) spectroscopy to noninvasively determine the protein, fat, and water contents of plastic-wrapped homogenized meat are evaluated. One hundred homogenized beef samples, ranging from 1 to 23% fat, wrapped in polyamide/polyethylene laminates, were used. Results of multivariate calibration and prediction for protein, fat, and water contents are presented. The optimal test set prediction errors (root mean square error of prediction, RMSEP), obtained with the use of the principal component regression method with NIR data, were 0.45, 0.29 and 0.50 weight % for protein, fat, and water, respectively, for plastic-wrapped meat (compared to 0.40, 0.28 and 0.45 wt % for unwrapped meat). The optimal prediction errors for the NIT method were 0.31, 0.52 and 0.42 wt % for protein, fat, and water, respectively, for plastic-wrapped meat samples (compared to 0.27, 0.38, and 0.37 wt % for unwrapped meat). We can conclude that the addition of the laminate only slightly reduced the abilities of the NIR and NIT method to predict protein, fat, and water contents in homogenized meat.

Near-IR Spectroscopic Determination of NaCl in Aqueous Solution

1992 ◽  
Vol 46 (12) ◽  
pp. 1809-1815 ◽  
Author(s):  
Jie Lin ◽  
Chris W. Brown
Keyword(s):  
Aqueous Solution ◽  
Temperature Effects ◽  
Continuous Monitoring ◽  
Principal Component Regression ◽  
Principal Component ◽  
Standard Errors ◽  
Near Ir ◽  
Linear And Nonlinear ◽  
Ir Spectroscopic

The concentrations of NaCl in aqueous solutions have been determined with the use of near-IR spectra between 1100 and 1900 nm. Models expressing the concentration of NaCl are developed with linear and nonlinear regression with the use of the absorbances at selected wavelengths and with principal component regression (PCR) using entire spectra. Temperature perturbations on water bands interfere with the measurement of NaCl but can be removed by linear or nonlinear regressions using the absorbances at the wavelengths where the temperature effects are zero, or they can be accounted for by PCR. Standard errors of 5 mM and a detection limit of IS mM are obtained for NaCl. This technique can be applied for quantitative analysis of NaCl in the laboratory or can be readily adapted for continuous monitoring in process control.

scholarly journals In Vitro Photothermal Destruction of Cancer Cells Using Gold Nanorods and Pulsed-Train Near-Infrared Laser

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Jui-Teng Lin ◽  
Yueh-Sheng Chiang ◽  
Guang-Hong Lin ◽  
Hsinyu Lee ◽  
Hsia-Wei Liu
Keyword(s):  
Cancer Cells ◽  
Diode Laser ◽  
Near Infrared ◽  
Irradiation Time ◽  
Laser System ◽  
Gold Nanorod ◽  
Near Ir ◽  
Laser Heated ◽  
Train System

We present a novel pulsed-train near-IR diode laser system with real-time temperature monitoring of the laser-heated cancer cell mixed in gold nanorod solution. Near-IR diode laser at 808 nm matching the gold nanorod absorption peak (with an aspect ratio about 4.0) was used in this study. Both surface and volume temperatures were measured and kept above 43°C, the temperature for cancer cells destruction. The irradiation time needed in our pulsed-train system with higher laser fluence for killing the cancel cells is about 1–3 minutes, much shorter than conventional methods (5–10 minutes). Cell viabilities in gold nanorod mixed and controlled solutions are studied by green fluorescence.

The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

1988 ◽  
Vol 42 (7) ◽  
pp. 1273-1284 ◽  
Author(s):  
Tomas Isaksson ◽  
Tormod Næs
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Data Sets ◽  
Prediction Errors ◽  
Regression Methods ◽  
Multiplicative Scatter Correction ◽  
Linearity Improvement

Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7–68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.

Identification of Electrolytes in Aqueous Solutions from Near-IR Spectra

1996 ◽  
Vol 50 (4) ◽  
pp. 444-448 ◽  
Author(s):  
Jie Lin ◽  
Jing Zhou ◽  
Chris W. Brown
Keyword(s):  
Aqueous Solutions ◽  
Ir Spectra ◽  
Principal Component Regression ◽  
Principal Component ◽  
Transition Metal Ions ◽  
Water Molecules ◽  
Spectral Library ◽  
Dipole Interactions ◽  
Near Ir ◽  
Solutions Of Electrolytes

Dissolution of electrolytes causes characteristic changes in the near-IR spectrum of water. These changes result from a decrease in the concentration of water; charge-dipole interactions between ions and water molecules; formation of hydrogen bonds between oxygen or nitrogen atoms in some ions and water molecules; production of H+ and OH− ions from dissociation and hydrolysis; absorptions due to OH, NH, and CH groups in some ions; and intrinsic colors of some transition metal ions. Changes in spectra were used for identification of electrolytes in aqueous solutions. Near-IR spectra of 71 solutions of single electrolytes were measured and used to develop a spectral library. This near-IR spectral library was processed with principal component regression (PCR) and used for the identification of single and multiple electrolytes in aqueous solutions with the use of their spectra. Most of the unknown electrolytes were identified correctly. For the others, very similar electrolytes were selected with one ion identified correctly. The near-IR spectral library of aqueous solutions of electrolytes can be used as a simple and fast approach for the identification of electrolytes.

Analisis Kandungan Nutrisi Kulit Kopi (Coffea sp.) dengan Menggunakan Metode Near Infrared Reflectance Spectroscopy (NIRS)

2017 ◽  
Vol 2 (4) ◽  
Author(s):  
Andika Boy Yuliansyah ◽  
Sitti Wajizah ◽  
Samadi Samadi
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Principal Component ◽  
Coffea Canephora ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Coffee Pulp ◽  
Infrared Reflectance Spectroscopy

Abstrak.     Tujuan penelitian ini adalah untuk mengevaluasi akurasi metode analisis pakan dengan metode (Near Infrared Reflectance Sectroscopy) NIRS dalam memprediksi kandungan nutrisi limbah kulit kopi serta mengetahui panjang gelombangnya.  Penelitian ini dilakukan di Laboratorium Ilmu Nutrisi dan Teknologi Pakan, Univeritas Syiah Kuala, dari Agustus hingga September 2017.  Penelitian ini menggunakan 30 sampel limbah kulit kopi yang terdiri dari 2 varietas kopi yaitu kopi arabika (Coffea arabica) dan kopi robusta (Coffea canephora). Spektrum diukur dengan menggunakan yaitu FT-IR IPTEK T-1516 pada rentang wavelengrh 1000-2500 nm dan di kalibrasi dan validasi dengan menggunakan software The Unscrambler X version 10.4.  Pretreatment yang digunakan yaitu Multiplicative scatter analysis (MSC) dan DeTrending (DT) dengan metode regresi Principal Component Regression (PCR). Parameter nutrisi yang dianalisis yaitu bahan kering (BK), protein kasar (PK) dan serat kasar (SK).  Hasil penelitian memperlihatkan bahwa NIRS dengan model yang telah dibangun tidak dapat menprediksi bahan kering dengan baik. Hal ini ditunjukkan dengan nilai r, R2 dan RPD yang rendah (0.58, 0.34 dan 3.06) serta RMSEC yang tinggi (3.06). Metode NIRS dapat memprediksi kandungan PK dan SK dengan baik pada penggunaan pretreatment MSC (PK= r: 0.87, R2: 0.76, RMSEC: 0.45 dan RPD: 2.07; SK= r: 0.87, R2: 0.75, RMSEC: 2.83 dan RPD: 2.03). Prediksi kasar untuk PK dan SK didapatkan dengan menggunakan pretreatment DT (PK= r: 0.75, R2: 0.57, RMSEC: 0.60 dan RPD: 1.55; SK= r: 0.84, R2: 0.71, RMSEC: 3.06 dan RPD: 1.88). Analysis of Coffee Pulp (Coffea sp.) Nutrition Content Using Near Infrared Reflectance Spectroscopy (NIRS) Method Abstract.   The aim of present study was to evaluate the accuration of feed analysis method of Near infrared reflectance spectroscopy (NIRS) in predicting nutritional content of Coffee pulp and to know its wavelength.  The study was conducted in  nutrition science and feed technology Laboratory,   Department of Animal Husbandry,  Faculty of Agriculture,  Syiah Kuala University,  august until september, 2017.   As many as 30 coffee pulps  were used in this study and seperated to 2 specieses of coffee, arabica coffee (Coffea arabica) and robusta coffee (Coffea canephora).  The spectrum was scanned using. FT-IR IPTEK T-1516 at 1000 to 2500 nm wavelength and calibrated and validated using The Unscrambler X version 10.4 software. Pretreatment used in this study was Multiplicative scatter analysis (MSC) dan DeTrending (DT) with Principal component regression (PCR) calibration method. Nutrition parameters analyzed were dry matter (DM), crude protein (CP) and dietary fiber (DF). The results of study showed that NIRS with prediction models that have been build cannot predicted DM content in coffee pulp. This was shown with low value of r, R2 dan RPD (0.58, 0.34 dan 3.06) and high value of RMSEC (3.60). NIRS method can predicted CP and DF content quite well using MSC pretreatment (CP= r: 0.87, R2: 0.76, RMSEC: 0.45 dan RPD: 2.07; DF= r: 0.87, R2: 0.75, RMSEC: 2.83 dan RPD: 2.03). Rough prediction for CP and DM content was obtained by using DT pretreatment (CP= r: 0.75, R2: 0.57, RMSEC: 0.60 dan RPD: 1.55; DF= r: 0.84, R2: 0.71, RMSEC: 3.06 dan RPD: 1.88). 

Use of residual maximum likelihood to evaluate accuracy of two NIRS calibration procedures, relative to Kjeldahl, for determining nitrogen concentration of soybeans

1991 ◽  
Vol 71 (2) ◽  
pp. 385-392 ◽  
Author(s):  
G. B. Schaalje ◽  
H. -H. Mündel
Keyword(s):  
Maximum Likelihood ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Field Trials ◽  
Near Infrared Reflectance ◽  
Residual Maximum Likelihood ◽  
Maximum Likelihood Procedure ◽  
Calibration Methods

The accuracy of estimates of plant properties based on near-infrared reflectance spectroscopy (NIRS) varies with many factors including the biological material in question and the method used to calibrate the NIRS instrument. This study investigated the accuracy, relative to Kjeldahl analysis, of NIRS analysis based on two calibration methods in estimating nitrogen concentration of four stages and/or parts of soybean (Glycine max (L.) Merr.) plants. Samples of whole top growth at anthesis, whole top growth at maturity, whole top growth at maturity excluding seeds, and seeds were obtained from two field trials and one phytotron experiment. Two Kjeldahl determinations of nitrogen concentration were obtained for each sample, as well as reflectance values at each of 19 infrared wavelengths, using a Technicon InfraAlyser 400R. Different subsets of the sample data were used for calibration and assessment of accuracy. The instrument was calibrated using stepwise multiple linear regression (SMLR) and principal component regression (PCR). The residual maximum likelihood procedure was useful in showing that NIRS estimates based on either SMLR or PCR were at least as accurate as Kjeldahl estimates for all stages and/or parts except whole top growth at maturity excluding seeds. Key words: Calibration, principal component regression, stepwise regression

Sign in / Sign up

Export Citation Format

Share Document