Calibration Transfer between Two Near-Infrared Spectrometers Based on a Wavelet Packet Transform

Tan, Chao; Li, Menglong

doi:10.2116/analsci.23.201

Cited by 23 publications

(9 citation statements)

References 23 publications

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“…Since internal factors (e.g., detector performance, calibration quality, dispersion element and stability, stray light) are out of the instrument operator's control, all other possible external factors must be kept constant (Tan and Menglong, 2007;Feudale et al, 2002;Wüliams, 1987). On die basis of these criteria and on years of experience in spectroscopic measurements of soils, we established a strict protocol for spectroscopic soil measurements that works in both the field and the laboratory (hereafter termed TAU protocol).…”

Section: Protocol For Spectral Measurementsmentioning

confidence: 99%

Performance of Three Identical Spectrometers in Retrieving Soil Reflectance under Laboratory Conditions

Pimstein

Notesco

Ben‐Dor

2011

Soil Science Soc of Amer J

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A wide range of electronic md mechanical noise factors can affect soil spectra when using different instruments or even when repeating a specific sample's measurements with the same spectrometer. In soil samples where very weak spectral features are monitored for chemometric purposes, alterations in wavelength location, peak absorption shape, or albedo intensity can limit the use of previously developed spectral models. To quantify this alteration and propose a standardization method, 12 soil samples and three different materials for internal standards (sand, glass and polyethylene) were analyzed. This population was concurrently measured with three identical spectrometers using a strict measurement protocol, and then by different operators with different protocols. Significant changes in the soil spectra were found when different operators performed the measurements, being reduced >50% when the strict protocol was applied. Sand was found to be the ideal internal standard for correcting the spectra to a reference spectrometer, even when different measuring protocols were used. This standardization also showed an improvement in the prediction of soil properties when applying chemometric spectral models even with different instruments, concluding that the use of an internal standard and a strict protocol must be applied tor soil spectral measurements. As the measuring factors described in this research also affect any infrared diffuse reflectance spectroscopy measurements, the proposed method should be applicable to any instrumentation and configuration being used. This is crucial to enabling spectral comparisons between different spectrometers or, more importantly, to establishing robust chemometric models and to exchange soil spectral information.Abbreviations: ASD, Analytical Spectral Devices, Inc.; CR, continuum removal; NIRS, near infrared analysis; PLS, partial least squares; RGB, red-greeen-blue color model; RMSEP, root mean square error of prediction; SAM, spectral angle mapper; TAU, Tel Aviv University.

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Section: Protocol For Spectral Measurementsmentioning

confidence: 99%

Performance of Three Identical Spectrometers in Retrieving Soil Reflectance under Laboratory Conditions

Pimstein

Notesco

Ben‐Dor

2011

Soil Science Soc of Amer J

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“…Many research groups (59)(60)(61)(62)(63)(64)(65)(66)(67)(68)(69)(70)(71)(72)(73)(74)(75)(76) have studied the calibrations of NIR systems as problems of calibration transfer or standardization. For example, Wang et al (59) proposed direct standardization (DS) and piecewise direct standardization (PDS), and Shenk et al (60) developed an algorithm with wavelength index correction and spectral intensity correction, a patented method.…”

Section: Regarding the Calibration Transfer Methods Proposed Previouslymentioning

confidence: 99%

A Review of Online Real-Time Process Analyses of Melt-State Polymer Using the Near-Infrared Spectroscopy and Chemometrics

Watari

2013

Applied Spectroscopy Reviews

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This review describes the online process monitoring of the melt-state polymer by near-infrared (NIR) spectroscopy and chemometrics. The spectra of linear low-density polyethylene (LLDPE), random polypropylene (RPP), block polypropylene (BPP), and ethylene-vinylacetate (EVA) copolymer in melt states measured by an online monitoring system with a Fourier transform near-infrared spectrometer are discussed. The calibration models for the density of LLDPE, the content of ethylene copolymers in RPP and BPP, and vinylacetate concentration (VA) in EVA copolymers using partial least squares regression are reported. The continuous monitoring of the LLDPE density at the real plant is described as an example of online monitoring using NIR spectroscopy and chemometrics. For the precise prediction of VA in EVA, a combination method using regression and discrimination was inducted. Three compensation methods for the effect of the temperature change in the RPP and BPP samples are shown. Conventional calibration transfer methods are introduced, and a practical calibration transfer method using two samples and its performance are reported using BPP and RPP spectra. Moreover, the possibility of a calibration correction method using one sample for the realization of long-term traceability is indicated by the example of the relocation.

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“…They found that normalized energy and entropy were suitable features for classification of ECG signals. Tan and Li (2007) proposed wavelet packet transform and entropy based criteria to transfer the spectral data between two spectrometers. Calibration transfer between two NIR spectrometers is a very challenging task due to the differences in instrumental responses and varying environmental conditions.…”

Section: Near-infrared Spectroscopymentioning

confidence: 99%

Wavelet Analysis of Signals in Agriculture and Food Quality Inspection

Singh

Choudhary

Jayas

et al. 2008

Food Bioprocess Technol

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Food quality and safety have become the top priorities for agriculture and food processing industry due to the increasing consumer demand for high-quality healthy food. The food processing industry is currently focusing on using fast, precise, and nondestructive automated quality inspection techniques. Near-infrared spectroscopy, image processing, hyperspectral imaging, X-rays, and ultrasonic techniques have been researched and shown to have high potential for automated inspection. The biggest challenge in the automated inspection systems deals with signal preprocessing, denoising, feature extraction, and its re-synthesis for classification purposes. Several research studies have established that the technique of wavelet analysis can very well resolve these issues of signal processing in many systems used for quality inspection of agricultural and food products. The objective of this paper is to discuss the theory of wavelet analysis and review its application in signal processing and feature extraction for quality monitoring of agricultural and food products.

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Calibration Transfer between Two Near-Infrared Spectrometers Based on a Wavelet Packet Transform

Cited by 23 publications

References 23 publications

Performance of Three Identical Spectrometers in Retrieving Soil Reflectance under Laboratory Conditions

Performance of Three Identical Spectrometers in Retrieving Soil Reflectance under Laboratory Conditions

A Review of Online Real-Time Process Analyses of Melt-State Polymer Using the Near-Infrared Spectroscopy and Chemometrics

Wavelet Analysis of Signals in Agriculture and Food Quality Inspection

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