Review of Process and Non-invasive Near-Infrared and Infrared Spectroscopy: 1993–1999

Workman, Jerry

doi:10.1081/asr-100100839

Cited by 116 publications

(46 citation statements)

References 246 publications

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“…It is clear that VIS-NIR-SWIR spectra contain useful information that can be used to derive estimates of soil properties. For example, absorption features in the VIS-NIR wavelength (400-1000 nm) are characteristics of the presence of soil carbon and iron oxide [34,35,[48][49][50], and those in the SWIR (1000-2500 nm) are from water, clay minerals and organic matter [16,51]. The important spectra absorption features through the use of some data mining algorithms have been studied by Viscarra Rossel and Behrens [15] and Gholizadeh et al [17].…”

Section: Soil Spectral Reflectance Patternmentioning

confidence: 99%

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

et al. 2017

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Abstract:Soil spectroscopy has shown to be a fast, cost-effective, environmentally friendly, non-destructive, reproducible and repeatable analytical technique. Soil components, as well as types of instruments, protocols, sampling methods, sample preparation, spectral acquisition techniques and analytical algorithms have a combined influence on the final performance. Therefore, it is important to characterize these differences and to introduce an effective approach in order to minimize the technical factors that alter reflectance spectra and consequent prediction. To quantify this alteration, a joint project between Czech University of Life Sciences Prague (CULS) and Tel-Aviv University (TAU) was conducted to estimate Cox, pH-H 2 O, pH-KCl and selected forms of Fe and Mn. Two different soil spectral measurement protocols and two data mining techniques were used to examine seventy-eight soil samples from five agricultural areas in different parts of the Czech Republic. Spectral measurements at both laboratories were made using different ASD spectroradiometers. The CULS protocol was based on employing a contact probe (CP) spectral measurement scheme, while the TAU protocol was carried out using a CP measurement method, accompanied with the internal soil standard (ISS) procedure. Two spectral datasets, acquired from different protocols, were both analyzed using partial least square regression (PLSR) technique as well as the PARACUDA II ® , a new data mining engine for optimizing PLSR models. The results showed that spectra based on the CULS setup (non-ISS) demonstrated significantly higher albedo intensity and reflectance values relative to the TAU setup with ISS. However, the majority of statistics using the TAU protocol was not noticeably better than the CULS spectra. The paper also highlighted that under both measurement protocols, the PARACUDA II ® engine proved to be a powerful tool for providing better results than PLSR. Such initiative is not only a way to unlock current limitations of soil spectroscopy, but also offers considerable efficiency and cost-and time-saving possibilities, which lead to further improvements in prediction performance of spectral models.

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Section: Soil Spectral Reflectance Patternmentioning

confidence: 99%

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

et al. 2017

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“…The final spectrum was an average based on 20 iterations from 4 directions, with 5 iterations per direction to improve the SNR. Each sample spectrum was corrected for background absorption before each single measurement to account for changes in temperature and air humidity; the spectral transmission of the fingertip was also corrected using a reference spectrum through a 1-mm layer of a white BaSO 4 panel standard [43,47].…”

Section: Spectral Data Measurementsmentioning

confidence: 99%

A Memory-Based Learning Approach as Compared to Other Data Mining Algorithms for the Prediction of Soil Texture Using Diffuse Reflectance Spectra

et al. 2016

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Successful determination of soil texture using reflectance spectroscopy across Visible and Near-Infrared (VNIR, 400-1200 nm) and Short-Wave-Infrared (SWIR, 1200-2500 nm) ranges depends largely on the selection of a suitable data mining algorithm. The objective of this research was to explore whether the new Memory-Based Learning (MBL) method performs better than the other methods, namely: Partial Least Squares Regression (PLSR), Support Vector Machine Regression (SVMR) and Boosted Regression Trees (BRT). For this purpose, we chose soil texture (contents of clay, silt and sand) as testing attributes. A selected set of soil samples, classified as Technosols, were collected from brown coal mining dumpsites in the Czech Republic (a total of 264 samples). Spectral readings were taken in the laboratory with a fiber optic ASD FieldSpec III Pro FR spectroradiometer. Leave-one-out cross-validation was used to optimize and validate the models. Comparisons were made in terms of the coefficient of determination (R 2 cv ) and the Root Mean Square Error of Prediction of Cross-Validation (RMSEP cv ). Predictions of the three soil properties by MBL outperformed the accuracy of the remaining algorithms. We found that the MBL performs better than the other three methods by about 10% (largest R 2 cv and smallest RMSEP cv ), followed by the SVMR. It should be pointed out that the other methods (PLSR and BRT) still provided reliable results. The study concluded that in this examined dataset, reflectance spectroscopy combined with the MBL algorithm is rapid and accurate, offers major efficiency and cost-saving possibilities in other datasets and can lead to better targeting of management interventions.

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“…During the last decade of the 20 th century, NIR spectroscopy has quickly surpassed FT-IR spectroscopy as the method-of-choice for process monitoring (Workman 1999). When it comes to remote sensing using long and low-cost fibers, both NIR and Raman techniques definitely have significant advantages (Marteau et al 1995).…”

Section: Choice Of Instrumentation and Justificationmentioning

confidence: 99%

A multimodal spectrometer for Raman scattering and near-infrared absorption measurement

Wang

Paliwal

2014

Vibrational Spectroscopy

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In food and pharmaceutical industries, the compositional complexity of raw materials and products promotes the use of multiple spectroscopic techniques. Availability of complementary methods, e.g., infrared absorption and Raman, is highly desirable. A multimodal Raman/NIR spectroscopic device was proposed. The device offered fast, non-destructive measurement, and remote sensing capability. A prototype was built.Spectral range covered 300-2600 cm -1 for Raman channel. Wavelength accuracy was better than 0.57 nm. Spectral resolution was better than 14.39 cm -1 (1 nm) for Raman (NIR) channel. Spectral response linearity was better than 0.998. Instrumental stability was better than 0.72% (0.39%) for Raman (NIR) channel.The device was applied to quantify ethanol aqueous solutions (2%-10% (v/v)).Quantitation results gave a root mean squared error of prediction (RMSEP) of 0.45% (v/v). Calibration using NIR absorbance spectra produced a RMSEP value of 0.49% Conformational change of ovalbumin upon thermal denaturation was studied using the height and area ratios between peaks at 1243.35 and 1662.66 cm -1 . Thermal denaturation of ovalbumin occurred at 70 °C and extensive formation of anti-parallel β-sheet structure was found at 90 °C.ii The unique capability of this multimodal device was proven by first performing data fusion of Raman and NIR spectra for quantitation. Using the same ethanol spectra set gave a more complicated model and a higher RMSEP of 0.73% (v/v). However, important variables in fused spectra corresponded well with the spectral signatures of ethanol and water. Two-dimensional correlation spectra were calculated using concentration as the external perturbation. Paliwal, for his generous support, unfailing guidance, and inspiring optimism that helped me grow both professionally and academically throughout the years.

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Review of Process and Non-invasive Near-Infrared and Infrared Spectroscopy: 1993–1999

Cited by 116 publications

References 246 publications

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

A Memory-Based Learning Approach as Compared to Other Data Mining Algorithms for the Prediction of Soil Texture Using Diffuse Reflectance Spectra

A multimodal spectrometer for Raman scattering and near-infrared absorption measurement

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