Evaluation of the standard normal variate method for Laser-Induced Breakdown Spectroscopy data treatment applied to the discrimination of painting layers

Syvilay, Delphine; Wilkie-Chancellier, Nicolas; Trichereau, Barbara; Texier, Annick; Martinez, L.; Serfaty, Sylvia; Detalle, Vincent

doi:10.1016/j.sab.2015.09.022

Cited by 54 publications

(25 citation statements)

References 48 publications

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“…While numerous works evaluated pre-processing of spectral data with a view of improving the prediction models of a variable [32,35,36], results varied depending on the type of variable that was determined and whether or not the pre-processing was combined with other kinds of pre-processing. Combination could explain the unsuitability of models computed with pre-processed spectral data.…”

Section: Discussionmentioning

confidence: 99%

“…Spectral data pre-processed by a jump correction tool were transformed using five techniques: (i) Derivatives, (ii) SNV, (iii) MSC, (iv) de-trending, and (v) CR transformation. In this section, we describe an approach based on work described in the literature (mainly [32], but also [35,36]).…”

Section: Transformationmentioning

confidence: 99%

“…SNV is a pre-processing method that can be applied to each spectrum individually to remove scatter. SNV calculates the average and standard deviation values of all the data points for a given spectrum, obtaining the mean value for absorbance for each data point and dividing the result by the standard deviation [35]. Previous research found that, in using SNV, the diffuse reflectance spectra was free from multi-collinearity and there was no confusion from the complexity of shapes encountered in derivative spectroscopy [30].…”

Section: Introductionmentioning

confidence: 99%

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Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards

et al. 2019

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Water status controls plant physiology and is key to managing vineyard grape quality and yield. Water status is usually estimated by leaf water potential (LWP), which is measured using a pressure chamber; however, this method is difficult, time-consuming, and error-prone. While traditional spectral methods based on leaf reflectance are faster and non-destructive, most are based on vegetation indices derived from satellite imagery (and so only take into account discrete bandwidths) and do not take full advantage of modern hyperspectral sensors that capture spectral reflectance for thousands of wavelengths. We used partial least squares regression (PLSR) to predict LWP from reflectance values (wavelength 350–2500 nm) captured with a field spectroradiometer. We first identified wavelength ranges that minimized regression error. We then tested several common data pre-processing methods to analyze the impact on PLSR prediction precision, finding that derivative pre-processing increased the determination coefficients of our models and reduced root mean squared error (RMSE). The models fitted with raw data obtained their best results at around 1450 nm, while the models with derivative pre-processed achieved their best estimates at 826 nm and 1520 nm.

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Section: Discussionmentioning

confidence: 99%

Section: Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards

et al. 2019

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“…Background normalization has proved to be an effective method for the problem of the matrix effect, since the intensity of the continuum radiation directly relates to the plasma [ 16 ]; however, it may restrict the laser energy in a small range [ 26 ]. Standard normal variate (SNV) can be used to minimize unwanted variations caused by particle size distribution [ 27 ], as well as to deal with multivariate analysis [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

Liu

Peng

et al. 2018

Sensors

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Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where Rc2 and Rp2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice.

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“…Among the preprocessing methods, best calibration results were achieved with the MSC and SNV normalizations. It has been proven that SNV normalization could be used to compensate the laser fluctuation [ 40 ]. The improvement of background and area normalizations was limited, and the sensitivity of those was even reduced.…”

Section: Resultsmentioning

confidence: 99%

Comparative Study of the Detection of Chromium Content in Rice Leaves by 532 nm and 1064 nm Laser-Induced Breakdown Spectroscopy

Peng

Liu

Shen

et al. 2018

Sensors

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Fast detection of toxic metals in crops is important for monitoring pollution and ensuring food safety. In this study, laser-induced breakdown spectroscopy (LIBS) was used to detect the chromium content in rice leaves. We investigated the influence of laser wavelength (532 nm and 1064 nm excitation), along with the variations of delay time, pulse energy, and lens-to-sample distance (LTSD), on the signal (sensitivity and stability) and plasma features (temperature and electron density). With the optimized experimental parameters, univariate analysis was used for quantifying the chromium content, and several preprocessing methods (including background normalization, area normalization, multiplicative scatter correction (MSC) transformation and standardized normal variate (SNV) transformation were used to further improve the analytical performance. The results indicated that 532 nm excitation showed better sensitivity than 1064 nm excitation, with a detection limit around two times lower. However, the prediction accuracy for both excitation wavelengths was similar. The best result, with a correlation coefficient of 0.9849, root-mean-square error of 3.89 mg/kg and detection limit of 2.72 mg/kg, was obtained using the SNV transformed signal (Cr I 425.43 nm) induced by 532 nm excitation. The results indicate the inspiring capability of LIBS for toxic metals detection in plant materials.

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Evaluation of the standard normal variate method for Laser-Induced Breakdown Spectroscopy data treatment applied to the discrimination of painting layers

Cited by 54 publications

References 48 publications

Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards

Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

Comparative Study of the Detection of Chromium Content in Rice Leaves by 532 nm and 1064 nm Laser-Induced Breakdown Spectroscopy

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