Non-Destructive Measurement of Acidity, Soluble Solids, and Firmness of Jonagold Apples Using Nir-Spectroscopy

Lammertyn, Jeroen; Nicolaı̈, Bart; Ooms, Kristien; Smedt, Valentijn De; Baerdemaeker, J. De

doi:10.13031/2013.17238

Cited by 239 publications

(139 citation statements)

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“…The statistic parameters were summarised in Large difference indicates either too many latent variables or PCs are used or 'noise' is modelled. 48 The results obtained by PCR and PLS-2 methods using only 3 factors/PCs are quite similar to each other. This is clearly observed for both the loading (Fig.…”

Section: +supporting

confidence: 65%

Simultaneous Spectrophotometric Determination of Copper, Nickel, and Zinc Using 1-(2-Thiazolylazo)-2-Naphthol in the Presence of Triton X-100 Using Chemometric Methods

Low¹,

Zain²,

Abas³

et al. 2009

Journal of the Korean Chemical Society

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주제어: 검정, 화학계량학적, 금속, 경감, 분광광도법적ABSTRACT. Multivariate models were developed for the simultaneous spectrophotometric determination of copper (II), nickel (II) and zinc (II) in water with 1-(2-thiazolylazo)-2-naphthol as chromogenic reagent in the presence of Triton X-100. To overcome the drawback of spectral interferences, principal component regression (PCR) and partial least square (PLS) multivariate calibration approaches were applied. Performances were validated with several test sets, and their results were then compared. In general, no significant difference in analytical performance between PLS and PCR models. The root mean square error of prediction (RMSEP) using three components for Cu 2+, Ni 2+ and Zn 2+ were 0.018, 0.010, 0.011 ppm, respectively. Figures of merit such as sensitivity, analytical sensitivity, limit of detection (LOD) were also estimated. High reliability was achieved when the proposed procedure was applied to simultaneous determination of Cu 2+ , Ni 2+ and Zn 2+ in synthetic mixture and tap water.

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Section: +supporting

confidence: 65%

Simultaneous Spectrophotometric Determination of Copper, Nickel, and Zinc Using 1-(2-Thiazolylazo)-2-Naphthol in the Presence of Triton X-100 Using Chemometric Methods

Low¹,

Zain²,

Abas³

et al. 2009

Journal of the Korean Chemical Society

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show abstract

“…A good model should have a low RMSEC, a low RMSEP, a high correlation coefficient, but also a small difference between RMSEC and RMSEP. A large difference indicates that too many latent variables (principal components) are used in the model and noise is modeled (Lammertyn et al, 1998). Figure 8 shows the total explained variance and root mean square error (a), and correlation coefficient and optimal numbers of principal components Although the RMSEP values of the second derivative dataset have increased (and hence decreased prediction accuracy) relative to the raw data, the difference was small (only about 2.5%).…”

Section: Analysis Of Savitzky-golay Second Derivative Datamentioning

confidence: 99%

Detection of Sclerotinia rot disease on celery using hyperspectral data and partial least squares regression

Huang

Apan²

2006

Journal of Spatial Science

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There is a need to detect and assess the incidence of Sclerotinia rot disease in celery (Apium graveolens). In this study, we examined the potential of hyperspectral sensing to detect the symptoms of this disease in celery crop. Using a portable spectrometer, sample measurements of diseased and healthy leaves were collected from celery leaves in the field. Both raw and transformed spectral data were used in the development of Partial Least Squares regression models. The cross-validated results showed that the incidence of disease on celery could be predicted using the raw spectra and the first and second derivative data, with prediction errors ranging from 11.08 to 13.62%. The visible and near-infrared wavelengths (400-1300nm) produced similar detection ability with that of the full range wavelengths (400-2500nm).

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“…Early stopping (ES) was employed to avoid the over-fitting problem. The quality of the calibration model was determined by the standard error of calibration (SEC), standard error of prediction (SEP) and the correlation coefficient (r) between the predicted and measured values (Lammertyn et al 1998). The SEC and SEP were defined by the following equations.…”

Section: (B) Neural Network (Nn)mentioning

confidence: 99%

Textural analysis of soil images to quantify and characterize the spatial variation of soil properties using a real-time soil sensor

2006

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The primary aim of this work was to predict soil moisture content and soil organic matter using soil image texture statistics. Co-occurrence method texture statistics were used to characterize Andisol soils to extend the possibility of using RGB color space in representing composite soil color. Four co-occurrence method textural features; angular second moment (ASM), contrast (CON), correlation (COR) and inverse difference moment (IDM) calculated from generalized matrix for image texture representation were used to describe soil moisture content variation under laboratory conditions. It was found that CON and COR had negative responses to moisture content (MC) and ASM had positive response to MC. The same were also observed in direct captured field soil images in terms of textural indices against MC and soil organic matter (SOM). The correlations were significant for ASM and COR in fertilizer and combined (fertilizer-manure) plots and insignificant in manure plots. To relate sub-surface image textural indices and soil properties for individual years, stepwise multiple linear regression (SMLR) and supervised feed-forward neural networks (NN) were investigated in an attempt to provide minimal prediction errors. The improvements achieved by NN with minimal prediction errors were better than SMLR in different years. It was assumed that several years of data sets with a much larger number of observations could be used to differentiate fundamental soil properties.

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Non-Destructive Measurement of Acidity, Soluble Solids, and Firmness of Jonagold Apples Using Nir-Spectroscopy

Cited by 239 publications

References 0 publications

Simultaneous Spectrophotometric Determination of Copper, Nickel, and Zinc Using 1-(2-Thiazolylazo)-2-Naphthol in the Presence of Triton X-100 Using Chemometric Methods

Simultaneous Spectrophotometric Determination of Copper, Nickel, and Zinc Using 1-(2-Thiazolylazo)-2-Naphthol in the Presence of Triton X-100 Using Chemometric Methods

Detection of Sclerotinia rot disease on celery using hyperspectral data and partial least squares regression

Textural analysis of soil images to quantify and characterize the spatial variation of soil properties using a real-time soil sensor

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