Applications of Discrete Wavelet Analysis for Predicting Internal Quality of Cherry Tomatoes using VIS/NIR Spectroscopy

Kim, Ghiseok; Kim, Dae-Yong; Kim, Geon Hee; Cho, Byoung‐Kwan

doi:10.5307/jbe.2013.38.1.048

Cited by 17 publications

(6 citation statements)

References 14 publications

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“…DWT can be seen as a process of filtering and size reduction [56] in which a filter basis, often referred to as wavelet base, is applied in a linear transformation. Besides filtering noise from the signal of interest, wavelet transformations have also been shown capable of separating spectral features in multispectral images [57], which can be beneficial to classification models like PLS [58]. DWT of a multispectral signal consists of two or more arrays in which one comprises the approximation of the signal and where the rest is formed by wavelet coefficients, the noise.…”

Section: Pre-processingmentioning

confidence: 99%

“…An infinite number of wavelet and scaling bases can be defined, which makes wavelet transforms broadly applicable. A number of ways for choosing suitable bases is described in literature, such as matching the shape of base functions to the shape of the spectral features of interest [56,58] or more quantitative approaches which try all available bases and measure either the de-noising effect [59] or the performance of a prediction algorithm using leave-one-out cross validation [56]. In this study, the wavelet bases delivering the best prediction performance were chosen.…”

Section: Pre-processingmentioning

confidence: 99%

“…Partial least squares regression is a widely adopted technique for the development of predictive models, for example with (NIR) spectroscopy where it is primarily used for approximation of concentrations of specific compounds in specimens [44,63], but sometimes also applied for the prediction of other variables which are indirectly or not at all linked to the chemical composition of the analytes [58,64]. PLS-R is related to conventional PCA.…”

Section: Pls-rmentioning

confidence: 99%

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An Innovative Route to Circular Rigid Plastics

et al. 2019

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An innovative route for plastics recycling is proposed, based on a combination of a logarithmic sorting process and colour plus high-resolution near-infrared (NIR) sensors. Although counterintuitive, it is shown that such a technology could sort clean flakes from rigid packaging waste into a very large number of different plastic grades with modest sorter capacity, provided that the chosen sensor is able to differentiate correctly between any two grades of plastics in the waste. Tests with high-resolution NIR on single pixels of transparent flakes from different types and brands of packaging show that this is indeed the case for a selection of 20 different packaging items bought from shops. Moreover, the results seem to indicate, in line with previous research, that high-resolution NIR data can be linked to important physical plastic properties like the melt flow viscosity and tensile strength. The attraction of deep sorting of waste plastics with relatively cheap sensors and modest sorter capacity is that the present industrial practice of tuning plastic grades to specific applications could coexist with commercial high-grade recycling at high levels of circularity and low carbon footprint. Therefore, advanced recycling technology is likely to be a societal alternative to phasing out plastics for rigid applications.

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Section: Pre-processingmentioning

confidence: 99%

Section: Pre-processingmentioning

confidence: 99%

Section: Pls-rmentioning

confidence: 99%

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An Innovative Route to Circular Rigid Plastics

et al. 2019

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“…Moreover, they can acquire both quantitative and qualitative information without the need for separate analyses. The near-infrared (NIR) spectral region has been widely used to assess the internal quality of fruits [ 7 ]. Specifically, NIR spectroscopy has been used to determine the content of soluble solids, lycopene, and β-carotene in tomatoes [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System

Lee

Kim

Jeong

et al. 2014

Sensors

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The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000–1700 nm was used to obtain hyperspectral reflectance images of 224 tomatoes: 112 with and 112 without cracks along the stem-scar region. The hyperspectral images were subjected to partial least square discriminant analysis (PLS-DA) to classify and detect cracks on the tomatoes. Two morphological features, roundness (R) and minimum-maximum distance (D), were calculated from the PLS-DA images to quantify the shape of the stem scar. Linear discriminant analysis (LDA) and a support vector machine (SVM) were then used to classify R and D. The results revealed 94.6% and 96.4% accuracy for classifications made using LDA and SVM, respectively, for tomatoes with and without crack defects. These data suggest that the hyperspectral near-infrared reflectance imaging system, in addition to traditional NIR spectroscopy-based methods, could potentially be used to detect crack defects on tomatoes and perform quality assessments.

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“…Recently, spectroscopy techniques have been widely used to nondestructively analyze the quality of crops, chemicals, and biomaterials [13][14][15][16][17][18]. Among them, a near-infrared spectroscopy (NIRS) technique is being considered as a promising spectroscopic method that treats the near-infrared (NIR) region of an electromagnetic spectrum, which corresponds to the wavelength range of 780 to 2500 nm [19].…”

Section: Introductionmentioning

confidence: 99%

Moisture Content Measurement of Broadleaf Litters Using Near-Infrared Spectroscopy Technique

et al. 2017

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Near-infrared spectroscopy (NIRS) was implemented to monitor the moisture content of broadleaf litters. Partial least-squares regression (PLSR) models, incorporating optimal wavelength selection techniques, have been proposed to better predict the litter moisture of forest floor. Three broadleaf litters were used to sample the reflection spectra corresponding the different degrees of litter moisture. The maximum normalization preprocessing technique was successfully applied to remove unwanted noise from the reflectance spectra of litters. Four variable selection methods were also employed to extract the optimal subset of measured spectra for establishing the best prediction model. The results showed that the PLSR model with the peak of beta coefficients method was the best predictor among all of the candidate models. The proposed NIRS procedure is thought to be a suitable technique for on-the-spot evaluation of litter moisture.

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Applications of Discrete Wavelet Analysis for Predicting Internal Quality of Cherry Tomatoes using VIS/NIR Spectroscopy

Cited by 17 publications

References 14 publications

An Innovative Route to Circular Rigid Plastics

An Innovative Route to Circular Rigid Plastics

Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System

Moisture Content Measurement of Broadleaf Litters Using Near-Infrared Spectroscopy Technique

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