Feasibility Investigation on Determining Soluble Solids Content of Peaches Using Dielectric Spectra

Zhu, Xinhua; Fang, Lijie; Gu, Jingsi

doi:10.1007/s12161-015-0348-7

Cited by 15 publications

(6 citation statements)

References 31 publications

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“…The dielectric loss factor behavior of fresh fruits was also found in apples and peaches in the range of 20-4500 MHz. [31,32] The overriding dielectric relaxation behavior of dielectric loss factor might be caused by bound water and Maxwell-Wagner relaxations. [33] The statistics for the SSC values of all used persimmons are shown in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…The FS whose stabilities were within the cutoff threshold were regarded as uninformative variables. Finally, 174 characteristic variables, including 76 for ε' and 98 for ε'', were of ε'' near 20.0 and 2234.9 MHz were also found in determining the SSC of peaches [8] and Korla fragrant pears. [34] It indicates that these frequencies might be related to the SSC of fruits.…”

Section: Selection Of Characteristic Variablesmentioning

confidence: 99%

“…Like visible/near-infrared spectroscopy, dielectric spectroscopy, which describes permittivities over a wide frequency range, is also a fast, easy, and nondestructive technique. Dielectric spectroscopy technique has been successfully applied to predict SSC of various fruits such as apples, [6] honeydew melon, [7] peaches, [8] and watermelons. [9,10] These researches have indicated that dielectric spectroscopy is potential to measure the SSC of fruits.…”

Section: Introductionmentioning

confidence: 99%

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Nondestructive determination of soluble solids content of persimmons by using dielectric spectroscopy

Liu

2017

International Journal of Food Properties

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To explore the feasibility of dielectric spectroscopy in predicting soluble solids content (SSC) of persimmons during postharvest storage period, the dielectric constant spectra and dielectric loss factor spectra of 105 'Shui' persimmons were measured from 20 MHz to 4500 MHz. Based on the joint x-y distances algorithm, the persimmon samples were divided into two sets: 70 samples in calibration set and 35 samples in prediction set. One hundred and seventyfour, 14, and 24 variables were extracted as characteristic variables from full dielectric spectra (FS) by uninformative variables elimination (UVE), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS), respectively. Partial least squares (PLS) and least squares support vector machine (LSSVM) were applied to build SSC prediction models using FS and characteristic variables extracted by UVE, SPA, and CARS. The results indicated that LSSVM models offered better performance than PLS models at same input variables. CARS-LSSVM had the best SSC determination performance with the correlation coefficient and root-mean-square error of prediction set of 0.970 and 0.494°Brix. This study indicates that dielectric spectroscopy technique combined with characteristic variables selection methods is promising for determining SSC of persimmons. ARTICLE HISTORY

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Section: Resultsmentioning

confidence: 99%

Section: Selection Of Characteristic Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nondestructive determination of soluble solids content of persimmons by using dielectric spectroscopy

Liu

2017

International Journal of Food Properties

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“…The network is trained by initially selecting the weights at random and then presenting all training data repeatedly. The weights are then adjusted after every trial using external information to specify the correct result until the weights converge and the errors are reduced to acceptable values (Zhu et al 2016). The neural network used in this study was established and consisted of an input layer, one or more hidden layers, and one output layer.…”

Section: Back Propagation Neural Network (Bpnn)mentioning

confidence: 99%

Potential of Near-infrared Spectroscopy to Detect Defects on the Surface of Solid Wood Boards

et al. 2016

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Defects on the surface of solid wood boards directly affect their mechanical properties and product grades. This study investigated the use of near-infrared spectroscopy (NIRS) to detect and classify defects on the surface of solid wood boards. Pinus koraiensis was selected as the raw material. The experiments focused on the ability to use the model to sort defects on the surface of wood into four types, namely live knots, dead knots, cracks, and defect-free. The test data consisted of 360 NIR absorption spectra of the defect samples using a portable NIR spectrometer, with the wavelength range of 900 to 1900 nm. Three preprocessing methods were used to compare the effects of noise elimination in the original absorption spectra. The NIR discrimination models were developed based on partial least squares and discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM), and backpropagation neural network (BPNN) from 900 to approximately 1900 nm. The results demonstrated that the BPNN model exhibited the highest classification accuracy of 97.92% for the model calibration and 97.50% for the prediction set. These results suggest that there is potential for the NIR method to detect defects and differentiate between types of defects on the surface of solid wood boards.

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“…In the calibration sample set, there will inevitably be a very small number of discrete samples, and the traditional distance metric criterion cannot eliminate the effect of discrete samples, which may lead to suboptimal modeling. Considering only the input distribution on the calibration sample space will make the prediction model performance poor, and if the spatial distance of the sample reference values is added, a better model can be built ( 7 Ramirez‐Lopez et al, 24). If the impacts of the spectra space and the reference space aren't equally significant, tweaking their weights can lead to enhanced model accuracy 8 .…”

Section: Introductionmentioning

confidence: 99%

Sample selection method using near‐infrared spectral information entropy as similarity criterion for constructing and updating peach firmness and soluble solids content prediction models

Liu,

He,

Jiang

2023

Journal of Chemometrics

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When using near‐infrared (NIR) techniques for analysis, model construction and maintenance updates are essential. When model construction is performed in machine learning, the sample set is usually divided into the calibration set and the validation set. The representativeness of the calibration set and the reasonable distribution of the validation set affects the accuracy of the established model. In addition, when maintaining and updating models, selecting the most informative updated sample not only improves the model prediction accuracy but also reduces sample preparation. In this paper, the spectral information entropy (SIE) is proposed to be used as a similarity criterion for dividing the sample set and use this criterion to select updated samples. The Kennard–Stone (KS) and the sample set portioning based on joint x–y distance (SPXY) methods were used for comparison to verify the superiority of the proposed method. The results showed that the model built after dividing the sample set using the SIE method has good prediction effect compared with KS and SPXY method. When predicting soluble solid content (SSC) and hardness, the prediction determination coefficient ( ) was improved by more than 15%, and the root mean square error (RMSE) of prediction was reduced by 50%. In terms of model updating, selecting a small number of updated samples using the SIE method can improve the correlation coefficient ( ) to more than 80%, and updated models' prediction accuracy is higher than that of KS and SPXY method. It is confirmed that the SIE method can make the NIR analysis technique more reliable.

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Feasibility Investigation on Determining Soluble Solids Content of Peaches Using Dielectric Spectra

Cited by 15 publications

References 31 publications

Nondestructive determination of soluble solids content of persimmons by using dielectric spectroscopy

Nondestructive determination of soluble solids content of persimmons by using dielectric spectroscopy

Potential of Near-infrared Spectroscopy to Detect Defects on the Surface of Solid Wood Boards

Sample selection method using near‐infrared spectral information entropy as similarity criterion for constructing and updating peach firmness and soluble solids content prediction models

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