Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system

Wu, Di; Shi, Hui; Wang, Songjing; He, Yong; Bao, Yukun; Liu, Kangsheng

doi:10.1016/j.aca.2012.03.038

Cited by 168 publications

(69 citation statements)

References 31 publications

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“…For example, compared between PLS and LSSVM models based on UVE, the values of R c , RMSEC, R p , RMSEP, and RPD of UVE-LSSVM and UVE-PLS were 0.993 vs. 0.988, 0.285 ºBrix vs. 0.323 ºBrix, 0.969 vs. 0.968, 0.624 ºBrix vs. 0.635 ºBrix, and 3.204 vs. 3.147, respectively. The better performance of LSSVM than that of PLS was also found in other studies, such as in determining organic acids of plum vinegar, [38] in predicting the moisture content of prawns, [39] and in determining the SSC of kiwifruits. [40] Among 8 developed SSC prediction models, the CARS-LSSVM had the best calibration performance with R c of 0.997 and RMSEC of 0.161°Brix, and the best prediction performance with R p of 0.970 and RMSEP of 0.494°Brix.…”

Section: Comparisonmentioning

confidence: 60%

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: Comparisonmentioning

confidence: 60%

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

Liu

2017

International Journal of Food Properties

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“…The better determination performance of LSSVM than that of PLS had also been noticed in other researches, such as in predicting SSC, pH, and firmness of pears (Li et al 2013), in determining organic acids of plum vinegar (Liu and He 2009), in quantifying common adulterants in powdered milk (Borin et al 2006), in determining the moisture content of prawns (Wu et al 2012), and in discriminating the varieties of fruit vinegars (Liu et al 2008).…”

Section: Discussionmentioning

confidence: 89%

Nondestructive Measurement of Soluble Solids Content of Kiwifruits Using Near-Infrared Hyperspectral Imaging

Zhao

Dong

2015

Food Anal. Methods

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This study aims to determine the soluble solids content (SSC) of intact kiwifruits of varieties BXixuan^and BHuayou^by using near-infrared (NIR) hyperspectral imaging and to investigate which model, developed for a single variety or for two varieties together, had better SSC determination performance. The NIR hyperspectral reflectance images of 200 kiwifruits (100 kiwifruits for each variety) were obtained over the wavelength of 865.11-1,711.71 nm. Mean spectra were extracted from the regions of interest in hyperspectral images of each kiwifruit. The samples were divided into calibration set and prediction set based on the joint x-y distance sample set partitioning method. There were 67 samples in calibration set and 33 samples in prediction set when a single variety was used to establish SSC calibration model, and there were 134 samples (67 BXixuan^and 67 BHuayou^) in calibration set and 66 samples (33 BXixuanâ nd 33 BHuayou^) in prediction set when the two varieties were used together. Successive projections algorithm (SPA) was applied to extract the effective wavelengths (EWs) from full spectra (FS). Nine EWs were selected when a single variety (BXixuan^or BHuayou^) was used in modeling, and 19 EWs were selected when the two varieties were used together. SSC calibration models were developed based on the partial least squares (PLS) regression and least square support vector machine (LSSVM) modeling methods using the full spectra and extracted EWs as inputs, respectively. The results showed that both calibration and prediction performances of LSSVM models were better than those of PLS. The best SSC determination model for BXixuan,^BHuayou,^and two varieties together were SPA-LSSVM, FS-LSSVM, and FS-LSSVM with the correlation coefficient of prediction set of 0.766, 0.971, and 0.911, and the root-mean-square error of prediction set of 0.968, 0.589, and 1.137, respectively. The study demonstrates the feasibility of using NIR hyperspectral reflectance imaging technique as a noninvasive method for predicting SSC of kiwifruits and indicates that developing a model for a specific variety is helpful to decreasing prediction error and to improving calculation speed.

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“…This method is a novel variable selection algorithm designed to solve the co-linearity problems by selecting variables with minimal redundancy. Nowadays SPA method has found its way with different modelling (Wu et al, 2012). Basically, SPA comprises three main stages.…”

Section: Optimum Wavelengths Selectionmentioning

confidence: 99%

“…In this study, SPA was used to select the optimum wavelengths. The technique has been used to solve the co-linearity problems by selecting variables with minimal redundancy with different modelling techniques such as SPA-PLS, SPA-least square support vector machine (LS-SVM) SPAartificial neural network (ANN), SPA-linear discriminate analysis (LDA) ( Wu et al, 2012). By using SPA method, seven wavelengths (930, 1067, 1396, 1460, 1658, 1668, and 1702 nm) were selected as optimum wavelengths for detecting adulteration in minced lamb as shown in Fig.…”

Section: Optimum Wavelength Selectionmentioning

confidence: 99%

Hyperspectral imaging technique for offal quantification in minced meat

Kamruzzaman

Haque²,

Ali

2014

J Bangladesh Agric Univ

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Spectral imaging is a new technique that combines conventional imaging and spectroscopy in a single system to obtain both spatial and spectral information simultaneously from an object. In this study, potential of hyperspectral imaging in the spectral range of 910-1700 nm was investigated for detecting adulteration in minced lamb meat. Spectral data were extracted to develop a partial least squares regression (PLSR) model to predict the level of adulteration in minced lamb. Good prediction model was obtained using the whole spectral range with a coefficient of determination (R 2 CV) of 0.97 and root-mean-square errors estimated by cross validation (RMSECV) of 1.80%. Successive projection algorithm (SPA) was employed for optimal waveband selection. The PLSR model using only 7 optimum wavelengths (930, 1067, 1396, 1460, 1658, 1668, and 1702 nm) resulted in a coefficient of determination (R 2 CV) of 0.97 and RMSECV of 1.84%. The study demonstrated the ability of the hyperspectral imaging as a rapid and alternative to the time-consuming and conventional methods to detect adulteration in minced lamb meat.

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Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system

Cited by 168 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

Nondestructive Measurement of Soluble Solids Content of Kiwifruits Using Near-Infrared Hyperspectral Imaging

Hyperspectral imaging technique for offal quantification in minced meat

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