Prediction of Soluble Solids Content and Firmness of Pears Using Hyperspectral Reflectance Imaging

Fan, Shuxiang; Huang, Wenqian; Guo, Zhiming; Zhang, Baohua; Zhao, Chunjiang

doi:10.1007/s12161-014-0079-1

Cited by 103 publications

(56 citation statements)

References 40 publications

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“…It is very important to preprocess spectra before model building. To this end, various models including standard normal variate transformation (SNV), multiplicative scatter correction (MSC), mean centering (MC), and baseline correction have been applied (Chen et al 2012a, b;Fan et al 2015). Indeed, SNV is a mathematical transformation technique of the log (1/R) spectra which is applied to eliminate slope difference and to correct for scatter effects.…”

Section: Spectral Preprocessing and Partial Least Squares Regressionmentioning

confidence: 99%

Rapid Determination of Antioxidant Compounds and Antioxidant Activity of Sudanese Karkade (Hibiscus sabdariffa L.) Using Near Infrared Spectroscopy

et al. 2015

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The extraction conditions applied in destructive analysis of Hibiscus sabdariffa L. (Hs) matrices before the measurement of antioxidant compounds may reduce their activities during isolation and purification due to decomposition. Hence, near infrared spectroscopic system was applied to determine these compounds. The calibration models developed by using partial least squares regression (PLSR) with 48 Hs samples and 24 unknown samples were used to confirm the robustness of the developed model. The results of calibration models effectively allowed the determination of the concentrations of total phenol content (TPC), total flavonoid content (TFC), total antioxidant content (TAC), ferric reducing antioxidant power (FRAP), and 1,1-diphenyl-2-picrylhydrazyl (DPPH) with high correlation coefficients (0.85-0.91) and small standard error of cross-validation (RMSECV) ranged from 0.08 to 0.33. The prediction capacity (RPD) for TPC, TFC, TAC, FRAP, and DPPH ranged from 3.30 to 8.25, thus demonstrating that the near infrared spectroscopic (NIRS) equations developed were applicable to unknown samples. This study demonstrates that NIRS can be considered as a fast tool for the nondestructive determination of antioxidant compounds (phenolics and flavonoids) and antioxidant activity of the Hs.

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Section: Spectral Preprocessing and Partial Least Squares Regressionmentioning

confidence: 99%

Rapid Determination of Antioxidant Compounds and Antioxidant Activity of Sudanese Karkade (Hibiscus sabdariffa L.) Using Near Infrared Spectroscopy

et al. 2015

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“…Furthermore, optimal wavelengths were selected by competitive adaptive reweighted sampling (CARS) method, and the spectra at those wavelengths were used to build a simplified SVM classification model. CARS method is used to select an optimal combination of wavelengths with large absolute coefficients in the full spectrum coupled with PLS regression [27]. The absolute values of regression coefficients of PLS model are used as an index when evaluating the importance of each wavelength.…”

Section: Model Development Methodsmentioning

confidence: 99%

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

Chu

Wang

et al. 2018

Applied Sciences

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Visible/near-infrared (Vis/NIR) hyperspectral imaging (400-1000 nm) was applied to identify the growth process of Aspergillus flavus and Aspergillus parasiticus. The hyperspectral images of the two fungi that were growing on rose bengal medium were recorded daily for 6 days. A band ratio using two bands at 446 nm and 460 nm separated A. flavus and A. parasiticus on day 1 from other days. Image at band of 520 nm classified A. parasiticus on day 6. Principle component analysis (PCA) was performed on the cleaned hyperspectral images. The score plot of the second to sixth principal components (PC 2 to PC 6 ) gave a rough clustering of fungi in the same incubation time. However, in the plot, A. flavus on day 3 and day 4 and A. parasiticus on day 2 and day 3 overlapped. The average spectra of each fungus in each growth day were extracted, then PCA and support vector machine (SVM) classifier were applied to the full spectral range. SVM models built by PC 2 to PC 6 could identify fungal growth days with accuracies of 92.59% and 100% for A. flavus and A. parasiticus individually. In order to simplify the prediction models, competitive adaptive reweighted sampling (CARS) was employed to choose optimal wavelengths. As a result, nine (402, 442, 487, 502, 524, 553, 646, 671, 760 nm) and seven (461, 538, 542, 742, 753, 756, 919 nm) wavelengths were selected for A. flavus and A. parasiticus, respectively. New optimal wavelengths SVM models were built, and the identification accuracies were 83.33% and 98.15% for A. flavus and A. parasiticus, respectively. Finally, the visualized prediction images for A. flavus and A. parasiticus in different growth days were made by applying the optimal wavelength's SVM models on every pixel of the hyperspectral image.

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“…[22] Up to now, CARS has been successfully used in characteristic variables selection and has obtained excellent determination performance. [23][24][25] …”

Section: Uninformative Variables Eliminationmentioning

confidence: 99%

“…The better variable selection methods performance of CARS than that of UVE and SPA had also been noticed in other studies. [22,23,37] When comparing the SSC determination performance of PLS and LSSVM models, the results showed that the LSSVM models offered better performance at the same characteristic variables selection method. 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.…”

Section: Comparisonmentioning

confidence: 99%

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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Prediction of Soluble Solids Content and Firmness of Pears Using Hyperspectral Reflectance Imaging

Cited by 103 publications

References 40 publications

Rapid Determination of Antioxidant Compounds and Antioxidant Activity of Sudanese Karkade (Hibiscus sabdariffa L.) Using Near Infrared Spectroscopy

Rapid Determination of Antioxidant Compounds and Antioxidant Activity of Sudanese Karkade (Hibiscus sabdariffa L.) Using Near Infrared Spectroscopy

Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

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

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