Effect of fruit moving speed on predicting soluble solids content of ‘Cuiguan’ pears (Pomaceae pyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression

Sun, Tong; Lin, Hongjian; Xu, Huirong; Ying, Yibin

doi:10.1016/j.postharvbio.2008.06.003

Cited by 76 publications

(37 citation statements)

References 20 publications

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“…A number of independent research groups are exploring nondestructive visible/near infrared (Vis/NIR) spectroscopy technology for fruit analysis (McGlone and Kawano 1998;McGlone et al 2002McGlone et al , 2003. Additionally, many other newly published papers have also investigated the feasibility of Vis/ NIR and NIR spectroscopy for predicting the SSC and pH of various fruits, such as apple (Bobelyn et al 2010;Bertone et al 2012;Mendoza et al 2012), pear (Sun et al 2009;Paz et al 2009), grape (Cao et al 2010;Parpinello et al 2013), pineapple (Seng Chia et al 2012, kiwifruit (Moghimi et al 2010), etc, showing the capability of spectroscopic technique for the prediction of fruit internal quality characteristics. However, spectroscopic method has a drawback compared with hyperspectral imaging because it acquires the spectral data from a single point or from a small portion of the tested fruit.…”

Section: Introductionmentioning

confidence: 99%

Soluble Solids Content and pH Prediction and Maturity Discrimination of Lychee Fruits Using Visible and Near Infrared Hyperspectral Imaging

Liú

Wang

et al. 2015

Food Anal. Methods

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Hyperspectral imaging (HSI) technique has shown promise as a rapid and nondestructive tool to evaluate various internal quality attributes of fruits and vegetables. The objective of this study was to investigate the nondestructive prediction of soluble solids content (SSC) and pH of lychees and maturity discrimination. Two hyperspectral imaging systems of visible/short-wave near infrared range (600-1000 nm, Spectral Set I) and long-wave near infrared range (1000-2500 nm, Spectral Set II) were employed. Results showed that Spectral Set II (SSC: r p =0.877, RMSEP=0.911°Brix; pH: r p =0.745, RMSEP=0.291) performed better than Spectral Set I (SSC: r p =0.790, RMSEP=1.279°Brix; pH: r p =0.701, RMSEP=0.308) for the internal quality prediction of litchi and maturity discrimination. The partial least square discriminant analysis (PSL-DA) model had a discrimination rate of 90.63 % for Spectral Set I and 96.88 % for Spectral Set II. β-Coefficients of partial least squares regression (PLSR) models were used to choose optimal wavelengths for quality predictions. The performance of optimized PLSR in both spectral sets were comparable to the models developed using the whole spectral range.

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Section: Introductionmentioning

confidence: 99%

Soluble Solids Content and pH Prediction and Maturity Discrimination of Lychee Fruits Using Visible and Near Infrared Hyperspectral Imaging

Liú

Wang

et al. 2015

Food Anal. Methods

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“…Recently, much attention has been paid on online detection of SSC using Vis/NIR spectroscopy. Several studies about online SSC determination using diffuse transmittance mode were reported for fruits such as pear (Sun et al 2009;Xu et al 2012) and watermelon with thick skin (Jie et al 2014), which indicated that diffused transmittance mode was suitable for internal quality determination and was a viable option for high-speed fruit measurement. Therefore, a prototype of diffused transmittance system was realized in our laboratory to provide some reference for the online detection of apple SSC.…”

Section: Introductionmentioning

confidence: 99%

Using Vis/NIR Diffuse Transmittance Spectroscopy and Multivariate Analysis to Predicate Soluble Solids Content of Apple

et al. 2015

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The objectives of this research were to compare the effect of different fruit orientations on the quality of acquired spectra and to provide a suitable calibration model for further online determination of soluble solids content (SSC) of BFujiâ pples using visible and near-infrared (Vis/NIR) diffuse transmittance. The diffuse transmittance spectra between 650 and 910 nm were collected with the designed spectrum measurement system in two fruit orientations: stem-calyx axis horizontal (T1) and stem-calyx axis vertical (T2). Area change rate (ACR) was used to evaluate the stability of spectra collected in two fruit orientations. Results showed that the fruit orientation T1 was better for our designed spectrum measurement system. Then, the performance of partial least squares (PLS) models based on spectral data after the pretreatment of several preprocessing methods was analyzed and compared. Finally, the modified competitive adaptive reweighted sampling (MCARS), successive projection algorithm (SPA), and their combination were investigated to select the effective variables for the determination of SSC. It concluded that the MCARS-SPA-PLS model based on the spectra after preprocessing of Savitzky-Golay (SG) smoothing achieved better results for SSC prediction. The correlation coefficients between measured and predicted SSC were 0.962 and 0.946, and the root mean square errors were 0.510 and 0.527°Brix for calibration and prediction set, respectively. Moreover, the physicochemical properties of 27 variables selected by MCARS-SPA were discussed to obtain a better interpretation of the calibration model. The overall results indicated that the designed diffuse transmittance spectrum measurement system together with the PLS calibration model with 27 effective variables selected by MCARS-SPA method had a potential application for online SSC detection of apple.

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“…Few studies have reported the ability of an online Vis-NIR technology to determine SSC. Sun et al 30 evaluated the use of Vis-NIR in measuring SSC of intact pears online, and better results were acquired with RMSEP of 0.53 Brix. However, no reports were found about online SSC measurements of navel oranges.…”

Section: Comparison Of Predicted Results By Mw-pls Mc-uve-pls and Plmentioning

confidence: 99%

Online quantitative analysis of soluble solids content in navel oranges using visible-near infrared spectroscopy and variable selection methods

Liu

Zhou

Pan

2014

J. Innov. Opt. Health Sci.

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Variable selection is applied widely for visible-near infrared (Vis-NIR) spectroscopy analysis of internal quality in fruits. Di®erent spectral variable selection methods were compared for online quantitative analysis of soluble solids content (SSC) in navel oranges. Moving window partial least squares (MW-PLS), Monte Carlo uninformative variables elimination (MC-UVE) and wavelet transform (WT) combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges. The performances of these methods were compared for modeling the Vis-NIR data sets of navel orange samples. Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation coe±cient ðrÞ of 0.89 and lower root mean square error of prediction (RMSEP) of 0.54 at 5 fruits per second. It concluded that Vis-NIR spectroscopy coupled with WT-MC-UVE may be a fast and e®ective tool for online quantitative analysis of SSC in navel oranges.

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Effect of fruit moving speed on predicting soluble solids content of ‘Cuiguan’ pears (Pomaceae pyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression

Cited by 76 publications

References 20 publications

Soluble Solids Content and pH Prediction and Maturity Discrimination of Lychee Fruits Using Visible and Near Infrared Hyperspectral Imaging

Soluble Solids Content and pH Prediction and Maturity Discrimination of Lychee Fruits Using Visible and Near Infrared Hyperspectral Imaging

Using Vis/NIR Diffuse Transmittance Spectroscopy and Multivariate Analysis to Predicate Soluble Solids Content of Apple

Online quantitative analysis of soluble solids content in navel oranges using visible-near infrared spectroscopy and variable selection methods

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