Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN.

By Yande Liu, Xudong Sun, Aiguo Ouyang. 2010

218 citations recorded by the source corpus.

Abstract A relationship was established between the soluble solid content (SSC) of navel orange fruit determined by destructive measurement and visible–near infrared diffuse reflectance spectra in the wavelength range of 350–1800 nm. Multiplicative scatter correction (MSC) and standard normal variate correction (SNV) were applied to the spectra, partial least squares regression (PLSR) and back propagation neural network (BPNN) based on principal component analysis (PCA) were used to develop the models for predicting the SSC of intact navel orange fruit. Thirty-eight unknown samples were used to evaluate the performance of these models. The principal component analysis-back propagation (PCA-BPNN) method with MSC spectral pretreatment obtain the best predictive results, resulting in correlation coefficient, root mean square error of prediction (RMSEP), average difference between predicted and measured values (Bias) of 0.90, 0.68 °Brix and 0.16 °Brix, respectively. Experimental results indicate that PCA-BPNN is a suitable tool to model the non-linear complex system, with additional advantages over PLSR, and the vis/NIR spectrometric technique can be used for measuring the SSC of intact navel orange fruit, nondestructively.

Subfields: Analytical Chemistry, Biomedical Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/b8d87ca723a6fcc732001a235340407c0e67ee5c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Hongbin Pu
2. Da‐Wen Sun
3. Lu Wang
4. Dan Liu
5. Umezuruike Linus Opara
6. Z. Xiong
7. Stuart O. Nelson
8. Liang Shang

In Biomedical Engineering

1. Hongbin Pu
2. Da‐Wen Sun
3. Lu Wang
4. Dan Liu
5. Z. Xiong
6. Umezuruike Linus Opara
7. Wouter Saeys
8. Jun‐Hu Cheng

In Industrial and Manufacturing Engineering

1. Da‐Wen Sun
2. Dan Liu
3. Wenchuan Guo
4. Jun‐Hu Cheng

Citations in the corpus

• Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice (2015, background): “BPNN is a nonlinear neural network, which simulates the human brain thinking to solve complex problems (Dubey et al. 2006; Liu et al. 2010).”
• Nondestructive Detection of Soluble Solids Content of Apples from Dielectric Spectra with ANN and Chemometric Methods (2015, methodology): “…the measured one of the j-th sample in the prediction set, respectively; ymc and ymp are the mean values of the samples in the calibration set and the prediction set, respectively; nc and np are the numbers of samples in the calibration set and the prediction set, respectively (Liu et al. 2010).”
• NIR Spectroscopy Applications for Internal and External Quality Analysis of Citrus Fruit—A Review (2012, methodology): “Most of the NIRS research on citrus fruit have focused on assessing internal quality attributes (Liu et al. 2010a, b; Gómez et al. 2006; Fraser et al. 2003).”
• Fruit Quality Evaluation Using Spectroscopy Technology: A Review (2015, methodology): “SSC C-H 910 [69] O-H 960, 1450 [14] C-H and O-H 1210 O-H 975 [70] O-H 960, 1180, 1450, 2000 [27] C-H 963 Combination bands of C-H and O-H 2000–2500 O-H and C-H 950–1075 [71]”
• Nondestructive Measurement of Soluble Solids Content of Kiwifruits Using Near-Infrared Hyperspectral Imaging (2015, unknown): “2006), and navel orange (Liu et al. 2010).”