Using Genetic Algorithm Interval Partial Least Squares Selection of the Optimal near Infrared Wavelength Regions for Determination of the Soluble Solids Content of “Fuji” Apple

2007 ◽  
Vol 15 (3) ◽  
pp. 153-159 ◽  
Author(s):  
Zou Xiaobo ◽  
Li Yanxiao ◽  
Zhao Jiewen
Keyword(s):  
Genetic Algorithm ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content ◽  
Selection Of

A near infrared (NIR) spectroscopy acquisition device was developed in this study using an apple as the test sample. With this device, the apple was rolled while collecting the NIR spectra. The feasibility of using efficient selection of wavelength regions in Fourier transform NIR for a rapid and conclusive determination of the inner qualities of fruit such as soluble solids content (SSC) of apples was investigated. Graphically-oriented local multivariate calibration modelling procedures called genetic algorithm interval partial least-squares (GA-iPLS) were applied to select efficient spectral regions that provide the lowest prediction error, in comparison to the full-spectrum model. The optimal SSC predictions were obtained from a seven-factor model using five intervals among 40 intervals selected by GA-iPLS. In the determination, a root mean square error of prediction of 0.42 °Brix for SSC of apples was obtained. The result demonstrated that the new method is a very useful and effective method for developing high precision PLS models based on optimal wavelength regions.

Quantitative detection of soluble solids content, pH, and total phenol in Cabernet Sauvignon grapes based on near infrared spectroscopy

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yijia Luo ◽  
Juan Dong ◽  
Xuewei Shi ◽  
Wenxia Wang ◽  
Zhuoman Li ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Soluble Solids ◽  
Cabernet Sauvignon ◽  
Soluble Solids Content ◽  
Solids Content

Abstract Determination of Cabernet Sauvignon grapes quality plays an important role in commercial processing. In this research, a rapid approach based on near infrared spectroscopy was proposed to the determination of soluble solids content (SSC), pH, and total phenol content (TPC) in entire bunches of Cabernet Sauvignon grapes. Standardized normal variate (SNV) and competitive adaptive weighted sampling (CARS), genetic algorithm (GA), and synergy interval partial least squares (si-PLS) were used to optimize the spectral data. With optimal combination input, the prediction accuracy of partial least squares regression (PLSR) and support vector regression (SVR) models was compared. The results showed that these models based on variable optimization method could predict well the SSC, pH, and TPC of Cabernet Sauvignon grapes. The correlation coefficient of prediction for SSC, pH, and TPC had reached more than 0.85. This work provides an alternative to analyze the chemical parameters in whole bunch of Cabernet Sauvignon grape.

Comparison of Multivariate Calibrations for the Determination of Soluble Solids Content of Tea Beverage Using UV-VIS-NIR Spectroscopy

2012 ◽  
Vol 236-237 ◽  
pp. 83-88 ◽  
Author(s):  
Wei Qiang Luo ◽  
Hai Qing Yang ◽  
Wei Cheng Dai
Keyword(s):  
Least Squares ◽  
Multivariate Calibration ◽  
Fiber Type ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Soluble Solids Content ◽  
Solids Content

Ultra-violet, visible and near infrared (UV-VIS-NIR) spectroscopy combined with chemometrics was investigated for fast determination of soluble solids content (SSC) of tea beverage. In this study, a total of 120 tea samples with SSC range of 4.0-9.5 ºBrix were tested. Samples were randomly divided for calibration (n=90) and independent validation (n=30). Spectra were collected by a mobile fiber-type UV-VIS-NIR spectrophotometer in transmission mode with recorded wavelength range of 203.64-1128.05 nm. Various calibration approaches, i.e., principal components analysis (PCA), partial least squares (PLS) regression, least squares support vector machine (LSSVM) and back propagation artificial neural network (BPANN), were investigated. The combinations of PCA-BPANN, PCA-LSSVM, PLS-BPANN and PLS-LSSVM were also investigated to build calibration models. Validation results indicated that all these investigated models achieved high prediction accuracy. Especially, PLS-LSSVM achieved best performance with mean coefficient of determination (R2) of 0.99, root-mean-square error of prediction (RMSEP) of 0.12 and residual prediction deviation (RPD) of 15.16. This experiment suggests that it is feasible to measure SSC of tea beverage using UV-VIS-NIR spectroscopy coupled with appropriate multivariate calibration, which may allow using the proposed method for off-line and on-line quality supervision in the production of soft drink.

scholarly journals Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Lin Zhang ◽  
Baohua Zhang ◽  
Jun Zhou ◽  
Baoxing Gu ◽  
Guangzhao Tian
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Near Infrared ◽  
Scatter Correction ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Biological Variability ◽  
Wavelength Selection ◽  
Calibration Models ◽  
Solids Content

Uninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and mean normalization, as well as their combinations were conducted on raw Fourier transform near-infrared spectra to eliminate the uninformative biological variability. Subsequently, robust calibration models were established by using partial least squares regression analysis and wavelength selection algorithms. Results indicated that the partial least squares calibration models with characteristic variables selected by CARS method coupled with preprocessing of Savitzky-Golay smoothing and multiplicative scatter correction had a considerable potential for predicting apple soluble solids content regardless of the biological variability.

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