Application of Least Squares-Support Vector Machine for Measurement of Soluble Solids Content of Rice Vinegars Using Vis/NIR Spectroscopy

2007 ◽  
Author(s):  
Fei Liu ◽  
Yong He ◽  
Li Wang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Support Vector ◽  
Soluble Solids Content ◽  
Solids Content

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.

Predicting Apple Firmness and Soluble Solids Content Based on Hyperspectral Scattering Imaging Using Fourier Series Expansion

2017 ◽  
Vol 60 (4) ◽  
pp. 1053-1062
Author(s):  
Wei Wang ◽  
Min Huang ◽  
Qibing Zhu
Keyword(s):  
Fourier Series ◽  
Least Squares ◽  
Series Expansion ◽  
Fourier Coefficients ◽  
Expansion Method ◽  
Soluble Solids ◽  
Fourier Series Expansion ◽  
Support Vector ◽  
Soluble Solids Content ◽  
Solids Content

Abstract. This article reports on using a Fourier series expansion method to extract features from hyperspectral scattering profiles for apple fruit firmness and soluble solids content (SSC) prediction. Hyperspectral scattering images of ‘Golden Delicious’ (GD), ‘Jonagold’ (JG), and ‘Delicious’ (RD) apples, harvested in 2009 and 2010, were acquired using an online hyperspectral imaging system over the wavelength region of 500 to 1000 nm. The moment method and Fourier series expansion method were used to analyze the scattering profiles of apples. The zeroth-first order moment (Z-FOM) spectra and Fourier coefficients were extracted from each apple, which were then used for developing fruit firmness and SSC prediction models using partial least squares (PLS) and least squares support vector machine (LSSVM). The PLS models based on the Fourier coefficients improved the standard errors of prediction (SEP) by 4.8% to 19.9% for firmness and by 2.4% to 13.5% for SSC, compared with the PLS models using the Z-FOM spectra. The LSSVM models for the prediction set of Fourier coefficients achieved better SEP results, with improvements of 4.4% to 11.3% for firmness and 2.8% to 16.5% for SSC over the LSSVM models for the Z-FOM spectra data and 3.7% to 12.6% for firmness and 5.4% to 8.6% for SSC over the PLS models for the Fourier coefficients. Experiments showed that Fourier series expansion provides a simple, fast, and effective means for improving Keywords: Apples, Firmness, Fourier series expansion, Hyperspectral scattering imaging, Least squares support vector machine, Partial least squares, Soluble solids content.

scholarly journals A Comparative Study of PLSR and SVM-R with Various Preprocessing Techniques for the Quantitative Determination of Soluble Solids Content of Hardy Kiwi Fruit by a Portable Vis/NIR Spectrometer

Foods ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1078
Author(s):  
Shagor Sarkar ◽  
Jayanta Kumar Basak ◽  
Byeong Eun Moon ◽  
Hyeon Tae Kim
Keyword(s):  
Support Vector Machine ◽  
Correlation Coefficient ◽  
Prediction Models ◽  
Partial Least Square ◽  
Least Square ◽  
Soluble Solids ◽  
Support Vector ◽  
Soluble Solids Content ◽  
Support Vector Machine Regression ◽  
Solids Content

Linear partial least square and non-linear support vector machine regression analysis with various preprocessing techniques and their combinations were used to determine the soluble solids content of hardy kiwi fruits by a handheld, portable near-infrared spectroscopy. Fruits of four species, namely Autumn sense (A), Chungsan (C), Daesung (D), and Green ball (Gb) were collected from five different areas of Gwangyang (G), Muju (M), Suwon (S), Wonju (Q), and Yeongwol (Y) in South Korea. The dataset for calibration and prediction was prepared based on each area, species, and in combination. Half of the dataset of each area, species, and combined dataset was used as calibrated data and the rest was used for model validation. The best prediction correlation coefficient ranges between 0.67 and 0.75, 0.61 and 0.77, and 0.68 for the area, species, combined dataset, respectively using partial least square regression (PLSR) method with different preprocessing techniques. On the other hand, the best correlation coefficient of predictions using the support vector machine regression (SVM-R) algorithm was 0.68 and 0.80, 0.62 and 0.79, and 0.74 for the area, species, and combined dataset, respectively. In most cases, the SVM-R algorithm produced better results with Autoscale preprocessing except G area and species Gb, whereas the PLS algorithm shows a significant difference in calibration and prediction models for different preprocessing techniques. Therefore, the SVM-R method was superior to the PLSR method in predicting soluble solids content of hardy kiwi fruits and non-linear models may be a better alternative to monitor soluble solids content of fruits. The finding of this research can be used as a reference for the prediction of hardy kiwi fruits soluble solids content as well as harvesting time with better prediction models.

Prediction of Soluble Solids Content During Storage of Apples with Different Maturity Based on VIS / NIR Spectroscopy

2020 ◽  
Author(s):  
Bo Zhang ◽  
Mengsheng Zhang ◽  
Maosheng Shen ◽  
Hao Li ◽  
Haihui Zhang ◽  
...  
Keyword(s):  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content

Non-destructive measurement of firmness and soluble solids content in bell pepper using NIR spectroscopy

2009 ◽  
Vol 94 (3-4) ◽  
pp. 267-273 ◽  
Author(s):  
Pathompong Penchaiya ◽  
Els Bobelyn ◽  
Bert E. Verlinden ◽  
Bart M. Nicolaï ◽  
Wouter Saeys
Keyword(s):  
Nir Spectroscopy ◽  
Bell Pepper ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content ◽  
Destructive Measurement ◽  
Non Destructive

scholarly journals A New Plant Indicator (Artemisia lavandulaefolia DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Lu Xu ◽  
Qiong Shi ◽  
Bang-Cheng Tang ◽  
Shunping Xie
Keyword(s):  
Support Vector Machine ◽  
Fourier Transform ◽  
Least Squares ◽  
Near Infrared ◽  
Mercury Level ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Classification Model ◽  
Support Vector ◽  
Reference Analysis

A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0–3 kilometers, 7.52–88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0–0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.

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