Spectroscopic determination of soluble solids content of ‘Qinmei’ kiwifruit using partial least squares

2012 ◽  
Vol 11 (10) ◽  
Author(s):  
Xiangwei Chen
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Soluble Solids ◽  
Spectroscopic Determination ◽  
Soluble Solids Content ◽  
Solids Content

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.

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.

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.

Investigating aquaphotomics for temperature-independent prediction of soluble solids content of pure apple juice

2020 ◽  
Vol 28 (2) ◽  
pp. 103-112 ◽  
Author(s):  
Harpreet Kaur ◽  
Rainer Künnemeyer ◽  
Andrew McGlone
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Apple Juice ◽  
Scatter Correction ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Least Squares Regression ◽  
Multiplicative Scatter Correction ◽  
Solids Content

The methods of aquaphotomics were explored as an aid to improve near infrared spectroscopic predictive modelling of the soluble solids content of pure apple juice at different temperatures. The study focussed on the first overtone region of the O–H stretching vibration of water (1300–1600 nm). A transmission-based FT-NIR (Fourier transform near infrared) spectrometer was used to acquire 103 spectra of freshly expressed juice samples from individual ‘Braeburn’ apples over the wavelength range of 870–1800 nm with a 1 mm cuvette at three temperatures, 20, 25 and 30°C. The aquagram of the first overtone water region showed a trend of increasing bound water absorption with rising soluble solids content, from 7.3 to 13.7°Brix, and increasing free water absorption with rising temperature from 20 to 30°C. Predictive models for apple juice soluble solids content at 25°C were developed using partial least squares regression with spectral pre-processing by standard normal variate (SNV) followed by second derivative transformation (SNV + 2D) or no pre-processing on absorbance spectra at all. The best result, with lowest standard error of prediction of 0.38°Brix, was obtained using the first overtone water region with partial least squares regression on the SNV + 2D spectra. The method of extended multiplicative scatter correction was used, as an additional pre-processing step, to improve apple juice soluble solids content prediction at different temperatures. The interference component selected for the extended multiplicative scatter correction method was the first principal component loading measured using pure water samples taken at the same three temperatures (20, 25 and 30°C). Such extended multiplicative scatter correction pre-processing greatly reduced the soluble solids content prediction bias, when applying the partial least squares regression model developed at 20°C to samples measured at 25 and 30°C, from 0.23 to 0.08 and 0.36 to 0.13°Brix, respectively. Model precision (in terms of standard error of prediction) was also slightly improved by 0.02°Brix in each case, from 0.40 to 0.38 and 0.46 to 0.44°Brix at 25 and 30°C respectively.

scholarly journals Prediksi Kadar Total Padatan Terlarut (TPT) dan Vitamin C Buah Mangga Arumanis (Mangifera indica L) Menggunakan Near Infrared Spectroscopy (NIRS) dengan Metode Partial Least Square (PLS)

2019 ◽  
Vol 4 (2) ◽  
pp. 349-358
Author(s):  
Salman Rivaldi ◽  
Yuswar Yunus ◽  
Agus Arip Munawar
Keyword(s):  
Vitamin C ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mangifera Indica ◽  
Correction Method ◽  
Partial Least Square ◽  
Soluble Solids ◽  
Baseline Correction ◽  
Soluble Solids Content ◽  
Solids Content

Abstrak. Total padatan terlarut (TPT) dan vitamin C merupakan suatu komponen penting dalam mangga. Total padatan terlarut (TPT) merupakan total unsur atau elemen mineral yang terlarut didalam suatu larutan. TPT disebut juga dengan kadar gula total, karena  kualitas rasa  manis dari buah diukur dengan pengukuran kadar gula. Vitamin C merupakan vitamin yang larut dalam air dan esensial untuk biosintesis kolagen. Pengukuran TPT dapat dilakukan menggunakan alat refraktometer, sedangkan vitamin C diukur menggunakan metode titrasi, serta dengan penggunaan gelombang elektromagnetik seperti NIRS. Penelitian ini bertujuan menguji dan mengevaluasi teknologi NIRS sebagai metode cepat dan tepat dalam memprediksi kandungan TPT dan vitamin C pada mangga dengan metode Partial Least Squares (PLS) serta menentukan metode koreksi spektrum yang terbaik dan akurat untuk memprediksi TPT dan vitamin C pada mangga dengan menggunakan pre-treatment Baseline Correction, dan Multiplicative Scatter Correction (MSC). Penelitian ini menggunakan buah mangga jenis Arumanis, yang berjumlah 30 sampel. Prediksi TPT dan vitamin C dengan NIRS menggunakan alat FT-IR IPTEK T-1516. Pengolahan data menggunakan Unscramble software® X version 10.5. Hasil penelitian menunjukkan prediksi TPT pada mangga dengan metode Partial Least Squares (PLS) menghasilkan sufficient performance dengan nilai RPD yang didapat yaitu 1,29 dan metode koreksi terbaik pada pendugaan TPT ialah Baseline Correction dengan nilai RPD 1,53, r sebesar 0,4783 , R2 sebesar 0,5600 dan RMSEC sebesar 2,9048. Sedangkan hasil prediksi vitamin C pada mangga menghasilkan good model performance dengan nilai RPD yang didapat yaitu 2,18 dan metode koreksi terbaik pada pendugaan vitamin C ialah MSC dengan nilai RPD 2,36, r sebesar 0,9026, R2 sebesar 0,8147 dan RMSEC sebesar 4,4067.Prediction of Soluble Solids Content (SSC) and Vitamin C on Mangoes (Mangifera Indica L) Using Near Infrared Spectroscopy (NIRS) with Partial Least Square (PLS) MethodAbstract. Soluble solids content (SSC) and vitamin C are important components in mangoes. Soluble solids content (SSC) is the total element or mineral element dissolved in a solution. SSC is also called total sugar content, because the quality of the sweet taste of the fruit is measured by measuring the total sugar. Vitamin C is a water-soluble vitamin and essential for collagen biosynthesis. Measurement of SSC can be done using a refractometer, while vitamin C is measured using the titration method, and by the use of electromagnetic waves such as NIRS. This study aims to test and evaluate NIRS technology as a fast and appropriate method in predicting the content of SSC and vitamin C in mangoes using the Partial Least Squares (PLS) method and determining the best and accurate spectrum correction method to predict SSC and vitamin C in mango by using pre-treatment Baseline Correction, and Multiplicative Scatter Correction (MSC). This study used Arumanis mangoes, which amounted to 30 samples. Prediction of TPT and vitamin C with NIRS using FT-IR tool of Science and Technology T-1516. Processing data using the Unscramble software® X version 10.5. The results showed that the prediction of SSC on mangoes using the Partial Least Squares (PLS) method resulted in sufficient performance with the obtained RPD value is 1,29 and the best correction method in estimating SSC is Baseline Correction with a value of RPD is 1,53, r is 0,4783, R2 is 0,5600 and RMSEC is 2,9048. While the prediction results of vitamin C on mangoes obtain a good performance model with the value of RPD obtained is 2,18 and the best correction method for estimating vitamin C is MSC with a value of RPD is 2,36, r is 0,9026, R2 is 0,8147 and RMSEC is 4,4067.

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.

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