NON-DESTRUCTIVE DETERMINATION OF SOLUBLE SOLIDS CONTENT IN STRAWBERRIES USING NEAR INFRARED (NIR) SPECTROSCOPY WITH FIBER OPTICS IN INTERACTANCE MODES: WHAT IS NEEDED FOR THE INSTRUMENT?

2005 ◽  
pp. 271-276 ◽  
Author(s):  
H. Ito ◽  
N. Fukino-Ito ◽  
H. Horie
Keyword(s):  
Fiber Optics ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content ◽  
Non Destructive

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

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.

Non-Destructive Measurement of Soluble Solids Content and Vitamin C in Gannan Navel Oranges by Vis-NIR Spectroscopy

2011 ◽  
Vol 9 (3) ◽  
pp. 1133-1139 ◽  
Author(s):  
Yande Liu ◽  
Xudong Sun ◽  
Xiaoling Dong ◽  
Aiguo Ouyang ◽  
Rongjie Gao ◽  
...  
Keyword(s):  
Vitamin C ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content ◽  
Destructive Measurement ◽  
Non Destructive

scholarly journals Determination of soluble solids content in Prunus avium by Vis/NIR equipment using linear and non-linear regression methods

2020 ◽  
Vol 17 (4) ◽  
pp. e0207
Author(s):  
Victoria Lafuente ◽  
Luis J. Herrera ◽  
Jesús Val ◽  
Razvan Ghinea ◽  
Angel I. Negueruela
Keyword(s):  
Prunus Avium ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Regression Methods ◽  
Variable Selection Method ◽  
Different Seasons ◽  
Solids Content ◽  
Calibration Equations

Aim of study: Developing models to determine soluble solids content (SSC) in cherry trees by means of Vis/NIR spectroscopy.Area of study: The Spanish Autonomous Community of Aragón (Spain).Material and methods: Vis/NIR spectroscopy was applied to Prunus avium fruit ‘Chelan’ (n=360) to predict total SSC using a range 400-2420 nm. Linear (PLS) and nonlinear (LSSVM) regression methods were applied to establish prediction models.Main results: The two regression methods applied obtained similar results (Rcv2=0.97 and Rcv2=0.98 respectively). The range 700-1060 nm attained better results to predict SSC in different seasons. Forty variables selected according to the variable selection method achieved Rcv2 value, 0.97 similar than full range.Research highlights: The development of this methodology is of great interest to the fruit sector in the area, facilitating the harvest for future seasons. Further work is needed on the development of the NIRS methodology and on new calibration equations for other varieties of cherry and other species.

scholarly journals Investigation of Parameters That Affect the Acquired Near Infrared Diffuse Reflected Signals in Non-Destructive Soluble Solids Content Prediction

2020 ◽  
Vol 24 (6) ◽  
pp. 79-90
Author(s):  
Kim Seng Chia ◽  
Fan Wei Hong
Keyword(s):  
Predictive Model ◽  
Near Infrared ◽  
Diffuse Reflection ◽  
Model Performance ◽  
Middle Part ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Geometrical Effects ◽  
Solids Content ◽  
Non Destructive

Near infrared spectroscopy is a susceptible technique which can be affected by various factors including the surface of samples. According to the Lambertian reflection, the uneven and matte surface of fruits will provide Lambertian light or diffuse reflectance where the light enters the sample tissues and that uniformly reflects out in all orientations. Bunch of researches were carried out using near infrared diffuse reflection mode in non-destructive soluble solids content (SSC) prediction whereas fewer of them studying about the geometrical effects of uneven surface of samples. Thus, this study aims to investigate the parameters that affect the near infrared diffuse reflection signals in non-destructive SSC prediction using intact pineapples. The relationship among the reflectance intensity, measurement positions, and the SSC value was studied. Next, three independent artificial neural networks were separately trained to investigate the geometrical effects on three different measurement positions. Results show that the concave surface of top and bottom parts of pineapples would affect the reflectance of light and consequently deteriorate the predictive model performance. The predictive model of middle part of pineapples achieved the best performance, i.e. root mean square error of prediction (RMSEP) and correlation coefficient of prediction (Rp) of 1.2104 °Brix and 0.7301 respectively.

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