A multi-region combined model for non-destructive prediction of soluble solids content in apple, based on brightness grade segmentation of hyperspectral imaging

2019 ◽  
Vol 183 ◽  
pp. 110-120 ◽  
Author(s):  
Xi Tian ◽  
Jiangbo Li ◽  
Qingyan Wang ◽  
Shuxiang Fan ◽  
Wenqian Huang ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Combined Model ◽  
Solids Content ◽  
Non Destructive

scholarly journals Modelling Soluble Solids Content Accumulation in ‘Braeburn’ Apples

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 302
Author(s):  
Konni Biegert ◽  
Daniel Stöckeler ◽  
Roy J. McCormick ◽  
Peter Braun
Keyword(s):  
Explanatory Power ◽  
Partial Least Square ◽  
Optical Measurements ◽  
Soluble Solids ◽  
Sensor Data ◽  
Soluble Solids Content ◽  
Mixed Effect ◽  
Laboratory Errors ◽  
Solids Content ◽  
Non Destructive

Optical sensor data can be used to determine changes in anthocyanins, chlorophyll and soluble solids content (SSC) in apple production. In this study, visible and near-infrared spectra (729 to 975 nm) were transformed to SSC values by advanced multivariate calibration models i.e., partial least square regression (PLSR) in order to test the substitution of destructive chemical analyses through non-destructive optical measurements. Spectral field scans were carried out from 2016 to 2018 on marked ‘Braeburn’ apples in Southwest Germany. The study combines an in-depth statistical analyses of longitudinal SSC values with horticultural knowledge to set guidelines for further applied use of SSC predictions in the orchard to gain insights into apple carbohydrate physiology. The PLSR models were investigated with respect to sample size, seasonal variation, laboratory errors and the explanatory power of PLSR models when applied to independent samples. As a result of Monte Carlo simulations, PLSR modelled SSC only depended to a minor extent on the absolute number and accuracy of the wet chemistry laboratory calibration measurements. The comparison between non-destructive SSC determinations in the orchard with standard destructive lab testing at harvest on an independent sample showed mean differences of 0.5% SSC over all study years. SSC modelling with longitudinal linear mixed-effect models linked high crop loads to lower SSC values at harvest and higher SSC values for fruit from the top part of a tree.

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

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

Prediction of Soluble Solids Content of Jujube Fruit Using Hyperspectral Reflectance Imaging

2013 ◽  
Vol 706-708 ◽  
pp. 201-204
Author(s):  
Jian Guo He ◽  
Yang Luo ◽  
Gui Shan Liu ◽  
Shuang Xu ◽  
Zhen Hua Si ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Bp Neural Network ◽  
Imaging Technique ◽  
Principal Component ◽  
Regions Of Interest ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Hyperspectral Reflectance ◽  
Jujube Fruit ◽  
Solids Content

To predict soluble solids content (SSC) of jujube fruits, a hyperspectral imaging technique has been used for acquiring reflectance images from 200 samples in the spectral regions of 900-1700nm. Hyperspectral images of jujubes were evaluated from the regions of interest using principal component analysis (PCA) with the goal of selecting five optimal wavelengths (1034, 1109，1231，1291 and 1461nm). Prediction model of SSC (Rp=0.9027, RMSEP=1.9845) were built based on BP neural network. This research has demonstrated the feasibility of implementing hyperspectral imaging technique for sorting jujube fruit for SSC to enhance the product quality and marketability.

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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