Non-destructive and fast method of mapping the distribution of the soluble solids content and pH in kiwifruit using object rotation near-infrared hyperspectral imaging approach

2021 ◽  
Vol 174 ◽  
pp. 111440
Author(s):  
Te Ma ◽  
Yu Xia ◽  
Tetsuya Inagaki ◽  
Satoru Tsuchikawa
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Soluble Solids ◽  
Fast Method ◽  
Soluble Solids Content ◽  
Imaging Approach ◽  
Object Rotation ◽  
Solids Content ◽  
Non Destructive

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.

Noncontact evaluation of soluble solids content in apples by near-infrared hyperspectral imaging

2018 ◽  
Vol 224 ◽  
pp. 53-61 ◽  
Author(s):  
Te Ma ◽  
Xinze Li ◽  
Tetsuya Inagaki ◽  
Haoyu Yang ◽  
Satoru Tsuchikawa
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content
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