Open AccessInvestigation of Parameters That Affect the Acquired Near Infrared Diffuse Reflected Signals in Non-Destructive Soluble Solids Content Prediction
Author(s) -
Kim Seng Chia,
Hong Fan
Publication year - 2020
Publication title -
warasan witsawakammasat, chulalongkorn university
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.246
H-Index - 20
ISSN - 0125-8281
DOI - 10.4186/ej.2020.24.6.79
Subject(s) - diffuse reflection , diffuse reflectance infrared fourier transform , optics , reflection (computer programming) , near infrared spectroscopy , infrared , intensity (physics) , materials science , correlation coefficient , reflectivity , root mean square , biological system , analytical chemistry (journal) , mathematics , chemistry , statistics , physics , computer science , chromatography , biochemistry , photocatalysis , quantum mechanics , programming language , catalysis , biology
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.