Determination of soluble solids content in apple using hyperspectral imaging and variable selection algorithms

2013 ◽  
Author(s):  
Wenqian Huang ◽  
Liping Chen ◽  
Jiangbo Li ◽  
Zhiming Guo
Keyword(s):  
Variable Selection ◽  
Hyperspectral Imaging ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content ◽  
Selection Algorithms

scholarly journals Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhenzhu Su ◽  
Chu Zhang ◽  
Tianying Yan ◽  
Jianan Zhu ◽  
Yulan Zeng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Soluble Solids ◽  
Quality Inspection ◽  
Soluble Solids Content ◽  
Saliency Maps ◽  
Solids Content ◽  
Maturity Degree

Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R2) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.

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.

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.

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