Optimizing genetic algorithm–partial least squares model of soluble solids content in Fukumoto navel orange based on visible–near‐infrared transmittance spectroscopy using discrete wavelet transform

2019 ◽  
Vol 99 (11) ◽  
pp. 4898-4903 ◽  
Author(s):  
Jie Song ◽  
Guanglin Li ◽  
Xiaodong Yang
Keyword(s):  
Genetic Algorithm ◽  
Wavelet Transform ◽  
Near Infrared ◽  
Soluble Solids ◽  
Discrete Wavelet ◽  
Navel Orange ◽  
Soluble Solids Content ◽  
Infrared Transmittance ◽  
Partial Least Squares Model ◽  
Solids Content

Nondestructive Detection of Soluble Solids Content in Navel Orange Based on GEP Algorithm

2014 ◽  
Vol 988 ◽  
pp. 552-556
Author(s):  
Long Sheng Huang ◽  
Wen Xia Yang ◽  
Yu Bo Liao ◽  
Qi Zhong
Keyword(s):  
Genetic Algorithm ◽  
Gene Expression Programming ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Navel Orange ◽  
Soluble Solids Content ◽  
Fiber Optic Probe ◽  
Data Points ◽  
Optic Probe ◽  
Solids Content

A rapid quantification technique was developed and validated for nondestructively quantifying the soluble solids content of navel orange using NIR spectroscopy in conjunction with Gene expression programming (GEP) algorithm and genetic algorithm (GA).The spectra were recorded in the NIR region using the fiber optic probe method. Four data points of the navel orange NIR spectra were selected by GEP algorithm and genetic algorithm by cross validation. In our experiment, the mean standard error Ribetween the prediction values and the real values in the train group is 0.0256, and the validation group is 0.0194, the results show that the proposed method can be built to a concise easily computed model, which has higher accuracy.

scholarly journals Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy

2020 ◽  
Vol 279 ◽  
pp. 109955 ◽  
Author(s):  
Zhiming Guo ◽  
MingMing Wang ◽  
Akwasi Akomeah Agyekum ◽  
Jingzhu Wu ◽  
Quansheng Chen ◽  
...  
Keyword(s):  
Near Infrared ◽  
Soluble Solids ◽  
Quantitative Detection ◽  
Soluble Solids Content ◽  
Infrared Transmittance ◽  
Solids Content

Nondestructive Detection of Soluble Solids Content on Navel Orange with Vis/NIR Based on Genetic Algorithm

2010 ◽  
Vol 47 (12) ◽  
pp. 123001 ◽  
Author(s):  
薛龙 Xue Long ◽  
黎静 Li Jing ◽  
刘木华 Liu Muhua ◽  
王晓 Wang Xiao ◽  
罗春生 Luo Chunsheng
Keyword(s):  
Genetic Algorithm ◽  
Soluble Solids ◽  
Navel Orange ◽  
Soluble Solids Content ◽  
Nondestructive Detection ◽  
Solids Content

Using Genetic Algorithm Interval Partial Least Squares Selection of the Optimal near Infrared Wavelength Regions for Determination of the Soluble Solids Content of “Fuji” Apple

2007 ◽  
Vol 15 (3) ◽  
pp. 153-159 ◽  
Author(s):  
Zou Xiaobo ◽  
Li Yanxiao ◽  
Zhao Jiewen
Keyword(s):  
Genetic Algorithm ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content ◽  
Selection Of

A near infrared (NIR) spectroscopy acquisition device was developed in this study using an apple as the test sample. With this device, the apple was rolled while collecting the NIR spectra. The feasibility of using efficient selection of wavelength regions in Fourier transform NIR for a rapid and conclusive determination of the inner qualities of fruit such as soluble solids content (SSC) of apples was investigated. Graphically-oriented local multivariate calibration modelling procedures called genetic algorithm interval partial least-squares (GA-iPLS) were applied to select efficient spectral regions that provide the lowest prediction error, in comparison to the full-spectrum model. The optimal SSC predictions were obtained from a seven-factor model using five intervals among 40 intervals selected by GA-iPLS. In the determination, a root mean square error of prediction of 0.42 °Brix for SSC of apples was obtained. The result demonstrated that the new method is a very useful and effective method for developing high precision PLS models based on optimal wavelength regions.

scholarly journals THE DEVELOPMENT OF PORTABLE DETECTOR FOR APPLE' S SOLUBLE SOLIDS CONTENT BASED ON VISIBLE AND NEAR INFRARED SPECTRUM

2020 ◽  
pp. 277-288
Author(s):  
Fa Peng ◽  
ShuangXi Liu ◽  
Hao Jiang ◽  
XueMei Liu ◽  
JunLin Mu ◽  
...  
Keyword(s):  
Infrared Spectrum ◽  
Prediction Model ◽  
Spectral Data ◽  
Near Infrared ◽  
Mobile App ◽  
Partial Least Square ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content ◽  
Near Infrared Spectrum

In order to detect the soluble solids content of apples quickly and accurately, a portable apple soluble solids content detector based on USB2000 + micro spectrometer was developed. The instrument can communicate with computer terminal and mobile app through network port, Bluetooth and other ways, which can realize the rapid acquisition of apple spectral information. Firstly, the visible / near-infrared spectrum data and soluble solids content information of 160 apple samples were collected; secondly, the spectral data preprocessing methods were compared, and the results showed that the prediction model of sugar content based on partial least square (PLS) method after average smoothing preprocessing was accurate. The correlation coefficient (RP) and root mean square error (RMSEP) of the prediction model were 0.902 and 0.589 ° Brix, respectively. Finally, on the basis of average smoothing preprocessing, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the wavelength of spectral data, and PLS model was constructed based on the selected 17 characteristic wavelengths, which can increase the accuracy of soluble solids content prediction model, increase the RP to 0.912, and reduce RMSEP to 0.511 ° Brix. The portable visible / near infrared spectrum soluble solids prediction model based on the instrument and method has high accuracy, and the detector can quickly and accurately measure the soluble solids content of apple.

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