Rapid assessment of soluble solids content in navel orange by near infrared diffuse reflectance spectra

2006 ◽  
Author(s):  
Yande Liu ◽  
Ji Luo ◽  
Xingmiao Chen ◽  
Yibin Ying
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Rapid Assessment ◽  
Reflectance Spectra ◽  
Soluble Solids ◽  
Navel Orange ◽  
Soluble Solids Content ◽  
Diffuse Reflectance Spectra ◽  
Solids Content

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.

Prediction of Soluble Solids Content and Post-Storage Internal Quality of Bulida Apricots Using near Infrared Spectroscopy

2007 ◽  
Vol 15 (3) ◽  
pp. 179-188 ◽  
Author(s):  
Marena Manley ◽  
Elizabeth Joubert ◽  
Lindie Myburgh ◽  
Ester Lotz ◽  
Martin Kidd
Keyword(s):  
Cold Storage ◽  
Regression Models ◽  
Near Infrared ◽  
Storage Period ◽  
Multivariate Adaptive Regression Splines ◽  
Soluble Solids ◽  
Internal Quality ◽  
Soluble Solids Content ◽  
Classification Rate ◽  
Solids Content

The development of internal breakdown of South African Bulida apricots during cold storage, rendering the fruit unsuitable for canning, causes significant post-harvest losses. Regression models to predict internal post-storage quality using near infrared (NIR) spectroscopy and multivariate classification techniques were developed using NIR spectra of the intact fruit collected prior to storage and subjective quality evaluations performed after a cold storage period of four weeks. A correct classification rate of 69% was obtained using multivariate adaptive regression splines (MARS) compared to 50% obtained by soft independent modelling by class analogy (SIMCA). NIR regression models developed for soluble solids content (SSC) of intact fruit as well as for direct NIR measurements on the exposed fruit tissue gave similar results, thus confirming sufficient NIR light penetration into the intact fruit. The best prediction results were obtained when two spectral measurements per fruit (one on each half of the fruit), compared to single measurements, were used.

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