Accurate and nondestructive detection of apple brix and acidity based on visible and near-infrared spectroscopy

2021 ◽  
Author(s):  
Zhang yunqi ◽  
Yong Chen ◽  
Yun Wu ◽  
Chaoyuan Cui
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nondestructive Detection

scholarly journals Rapid and nondestructive detection of watercore and sugar content in Asian pear by near infrared spectroscopy for commercial trade

2014 ◽  
Vol 07 (06) ◽  
pp. 1350073 ◽  
Author(s):  
Ronnarit Rittiron ◽  
Sureeporn Narongwongwattana ◽  
Unaruj Boonprakob ◽  
Worapa Seehalak
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Short Wavelength ◽  
Near Infrared ◽  
High Efficiency ◽  
Sugar Content ◽  
Wavelength Region ◽  
Nondestructive Detection ◽  
First Derivative ◽  
Asian Pear

Watercore and sugar content are internal qualities which are impossible for exterior determination. Therefore the aims of this study were to develop models for nondestructive detection of watercore and predicting sugar content in pear using Near Infrared Spectroscopy (NIR) technique. A total of 93 samples of Asian pear variety "SH-078" were used. For sugar content, spectrum of each fruit was measured in the short wavelength region (700–1100 nm) in the reflection mode and the first derivative of spectra were then correlated with the sugar content in juice determined by digital refractometer. Prediction equation was performed by multiple linear regression. The result showed Standard Error of Prediction (SEP) = 0.58°Bx, and Bias=0.11. The result from t-test showed that sugar content predicted by NIR was not significantly different from the value analyzed by refractometer at 95% confidence. For watercore disorder, NIR measurement was performed over the short wavelength range (700–850 nm) in the transmission mode. The first derivative spectra were correlated with internal qualities. Then principle component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were used to perform discrimination models. The accuracy of the PCA model was greater than the PLSDA one. The scores from PC1 were separated into two boundaries, one predicted rejected pears with 100% classification accuracy, and the other was accepted pears with 92% accuracy. The high accuracy of sugar content determining and watercore detecting by NIR reveal the high efficiency of NIR technique for detecting other internal qualities of fruit in the future.

Sign in / Sign up

Export Citation Format

Share Document