Longitudinal Study Comparing Orthogonal Signal Correction Algorithms Coupled with Partial Least-Squares for Quantitative Near-Infrared Spectroscopy

2021 ◽  
pp. 1-18
Author(s):  
Austin Gessell ◽  
Gary W. Small
Keyword(s):  
Infrared Spectroscopy ◽  
Longitudinal Study ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Orthogonal Signal ◽  
Orthogonal Signal Correction ◽  
Signal Correction

Application of OSC in Sugar Content Evaluation of Chestnut Based on near Infrared Spectroscopy

2014 ◽  
Vol 701-702 ◽  
pp. 577-580
Author(s):  
Jie Liu ◽  
Xiao Yu Li ◽  
Wei Wang ◽  
Jun Zhang ◽  
Wei Zhou
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Sugar Content ◽  
Model Performance ◽  
Nir Spectroscopy ◽  
Orthogonal Signal ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Validation Set

It is important in chestnut industry to evaluate the sugar content of nuts since sugar content is one of parameters for classifying the fruit to different productions. Previous work had proved the near infrared (NIR) spectroscopy could be used to measuring the sugar content in intact and peeled chestnut nondestructively; however, the performance of the predictive model would need more improvement. In this work, the orthogonal signal correction (OSC) algorithm was employed to optimize the predictive models. The results shown that, for the peeled chestnut sample, OSC could increase the correlation coefficient (R2) of validation set from 0.8649 to 0.8961while decrease the root mean and square error of prediction from 0.739 to 0.626. For the intact chestnut sample, this algorithm did not improve the model performance. The results indicated that the OSC had potential to optimized the prediction accuracy of sugar content in chestnut based on near infrared spectroscopy.

scholarly journals Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 666
Author(s):  
Rafael Font ◽  
Mercedes del Río-Celestino ◽  
Diego Luna ◽  
Juan Gil ◽  
Antonio de Haro-Bailón
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Cicer Arietinum ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Neutral Detergent Fiber ◽  
Acid Detergent Fiber ◽  
Cicer Arietinum L

The near-infrared spectroscopy (NIRS) combined with modified partial least squares (modified PLS) regression was used for determining the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) fractions of the chickpea (Cicer arietinum L.) seed. Fifty chickpea accessions (24 desi and 26 kabuli types) and fifty recombinant inbred lines F5:6 derived from a kabuli × desi cross were evaluated for NDF and ADF, and scanned by NIRS. NDF and ADF values were regressed against different spectral transformations by modified partial least squares regression. The coefficients of determination in the cross-validation and the standard deviation from the standard error of cross-validation ratio were, for NDF, 0.91 and 3.37, and for ADF, 0.98 and 6.73, respectively, showing the high potential of NIRS to assess these components in chickpea for screening (NDF) or quality control (ADF) purposes. The spectral information provided by different chromophores existing in the chickpea seed highly correlated with the NDF and ADF composition of the seed, and, thus, those electronic transitions are highly influenced on model fitting for fiber.

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