Principal components transform-partial least squares: a novel method to accelerate cross-validation in PLS regression

This paper shows the determination of iodine value (IV) of pure and frying palm oils using Partial Least Squares (PLS) regression with application of variable selection. A total of 28 samples consisting of pure and frying palm oils which acquired from markets. Seven of them were considered as high-priced palm oils while the remaining was low-priced. PLS regression models were developed for the determination of IV using Fourier Transform Infrared (FTIR) spectra data in absorbance mode in the range from 650 cm-1 to 4000 cm-1. Savitzky Golay derivative was applied before developing the prediction models. The models were constructed using wavelength selected in the FTIR region by adopting selectivity ratio (SR) plot and correlation coefficient to the IV parameter. Each model was validated through Root Mean Square Error Cross Validation, RMSECV and cross validation correlation coefficient, R2cv. The best model using SR plot was the model with mean centring for pure sample and model with a combination of row scaling and standardization of frying sample. The best model with the application of the correlation coefficient variable selection was the model with a combination of row scaling and standardization of pure sample and model with mean centering data pre-processing for frying sample. It is not necessary to row scaled the variables to develop the model since the effect of row scaling on model quality is insignificant.

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Determination of the Degree of Degradation of Frying Rapeseed Oil Using Fourier-Transform Infrared Spectroscopy Combined with Partial Least-Squares Regression

International Journal of Analytical Chemistry ◽

10.1155/2015/185367 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 9

Author(s):

Jie Yu Chen ◽

Han Zhang ◽

Jinkui Ma ◽

Tomohiro Tuchiya ◽

Yelian Miao

Keyword(s):

Fourier Transform ◽

Ftir Spectroscopy ◽

Least Squares ◽

Partial Least Squares ◽

Cross Validation ◽

Standard Errors ◽

Calibration Model ◽

Second Derivative ◽

Pls Regression ◽

Degree Of Degradation

This rapid method for determining the degree of degradation of frying rapeseed oils uses Fourier-transform infrared (FTIR) spectroscopy combined with partial least-squares (PLS) regression. One hundred and fifty-six frying oil samples that degraded to different degrees by frying potatoes were scanned by an FTIR spectrometer using attenuated total reflectance (ATR). PLS regression with full cross validation was used for the prediction of acid value (AV) and total polar compounds (TPC) based on raw, first, and second derivative FTIR spectra (4000–650 cm−1). The precise calibration model based on the second derivative FTIR spectra shows that the coefficients of determination for calibration(R2)and standard errors of cross validation (SECV) were 0.99 and 0.16 mg KOH/g and 0.98 and 1.17% for AV and TPC, respectively. The accuracy of the calibration model, tested using the validation set, yielded standard errors of prediction (SEP) of 0.16 mg KOH/g and 1.10% for AV and TPC, respectively. Therefore, the degradation of frying oils can be accurately measured using FTIR spectroscopy combined with PLS regression.

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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 ◽

10.3390/agronomy11040666 ◽

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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