TY - JOUR
T1 - Predicting the ethanol potential of wheat straw using near-infrared spectroscopy and chemometrics
T2 - The challenge of inherently intercorrelated response functions
AU - Rinnan, Åsmund
AU - Bruun, Sander
AU - Lindedam, Jane
AU - Decker, Stephen R.
AU - Turner, Geoffrey B.
AU - Felby, Claus
AU - Engelsen, Søren Balling
PY - 2017
Y1 - 2017
N2 - The combination of NIR spectroscopy and chemometrics is a powerful correlation method for predicting the chemical constituents in biological matrices, such as the glucose and xylose content of straw. However, difficulties arise when it comes to predicting enzymatic glucose and xylose release potential, which is matrix dependent. Further complications are caused by xylose and glucose release potential being highly intercorrelated. This study emphasizes the importance of understanding the causal relationship between the model and the constituent of interest. It investigates the possibility of using near-infrared spectroscopy to evaluate the ethanol potential of wheat straw by analyzing more than 1000 samples from different wheat varieties and growth conditions. During the calibration model development, the prime emphasis was to investigate the correlation structure between the two major quality traits for saccharification of wheat straw: glucose and xylose release. The large sample set enabled a versatile and robust calibration model to be developed, showing that the prediction model for xylose release is based on a causal relationship with the NIR spectral data. In contrast, the prediction of glucose release was found to be highly dependent on the intercorrelation with xylose release. If this correlation is broken, the model performance breaks down. A simple method was devised for avoiding this breakdown and can be applied to any large dataset for investigating the causality or lack of causality of a prediction model.
AB - The combination of NIR spectroscopy and chemometrics is a powerful correlation method for predicting the chemical constituents in biological matrices, such as the glucose and xylose content of straw. However, difficulties arise when it comes to predicting enzymatic glucose and xylose release potential, which is matrix dependent. Further complications are caused by xylose and glucose release potential being highly intercorrelated. This study emphasizes the importance of understanding the causal relationship between the model and the constituent of interest. It investigates the possibility of using near-infrared spectroscopy to evaluate the ethanol potential of wheat straw by analyzing more than 1000 samples from different wheat varieties and growth conditions. During the calibration model development, the prime emphasis was to investigate the correlation structure between the two major quality traits for saccharification of wheat straw: glucose and xylose release. The large sample set enabled a versatile and robust calibration model to be developed, showing that the prediction model for xylose release is based on a causal relationship with the NIR spectral data. In contrast, the prediction of glucose release was found to be highly dependent on the intercorrelation with xylose release. If this correlation is broken, the model performance breaks down. A simple method was devised for avoiding this breakdown and can be applied to any large dataset for investigating the causality or lack of causality of a prediction model.
KW - Calibration
KW - Correlated response variables
KW - Enzymatic sugar release
KW - NIR
KW - Straw
U2 - 10.1016/j.aca.2017.02.001
DO - 10.1016/j.aca.2017.02.001
M3 - Journal article
C2 - 28231876
AN - SCOPUS:85012883293
SN - 0003-2670
VL - 962
SP - 15
EP - 23
JO - Analytica Chimica Acta
JF - Analytica Chimica Acta
ER -