Construction and optimization of prediction models for cassava ash and water content by using near-infrared spectroscopy
Ash content and moisture content are important indexes for cassava quality evaluation.It is of great significance to establish a method for rapid detection of ash and moisture contents in cassava by using near infrared spectroscopy combined with multivariate mathematical analysis.Tubers of 137 accessions of cassava were harvested at the cassava growing site and their ash and water contents were determined by using the methods described in the standards GB5009.3-2016 and GB/T5009.4-2016 and at the same time the near-infrared spectroscopy.The NIR spectral data collected were analyzed by using the software TQ Analyst 9.0.The NIR calibration models for ash and water contents of cassava were constructed by using the partial least squares(PLS)method.The results showed that correlation coefficients(R)of the ash and water content models were 0.94 and 0.93,respectively.Root mean square error of correction(RMSEC)was 0.22 and 0.48,respectively.Root mean square error of prediction(RMSEP)was 0.21 and 1.46,respectively.Root mean square error of cross-validation(RMSECV)was 0.40 and 1.54,respectively.Twenty accessions of cassava germplasm that were not involved in the modelling were selected for external validation of the model.The results showed that there is no significant difference between the predicted value and the actual value,with the p-values(P＞0.05)being 0.464 and 0.459,respectively,indicating that the model could be applied to the detection of cassava ash and water contents.The near-infrared quantitative detection models of ash and moisture established can be used for rapid detection.
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