SOFTWARE SUPPORT FOR QUANTITATIVE NEAR-INFRARED ANALYSIS AND BENCHMARKING OF CHEMOMETRIC METHODS - A CASE STUDY ON SINGLE KERNEL SAMPLES

Abstract

During the past decades, the technology of the Near-Infrared Spectroscopy (NIRS) has been widely adopted as a non-destructive analytical tool in various fields. In agriculture and chemometrics, NIRS analysis at single kernel level can improve not only the sample uniformity and purity but also the quality and economic value of a seed batch. However, many limitations and challenges exist in the single kernel Near-Infrared Spectroscopy (SKNIRS) analysis and applications. The first contribution of this PhD thesis was to develop an integrated software system to support data collection, processing and analysis for single kernel sample. IDEF0 Functional Modelling has been used to guide the development and implementation of the integrated software system. Two real-world applications supported by the integrated software system were reported as the validation of the integrated software system. Another contribution of this PhD thesis was to provide a benchmark of chemometric methods for comparative single kernel near-infrared spectroscopy analysis based on the proposed stepwise process. Sixteen methods including two dataset partition methods, three pre-processing methods, eight variable selection methods and three calibration methods were assessed and compared based on two statistics: root mean squared error of prediction (RMSEP) and coefficient of determination (R2). Conclusions were discussed in detail based on the results of benchmarking analysis, which is appropriately general and may assist the choice of chemometric methods for SKNIRS.

Date of Award	1 Aug 2020
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Xiaojun Zeng (Supervisor) & Liping Zhao (Supervisor)