Abstract
Recent developments in compact near infrared (NIR) instruments, including both handheld and process instruments, have enabled easy and affordable deployment of multiple instruments for various field and online or inline applications. However, historically, instrument-to-instrument variations could prohibit success when applying calibration models developed on one instrument to additional instruments. Despite the usefulness of calibration transfer techniques, they are difficult to apply when a large number of instruments and/or a large number of classes are involved. Direct model transferability was investigated in this study using miniature near-infrared (MicroNIR™) spectrometers for both classification and quantification problems. For polymer classification, high cross-unit prediction success rates were achieved with both conventional chemometric algorithms and machine learning algorithms. For active pharmaceutical ingredient quantification, low cross-unit prediction errors were achieved with the most commonly used partial least squares (PLS) regression method. This direct model transferability is enabled by the robust design of the MicroNIR™ hardware and will make deployment of multiple spectrometers for various applications more manageable.
Highlights
In recent years, compact near infrared (NIR) instruments, including both handheld and process instruments, have attracted considerable attention and received wider adoption due to their cost-effectiveness, portability, ease of use, and flexibility in installation
In this study polymer classification was used as an example to investigate direct model transferability
Resin kits polymer classification was used as an example to investigate direct model transferability
Summary
Compact near infrared (NIR) instruments, including both handheld and process instruments, have attracted considerable attention and received wider adoption due to their cost-effectiveness, portability, ease of use, and flexibility in installation. These instruments have been used for various applications in different industries, such as the pharmaceutical industry, agriculture, the food industry, the chemical industry, and so on. A calibration data set is collected using an NIR instrument to develop a calibration model. When multiple instruments are deployed for the same application, it is too time and labor consuming to collect calibration sets and develop calibration models for these instruments individually. When multiple instruments are involved for a particular application, the calibration model is often developed on one instrument and applied
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