Abstract
Near-infrared (NIR) spectroscopy is a widespread technology for
fruit and vegetable quality assessment. New fields of application of
this technology, like mobile food analysis with handheld low-cost
spectrometers, increase the demand for chemometric calibration models
that are able to deal with multiple products and varieties thereof at
once (so-called multi-product calibration models). While
there are well studied methods for single-product calibration as
partial least squares regression (PLSR), multi-product
calibration is still challenging. Conventional approaches that work
well for single-product calibration can lead to high errors for
multi-product calibration. However, nonlinear methods as local
regression and artificial neural networks were found to be
suitable
E. Micklander, K. Kjeldahl, M. Egebo, and L. Norgaard.
Multi-product calibration models of near-infrared spectra of foods.
Journal of Near Infrared Spectroscopy, 14:395–402, 2006.
L. R. Lopez, T. Behrens, K. Schmidt, A. Stevens, J. A. M. Dematte, and
T. Scholten.
The spectrum-based learner: A new local approach for modeling soil
vis-NIR spectra of complex datasets.
Geoderma, 195–196:268–279, 2013.
. Preliminary studies in
multi-product calibration for quantitative analysis of food with
near-infrared spectroscopy showed good results for
memory-based learning (MBL) and a classification
prediction hierarchy (CPH)
M. C. Kopf and R. Gruna.
Examination of multiproduct calibration approaches for quantitative
analysis of food with near infrared spectroscopy.
Bachelor's thesis, Karlsruhe Institute of Technology KIT, 2016.
. In this study, three
varieties of apples, pears and tomatoes with known sugar content (in
○Brix) are analysed with NIR hyperspectral imaging
spectroscopy in the range from 900 nm to
2400 nm. Predictive performance of a linear PLSR model, two
nonlinear models (CPH and MBL) and different pre-processing techniques
are tested and evaluated. For error estimation,
leave-one-product-out and leave-one-out
cross-validation are used.
Cobot User Frame Calibration: Evaluation and Comparison between Positioning Repeatability Performances Achieved by Traditional and Vision-Based Methods
