In:
tm - Technisches Messen, Walter de Gruyter GmbH, Vol. 85, No. 3 ( 2018-3-26), p. 184-190
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.
Type of Medium:
Online Resource
ISSN:
2196-7113
,
0171-8096
DOI:
10.1515/teme-2017-0069
Language:
English
Publisher:
Walter de Gruyter GmbH
Publication Date:
2018
detail.hit.zdb_id:
2025790-9
SSG:
15,3
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