Ramin Nikzad

Ramin Nikzad
Software Competence Center Hagenberg | SCCH · Data Analysis Systems Group

Dr. rer. nat.

About

26
Publications
5,075
Reads
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301
Citations
Citations since 2016
26 Research Items
299 Citations
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2016201720182019202020212022020406080100
2016201720182019202020212022020406080100

Publications

Publications (26)
Preprint
Full-text available
The ongoing transition from a linear (produce-use-dispose) to a circular economy poses significant challenges to current state-of-the-art information and communication technologies. In particular, the derivation of integrated, high-level views on material, process, and product streams from (real-time) data produced along value chains is challenging...
Preprint
Full-text available
In contrast to classical techniques for exploratory analysis of high-dimensional data sets, such as principal component analysis (PCA), neighbor embedding (NE) techniques tend to better preserve the local structure/topology of high-dimensional data. However, the ability to preserve local structure comes at the expense of interpretability: Technique...
Preprint
Full-text available
Partial least squares (PLS) regression enjoys great popularity as a multivariate regression method that is relatively easy to use while at the same time offering a high level of interpretability. These traits make PLS especially useful for the analysis of spectroscopic measurements in industries with stringent quality standards such as the (bio-) p...
Article
Uniform Manifold Approximation and Projection (UMAP) and related manifold embedding techniques generate nonlinear projections of high‐dimensional data onto low‐dimensional subspaces. However, due to the non‐linearity of these methods, there is no analogous transformation matrix (i.e., the loadings or eigenvectors) that allows one to see the corresp...
Chapter
Digitization of industrial processes requires an ever increasing amount of resources to store and process data. However, integration of the business process including expert knowledge and (real-time) process data remains a largely open challenge. Our study is a first step towards better integration of these aspects by means of knowledge graphs and...
Article
Domain adaptation (DA) and Transfer Learning (TL) are terms coined by the machine learning community, particularcomputer vision. However, the chemometrics community has been working on similar problems (with chemical andspectroscopic contexts) for much longer and these techniques go under the moniker of calibration transfer and mainte-nance (CTM)....
Article
Transfer learning (TL), the sub-discipline of machine learning devoted to learning from different domains, has gained increasing attention over the past decade. With the current contribution, we aim at giving a concise overview on theory, concepts, and applications of TL from a chemometrician's perspective and draw some connections to previous work...
Preprint
Full-text available
Transfer learning (TL), the sub-discipline of machine learning devoted to learning from different domains, has gained increasing attention over the past decade. With the current contribution, we aim at giving a concise overview on theory, concepts and applications of TL from a chemometrician's perspective and draw some connections to previous work...
Article
In this work, mid-infrared hyperspectral images of multilayer polymer film (MLPF) cross sections are acquired with a high-speed quantum cascade laser (QCL) based mid-infrared microscope and analyzed using different data analysis techniques. The investigated MLPF is a polypropylene (PP) and ethylene-vinyl alcohol co-polymer (EVOH) composite commonly...
Article
Full-text available
Near-infrared (NIR) calibration models are widely developed and routinely used for the prediction of physicochemical properties of samples. However, the main challenge with NIR models is that they are highly specific to the physical form of the samples. For example, a NIR calibration established for solid samples can usually not be used for the sam...
Article
Full-text available
Calibration transfer (CT) refers to the set of chemometric techniques used to transfer (near-infrared) calibration models between spectrometers. The requirement of traditional CT methods to measure calibration standard samples has been a challenge as such measurements are difficult in real-world applications, e.g. when the instruments are located f...
Article
We consider the problem of unsupervised domain adaptation (DA) in regression under the assumption of linear hypotheses (e.g. Beer–Lambert’s law) – a task recurrently encountered in analytical chemistry. Following the ideas from the non-linear iterative partial least squares (NIPALS) method, we propose a novel algorithm that identifies a low-dimensi...
Article
The problem of transferring calibrations from a primary to a secondary instrument, that is, calibration transfer (CT), has been a matter of considerable research in chemometrics over the past decades. Current state‐of‐the‐art (SoA) methods like (piecewise) direct standardization perform well when suitable transfer standards are available. However,...
Article
Full-text available
Calibration models required for near-infrared (NIR) spectroscopy-based analysis of fresh fruit frequently fail to extrapolate adequately to conditions not encountered during initial data acquisition. Such different conditions can be due to physical, chemical or environmental effects and might be encountered for instance when measurements are carrie...
Preprint
Full-text available
The problem of transferring calibrations from a primary to a secondary instrument, i.e. calibration transfer (CT), has been a matter of considerable research in chemometrics over the past decades. Current state-of-the-art (SoA) methods like (piecewise) direct standardization perform well when suitable transfer standards are available. However, stab...
Article
Full-text available
Real-time measurements and adjustments of critical process parameters are essential for the precise control of fermentation processes and thus for increasing both quality and yield of the desired product. However, the measurement of some crucial process parameters such as biomass, product, and product precursor concentrations usually requires time-...
Conference Paper
We consider the problem of unsupervised domain adaptation (DA) in regression under the assumption of linear hypotheses (e.g. Beer-Lambert's law) – a task recurrently encountered in analytical chemistry. Following the ideas from the non-linear iterative partial least squares (NIPALS) method, we propose a novel algorithm that identifies a low-dimensi...
Article
Establishing fuzzy models from time-series data with predictive capabilities for numerical targets typically requires dimension reduction techniques to overcome the severe curse of dimensionality effects. Linear projection methods are promising candidates in this context as they — unlike non-linear dimension reduction techniques — preserve interpre...
Article
Full-text available
We introduce a compressive sensing based approach for single pixel hyperspectral chemical imaging in a broad spectral range in the near-infrared. Fully integrated MEMS based Fabry-Pérot tunable filter spectrometers and a digital micro-mirror device were employed to achieve spectral and spatial resolution, respectively. The available spectral range...
Article
Multivariate calibration models often fail to extrapolate beyond the calibration samples due to changes associated with the instrumental response, environmental condition or sample matrix. Most of the current methods used to adapt a source calibration model to a target domain exclusively apply to calibration transfer between similar analytical devi...
Article
Full-text available
The physico-chemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. On-line supervision of the turbidity point by means of vibrational spectroscopy has recently emerged as a promising technique to monitor the DP of MF resins. However, spectroscopic deter...
Article
(Acetoxy-)valerenic acid and total essential oil content are important quality attributes of pharmacy grade valerian root (Valerianae radix). Traditional analysis of these quantities is time-consuming and necessitates (harmful) solvents. Here we investigated an application of attenuated total reflection Fourier transform infrared spectroscopy for e...
Article
Full-text available
Inflammation is a hallmark of some of today's most life-threatening diseases such as arteriosclerosis, cancer, diabetes and Alzheimer's disease. Herbal medicines (HMs) are re-emerging resources in the fight against these conditions and for many of them, anti-inflammatory activity has been demonstrated. However, several aspects of HMs such as their...
Article
In this paper, we propose a new strategy for retrospective identification of feed phases from online sensor-data enriched feed profiles of an Escherichia Coli (E. coli) fed-batch fermentation process. In contrast to conventional (static), data-driven multi-class machine learning (ML), we exploit process knowledge in order to constrain our classific...

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Projects

Projects (2)
Project
This project aims at identifying relevant research directions in machine learning that will likely have an impact on the field of chemometrics. Fellow chemometricians are warmly invited to contribute to the project by making suggestions on the topics that should be covered and to join the discussion.