R. De Maesschalck

Bio: R. De Maesschalck is an academic researcher from Vrije Universiteit Brussel. The author has contributed to research in topics: Mahalanobis distance & Euclidean distance. The author has an hindex of 7, co-authored 7 publications receiving 2101 citations.

On-Line Monitoring of Powder Blending with Near-Infrared Spectroscopy

Abstract: Powder blending was monitored on-line by taking near-infrared measurements at regular time intervals during the mixing process. The average standard deviation between the measurements taken at each time and the dissimilarity between each mixture spectrum and the ideal mixture spectrum were used to monitor the changes in the powder blend over time. The distribution of the pure compounds in the blend can be investigated by looking at the score plot for the first two principal components (PCs), the contribution of each variable to the dissimilarity and, in particular, the contrasts between two characteristic wavelengths for each compound. Statistical process monitoring charts were used to determine the blending time at which the mixture was within (spectroscopic) specifications. Shewhart charts monitor the blend at characteristic wavelengths for each substance separately. The Hotelling's T2 test defines a multivariate confidence interval. For spectral data, feature reduction is needed. This procedure is accomplished by using characteristic wavelengths for the pure compounds or the significant PCs after performing principal components analysis (PCA).

Genomic selection in plant breeding: from theory to practice.

Abstract: We intuitively believe that the dramatic drop in the cost of DNA marker information we have experienced should have immediate benefits in accelerating the delivery of crop varieties with improved yield, quality and biotic and abiotic stress tolerance. But these traits are complex and affected by many genes, each with small effect. Traditional marker-assisted selection has been ineffective for such traits. The introduction of genomic selection (GS), however, has shifted that paradigm. Rather than seeking to identify individual loci significantly associated with a trait, GS uses all marker data as predictors of performance and consequently delivers more accurate predictions. Selection can be based on GS predictions, potentially leading to more rapid and lower cost gains from breeding. The objectives of this article are to review essential aspects of GS and summarize the important take-home messages from recent theoretical, simulation and empirical studies. We then look forward and consider research needs surrounding methodological questions and the implications of GS for long-term selection.

An institutional approach to cross-national distance

Abstract: Cross-national distance is a key concept in the field of management. Previous research has conceptualized and measured cross-national differences mostly in terms of dyadic cultural distance, and has used the Euclidean approach to measuring it. In contrast, our goal is to disaggregate the construct of distance by proposing a set of multidimensional measures, including economic, financial, political, administrative, cultural, demographic, knowledge, and global connectedness as well as geographic distance. We ground our analysis and choice of empirical dimensions on institutional theories of national business, governance, and innovation systems. In order to overcome the methodological limitations of the Euclidean approach, we calculate dyadic distances using the Mahalanobis method, which is scale-invariant and takes into consideration the variance–covariance matrix. We empirically analyze four different foreign expansion choices of US companies to illustrate the importance of disaggregating the distance construct and the usefulness of our distance calculations, which we make freely available to managers and scholars.

Theory and application of near infrared reflectance spectroscopy in determination of food quality

Abstract: Near infrared reflectance spectroscopy (NIRS) is a non-destructive and rapid technique applied increasingly for food quality evaluation in recent years. It provides us more information to research the quality of food products. This review intends to give an overview of the type of information that can be obtained based on some developed theory and food research of NIRS. It includes the principle of NIRS technique, the specific techniques with chemometrics for data pre-processing methods, qualitative and quantitative analysis and model transfer, and the wide applications of NIRS in food science. In addition, the promise of NIRS technique for food quality evaluation is demonstrated, and some problems which need to be solved or investigated further are also discussed.