Nicolaas M. Faber

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Near infrared spectroscopy (NIRS) is one of the most promising techniques for large-scale meat quality evaluation. We investigated the potential of NIRS-based models to predict drip loss and shear force of pork samples. Near infrared reflectance spectra (1000-2500 nm), water-holding capacity, shear force, ultimate pH, and colour (L(∗), a(∗), b(∗)-value) of(More)
This paper critically reviews the problem of over-fitting in multivariate calibration and the conventional validation-based approach to avoid it. It proposes a randomization test that enables one to assess the statistical significance of each component that enters the model. This alternative is compared with cross-validation and independent test set(More)
Predictions obtained from a multivariate calibration model are sensitive to variations in the spectra such as baseline shifts, multiplicative effects, etc. Many spectral pretreatment methods have been developed to reduce these distortions, and the best method is usually the one that minimizes the prediction error for an independent test set. This paper(More)
The net analyte signal vector has been defined by Lorber as the part of a mixture spectrum that is unique for the analyte of interest; i.e., it is orthogonal to the spectra of the interferences. It plays a key role in the development of multivariate analytical figures of merit. Applications have been reported that imply its utility for spectroscopic(More)
The estimation of the pseudorank of a matrix, i.e., the rank of a matrix in the absence of measurement error, is a major problem in multivariate data analysis. In the practice of analytical chemistry it is often even the only problem. An important example is the determination, of the purity of a chromatographic peak. In this paper we discuss three(More)
The trilinear PARAFAC model occupies a central place in multiway analysis, because the components of a data array can often be uniquely resolved. This paper compares the resolution for a large variety of methods, namely the generalized rank annihilation method (GRAM), alternating least squares (ALS), alternating trilinear decomposition (ATLD), alternating(More)
Selecting the correct dimensionality is critical for obtaining partial least squares (PLS) regression models with good predictive ability. Although calibration and validation sets are best established using experimental designs, industrial laboratories cannot afford such an approach. Typically, samples are collected in an (formally) undesigned way, spread(More)