Reduction of the nonspecific serum protein adsorption on a gold surface to levels low enough to allow the detection of biomarkers in complex media has been achieved using the N-hydroxysuccinimide (NHS) ester of 16-mercaptohexadecanoic acid. Carboxymethylated dextran (CM dextran), which is widely used, nonspecifically adsorbs enough proteins to mask the signal from target biomarkers in complex solutions such as serum or blood. The use of short-chain thiols greatly reduces the amount of nonspecific protein adsorption. Mixed layers of 11-mercaptoundecanoic acid or the NHS ester of 11-mercaptoundecanoic acid mixed layers with either 11-mercaptoundecanol or undecanethiol, and 16-mercaptohexadecanoic acid or the NHS ester of 16-mercaptohexadecanoic acid with hexadecanethiol, were also investigated for nonspecific protein binding properties as well as for biomarker signal response. The NHS ester of 16-mercaptohexadecanoic acid exhibits the largest signal for the biomarker myoglobin (including CM dextran) while offering a significantly diminished amount of nonspecific binding. The sensor has also been shown to detect interleukin-6 in cell culture media containing protein concentrations of at least 4 mg/mL.
Parallel factor analysis (PARAFAC) is one of the most popular methods for evaluating multi-way data sets, such as those typically acquired by hyphenated measurement techniques. One of the reasons for PARAFAC popularity is the ability to extract directly interpretable chemometric models with little a priori information and the capability to handle unknown interferents and missing values. However, PARAFAC requires long computation times that often prohibit sufficiently fast analyses for applications such as online sensing. An additional challenge faced by PARAFAC users is the handling and storage of very large, high-dimensional data sets. Accelerating computations and reducing storage requirements in multi-way analyses are the topics of this manuscript. This study introduces a data pre-processing method based on multi-dimensional wavelet transforms (WTs), which enables highly efficient data compression applied prior to data evaluation. Because multidimensional WTs are linear, the intrinsic underlying linear data construction is preserved in the wavelet domain. In almost all studied examples, computation times for analyzing the much smaller, compressed data sets could be reduced so much that the additional effort for wavelet compression was more than recompensated. For 3-way and 4-way synthetic and experimental data sets, acceleration factors up to 50 have been achieved; these data sets could be compressed down to a few per cent of the original size. Despite the high compression, accurate and interpretable models were derived, which are in good agreement with conventionally determined PARAFAC models. This study also found that the wavelet type used for compression is an important factor determining acceleration factors, data compression ratios and model quality.
Multi-dimensional wavelet transforms (WTs) have been introduced for efficient data compression in order to accelerate chemometric calculations and to reduce requirements for data storage space. For hyphenated measurement techniques or hyperspectral imaging this wavelet compression becomes vital because such sensors acquire unprecedented amounts of information in short periods of time. Conventional, multi-dimensional wavelet compression uses the same wavelet for all dimensions. However, from a mathematical perspective there is no need for this restriction as the transforms in different dimensions are independent from each other. This manuscript presents multidimensional 'hybrid wavelet transforms', which utilize different wavelet types for different dimensions. Thus, hybrid wavelets optimize wavelet compression by adjusting WTs to the different types of data sets.In this manuscript we demonstrate that hybrid wavelets improve acceleration factors compared to conventional multi-dimensional wavelet compression and determine more precise models. Combinations of Haar, Daub4, Daub6, Daub8, Daub10, Daub12, Daub14, Daub16, Daub18 and Daub20 wavelets are considered in this study. Data obtained with two different experimental techniques are used for assessing hybrid wavelet compression: (i) a data cube obtained by means of mid-infrared hyperspectral imaging; (ii) data acquired by excitation-emission matrix (EEM) fluorescence spectroscopy in photo-catalytic studies. For the hyperspectral data cube hybrid wavelets were found, which are superior regarding acceleration and model quality to all 10 conventional WTs (Haar-Daub20). For the EEM example this was achieved in 9 out of 10 cases; thus in 19 out of 20 investigated cases hybrid WTs were found to be superior to conventional wavelet compression.
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