Rapid Determination of Nutmeg Shell Content in Ground Nutmeg Using FT-NIR Spectroscopy and Machine Learning.

Nutmeg is a popular spice often used in ground form, which makes it highly susceptible to food fraud. Therefore, the aim of the present study was to detect adulteration of ground nutmeg with nutmeg shell via Fourier transform near-infrared (FT-NIR) spectroscopy. For this purpose, 36 authentic nutmeg samples and 10 nutmeg shell samples were analyzed pure and in mixtures with up to 50% shell content. The spectra plot as well as a principal component analysis showed a clear separation trend as a function of shell content. A support vector machine regression used for shell content prediction achieved an R2 of 0.944 in the range of 0-10%. The limit of detection of the prediction model was estimated to be 1.5% nutmeg shell. Based on random sub-sampling, the likelihood was found to be 2% that a pure nutmeg sample is predicted with a nutmeg shell content of >1%. The results confirm the suitability of FT-NIR spectroscopy for rapid detection and quantitation of the shell content in ground nutmeg.

The Influence of Protein Charge and Molecular Weight on the Affinity of Aptamers.

Aptamers offer several advantages over antibodies. However, to ensure high affinity and specificity, a better understanding of the interactions between the nucleic-acid-based aptamers and their targets is mandatory. Therefore, we investigated the influence of two physical properties of proteins-molecular mass and charge-on the affinity of nucleic-acid-based aptamers. For this purpose, first, the affinity of two random oligonucleotides towards twelve proteins was determined. No binding was observed for proteins with a negative net charge towards the two oligonucleotides, while up to nanomolar affinity was determined for positively charged proteins with a high pI value. Second, a literature analysis comprising 369 aptamer-peptide/protein pairs was performed. The dataset included 296 different target peptides and proteins and is thus currently one of the largest databases for aptamers for proteins and peptides. The targets considered covered isoelectric points of 4.1-11.8 and a molecular weight range of 0.7-330 kDa, while the dissociation constants ranged from 50 fM to 29.5 µM. This also revealed a significant inverse correlation between the protein's isoelectric point and the affinity of aptamers. In contrast, no trend was observed between the affinity and the molecular weight of the target protein with either approach.

Rapid determination of the shell content in cocoa products using FT-NIR spectroscopy and chemometrics.

The determination of the cocoa shell content is of interest because a high shell content causes a reduction in the quality of cocoa products. Consequently, the aim of the present study was the development of a routinely applicable method for the quantitation of shell material in cocoa nibs. For this, 51 fermented cocoa samples of different varieties from 14 cocoa growing countries covering the crop years 2012-2017 were acquired. Admixtures of cocoa nibs with shell material were prepared in a range of 0-20% cocoa shell and subsequently analysed by Fourier transform near-infrared spectroscopy (FT-NIRS). Support vector machine regression models were created, which enabled the prediction of the cocoa shell content in a mixing ratio range of 0-20% with an RMSE of 2.05% and a R2 of 0.88 and in a range of 0-10% with an RMSE of 1.70% and a R2 of 0.72. This predictive capability suggests that the presented method is suitable for rapid determination of cocoa shell content in cocoa nibs. In addition, it was demonstrated that the method is applicable to other relevant cocoa matrices, as the prediction of the shell content of several industrial cocoa masses by the FT-NIRS-based model showed good consistency with the prediction by liquid chromatography-mass spectrometry. This emphasizes that FT-NIRS combined with chemometrics has great potential for the determination of cocoa shell content in cocoa nibs and cocoa masses in routine analysis, such as incoming inspection.

High-Throughput Selection and Characterisation of Aptamers on Optical Next-Generation Sequencers.

Aptamers feature a number of advantages, compared to antibodies. However, their application has been limited so far, mainly because of the complex selection process. 'High-throughput sequencing fluorescent ligand interaction profiling' (HiTS-FLIP) significantly increases the selection efficiency and is consequently a very powerful and versatile technology for the selection of high-performance aptamers. It is the first experiment to allow the direct and quantitative measurement of the affinity and specificity of millions of aptamers simultaneously by harnessing the potential of optical next-generation sequencing platforms to perform fluorescence-based binding assays on the clusters displayed on the flow cells and determining their sequence and position in regular high-throughput sequencing. Many variants of the experiment have been developed that allow automation and in situ conversion of DNA clusters into base-modified DNA, RNA, peptides, and even proteins. In addition, the information from mutational assays, performed with HiTS-FLIP, provides deep insights into the relationship between the sequence, structure, and function of aptamers. This enables a detailed understanding of the sequence-specific rules that determine affinity, and thus, supports the evolution of aptamers. Current variants of the HiTS-FLIP experiment and its application in the field of aptamer selection, characterisation, and optimisation are presented in this review.

Determination of the Geographical Origin of Walnuts (Juglans regia L.) Using Near-Infrared Spectroscopy and Chemometrics.

The prices of walnuts vary according to their geographical origin and, therefore, offer a financial incentive for adulteration. A reliable analysis method is required to quickly detect possible misdeclarations and thus prevent food fraud. In this study, a method to distinguish between seven geographical origins of walnuts using Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics as a fast, versatile, and easy to handle analytical tool was developed. NIR spectra of 212 ground and afterwards freeze-dried walnut samples, harvested in three consecutive years (2017-2019), were collected. We optimized the data pre-processing by applying and evaluating 50,545 different pre-processing combinations, followed by linear discriminant analysis (LDA) which was confirmed by nested cross-validation. The results show that in the scope of our research minimal pre-processing led to the best results: By applying just multiplicative scatter correction (MSC) and median centering, a classification accuracy of 77.00% ± 1.60% was achieved. Consequently, this complex model can be used to answer economically relevant questions e.g., to distinguish between European and Chinese walnuts. Furthermore, the great influence of the applied pre-processing methods, e.g., the selected wavenumber range, on the achieved classification accuracy is shown which underlines the importance of optimization of the pre-processing strategy.