Thin-layer chromatography in spices analysis

Thin layer chromatography coupled with surface enhanced Raman scattering for rapid separation and on-site detection of multi-components

The separation and detection of multi-component mixtures has always been a challenging task. Traditional detection methods often suffer from complex operation, high cost, and low sensitivity. Surface enhanced Raman scattering (SERS) technique is a high sensitivity, powerful and rapid detection tool, which can realize the specific detection of single substance components, but it must solve the problem that multi-component mixtures cannot be accurately determined. Thin layer chromatography (TLC) technology, as a high-throughput separation technology, uses chromatographic plate as the stationary phase, and could select different developing phases for separation experiments. The advantages of TLC technology in short distance and rapid separation are widely used in protein, dye and biomedical fields. However, TLC technology has limitations in detection ability and difficulty in obtaining ideal signal intensity. The combination of TLC technology and SERS technology made the operation procedure simple and the sample size small, which can achieve rapid on-site separation and quantitative detection of mixtures. Due to the rapid development of TLC-SERS technology, it has been widely used in the investigation of various complex systems. This paper reviews the application of TLC-SERS technology in food science, environmental pollution and biomedicine.

Identification of cumin and fennel from different regions based on generative adversarial networks and near infrared spectroscopy

Cumin (Cuminum cyminum) and fennel (Foeniculum vulgare) are widely used seasonings and play a very important role in industries such as breeding, cosmetics, winemaking, drug discovery, and nano-synthetic materials. However, studies have shown that cumin and fennel from different regions not only differ greatly in the content of lipids, phenols and proteins but also the substances contained in their essential oils are also different. Therefore, realizing precise identification of cumin and fennel from different regions will greatly help in quality control, market fraud and production industrialization. In this experiment, cumin and fennel samples were collected from each region, a total of 480 NIR spectra were collected. We used deep learning and traditional machine learning algorithms combined with near infrared (NIR) spectroscopy to identify their origin. To obtain the model with the best generalization performance and classification accuracy, we used principal component analysis (PCA) to reduce spectral data dimensionality after Rubberband baseline correction, and then established classification models including quadratic discriminant analysis based on PCA (PCA-QDA) and multilayer perceptron based on PCA (PCA-MLP). We also directly input the spectral data after baseline correction into convolutional neural networks (CNN) and generative adversarial networks (GAN). The experimental results show that GAN is more accurate than the PCA-QDA, PCA-MLP and CNN models, and the classification accuracy reached 100%. In the cumin and fennel classification experiment in the same region, the four models achieve great classification results from three regions under the condition that all model parameters remain unchanged. The experimental results show that when the training data are limited and the dimension is high, the model obtained by GAN using competitive learning has more generalization ability and higher classification accuracy. It also provides a new method for solving the problem of limited training data in food research and medical diagnosis in the future.

A new high-throughput screening method to determine multiple dyes in herbs and spices

The unauthorised addition of colours to herbs and spices is a recurrent issue affecting food business operators. Such a practice aims at improving food visual attractiveness, masking poor product quality, and/or compensating for natural colour variation with the ultimate goal to increase profits. To detect this fraud, a new LC-MS/MS method was developed for screening 58 dyes in both herbs and spices. This extended list of targets was established based on requirements from international spices organisations, past issues identified by web scouting and by notifications from the European Rapid Alert System for Food and Feed (RASFF). The method is intended to quickly detect fraudulent addition of dyes with Screening Target Concentrations ranging from 0.1 to 2.5 mg/kg. Validation was performed according to the European Community Reference Laboratories Residues Guidelines 20/1/2010. False positive and false negative rates were below 5% for all analytes and applicability of the method was further demonstrated by analysing 117 samples collected worldwide. None of the surveyed dyes was found in herbs (n = 28, 16 varieties) whereas 6% of spice samples (n = 89, 21 varieties) was found contaminated with one or two dyes at levels ranging from 0.12 to 255 mg/kg. Four out of the nine detected compounds have never been reported in the RASFF, thus demonstrating the usefulness of this analytical approach.