Wheat authentication：An overview on different techniques and chemometric methods.

Wheat (Triticum aestivum L.) is one of the most important cereal crops and is consumed as a staple food around the globe. Wheat authentication has become a crucial issue over the last decades. Recently, many techniques have been applied in wheat authentication including the authentication of wheat geographical origin, wheat variety, organic wheat, and wheat flour from other cereals. This paper collected related literature in the last ten years, and attempted to highlight the recent studies on the discrimination and authentication of wheat using different determination techniques and chemometric methods. The stable isotope analysis and elemental profile of wheat are promising tools to obtain information regarding the origin, and variety, and to differentiate organic from conventional farming of wheat. Image analysis, genetic parameters, and omics analysis can provide solutions for wheat variety, organic wheat, and wheat adulteration. Vibrational spectroscopy analyses, such as NIR, FTIR, and HIS, in combination with multivariate data analysis methods, such as PCA, LDA, and PLS-DA, show great potential in wheat authenticity and offer many advantages such as user-friendly, cost-effective, time-saving, and environment friendly. In conclusion, analytical techniques combining with appropriate multivariate analysis are very effective to discriminate geographical origin, cultivar classification, and adulterant detection of wheat.

Antioxidant capacity, phytochemical profiles, and phenolic metabolomics of selected edible seeds and their sprouts.

Sprouts are recognized as nutritional and functional vegetables. In this study, 17 selected seeds were germinated simultaneously. The antioxidant capacity and total phenolic content (TPC) were determined for seeds and sprouts of all species. Both seed and sprout of white radish, with the highest antioxidant capacity, and TPC among all the 17 species, were further determined for phenolic metabolomics. Four phenolic classes with 316 phenolic metabolites were identified. 198 significantly different metabolites with 146 up-regulated and 52 down-regulated were confirmed, and high amounts of phenolic acids and flavonoids were found to be accumulated in the sprout. Several metabolism and biosynthesis, including phenylpropanoid, favone and flavonol, phenylalanine, and various secondary metabolites, were significantly activated. Significant correlations were found among FRAP, DPPH, ABTS, TPC, and phenolic profiles. Therefore, white radish sprout could be served as antioxidant and could be a good source of dietary polyphenols.

[Application of near infrared spectral fingerprint technique in lamb meat origin traceability].

Near infrared spectra of 99 lamb meat samples from three pasturing areas and two farming areas of China were scanned and analyzed to seek a cheap, rapid and effective method for lamb meat origin traceability. Two chemometric methods including linear discriminant analysis based on principal component analysis (PCA+LDA) and partial least squares discriminant analysis (PLS-DA) were used to develop the discriminate models. It was showed that there were significantly differences among the lamb meat samples from five regions based on NIR spectra after second derivative (Savitzky-Golay, 9 point) and multiplicative scattering correction (MSC) transformation in the whole wavelength. The discrimination of two models was best for classification of pasturing area and farming area, with both correctly classified by 100%. The correct classification rate of samples from five different regions using PCA+LDA model was 91.2%, higher than using PLS-DA model (76.7%). These results demonstrate that near infrared reflectance spectroscopy (NIRS) combined with chemometric analysis can be used as an effective method to classify lamb meat according to its geographical origin.

[Determination of geographical origin of beef based on FTIR spectroscopy analysis].

The aim of the present study was to investigate the feasibility of tracing the geographical origin of beef with FT-NIR spectroscopy, set up the model for identifying the beef geographical origin, and validate the rate of discrimination. Fifty eight defatted beef samples from Jilin, Guizhou, Ningxia, and Hebei in China were dried and milled. Based on the NIR spectra of the pre-processing beef, the samples were subjected to principal component analysis (PCA), cluster analysis (CA), discriminant analysis (DA), and a qualitative model was established to do discriminant analysis and validated. The results showed that there were some differences in NIR spectra from different geographical origins, and the element contents in the beef samples were different from different locations. According to the Euclidean distance of NIR spectra, the geographical origin can be identified by cluster analysis. The distance of spectra is the shortest between the samples from Guizhou and Hebei province. Meanwhile there is a certain crossover. This model calibrated by 40 samples was used to predict the varieties of 18 unknown beef samples. The recognition rate of 100% was achieved. So applying FT-NIR fingerprint spectroscopy to trace geographical origin of beef is accurate, rapid and low-cost.