Discrimination of Corsican honey by FT-Raman spectroscopy and chemometrics

Honey is a complex and challenging product to analyze due mainly to its composition consisting on various botanical sources. The discrimination of the origin of honey is of prime importance in order to reinforce the consumer trust in this typical food product. But this is not an easy task as usually no single chemical or physical parameter is sufficient. The aim of our paper is to investigate whether FT-Raman spectroscopy as spectroscopic fingerprint technique combined with some chemometric tools can be used as a rapid and reliable method for the discrimination of honey according to their source. In addition to that, different chemometric models are constructed in order to discriminate between Corsican honeys and honey coming from other regions in France, Italy, Austria, Germany and Ireland based on their FT-Raman spectra. These regions show a large variation in their plants. The developed models include the use of exploratory techniques as the Fisher criterion for wavenumber selection and supervised methods as Partial Least Squares-Discriminant Analysis (PLS-DA) or Support Vector Machines (SVM). All these models showed a correct classification ratio between 85% and 90% of average showing that Raman spectroscopy combined to chemometric treatments is a promising way for rapid and non-expensive discrimination of honey according to their origin

Origin authentication of distillers' dried grains and solubles (DDGS) - Application and comparison of different analytical strategies

In the context of products from certain regions or countries being banned because of an identified or non-identified hazard, proof of geographical origin is essential with regard to feed and food safety issues. Usually, the product labeling of an affected feed lot shows origin, and the paper documentation shows traceability. Incorrect product labeling is common in embargo situations, however, and alternative analytical strategies for controlling feed authenticity are therefore needed. In this study, distillers' dried grains and solubles (DDGS) were chosen as the product on which to base a comparison of analytical strategies aimed at identifying the most appropriate one. Various analytical techniques were investigated for their ability to authenticate DDGS, including spectroscopic and spectrometric techniques combined with multivariate data analysis, as well as proven techniques for authenticating food, such as DNA analysis and stable isotope ratio analysis. An external validation procedure (called the system challenge) was used to analyze sample sets blind and to compare analytical techniques. All the techniques were adapted so as to be applicable to the DDGS matrix. They produced positive results in determining the botanical origin of DDGS (corn vs. wheat), and several of them were able to determine the geographical origin of the DDGS in the sample set. The maintenance and extension of the databanks generated in this study through the analysis of new authentic samples from a single location are essential in order to monitor developments and processing that could affect authentication

Portable Infrared Laser Spectroscopy for On-site Mycotoxin Analysis

Mycotoxins are toxic secondary metabolites of fungi that spoil food, and severely impact human health (e.g., causing cancer). Therefore, the rapid determination of mycotoxin contamination including deoxynivalenol and aflatoxin B 1 in food and feed samples is of prime interest for commodity importers and processors. While chromatography-based techniques are well established in laboratory environments, only very few (i.e., mostly immunochemical) techniques exist enabling direct on-site analysis for traders and manufacturers. In this study, we present MYCOSPEC - an innovative approach for spectroscopic mycotoxin contamination analysis at EU regulatory limits for the first time utilizing mid-infrared tunable quantum cascade laser (QCL) spectroscopy. This analysis technique facilitates on-site mycotoxin analysis by combining QCL technology with GaAs/AlGaAs thin-film waveguides. Multivariate data mining strategies (i.e., principal component analysis) enabled the classification of deoxynivalenol-contaminated maize and wheat samples, and of aflatoxin B 1 affected peanuts at EU regulatory limits of 1250 μg kg -1 and 8 μg kg -1, respectively. © The Author(s) 2017

Influence function analysis applied to partial least squares

Partial least squares, Influence function analysis, Outliers,