Characterization of the aroma profile of edible flowers using HS-SPME/GC-MS and chemometrics.

In recent years the consumption of edible flowers has gained new popularity, and their use seems destined to grow thanks to their potential as functional elements and their ability to impart aroma to traditional foods. In this study, the volatile profile of several edible flowers was investigated to identify characteristic compounds to be used as product markers. 85 samples belonging to four cultivars were analyzed by HS-SPME/GC-MS. A PLS-DA was used to build a model capable of differentiating the investigated classes. The resulting model correctly predicted over 95% of the validation samples, highlighting a significant difference between the four types of edible flowers. The VIP analysis highlighted 29 compounds relevant for the characterization of different flowers, many of which were biologically active. The study aims to broaden the framework of objectively measurable tools useful for enhancing the qualitative peculiarity of one product compared to another and offering growth opportunities to emerging food chains.

Effects of thermal treatments on durum wheat pasta flavour during production process: A modelling approach to provide added-value to pasta dried at low temperatures.

Pasta is a key element of the Mediterranean Diet and it has been declared by Unesco intangible cultural heritage of humanity. Despite seems a simple food, only made of semolina and water, pasta is produced following a multi-step process that strongly affect the final product. Drying stage is the one that has the greater influence on its organoleptic/nutritional characteristics. This study aimed to analyse the flavour of pasta to test whether the different drying treatments (High Temperature-Short time or Low Temperature-Long time) have a direct impact on its composition and consequently whether they could influence the end-product quality. The headspace solid-phase microextraction was optimized using an experimental design and 52 samples were analysed by HS-SPME/GC-MS and classified by PLS-DA. The resulting classification model (validated by repeated double cross-validation and permutation tests) allowed correctly predicting more than 80% of samples, confirming that drying may have a significant impact on pasta flavour.

Monitoring of contaminants in recycled paperboard for food contact applications.

BACKGROUND: The lack of harmonized European legislation on food packaging led the Confederation of European Paper Industries to the proposal of a voluntary Industry Guideline for the compliance of paper and board materials for food contact applications. In the present work, a previously established method for the simultaneous determination of contaminants commonly found in recycled paperboard was improved and its applicability as a quality control tool in the paper industry was also assessed. The method involves a sample pre-treatment followed by gas chromatography/mass spectrometry (GC/MS) analysis. RESULTS: For analysis, paperboard samples were collected both from three sections of the same reel and from different reels belonging to the same production run. Results highlighted no significant differences in terms of contaminant distribution among samples, which ensured good sampling representativeness. The performance of the method was considerably improved in terms of linearity range, limits of detection and quantification (5- to 2-fold lower) by using a quadrupole GC/MS system instead of an ion trap GC/MS system. CONCLUSION: The proposed method could offer a key strategy for analysis of benzophenone derivatives, diisopropyl naphthalene and phthalates in recycled paperboard in order to assess compliance of food packaging with the voluntary limits recommended by the Industry Guideline. © 2016 Society of Chemical Industry.

Characterization of the Authenticity of Pasta di Gragnano Protected Geographical Indication Through Flavor Component Analysis by Gas Chromatography-Mass Spectrometry and Chemometric Tools.

An authentication study based on headspace solid-phase microextraction/GC-MS was performed with a set of 60 samples representative of traditional "Pasta di Gragnano protected geographical indication (PGI)" and the most common Italian pasta brands. Multivariate chemometric tools were used to classify the samples based on the chemical information provided from 20 target flavor compounds, including Maillard reaction and lipid oxidation products. Pattern recognition by principal component analysis and linear discriminant analysis showed a natural grouping of samples according to the drying process adopted for their production (i.e., the traditional Cirillo method versus a high-temperature approach). Subsequently, soft independent modeling by class analogy (SIMCA) and unequal dispersed classes (UNEQ) were used to build class models at 95% confidence and 100% sensitivity levels (forced models) for predictive classification purposes. The good performance obtained from the models in terms of cross-validation efficiency (SIMCA, 57.01%; UNEQ, 86.60%; 100% for both forced models) highlighted that targeted analysis of flavor profiles could be used to assess the authenticity of Pasta di Gragnano PGI samples. Hence, the proposed method may help to protect Pasta di Gragnano PGI from label frauds by verifying whether samples comply with statements concerning drying process conditions as stated in the product specification.