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

Application of experimental design in HPLC method optimisation for the simultaneous determination of multiple bioactive cannabinoids

The scientific interest in Cannabis sativa L. analysis has been rapidly increasing in recent years, especially for what concerns cannabinoids, plant secondary metabolites which are well known for having many biological properties. High-performance liquid chromatography (HPLC) is frequently used for both the qualitative and quantitative analysis of cannabinoids in plant extracts from C. sativa and its derived products. Many studies have been focused on the main cannabinoids, such as ∆⁹-tetrahydrocannabinolic acid (∆⁹-THCA), cannabidiolic acid (CBDA), cannabigerolic acid (CBGA) and their decarboxylated derivatives, such as ∆⁹-tetrahydrocannabinol (∆⁹-THC), cannabidiol (CBD) and cannabigerol (CBG). In addition to the abovementioned compounds, the plant produces other metabolites of the same chemical class, and some of them have shown interesting biological activities. In the light of this, it is important to have efficient analytical methods for the simultaneous separation of cannabinoids, which is quite complex since they present similar chemical-physical characteristics. The present work is focused on the use of the Design of Experiments technique (DoE) to develop and optimise an HPLC method for the simultaneous separation of 14 cannabinoids. Experimental design optimisation was applied by using a Central Composite Face-Centered design to achieve the best resolution with minimum experimental trials. Five significant variables affecting the chromatographic separation, including ammonium formate concentration, gradient elution, run time and flow rate, were studied. A multivariate strategy, based on Principal Component Analysis (PCA) and Partial Least Squared (PLS) regression, was used to define the best operative conditions. The developed method allowed for the separation of 12 out of 14 cannabinoids. Due to co-elution phenomena, HPLC coupled with a triple quadrupole mass analyser (HPLC-ESI-MS/MS) was applied, monitoring the specific transitions of each compound in the multiple reaction monitoring (MRM) mode. Finally, the optimised method was applied to C. sativa extracts having a different cannabinoid profile to demonstrate its efficiency to real samples. The methodology applied in this study can be useful for the separation of other cannabinoid mixtures, by means of appropriate optimisation of the experimental conditions.

Extension of the shelf-life of fresh pasta using modified atmosphere packaging and bioprotective cultures

Microbial stability of fresh pasta depends on heat treatment, storage temperature, proper preservatives, and atmosphere packaging. This study aimed at improving the microbial quality, safety, and shelf life of fresh pasta using modified atmosphere composition and packaging with or without the addition of bioprotective cultures (Lactobacillus acidophilus, Lactobacillus casei, Bifidobacterium spp., and Bacillus coagulans) into semolina. Three fresh pasta variants were made using (i) the traditional protocol (control), MAP (20:80 CO₂:N₂), and barrier packaging, (ii) the experimental MAP (40:60 CO₂:N₂) and barrier packaging, and (iii) the experimental MAP, barrier packaging, and bioprotective cultures. Their effects on physicochemical properties (i.e., content on macro elements, water activity, headspace O₂, CO₂ concentrations, and mycotoxins), microbiological patterns, protein, and volatile organic compounds (VOC) were investigated at the beginning and the end of the actual or extended shelf-life through traditional and multi-omics approaches. We showed that the gas composition and properties of the packaging material tested in the experimental MAP system, with or without bioprotective cultures, positively affect features of fresh pasta avoiding changes in their main chemical properties, allowing for a storage longer than 120 days under refrigerated conditions. These results support that, although bioprotective cultures were not all able to grow in tested conditions, they can control the spoilage and the associated food-borne microbiota in fresh pasta during storage by their antimicrobials and/or fermentation products synergically. The VOC profiling, based on gas-chromatography mass-spectrometry (GC-MS), highlighted significant differences affected by the different manufacturing and packaging of samples. Therefore, the use of the proposed MAP system and the addition of bioprotective cultures can be considered an industrial helpful strategy to reduce the quality loss during refrigerated storage and to increase the shelf life of fresh pasta for additional 30 days by allowing the economic and environmental benefits spurring innovation in existing production models.

Extraction of Intangible Cultural Heritage Visual Elements by Deep Learning and Its Application in Grassland Tourism of the Silk Road Culture

Inner Mongolia is rich in grassland tourism resources, and the development of grassland tourism is of great significance to Inner Mongolia tourism and promotion of grassland protection. To better promote the grassland tourism of the Silk Road culture, the Conditional Global Area Network (CGAN) and Morphology Connected Component Chan-Vase (MCC-CV) algorithm are used to enhance and segment the traditional embroidery patterns in Inner Mongolia. Firstly, the generative adversarial network (GAN) is optimized, and a new GAN is proposed with the feature vector extracted from the convolutional neural network (CNN) as the constraint condition. Secondly, the automatic segmentation algorithm of embroidery based on the MCC-CV model is proposed, and finally, the proposed algorithm is tested. The test results demonstrate that after 8000 iterations of the proposed image-enhancement algorithm, its personalized features are enhanced, and the segmentation accuracy of the proposed image segmentation algorithm is 60%. The proposed algorithm provides some ideas for the application of deep learning (DL) technology in the grassland tourism of the Silk Road culture and also helps operators to accurately grasp the market and make tourists more comfortable and pleasant.

Pasta-Making Process: A Narrative Review on the Relation between Process Variables and Pasta Quality

Pasta is an increasingly popular food worldwide and different formulations have been developed to improve its nutritional profile. Semolina that is high both in protein and gluten content is recognized as the ideal raw material to produce conventional dry pasta. When alternative raw materials are used, an understanding of the relationship between processing variables and pasta quality is crucial in order to optimize the redesign of the production process. This review aims to: (1) investigate the main challenges of the pasta-making process, highlighting the processing variables that most affect pasta quality; and (2) indicate the unknown factors that influence the pasta-making process and which need to be studied. After overviewing the last twenty years of research in the pasta sector, the interplay/relationship between processing variables and pasta quality is examined, together with the main innovations proposed for each step of pasta processing. An analysis of all the variables involved in the process and their influence on each other will elucidate how to optimize certain parameters to ensure the production of pasta with the desired characteristics.

Geographical Discrimination of Bell Pepper (Capsicum annuum) Spices by (HS)-SPME/GC-MS Aroma Profiling and Chemometrics

Dried and ground red pepper is a spice used as seasoning in various traditional dishes all over the world; nevertheless, the pedoclimatic conditions of the diverse cultivation areas provide different chemical characteristics, and, consequently, diverse organoleptic properties to this product. In the present study, the volatile profiles of 96 samples of two different ground bell peppers harvested in diverse Italian geographical areas, Altino (Abruzzo) and Senise (Lucania), and a commercial sweet paprika, have been studied by means of headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). The investigation of their volatile profile has led to the identification of 59 analytes. Eventually, a discriminant classifier, Partial Least Squares Discriminant Analysis (PLS-DA), was exploited to discriminate samples according to their geographical origin. The model provided very accurate results in external validation; in fact, it correctly classified all the 30 test samples, achieving 100% correct classification (on the validation set). Furthermore, in order to understand which volatiles contribute the most at differentiating the bell peppers from the different origins, a variable selection approach, Variable Importance in Projection (VIP), was used. This strategy led to the selection of sixteen diverse compounds which characterize the different bell pepper spices.