LC-HRMS for Characterizing Durum Wheat Pasta Production Variability and Consumer Overall Liking. (23rd November 2019)

• Record Type: Journal Article
• Authors: Lambertini, Francesca; Cavanna, Daniele; Catellani, Dante; Vigni, Mario Li; Durante, Caterina; D'Alessandro, Alessandro; Suman, Michele
• Is Part Of: Journal of AOAC International. Volume 101:Number 2(2018)
• Page Start: 360
• Page End: 366
• Publication Date: 2019-11-23
• Subjects: Agricultural chemistry -- Periodicals; Food -- Analysis -- Periodicals; 543
• DOI: 10.5740/jaoacint.17-0209
• Languages: English
• ISSNs: 1060-3271

Abstract

Abstract: Semolina pasta represents one of the most important dishes in Italian cuisine worldwide. Italy is the leader in its production and, recently, the worldwide diffusion of its production has begun to grow tremendously. The perceived quality of a food product, such as pasta, is a key feature that allows a company to increase and maintain the competitive advantage of a specific brand. The overall flavor perception of the consumer, therefore, has become as important as other key quality factors such as texture and color; thus, the food industry needs to meet consumer expectations and needs the tools to objectively "measure" the quality of food products. Untargeted fingerprinting by means of coupling LC with high-resolution MS (HRMS) has been well received within the analytical community, and different studies exploiting this approach for the characterization of high-value food products have recently been reported in the literature. In the present work, a tentative application of the sensomics approach to cluster analysis of semolina pasta obtained using different production conditions was developed to objectively define target molecules that correlate with consumer overall liking of an industrial standard product. Principal component analysis of chemical and physical testing, GC-MS, LC-HRMS, and sensory data were performed with the aim of identifying the main parameters to discern similarities and differences among samples and clustering them according to these features. The correlation between analytical data and compounds related to sensory data was further investigated, and lastly, a partial least-squares regression model for the prediction of consumer overall liking was reported.