In food manufacturing, the quality control procedure is a critical activity that consists in organizing, measuring, tracking, and filing the conditions of the production process and the final product, with the goal of guaranteeing the designed quality standard. During the last 30 years, due to a mounting concern by both consumers and lawmakers, the definition of quality and the application of quality control improved drastically, and new methodologies have been developed to ensure better control of food production and to understand the effect of raw materials and the process condition on the final quality of the food product. This thesis discusses the approaches to quality control procedures in food manufacture, focusing on the relationship between the conditions of the process and the quality profile of the final product, testing in a real-case scenario of a complex production process advanced data analysis procedures.
The statistical and analytical procedures proposed have been applied in a real case studio from Trentingrana cheese production, a dairy consortium in the northeast region of Italy producing a ripened semi-artisanal hard cheese under the Protected Denomination of Origin (PDO) of Grana Padano. The aim is developing tailored statistical procedures that infer the effect of the critical factors of production on quality properties of this PDO product considering its semi-artisanal production process and the presence of multiple confounding factors. The statistical analyses were applied to a dataset of measurements of physical, sensory, and chemical properties collected on cheese wheels sampled systematically to represent the variability of the production of the Trentingrana wheels over two years of production.
In the first introductory chapter, after a review of the different definitions of quality, the most important quality parameters for a food product and the standard measurement techniques adopted in quality control are presented. Then, in the chapter 2, the standard procedures of data analysis are reviewed, as well as the new approaches derived from the context of the foodomic sciences and machine learning models for the analysis of quality control data in food manufacturing. Two implemented and tested practical statistical procedures in the context of the Trentingrana consortium are reported: the results are discussed according to the objectives of the quality control process, the type of data, and the organization of food production. In the first case, reported in chapter 3, Linear Mixed Model ANOVA Simultaneous Component Analysis (LMM-ASCA) was developed to investigate the effect of the dairy factory, the bimester of production, and the variability within a cheese wheel using colorimetric and textural measurements. In the second case, reported in chapter 4, a standard ASCA model with the addition of a blocking factor to include systematic error was developed to investigate the relationship between the dairy factory and bimester of production and the volatile organic compounds (VOCs) profile of Trentingrana cheese wheels. In addition, in chapter 5, an approach to relate physical measurements on Trentingrana samples with sensory evaluations of texture by a trained panel is presented. The objective of this procedure is to incorporate the quality control procedure information from different quality parameters. The development of the Partial Least Squares (PLS) predictive model, its validation, and the evaluation of its performances are discussed. In the last section (chapter 6), the development of an image analysis procedure to measure the visual quality of the rind thickness of cheese wheels is reported, comparing the performances of two different algorithms. The data analysis tools proposed in this thesis have been proved to be useful for exploring, inferring, and plotting the process quality properties and suitable for analyzing complex and unbalanced experimental designs. Furthermore, the data analysis procedures proposed improve quality control activity both at the process level and at the product level, increasing the information that is possible to extract from the measurement collected in a context where standard statistical approaches cannot infer significant information.
| Identifer | oai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/380189 |
| Date | 13 June 2023 |
| Creators | Ricci, Michele |
| Contributors | Ricci, Michele, Aprea, Eugenio, Gasperi, Flavia |
| Publisher | Università degli studi di Trento, place:TRENTO |
| Source Sets | Università di Trento |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | firstpage:1, lastpage:155, numberofpages:155 |
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