Spectral fingerprinting and chemical mapping of plant-based meat analogues using portable hyperspectral imaging system and multivariate analysis
| dc.contributor.author | Dhanapal, Logesh | |
| dc.contributor.examiningcommittee | Bandara, Nandika (Food and Human Nutritional Sciences) | |
| dc.contributor.examiningcommittee | Malalgoda, Maneka (Food and Human Nutritional Sciences) | |
| dc.contributor.supervisor | Erkinbaev, Chyngyz | |
| dc.date.accessioned | 2024-03-21T15:41:40Z | |
| dc.date.available | 2024-03-21T15:41:40Z | |
| dc.date.issued | 2024-03-20 | |
| dc.date.submitted | 2024-03-20T21:24:28Z | en_US |
| dc.degree.discipline | Biosystems Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | |
| dc.description.abstract | The ultra-processed nature and multiple ingredients of plant-based meat analogues cause excessive-quality variation during storage suggesting the need for real-time quality monitoring and chemical mapping techniques. This research utilizes a non-destructive portable hyperspectral imaging technique in the visible and near-infrared ranges (400–1000 nm) to predict the quality (CIE- L*, a*, b*, moisture, texture) of plant-based meat burger patties followed by mapping of the quality distribution attributed to major ingredients and color variations over the storage period. Firstly, the study analyzed the effect of 10-30% textured vegetable protein (TVP), 5-25% pea protein (PP), and storage time (14 days) on quality using conventional quality assessment approaches, revealing the instability of overall quality even over a short storage period. Principal Components Analysis (PCA) applied to HSI data indicated that the primary variance stems from discoloration and moisture loss due to storage days, TVP, and PP. This variance is associated with the spectral variations at 500–650nm and 950–980 nm. Developed Partial Least Square Regression (PLSR) models possessed good prediction accuracies for a*, b*, moisture, and hardness. Pixel-by-pixel prediction maps highlighted the non-uniform component distribution. Secondly, the study examines the role of HSI in addressing the color instability of PBMB by visualizing color evolution during a 10-day storage period, influenced by TVP, PP, fava bean protein, and fat mimic. Simplified PLSR prediction models with improved accuracies were developed with interval-PLS (iPLS), recursive-PLS (rPLS), and genetic algorithm (GAs). Prediction maps showed the patterns of color deterioration. Findings highlighted that color evaluation of heterogenous plant-based food formulations using single-point colorimeters is insufficient to represent the whole sample. This research illustrated that spectral fingerprinting could visualize component distribution in complex food formulations, aiding in formulation optimization and understanding ingredient interactions. The findings are crucial for informed decisions on grading, labeling, and storage. Overall, VNIR-HSI in combination with multivariate analysis proves to be a valuable analytical tool for real-time quality inspection in plant-based food production, highlighting the potential of digital technologies in promoting sustainable foods. | |
| dc.description.note | May 2024 | |
| dc.description.sponsorship | University of Manitoba Graduate Fellowship (UMGF) 2023-2024 | |
| dc.identifier.uri | http://hdl.handle.net/1993/38065 | |
| dc.language.iso | eng | |
| dc.rights | open access | en_US |
| dc.subject | Meat alternatives | |
| dc.subject | Food quality | |
| dc.subject | Non-destructive food testing | |
| dc.subject | Smart sensing | |
| dc.subject | Hyperspectral imaging | |
| dc.subject | Chemometrics | |
| dc.subject | Plant proteins | |
| dc.title | Spectral fingerprinting and chemical mapping of plant-based meat analogues using portable hyperspectral imaging system and multivariate analysis | |
| dc.type | master thesis | en_US |
| local.subject.manitoba | no | |
| oaire.awardNumber | RGPIN-2021-03340 | |
| oaire.awardTitle | Discovery Grants Program - Individual | |
| oaire.awardURI | https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=743380 | |
| project.funder.name | Natural Sciences and Engineering Research Council of Canada |