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ARS Home » Pacific West Area » Albany, California » Western Regional Research Center » Healthy Processed Foods Research » Research » Publications at this Location » Publication #258475

Research Project: New Sustainable Processing Technologies to Produce Healthy, Value-Added Foods from Specialty Crops and their Co-Products

Location: Healthy Processed Foods Research

Title: Prediction of processing tomato peeling outcomes

item Milczarek, Rebecca
item Mccarthy, Michael - University Of California

Submitted to: Journal of Food Processing and Preservation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/17/2010
Publication Date: N/A
Citation: N/A

Interpretive Summary: Fruit processors seek methods for predicting the quality of their finished products from measurements of their raw materials (individual fruit). Such predictions will ensure optimal allocation and processing of each fruit, which will result in raw material cost savings, improved product quality, wastewater reduction, and energy efficiency. In the case of processing tomatoes, peeling outcome is the metric of interest. This study demonstrates the feasibility of predicting final product quality using Magnetic Resonance (MR) imaging of raw food materials.

Technical Abstract: Peeling outcomes of processing tomatoes were predicted using multivariate analysis of Magnetic Resonance (MR) images. Tomatoes were obtained from a whole-peel production line. Each fruit was imaged using a 7 Tesla MR system, and a multivariate data set was created from 28 different images. After imaging, the fruit were individually tagged and processed in a pilot peeling system. An expert grader then assessed the peeling outcome for each fruit; outcomes included “Whole Peel”, “Some Skin Attached”, and 7 others. The multivariate analysis techniques of Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modelling of Class Analogy (SIMCA) were used to predict peeling outcome from the 28 MR images. The PLS-DA model for the “Whole Peel” (best) outcome correctly classified 81% of the fruit that were in this category. The SIMCA model performed well for rejecting non-“Whole Peel” fruit but did not perform as well for identification of “Whole Peel” fruit.