Abstract

Crackers are a popular and convenient snack; however, the baking process can produce neo-formed contaminants (NFCs), such as acrylamide (AA) and 5-hydroxymethylfurfural (HMF), through non-enzymatic browning reactions. Conventional analytical methods for quantifying these NFCs are complex, labor-intensive, and require specialized personnel. The main objective of this study was to develop computer vision (CV) models based on surface digital image analysis for the rapid prediction of AA and HMF in crackers. Therefore, five baking temperatures (160, 170, 180, 190, and 200 degrees C) and times (15, 20, 25, 30, and 35 min) were tested and analyzed using CV alongside conventional analytical methods. CV estimates and analytical measurements for AA (4.35-829 & micro;g kg-1) and HMF (0.004-105.4 mg kg-1) contents were compared using cross-validation with a "leave-one-treatment-out" approach. The average error for missing measurements was 3.10% for AA and 3.28% for HMF, validating CV as an efficient tool for the rapid estimation of these NFCs in crackers. Among the cracker samples with AA content below the EU benchmark of 400 mu g/kg, evaluated using the Check-All-That-Apply (CATA) test, consumers preferred the samples baked at 180 degrees C for 25 min, which also exhibited the lowest levels of both AA and HMF.