## Results and Discussion  
### QIM Development  
The parameters that were selected and included in the preliminary QIM were based on the description of external characteristics (skin, texture, odor, appearance of eyes, gills, and anus), as they showed clear changes during the first storage observations (Figure 1). During the development of the preliminary QIM tables, successive modifications were made to obtain the highest possible number of descriptors. Figure 1: Greater amberjack (GrA) main visual changes during storage at 0 \({}^{\circ}\)C on ice. The panel performance evaluation was conducted by obtaining the QI for each specimen during the second experiment, when the attributes for the evaluation were fixed. A significant correlation (\(p<0.05\)) between all assessors' results was obtained. The slope and \(r^{2}\) for each panelist (P) were: P1 (0.9781-0.8945), P2 (1.0083-0.9481), P3 (1.0706-0.9445), P4 (1.0840-0.8510), P5 (1.0754-0.8443), P6 (1.1283-0.8381). The final scheme suggested in this work includes 25 demerit points, describing four quality attributes with 9 sensory attributes (Table 2). Figure 2 shows the average points registered for each individual attribute as a function of the storage days on ice. It is possible to see that all quality attributes present an increasing trend during storage. QI is the sum of demerit points of the different quality attributes (Figure 2b). The QI for GrA showed a significant linear increase during storage on ice (\(p<0.01\)), with a high coefficient of determination (\(r^{2}=0.9771\)) ranging from 0.4 to 25 from day 0 to 17, respectively. Principal component analysis (PCA) analysis indicated that the proportion of variance for the first two principal components was 96.2%, PC1-93.8%, and PC2-2.4% (Figure 3a). All variables presented a high positive correlation with the first component; therefore, they contributed with the same approximate weight to QI. Analysis of variable importance confirmed each attribute's importance. For this scheme, values above 0.8 were considered relevant for the QIM protocol [19]. Thus, the variable importance for each attribute was, in decreasing order: odor-0.959; gill odor-0.958; eye color-0.950; eye shape-0.947; gill mucus-0.946; gill color-0.939; skin-0.919; firmness-0.905, and anus-0.815. However, the correlation results for QI values showed a higher correlation (0.991) than for single attributes. This indicates that the proposed QIM scoring scheme is suitable to follow the deterioration progress and correlate it with time, which is in accordance with other developed QIM methods [20; 21]. For the GrA sensory scheme, the odor was one of the main contributors to the determination of deterioration progress. At the start of the trial, the skin and gill odors were described as seaweed or fresh and, from day 3, became neutral. The perception of the off-odors associated with degradation occurred on day 11, but the intensity was only considered sufficient for rejection on day 12. The QI average equations of observed and predicted values were subjected to the partial least-squares (PLS) regression model. The results showed that the obtained linear regression model had an estimation error of 0.54 days and a suitable linear regression (\(r^{2}=0.995\)) (Figure 3b). The predicted and measured values were statistically similar (\(p<0.05\)). Figure 2: **(a) Evolution of different QIM parameters for GrA (mean values of the six assessors). (b) QIM calibration curve (QI versus days on ice) for GrA during ice storage (\(0\pm 1\,^{\circ}\)C). Points that seem without error bars have in fact error bars smaller than the symbol size. **  
The developed QIM scheme successfully described different freshness levels of GrA and sustained individual panelists variation. Taking into consideration all sensory data, the rejection was estimated to occur between days 11 and 12, mainly due to the presence of significant off-odors on the skin and gills (rancid odors).