AI Article Synopsis
- Sensor fusion combines different methods to assess food quality, utilizing colorimetric sensor arrays and near-infrared spectroscopy to analyze wheat flour.
- A partial least squares model was used alongside low- and mid-level fusion strategies to quantify the data, leading to significant correlation and prediction accuracy.
- The mid-level fusion model outperformed others with strong performance metrics, indicating that the NIR-CSA approach is effective for predicting free fatty acids in wheat flour.
Article Abstract
The use of sensor fusion, a novel method of combining artificial senses, has become increasingly popular in the assessment of food quality. This study employed a combination of the colorimetric sensor array (CSA) and mobile near-infrared (NIR) spectroscopy to predict free fatty acids in wheat flour. In conjunction with a partial least squares model, Low- and mid-level fusion strategies were used for quantification. Accordingly, performance of the built model was evaluated based on higher correlation coefficients between calibration and prediction (R and R), lower root mean square error of prediction (RMSEP), and a higher residual predictive deviation (RPD). The mid-level fusion coupled PLS model produced superior data fusion findings, with R = 0.8793, RMSECV = 7.91 mg/100 g, R = 0.8747, RMSEP = 6.99 mg/100 g, and RPD = 2.27. The findings of the study suggest that the NIR-CSA fusion approach could be effectively applied to the prediction of free fatty acids in wheat flour.
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| http://dx.doi.org/10.1016/j.saa.2023.122798 | DOI Listing |
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