Fusing hyperspectral imaging and electronic nose data to predict moisture content in Penaeus vannamei during solar drying.

The control of moisture content (MC) is essential in the drying of shrimp, directly impacting its quality and shelf life. This study aimed to develop an accurate method for determining shrimp MC by integrating hyperspectral imaging (HSI) with electronic nose (E-nose) technology. We employed three different data fusion approaches: pixel-, feature-, and decision-fusion, to combine HSI and E nose data for the prediction of shrimp MC. We developed partial least squares regression (PLSR) models for each method and compared their performance in terms of prediction accuracy. The decision fusion approach outperformed the other methods, producing the highest determination coefficients for both calibration (0.9595) and validation sets (0.9448). Corresponding root-mean square errors were the lowest for the calibration set (0.0370) and validation set (0.0443), indicating high prediction precision. Additionally, this approach achieved a relative percent deviation of 3.94, the highest among the methods tested. The findings suggest that the decision fusion of HSI and E nose data through a PLSR model is an effective, accurate, and efficient method for evaluating shrimp MC. The demonstrated capability of this approach makes it a valuable tool for quality control and market monitoring of dried shrimp products.

Quantitative analysis of Chinese steamed bread staling using NIR, MIR, and Raman spectral data fusion.

For efficient and comprehensive detection of the staling degree of Chinese steamed bread (CSB), staled CSB samples stored for 0-16 days were prepared and analyzed using near-infrared (NIR), mid-infrared (MIR), and Raman spectroscopy combined with data fusion. Among three data fusion schemes, decision-level fusion achieved the best performance when quantifying the CSB staling degree according to the soluble starch amylose fraction, relative crystallinity, and hardness, with determination coefficients and root mean square errors for the validation set in the range of 0.928-0.986 and 0.015-1.290, respectively. The relative percent deviation values of the three indicators increased to 8.362, 4.735, and 3.617, respectively. These results indicate that the combination of NIR, MIR, and Raman spectroscopy as a decision-level fusion scheme can achieve efficient, comprehensive, and accurate quantification of the staling degree of CSB. This research has important applications for food quality, safety, and shelf-life evaluations.