**1. Introduction**

Shrimp (*Penaeus vannamei*) harvesting is one of the most economically significant fishing activities in China attracting attention from consumers due to the high protein content and rich nutritional composition of shrimp [1,2]. According to the China Fisheries Statistical Yearbook, the *Penaeus vannamei* aquaculture production in China was 1.1977 millions of tons in 2020. However, the shrimp harvest suffers from rapid deterioration due to biochemical reactions and microbial activity after death [3–5], which directly affect its shelf life. Hot air drying, a common and practical method of drying seafood, can prolong the shelf life of the shrimp harvest [6–8]. As a foodstuff, dried shrimp has the advantages of a unique flavor, rich nutrition, easy storage, and high consumer demand [9,10]. However, drying is a complex process involving water evaporation, protein degradation and denaturation, and the formation of flavor compounds [11,12]. Ineffective drying can adversely impact the color, texture, and nutrition attributes of the dried shrimp product [13]. Therefore, it is imperative to monitor and control critical quality parameters during the drying process to ensure consistency among batches, as well as uniformity of the end-product.

Current analytical methods employed to measure these quality characteristics in factories, such as oven drying and texture profile analysis (TPA), are time-consuming, destructive, cumbersome, and restricted to off-line usage [14–16]. Therefore, it is necessary to develop an effective, rapid, nondestructive, and real-time detection method for dried shrimp quality control. With the development of optical and spectroscopic technologies, hyperspectral imaging (HSI) has been successfully applied to evaluate food safety and

**Citation:** Xu, W.; Zhang, F.; Wang, J.; Ma, Q.; Sun, J.; Tang, Y.; Wang, J.; Wang, W. Real-Time Monitoring of the Quality Changes in Shrimp (*Penaeus vannamei*) with Hyperspectral Imaging Technology during Hot Air Drying. *Foods* **2022**, *11*, 3179. https://doi.org/10.3390/ foods11203179

Academic Editor: James Carson

Received: 25 August 2022 Accepted: 7 October 2022 Published: 12 October 2022

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quality, monitor food preparation processes, and identify adulteration [17,18]. HSI simultaneously captures both spectral and spatial information of a sample by integrating spectroscopic and computer vision or imaging techniques into one system [19–21]. Another unique characteristic is that HSI generates a visual distribution map of reference values to enable the prediction and quantification of internal sample constituents, as well as the simultaneous determination of their location on the sample surface [22,23].

Based on these advantages, HSI has been applied to monitor quality changes in meat, fruit, vegetable, and cereal foods during drying. For example, Sun et al. used HSI to monitor the moisture contents of scallops during drying, and reported a model prediction accuracy of greater than 0.9 [24]. Moreover, Netto et al. used HSI to evaluate the water uniformity of the melon drying process under different pretreatments by visualizing the moisture content in the samples [25]. However, most existing studies only employ spectral information for quality indicator evaluation, ignoring image information, such as color and texture in their modeling. To improve prediction accuracy, the importance of combining spectral and spatial HIS information has been emphasized by several researchers. This technique has been used to discriminate between different breeds of chicken [26], predict the storage time and moisture content of cooked beef [19], and assess the fat and moisture contents of salmon [27]. The results indicate that a combination of spectral and spatial HSI data is more comprehensive and intuitional than conventional analyses. Furthermore, considering that the shrimp drying process involves color and texture changes, it is crucial to include image information in the spectral model for quality control. To the best of our knowledge, there are no previous data fusion studies on the visualization of moisture and other quality indicators in dried shrimp. Additionally, previous studies only predicted moisture and other quality indicator contents, neglecting the link between moisture distribution and other quality characteristics, which may clarify the mechanisms governing shrimp quality changes during the drying process.

Therefore, the purpose of the current study is to explore the correlation between shrimp water distribution state and other quality indices and combine spatial and spectral information of the hypercube to measure shrimp quality changes during the drying process. The specific objectives are: (1) to quantify changes in shrimp during hot air drying through moisture content measurement, color properties (*L*\*, *a*\*, *b*\*) analysis, and texture profile analysis (hardness, adhesiveness, elasticity, stickiness, and chewiness); (2) to monitor the dynamic water sate and water migration by low field magnetic resonance (LF-NMR) and determine the correlation between water distribution and other quality indicators by Pearson correlation analysis; (3) to acquire hyperspectral reflectance images of shrimps at different drying stages, as well as spectral data and color and textural features from the region of interest (ROI); (4) to establish partial least squares regression (PLSR) and least squares support vector machine (LSSVM) models based on spectral, image, and fusion information; and (5) to visualize shrimp quality at the pixel level using the optimal models.
