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. 2024 Nov 10;13(22):3589.
doi: 10.3390/foods13223589.

Visualization of Moisture Content, Reducing Sugars, and Chewiness in Bread During Oral Processing Based on Hyperspectral Imaging Technology

Xiaoyu Tian  1 , Qin Fang  1 , Xiaorui Zhang  1 , Shanshan Yu  1 , Chunxia Dai  2 , Xingyi Huang  1
Affiliations

Visualization of Moisture Content, Reducing Sugars, and Chewiness in Bread During Oral Processing Based on Hyperspectral Imaging Technology

Xiaoyu Tian et al. Foods. .

Abstract

This study evaluated the differences in oral processing and texture perception of breads with varying compositions. The research investigated the dynamic changes in moisture content (MC), reducing sugars (RSs), and chewiness of the bolus formed from white bread (B0) and 50% whole-wheat bread (B50) during oral processing. Hyperspectral imaging (HSI) combined with chemometric methods was used to establish quantitative prediction models for MC, RSs, and chewiness, and to create visual distribution maps of these parameters. The results showed that B0 had a higher moisture content and a faster hydration rate than B50 during the initial stages of oral processing, indicating greater hydrophilicity and ease of saliva wetting. Additionally, the uniformity of moisture distribution in the bolus of B0 was higher than that of B50. B50 exhibited significantly lower RSs content and poorer distribution uniformity compared to B0. The primary differences in chewiness between the two types of bread were observed during the early stages of oral processing, with B50 requiring more chewing effort initially. This study demonstrated that HSI technology can effectively monitor and elucidate the compositional changes in food particles during oral processing, providing new insights into bread texture perception and offering a scientific basis for improving bread processing and texture.

Keywords: bread; chewiness; hyperspectral imaging; oral processing; visual distribution.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Schematic representation of the oral processing of bread, measurement of physical and chemical properties, and hyperspectral images acquisition.
Figure 2
Figure 2
Changes in moisture content (MC), reducing sugars (RSs), and chewiness during bread oral processing.
Figure 3
Figure 3
Spectral profile of bread at different stages of oral processing. (A) Average raw spectral at different stages of oral processing. (B) Spectral preprocessed by SG algorithm. (C) Spectral preprocessed by GF algorithm. (D) Spectral preprocessed by normalize algorithm.
Figure 4
Figure 4
Correlation between predicted and measured values for moisture content (MC), reducing sugars (RSs), and chewiness. The solid line represents the ideal prediction line, while the dashed line indicates the linear regression fit. (A) MC prediction, (B) RSs prediction, and (C) chewiness prediction.
Figure 5
Figure 5
Distribution map of moisture content (MC), reducing sugars (RSs), and chewiness of two types of bread at different stages of oral processing.
Figure 6
Figure 6
Correlation matrix of moisture content (MC), reducing sugars (RSs), chewiness, contrast MC, contrast RSs, and contrast chewiness during bread oral processing. * p ≤ 0.05, ** p ≤ 0.01.
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