数字农科院2.0

Infrared spectroscopy combined with deep learning to describe the textural properties of cooked rice from raw materials: revealing spectral variations and internal correlations during processing

文献类型： 外文期刊

作者： Rui Tang;Ting Yu;Zi Li;Junru Wu;Xiaoming Zheng;Leiqing Pan;Yang Chen;Kun Duan;Hui Dong;Weijie Lan

作者机构：

关键词： Cooked rice;Deep learning;Feature selection;Infrared spectroscopy;Texture properties

期刊名称： Food Control

ISSN： 0956-7135

年卷期： 2025 年 180 卷

页码：

收录情况： SCIE（2025版）

摘要： This study investigates the potential to describe the textural properties of cooked rice directly based on their infrared spectroscopy of raw materials, including hardness, adhesiveness, cohesiveness, springiness, gumminess, and chewiness. Near-infrared (NIR) and mid-infrared (MIR) spectroscopy were collected from a large variability of 122 rice varieties in Asian region. The analysis of spectral variance highlighted the thermal processing induced intensive variations of NIR wavelength at 1380 nm and MIR wavenumbers at 890 cm−1. Furthermore, specific spectral regions around 2000 nm and 980 cm−1 showed strong correlations during rice cooking, associated with starch and moisture changes. Convolutional neural networks models based on the NIR and MIR spectrum of cooked rice can satisfactorily predict their textural properties, particularly the hardness with Rv2 of 0.92 and 0.95, respectively. Notably, support vector machine models based on the selected MIR and NIR spectral variables of raw materials can directly describe the texture of cooked rice, with the Rv2 ≥ 0.90. These results demonstrate that infrared spectroscopy combined deep learning to describe the textural properties of cooked rice from raw materials.

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