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Nondestructively Determining Soluble Solids Content of Blueberries Using Reflection Hyperspectral Imaging Technique

文献类型： 外文期刊

作者： Qiu, Guangjun 1 ; Chen, Biao 2 ; Lu, Huazhong 3 ; Yue, Xuejun 2 ; Deng, Xiangwu 4 ; Ouyang, Haishan 5 ; Li, Bin 1 ; Wei, Xinyu 1 ;

作者机构： 1.Guangdong Acad Agr Sci, Inst Facil Agr, Guangzhou 510640, Peoples R China

2.South China Agr Univ, Coll Elect Engn, Coll Artificial Intelligence, Guangzhou 510642, Peoples R China

3.Guangdong Acad Agr Sci, Guangzhou 510640, Peoples R China

4.Guangdong Univ Petrochem Technol, Coll Elect & Informat Engn, Maoming 525000, Peoples R China

5.Guangzhou Kaibang Informat Technol Co Ltd, Guangzhou 510100, Peoples R China

关键词： blueberry; soluble solids content; nondestructive detection; hyperspectral imaging; machine learning; feature selection

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 10 期

页码：

收录情况： SCI

摘要： Effectively detecting the quality of blueberries is crucial for ensuring that high-quality products are supplied to the fresh market. This study developed a nondestructive method for determining the soluble solids content (SSC) of blueberry fruit by using a near-infrared hyperspectral imaging technique. The reflection hyperspectral images in the 900-1700 nm waveband range were collected from 480 fresh blueberry samples. An image analysis pipeline was developed to extract the spectrums of blueberries from the hyperspectral images. A regression model for quantifying SSC values was successfully established based on the full range of wavebands, achieving the highest RP2 of 0.8655 and the lowest RMSEP value of 0.4431 degrees Brix. Furthermore, three variable selection methods, namely the Successive Projections Algorithm (SPA), interval PLS (iPLS), and Genetic Algorithm (GA), were utilized to identify the feature wavebands for modeling. The models calibrated from feature wavebands generated an RMSEP of 0.4643 degrees Brix, 0.4791 degrees Brix, and 0.4764 degrees Brix, as well as the RP2 of 0.8507, 0.8397, and 0.8420 for SPA, iPLS, and GA, respectively. Furthermore, a pseudo-color distribution diagram of the SSC values within blueberries was successfully generated based on established models. This study demonstrated a novel approach for blueberry quality detection and inspection by jointly using hyperspectral imaging and machine learning methodologies. It can serve as a valuable reference for the development of grading equipment systems and portable testing devices for fruit quality assurance.

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