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Nondestructive Detecting Maturity of Pineapples Based on Visible and Near-Infrared Transmittance Spectroscopy Coupled with Machine Learning Methodologies

文献类型： 外文期刊

作者： Qiu, Guangjun 1 ; Lu, Huazhong 2 ; Wang, Xu 4 ; Wang, Chen 5 ; Xu, Sai 1 ; Liang, Xin 1 ; Fan, Changxiang 1 ;

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

2.Guangdong Lab Lingnan Modern Agr, Guangzhou 510640, Peoples R China

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

4.Guangdong Acad Agr Sci, Inst Qual Stand & Monitoring Technol Agroprod, Guangzhou 510640, Peoples R China

5.Jiangsu Univ, Sch Food & Biol Engn, Zhenjiang 212013, Peoples R China

关键词： transmittance spectrum; maturity; soluble solids content; pineapple; nondestructive; machine learning

期刊名称：HORTICULTURAE （ 影响因子：3.1； 五年影响因子：3.4 ）

ISSN：

年卷期： 2023 年 9 卷 8 期

页码：

收录情况： SCI

摘要： Pineapple is mainly grown in tropical regions and consumed fresh worldwide due to its attractive flavor and health benefits. With increasing global production and trade volume, there is an urgent need for nondestructive techniques for accurate and efficient detection of the internal quality of pineapples. Therefore, this study is dedicated to developing a nondestructive method for real-time determining the internal quality of pineapples by using VIS/NIR transmittance spectroscopy technique and machine learning methodologies. The VIS/NIR transmittance spectrums ranging in 400-1100 nm of total 195 pineapples were collected from a dynamic experimental platform. The maturity grade and soluble solids content (SSC) of individual pineapples were then measured as indicators of internal quality. The qualitative model for discriminating maturity grades of pineapple achieved a high accuracy of 90.8% by the PLSDA model for unknown samples. Meanwhile, the quantitative model for determining SSC also reached a determination coefficient (R-P(2)) of 0.7596 and a root mean square error of prediction (RMSEP) of 0.7879 ffi Brix by the ANN-PLS model. Overall, high model performance demonstrated that using VIS/NIR transmittance spectroscopy technique coupled with machine learning methodologies could be a feasible method for nondestructive and real-time detection of the internal quality of pineapples.

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