Nondestructive detection method for pineapple water core based on visible/near infrared spectroscopy

被引:2
作者
Xu S. [1 ,2 ]
Lu H. [2 ]
Wang X. [1 ,2 ]
Qiu G. [1 ,2 ]
Wang C. [1 ,2 ]
Liang X. [1 ,2 ]
机构
[1] Institute of Quality Standard and Monitoring Technology for Agro-products, Guangdong Academy of Agricultural Sciences, Guangzhou
[2] Guangdong Academy of Agricultural Sciences, Guangzhou
来源
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | 2021年 / 37卷 / 21期
关键词
Models; Nondestructive detection; Pineapple; Visible/near infrared spectroscopy; Water core;
D O I
10.11975/j.issn.1002-6819.2021.21.033
中图分类号
学科分类号
摘要
Water core is a serious physiological disorder of pineapple in recent years. Effective detection of internal water core is highly urgent for the market quality of pineapple after post-harvest treatments. In this study, A nondestructive detection platform was lab-developed for the water core of pineapple using Visible/Near-infrared (VIS/NIR) spectroscopy. The optimal parameters of the platform were set, where the integral time of 400-1 100 nm and 900-1 700 nm spectrometer were 600 and 2 000 ms, respectively, the intensity of light source was 500 W, the distance between the optical fiber and tray was 30 mm, the distance between the tray and input optical hole was 84 mm, while, all the light, input optical hole, pineapple sample, output optical hole, and optical fiber were in the same horizontal line. Three settings of spectrum wavelength (400-1 100 nm VIS/NIR spectrum, 900-1 700 nm NIR spectrum, and 400-1 700 nm VIS/NIR spectrum) were applied for the pineapple sampling. After that, the pineapple was cut open to artificially and immediately record the water core. The Savitzky Golay (SG) and Standard Normal Variate (SNV) were also applied for the subsequent data processing. Furthermore, the extraction of the feature was conducted using the Successive Projections Algorithm (SPA), Principal Component Analysis (PCA), and Euclidean Distance (ED). Some models were finally established using the Partial Least Squares Regression (PLSR) and Probabilistic Neural Network (PNN). The results showed that an optimal procedure of detection was achieved for the water core using three settings of spectrum wavelength: to take the full wavelength data for SG and SNV processing, and then build a detection model by PNN. Using 400-1 100 nm spectrum and the optimal detection, the accuracy of the model for the calibration set of the water core was 98.51%, while the accuracy of the model for the validation set was 91.18%. Using 900-1 700 nm spectrum and the optimal detection, the accuracy of the model for the calibration set of the water core was 100%, while, the accuracy of the model for the validation set was 62%. Using 400-1 700 nm spectrum and the optimal detection, the accuracy of the model for the calibration set of water core was 100%, while the accuracy of the model for the validation set was 91.18%. Besides, both PCA and PLSR showed that there was a relatively less significant improvement, even though the detection of water core was slightly improved by 400-1 700 nm spectrum, compare with only by 400-1 100 nm. Thus, a practical detection of water core was suggested to use the 400-1 100 nm spectrum that combined with SG + SNV + PNN modeling in industrial production. Specifically, the marking price of 400-1 100 nm spectrometer like QE pro was about 130 000 Yuan, and the marking price of 900-1 700 nm spectrometer like NIR QUEST was about 150 000 Yuan, while, the marking price of 400-1 700 nm spectrometer like a combination of QE pro and NIR QUEST was about 280 000 Yuan. Consequently, the VIS/NIR spectroscopy can be widely expected to nondestructively and rapidly identify the internal water core of pineapple in modern agriculture. © 2021, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
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页码:287 / 294
页数:7
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