基于可见/近红外光谱的菠萝水心病无损检测

水心病近年严重危害菠萝产业，探究一种菠萝水心病的无损检测方法对保证上市果品、指导采后处理、促进产业提升具有重要意义。该研究采用自行搭建的菠萝可见/近红外光谱无损智能检测平台，考虑实际应用成本与效果，搭载覆盖不同波段（400～1 100、900～1 700和400～1 700 nm）的检测器对菠萝样本进行采样，随后人工标定水心病发生程度。研究结果表明，3种不同光谱波段对菠萝水心程度检测的较优方法均为：采用全波段进行多项式平滑（Savitzky Golay，SG）处理，再进行标准正态变量校正（Standard Normal Variate，SNV），最后结合概率神经网络（Probabilistic Neural Network，PNN）建模识别。其中，400～1 100 nm所建模型对菠萝水心病训练集的回判正确率为98.51%，对验证集的检测正确率为91.18%；900～1 700 nm所建模型对菠萝水心病训练集的回判正确率为100%，对验证集的检测正确率为62%；400～ 1 700 nm所建模型对菠萝水心病训练集的回判正确率为100%，对验证集的检测正确率为91.18%。主成分分析（Principal Component Analysis，PCA）和偏最小二乘回归（Partial Least Squares Regression，PLSR）分析结果均显示，采用400～ 1 700 nm能轻微提升400～1 100 nm的检测效果。综合考虑实际应用成本与效果，实际应用建议采用400～1 100 nm光谱结合SG + SNV + PNN对菠萝水心病进行识别。研究结果证明可见/近红外光谱技术可为菠萝水心病无损、快速、智能检测提供有效的解决方案，为相关领域提供参考。

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

Abstract: 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 900-1700 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 400-1700 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 900-1700 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 400-1700 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.