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首页> 外文期刊>Food analytical methods >Nondestructive Detection of Postharvest Quality of Cherry Tomatoes Using a Portable NIR Spectrometer and Chemometric Algorithms
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Nondestructive Detection of Postharvest Quality of Cherry Tomatoes Using a Portable NIR Spectrometer and Chemometric Algorithms

机译：使用便携式NIR光谱仪和化学计量算法的樱桃番茄采后质量的无损检测

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摘要

The aim of this study was to assess the applicability of a portable NIR spectroscopy system and chemometric algorithms in intelligently detecting postharvest quality of cherry tomatoes. The postharvest quality of cherry tomatoes was evaluated in terms of firmness, soluble solids content (SSC), and pH, and a portable NIR spectrometer (950-1650nm) was used to obtain the spectra of cherry tomatoes. Partial least square (PLS), support vector machine (SVM), and extreme learning machine (ELM) were applied to predict the postharvest quality of cherry tomatoes from their spectra. The effects of different preprocessing techniques, including Savitzky-Golay (S-G), multiplicative scattering correction (MSC), and standard normal variate (SNV) on prediction performance were also evaluated. Firmness, SSC and pH values of cherry tomatoes decreased during storage period, based on which the tomato samples could be classified into two distinct clusters. Similarly, cherry tomatoes with different storage time could also be separated by the NIR spectroscopic characteristics. The best prediction accuracy was obtained from ELM algorithms using the raw spectra with R-p(2), RMSEP, and RPD values of 0.9666, 0.3141N, and 5.6118 for firmness; 0.9179, 0.1485%, and 3.6249 for SSC; and 0.8519, 0.0164, and 2.7407 for pH, respectively. Excellent predictions for firmness and SSC (RPD value greater than 3.0), good prediction for pH (RPD value between 2.5 and 3.0) were obtained using ELM model. NIR spectroscopy is capable of intelligently detecting postharvest quality of cherry tomatoes during storage.
机译：本研究的目的是评估便携式NIR光谱系统和化学计量算法在智能检测樱桃番茄的波脱质量方面的适用性。在坚固性，可溶性固体含量（SSC）和pH值方面评估了樱桃番茄的后氨基乙醇质质量，并且使用便携式NIR光谱仪（950-1650nm）来获得樱桃番茄的光谱。部分最小二乘（PLS），支持向量机（SVM）和极端学习机（ELM）以预测来自光谱的樱桃番茄的波脱质量。还评估了不同预处理技术的影响，包括Savitzky-GoLay（S-G），乘法散射校正（MSC）和标准正常变化（SNV）的预测性能。在储存期间，樱桃番茄的坚定，SSC和pH值下降，基于番茄样本可以分为两个不同的簇。类似地，具有不同储存时间的樱桃西红柿也可以通过NIR光谱特性分离。使用R-P（2），RmSEP和0.9666,0.3141N和5.6118的R-P（2），RMSEP和5.6118的R-P（2），RMSEP和5.6118的原始光谱从ELM算法获得最佳预测精度。 0.9179，0.1485％和3.6249用于SSC; pH值分别为0.8519,0.0164和2.7407。使用ELM模型获得了对坚固性和SSC的优异预测（RPD值大于3.0），对pH（2.5和3.0之间的RPD值之间）的良好预测。 NIR光谱能力能够在储存期间智能地检测樱桃番茄的波脱质量。

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