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采用近红外光谱法无损测量樱桃品质的研究
Fruit Quality Indexes Nondestructive Prediction of Cherry Using Near-infrared Spectroscopy
以樱桃常见品种‘雷尼尔’、‘布鲁克斯’、‘雷吉纳’、‘美早’为试材，采用近红外光谱(NIR)对可溶性固形物含量、干物质含量、果皮颜色和果实硬度4个质量指标进行无损测量，开发预测性NIR回归模型，并使用该模型测量‘雷尼尔’樱桃从果实开始着色到完全成熟阶段4个质量指标变化。结果表明，建立的可溶性固形物含量、干物质含量、果皮颜色和果实硬度模型的线性回归决定系数(R2)值分别为0.87、0.97、0.77、0.76。模型线性系数(R2)越接近1拟合效果越好，模型的拟合程度越高。R2值均大于0.7，说明模型能够达到性能预期。可溶性固形物和干物质模型进行定量测定的准确性高于果皮颜色和果实硬度模型。樱桃果实成熟阶段，可溶性固形物与干物质逐渐增加，果皮颜色逐渐加深，硬度逐渐降低。本研究建立的模型可用于其他樱桃品种品质预测，同时可为进一步探索樱桃果实成熟阶段内部物质变化提供参考依据。
Soluble solids content, dry matter, peel color and firmness of cherry ‘Rainier’, ‘Brooks’, ‘Regina’, ‘Tieton’ were measured with no damage by near-infrared spectroscopy (NIR) in the experiment. Predictive NIR regression model was developed to measure four quality indicators using the ‘Rainier’ cherry fruit from beginning of coloration to fully maturity. The result showed that the coefficients of determination (R2) of soluble solids content, dry matter, peel color and firmness were 0.87, 0.97, 0.77 and 0.76, respectively. The closer the model linearity (R2) of the model gets to 1, the better the fitting effect and the higher the fitting degree of the model. The R2 values were all greater than 0.7, indicating that the model could achieve the performance expectations. The accuracy of the soluble solids model and dry matter model was higher than that of models of peel color and firmness. The soluble solid and dry matter gradually increased, skin color gradually deepened and hardness gradually decreased during the ripening of cherry fruit. The model established in this study can be applied in predicting four quality indexes for other cultivars, providing the intuitive reference for exploring changes on endogenous substances in the fruit ripening stage.
樱桃 / 红外光谱 / 建模 / 测量 / 内部品质 {{custom_keyword}} /
cherry / spectroscopy / model building / prediction / fruit quality {{custom_keyword}} /
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In this study, an infrared-based prediction method was developed for easy, fast and non-destructive detection of pesticide residue levels measured by reference analysis in strawberry (Fragaria × ananassa Duch, cv. Albion) samples using near-infrared spectroscopy and demonstrating its potential alternative or complementary use instead of traditional pesticide determination methods. Strawberries of Albion variety, which were supplied directly from greenhouses, were used as the study material. A total of 60 batch sample groups, each consisting of eight strawberries, was formed, and each group was treated with a commercial pesticide at different concentrations (26.7% boscalid + 6.7% pyraclostrobin) and varying residual levels were obtained in strawberry batches. The strawberry samples with pesticide residuals were used both to collect near-infrared spectra and to determine reference pesticide levels, applying QuEChERS (quick, easy, cheap, rugged, safe) extraction, followed by liquid chromatographic-mass spectrometric analysis.Partial least squares regression (PLSR) models were developed for boscalid and pyraclostrobin active substances. During model development, the samples were randomly divided into two groups as calibration (n = 48) and validation (n = 12) sets. A calibration model was developed for each active substance, and then the models were validated using cross-validation and external sets. Performance evaluation of the PLSR models was evaluated based on the residual predictive deviation (RPD) of each model. An RPD of 2.28 was obtained for boscalid, while it was 2.31 for pyraclostrobin. These results indicate that the developed models have reasonable predictive power. © 2019 Society of Chemical Industry.© 2019 Society of Chemical Industry.
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