FT-NIR spectroscopy and multivariate classification strategies for the postharvest quality of green-fleshed kiwifruit varieties
FT-NIR spectroscopy and multivariate classification strategies for the postharvest quality of green-fleshed kiwifruit varieties
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DOI：
10.1016/j.scienta.2019.108622
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发表时间：
2019-11-17
影响因子：
4.3
通讯作者：
Carbone, Katya
中科院分区：
文献类型：
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作者：
Ciccoritti, Roberto;Paliotta, Mariano;Carbone, Katya
本研究将近红外光谱(NIRS)和化学计量学分析相结合，对绿色肉质猕猴桃(A.deliciosa、Hayward和Bo-Erica(R)CVS)进行了表征和商品分类。研究了两种方法：1)同时使用光谱和参考数据来评估水果的质量和营养水平的偏最小二乘模型；2)仅使用光谱数据的监督分类方法(LDA和SIMCA)，以便根据水果的成熟时间和消费者的接受程度对水果进行分类。对几个传统的品质指标(即可溶性固形物、硬度、可滴定酸度、干物质)以及营养指标如总酚、类胡萝卜素和抗氧化能力进行了评价。用偏最小二乘回归对品质性状进行了预测，特别是对可溶性固形物含量和干物质含量(R-2：0.993，RMSEP0.40和R2：0.983，RMSEP0.33)的预测效果较好。偏最小二乘模型对总可滴定酸度的预测精度较高(R-2：0.933，均方根误差：6.65)。首次对猕猴桃样品的主要营养成分进行预测，取得了较好的模型效果。总黄酮和维生素C的预测效果较好(R-2：0.813，RMSEP0.07；R2：0.870，RMSEP6.04)。线性判别分析(LDA)和软独立类比建模(SIMCA)模型的分类正确率分别为92%和99%。这项研究强调，FT-NIRS可以成功地用于改善猕猴桃采收和贮藏期间的质量控制，以及在配送前对样品进行识别和分离，以减少果实损失。
In the present study, combining near-infrared spectroscopy (NIRs) and chemometric analysis was tested for the characterization and commercial classification of green-fleshed kiwifruit (A. deliciosa, Hayward and Bo-Erica (R) cvs). Two approaches were studied: 1) development of PLS models using both spectral and reference data to assess the fruit quality and nutraceutical levels and 2) supervised classification methods (LDA and SIMCA) using only spectral data in order to sort fruit on the basis of their ripening time and consumer's acceptability. Several traditional quality indexes (i.e. soluble solids, firmness, titratable acidity, dry matter), as well as nutritional ones such as total phenols, carotenoids and antioxidant potential were evaluated. Very good prediction of the internal quality attributes, using partial least squares regressions (PLS), was observed, especially for both soluble solid content and dry matter (R-2: 0.993, RMSEP: 0.40 and R-2: 0.983, RMSEP: 0.33, respectively). An accurate predictive performance of PLS model was also obtained for the total titratable acidity (R-2: 0.933, RMSEP: 6.65). Good model performances were obtained for the prediction of the main nutraceutical traits of the samples, developed herein for the first time on kiwifruit. However, fairly good prediction performances were obtained for both total flavans and vitamin C (R-2: 0.813, RMSEP: 0.07 and R-2: 0.870, RMSEP: 6.04, respectively). Linear Discriminant Analysis (LDA) and Soft Indipendent Modelling by Class Analogy (SIMCA) models provided good results with rates of 92% and 99% of correctly classified samples. This study highlights that FT-NIRs can be used successfully to improve the quality control of kiwifruit at harvest and during storage as well as to identify and segregate samples prior to distribution in order to reduce fruit loss.