Department of Agricultural and Environmental Sciences - Production, Landscape, Agroenergy (DiSAA), Università degli Studi di Milano, via Celoria 2, 20133 Milano, Italy.
Department of Agricultural and Environmental Sciences - Production, Landscape, Agroenergy (DiSAA), Università degli Studi di Milano, via Celoria 2, 20133 Milano, Italy.
Talanta. 2014 Mar;120:368-75. doi: 10.1016/j.talanta.2013.12.014. Epub 2013 Dec 18.
The aim of this work was to investigate the applicability of non-destructive techniques in monitoring freshness decay of fresh-cut Valerianella locusta L. during storage at different temperature. The sampling was performed for 15 days for Valerianella samples preserved at 4 and 10 °C, and for 7 days for samples stored at 20 °C. The quality decay of samples was evaluated by quality parameters (pH, water content, total phenols, chlorophyll a fluorescence) and by non-destructive systems (electronic nose and visible-near infrared spectroscopy). Cluster Analysis (CA) was performed on quality indices and four clusters were identified, namely "fresh", "acceptable", "spoiled" and "very spoiled". Principal Component Analysis (PCA) was applied on the electronic nose data in order to evaluate the feasibility of this technique as a rapid and non-destructive approach for monitoring the freshness of fresh-cut Valerianella during storage. Linear Discriminant Analysis (LDA) and PLS-discriminant analysis (PLS-DA) models were developed to test the performance of electronic nose and VIS-NIR, respectively, to classify samples in the four classes of freshness. The average value of samples correctly classified using LDA was 95.5% and the cross validation error rate was equal to 8.7%. The results obtained from PLS-DA models, in validation, gave a positive predictive value (PPV) of classification between 74% and 96%. Finally, predictive models were performed using Partial Least Squares (PLS) regression analysis between quality indices and VIS-NIR data. RPD values <3 were obtained for water content and pH. Excellent results were obtained for total phenols with Rcv(2) and RPD equal to 0.89 and 3.19, and for chlorophyll a fluorescence with Rcv(2) and RPD equal to 0.92 and 3.22, respectively. Results demonstrated that electronic nose and VIS-NIR are complementary techniques able to support the conventional techniques in the shelf-life assessment of fresh-cut V. locusta L. providing information useful for a better management of the product along the distribution chain.
本工作旨在研究无损技术在监测不同温度下贮藏的新鲜切割独活草新鲜度衰变中的适用性。对 4 和 10°C 下贮藏的独活草样品进行了 15 天的采样,对 20°C 下贮藏的样品进行了 7 天的采样。通过质量参数(pH 值、水分含量、总酚、叶绿素 a 荧光)和无损系统(电子鼻和可见-近红外光谱)对样品的质量衰变进行了评估。对质量指标进行了聚类分析(CA),确定了四个聚类,分别为“新鲜”、“可接受”、“腐烂”和“非常腐烂”。对电子鼻数据进行了主成分分析(PCA),以评估该技术作为监测贮藏期间新鲜切割独活草新鲜度的快速无损方法的可行性。采用线性判别分析(LDA)和偏最小二乘判别分析(PLS-DA)分别建立电子鼻和 VIS-NIR 模型,以对新鲜度的四个等级的样品进行分类。使用 LDA 对样品进行分类的平均正确率为 95.5%,交叉验证错误率为 8.7%。在验证中,PLS-DA 模型的结果给出了分类的阳性预测值(PPV)在 74%到 96%之间。最后,使用偏最小二乘(PLS)回归分析在质量指标和 VIS-NIR 数据之间建立了预测模型。水分含量和 pH 的 RPD 值<3。总酚的结果非常好,Rcv(2)和 RPD 分别为 0.89 和 3.19,叶绿素 a 荧光的 Rcv(2)和 RPD 分别为 0.92 和 3.22。结果表明,电子鼻和 VIS-NIR 是互补的技术,能够在新鲜切割独活草 L.的货架期评估中支持常规技术,为更好地管理产品在分销链中的提供有用的信息。