Risoluti Roberta, Gullifa Giuseppina, Materazi Stefano
Department of Chemistry, Sapienza University of Rome, Rome, Italy.
Front Chem. 2020 Dec 1;8:614718. doi: 10.3389/fchem.2020.614718. eCollection 2020.
In this work, an innovative screening platform based on MicroNIR and chemometrics is proposed for the on-site and contactless monitoring of the quality of milk using simultaneous multicomponent analysis. The novelty of this completely automated tool consists of a miniaturized NIR spectrometer operating in a wireless mode that allows samples to be processed in a rapid and accurate way and to obtain in a single click a comprehensive characterization of the chemical composition of milk. To optimize the platform, milk specimens with different origins and compositions were considered and prediction models were developed by chemometric analysis of the NIR spectra using Partial Least Square regression algorithms. Once calibrated, the platform was used to predict samples acquired in the market and validation was performed by comparing results of the novel platform with those obtained from the chromatographic analysis. Results demonstrated the ability of the platform to differentiate milk as a function of the distribution of fatty acids, providing a rapid and non-destructive method to assess the quality of milk and to avoid food adulteration.
在这项工作中，提出了一种基于近红外光谱（MicroNIR）和化学计量学的创新筛选平台，用于通过同时多组分分析对牛奶质量进行现场非接触式监测。这个完全自动化工具的新颖之处在于一个以无线模式运行的小型近红外光谱仪，它能够快速、准确地处理样品，并通过一键操作获得牛奶化学成分的全面表征。为了优化该平台，考虑了不同来源和成分的牛奶样本，并使用偏最小二乘回归算法对近红外光谱进行化学计量分析，从而开发了预测模型。校准后，该平台用于预测市场上采集的样本，并通过将新平台的结果与色谱分析结果进行比较来进行验证。结果表明，该平台能够根据脂肪酸分布区分牛奶，提供了一种快速、无损的方法来评估牛奶质量并避免食品掺假。
