Department of Chemistry, University of Rome "La Sapienza", P.le Aldo Moro 5, 00185, Rome, Italy.
Institut National des Sciences Appliquees, Campus de Rouen, Avenue de l'Université, 76801, Saint-Étienne-du-Rouvray, France.
Environ Sci Pollut Res Int. 2018 Oct;25(29):28780-28786. doi: 10.1007/s11356-018-1755-2. Epub 2018 Mar 21.
Common hazelnuts are widely present in human diet all over the world, and their beneficial effects on the health have been extensively investigated and demonstrated. Different in-depth researches have highlighted that the harvesting area can define small variations in the chemical composition of the fruits, affecting their quality. As a consequence, it has become relevant to develop methodologies which would allow authenticating and tracing hazelnuts. In the light of this, the present work aims to develop a non-destructive method for the authentication of a specific high-quality Italian hazelnut, "Nocciola Romana," registered with a protected designation of origin (PDO). Thus, different samples of this fruit have been analyzed by near-infrared (NIR) spectroscopy and then classification models have been built, in order to distinguish between the PDO fruits and the hazelnuts not coming from the designated region. In particular, two different classification approaches have been tested, a discriminant one, partial least squares-discriminant analysis, and a class-modeling one, soft independent modeling of class analogies. Both methods led to very high prediction capability in external validation on a test set (classification accuracy in one case, and sensitivity and specificity in the other, all higher than 92%), suggesting that the proposed methodologies are suitable for a rapid and non-destructive authentication of the product.
普通榛子在全世界范围内广泛存在于人类饮食中，其对健康的有益影响已得到广泛研究和证实。不同的深入研究表明，收获区域可以定义果实化学成分的微小变化，从而影响其质量。因此，开发能够对榛子进行鉴定和溯源的方法变得非常重要。有鉴于此，本工作旨在开发一种用于鉴定特定优质意大利榛子“Nocciola Romana”（罗马榛子）的非破坏性方法，该榛子已获得受保护的原产地名称 (PDO) 注册。为此，对不同的榛子样本进行了近红外 (NIR) 光谱分析，然后建立了分类模型，以便区分 PDO 果实和非指定地区的榛子。特别是，测试了两种不同的分类方法，一种是判别方法，偏最小二乘判别分析，另一种是类建模方法，类模拟的软独立建模。这两种方法在外部验证测试集上都具有非常高的预测能力（一种方法是分类准确率，另一种方法是灵敏度和特异性，均高于 92%），表明所提出的方法适用于产品的快速和非破坏性鉴定。
