Caredda Marco, Ciulu Marco, Tilocca Francesca, Langasco Ilaria, Núñez Oscar, Sentellas Sònia, Saurina Javier, Pilo Maria Itria, Spano Nadia, Sanna Gavino, Mara Andrea
Department of Animal Science, AGRIS Sardegna, Loc. Bonassai, 07100 Sassari, Italy.
Department of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, Italy.
Foods. 2024 Sep 26;13(19):3062. doi: 10.3390/foods13193062.
Fraudulent practices concerning honey are growing fast and involve misrepresentation of origin and adulteration. Simple and feasible methods for honey authentication are needed to ascertain honey compliance and quality. Working on a robust dataset and simultaneously investigating honey traceability and adulterant detection, this study proposed a portable FTNIR fingerprinting approach combined with chemometrics. Multifloral and unifloral honey samples ( = 244) from Spain and Sardinia (Italy) were discriminated by botanical and geographical origin. Qualitative and quantitative methods were developed using linear discriminant analysis (LDA) and partial least squares (PLS) regression to detect adulterated honey with two syrups, consisting of glucose, fructose, and maltose. Botanical and geographical origins were predicted with 90% and 95% accuracy, respectively. LDA models discriminated pure and adulterated honey samples with an accuracy of over 92%, whereas PLS allows for the accurate quantification of over 10% of adulterants in unifloral and 20% in multifloral honey.
蜂蜜造假行为迅速增多，包括产地虚假标注和掺假。需要简单可行的蜂蜜鉴定方法来确保蜂蜜符合标准并保证质量。本研究基于一个强大的数据集，同时研究蜂蜜的可追溯性和掺假物检测，提出了一种结合化学计量学的便携式傅里叶变换近红外光谱指纹识别方法。对来自西班牙和撒丁岛（意大利）的多花种和单花种蜂蜜样本（n = 244）按植物来源和地理来源进行了区分。使用线性判别分析（LDA）和偏最小二乘法（PLS）回归开发了定性和定量方法，以检测掺有两种糖浆（由葡萄糖、果糖和麦芽糖组成）的掺假蜂蜜。植物来源和地理来源的预测准确率分别为90%和95%。LDA模型对纯蜂蜜样本和掺假蜂蜜样本的判别准确率超过92%，而PLS能够准确量化单花种蜂蜜中超过10%的掺假物以及多花种蜂蜜中超过20%的掺假物。
