Islam Md Khairul, Vinsen Kevin, Sostaric Tomislav, Lim Lee Yong, Locher Cornelia
Division of Pharmacy, School of Allied Health, University of Western Australia, Crawley, WA, Australia.
Cooperative Research Centre for Honey Bee Products Limited (CRC HBP), Perth, WA, Australia.
PeerJ. 2021 Sep 22;9:e12186. doi: 10.7717/peerj.12186. eCollection 2021.
High-Performance Thin-Layer Chromatography (HPTLC) was used in a chemometric investigation of the derived sugar and organic extract profiles of two different honeys (Manuka and Jarrah) with adulterants. Each honey was adulterated with one of six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrups) in five different concentrations (10%, 20%, 30%, 40%, and 50% w/w). The chemometric analysis was based on the combined sugar and organic extract profiles' datasets. To obtain the respective sugar profiles, the amount of fructose, glucose, maltose, and sucrose present in the honey was quantified and for the organic extract profile, the honey's dichloromethane extract was investigated at 254 and 366 nm, as well as at T (Transmittance) white light and at 366 nm after derivatisation. The presence of sugar syrups, even at a concentration of only 10%, significantly influenced the honeys' sugar and organic extract profiles and multivariate data analysis of these profiles, in particular cluster analysis (CA), principal component analysis (PCA), principal component regression (PCR), partial least-squares regression (PLSR) and Machine Learning using an artificial neural network (ANN), were able to detect post-harvest syrup adulterations and to discriminate between neat and adulterated honey samples. Cluster analysis and principal component analysis, for instance, could easily differentiate between neat and adulterated honeys through the use of CA or PCA plots. In particular the presence of excess amounts of maltose and sucrose allowed for the detection of sugar adulterants and adulterated honeys by HPTLC-multivariate data analysis. Partial least-squares regression and artificial neural networking were employed, with augmented datasets, to develop optimal calibration for the adulterated honeys and to predict those accurately, which suggests a good predictive capacity of the developed model.
高效薄层色谱法（HPTLC）被用于对两种不同蜂蜜（麦卢卡蜂蜜和红柳桉树蜂蜜）及其掺假品的衍生糖和有机提取物谱进行化学计量学研究。每种蜂蜜分别用六种不同糖浆（大米糖浆、玉米糖浆、金糖浆、糖蜜、葡萄糖浆和枫糖浆）中的一种以五种不同浓度（10%、20%、30%、40%和50% w/w）进行掺假。化学计量学分析基于糖和有机提取物谱的组合数据集。为了获得各自的糖谱，对蜂蜜中存在的果糖、葡萄糖、麦芽糖和蔗糖的量进行了定量，对于有机提取物谱，对蜂蜜的二氯甲烷提取物在254和366 nm处进行了研究，以及在T（透光率）白光下和衍生化后在366 nm处进行了研究。即使糖浆浓度仅为10%，其存在也会显著影响蜂蜜的糖和有机提取物谱，并且对这些谱进行多变量数据分析，特别是聚类分析（CA）、主成分分析（PCA）、主成分回归（PCR）、偏最小二乘回归（PLSR）以及使用人工神经网络（ANN）的机器学习，能够检测收获后糖浆掺假情况，并区分纯蜂蜜和掺假蜂蜜样品。例如，聚类分析和主成分分析可以通过使用CA或PCA图轻松区分纯蜂蜜和掺假蜂蜜。特别是过量的麦芽糖和蔗糖的存在使得通过HPTLC - 多变量数据分析能够检测到糖掺假剂和掺假蜂蜜。使用增强数据集采用偏最小二乘回归和人工神经网络，为掺假蜂蜜开发最佳校准并准确预测它们，这表明所开发模型具有良好的预测能力。
