Department of Agricultural Engineering, Faculty of Agriculture, The University of Lampung, Jl. Prof. Dr. Soemantri Brojonegoro No.1, Bandar Lampung 35145, Indonesia.
Department of Agricultural Technology, Lampung State Polytechnic, Jl. Soekarno Hatta No. 10, Rajabasa, Bandar Lampung 35141, Indonesia.
Molecules. 2021 Feb 9;26(4):915. doi: 10.3390/molecules26040915.
As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190-400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky-Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250-400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.
作为一种功能性食品，蜂蜜是一种容易受到食品欺诈风险影响的食品产品。为了减轻这种风险，建立一个针对蜂蜜的认证系统非常重要，这既能保护生产者，又能保护消费者免受可能的经济损失。本研究提出了一种简单的分析方法，用于根据印度尼西亚蜂蜜的植物学、昆虫学和地理起源，使用紫外（UV）光谱和 SIMCA（软独立建模分类法）对其进行认证和分类。使用台式紫外-可见分光光度计（190-400nm）共采集了 1040 个样本的光谱数据，这些样本代表了六种不同植物学、昆虫学和地理起源的印度尼西亚蜂蜜。同时评估了三种不同的预处理算法，即 11 点移动平均平滑、均值归一化和 Savitzky-Golay 一阶导数（11 点，二阶多项式拟合（ordo2），以改善原始光谱数据。使用化学计量学方法，包括 PCA 的探索性分析和 SIMCA 分类方法，对蜂蜜样本进行分类。基于植物学、昆虫学和地理起源，使用 PCA 从 250-400nm 预处理光谱计算，可以清楚地分离六种不同的印度尼西亚蜂蜜。SIMCA 分类方法根据蜂蜜的植物学、昆虫学和地理起源对蜂蜜样本进行分类，取得了令人满意的结果，准确率、灵敏度和特异性均达到 100%。确定了几个波长（266、270、280、290、300、335 和 360nm）对不同印度尼西亚蜂蜜样本的区分最敏感。
