Karabagias Ioannis Konstantinos, Nayik Gulzar Ahmad
Department of Food Science & Technology, School of Agricultural Sciences, University of Patras, G. Seferi 2, 30100 Agrinio, Greece.
Department of Food Science & Technology, Government Degree College Shopian, Jammu & Kashmir 192303, India.
Foods. 2023 Jan 22;12(3):509. doi: 10.3390/foods12030509.
The scope of the current study was to monitor if semi-quantitative data of volatile compounds (volatilome) of citrus honey (ch) produced in different countries could potentially lead to a new index of citrus honey authentication using specific ratios of the identified volatile compounds in combination with machine learning algorithms. In this context, the semi-quantitative data of the volatilome of 38 citrus honey samples from Egypt, Morocco, Greece, and Spain (determined by headspace solid phase microextraction coupled to gas chromatography mass spectrometry (HS-SPME/GC-MS)) was subjected to supervised and unsupervised chemometrics. Results showed that honey samples could be classified according to the geographical origin based on specific volatile compounds. Data were further evaluated with additional nectar honey samples introduced in the multivariate statistical analysis model and the classification results were not affected. Specific volatile compounds contributed to the discrimination of citrus honey in different amounts according to geographical origin. These were lilac aldehyde D, dill ether, 2-methylbutanal, heptane, benzaldehyde, α,4-dimethyl-3-cyclohexene-1-acetaldehyde, and herboxide (isomer II). The numerical data of these volatile compounds was summed up and divided by the total semi-quantitative volatile content (R, Karabagias-Nayik index) of citrus honey, according to geographical origin. Egyptian citrus honey had a value of R = 0.35, Moroccan citrus honey had a value of R = 0.29, Greek citrus honey had a value of R = 0.04, and Spanish citrus honey had a value of R = 0.27, leading to a new hypothesis and a complementary index for the control of citrus honey authentication.
本研究的范围是监测不同国家生产的柑橘蜂蜜（ch）中挥发性化合物（挥发组）的半定量数据，是否有可能结合机器学习算法，利用已鉴定挥发性化合物的特定比例，得出一个新的柑橘蜂蜜认证指标。在此背景下，对来自埃及、摩洛哥、希腊和西班牙的38个柑橘蜂蜜样品的挥发组半定量数据（通过顶空固相微萃取结合气相色谱 - 质谱联用仪（HS-SPME/GC-MS）测定）进行了有监督和无监督的化学计量学分析。结果表明，基于特定的挥发性化合物，蜂蜜样品可以根据地理来源进行分类。在多变量统计分析模型中引入额外的花蜜蜂蜜样品对数据进行进一步评估，分类结果未受影响。根据地理来源，特定的挥发性化合物对柑橘蜂蜜的区分贡献量不同。这些化合物为丁香醛D、莳萝醚、2-甲基丁醛、庚烷、苯甲醛、α,4-二甲基-3-环己烯-1-乙醛和除草醚（异构体II）。根据地理来源，将这些挥发性化合物的数值数据相加，再除以柑橘蜂蜜的总半定量挥发性含量（R，卡拉巴吉亚斯 - 奈伊克指数）。埃及柑橘蜂蜜的R值为0.35，摩洛哥柑橘蜂蜜的R值为0.29，希腊柑橘蜂蜜的R值为0.04，西班牙柑橘蜂蜜的R值为0.27，由此得出一个关于柑橘蜂蜜认证控制的新假设和一个补充指标。
