Untargeted and Targeted Discrimination of Honey Collected by Apis cerana and Apis mellifera Based on Volatiles Using HS-GC-IMS and HS-SPME-GC–MS

Abstract

Fraudulent acts regarding honey authenticity that use Apis mellifera honey as a substitute for Apis cerana honey have garnered considerable concern in China and triggered a trust crisis from consumers. In this study, untargeted metabolomics analysis was carried out based on volatile fractions in honey from A. cerana and A. mellifera using headspace gas chromatography−ion mobility spectrometry (HS-GC-IMS). Honey from A. cerana and A. mellifera was discriminated by HS-GC-IMS profiling, principal component analysis, and orthogonal partial least-squares discrimination analysis. Tentative markers were identified from p-values and the variable importance in projection analysis and confirmed using the retention index, mass fragments, and reference standards by gas chromatography−mass spectrometry (GC−MS). A targeted method was established using the headspace solid phase coupled with microextraction GC−MS (HS-SPME-GC−MS) to quantitate the markers. The results demonstrated that the developed untargeted and targeted metabolomics approach performed well when discriminating honey from A. cerana and A. mellifera.

The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical, Entomological and Geographical Origins

“…Most reported works on honey authentication mainly focus on the floral type (botanical origin) and geographical origins, and less frequently on entomological origins [10][11][12][13][14]. Few reported works have been reported on the honey authentication based on entomological origins [15,16].…”

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

“…Furthermore, HS-GC-IMS with NTS was successfully used for reliable classification of geographical origins for both olive oil (EVOO) [ 34 , 80 , 81 ] and wine [ 30 ]. Moreover, HS-GC-IMS with NTS was applied for the classification of honey according to botanical origin [ 52 , 81 , 82 ], as well as for the detection and quantification of honey adulterated with sugar cane or corn syrups [ 83 , 84 ]. Recently, HS-GC-IMS with NTS has been applied to assess the freshness of food [ 85 ] and for the detection of mold formation on milled rice [ 86 ], peanut kernels [ 87 ], and wheat kernels [ 88 ].…”

“…Scaling methods, such as unit variance (UnVa) scaling, Pareto (Par) scaling, and mean centering (Ctr) scaling, can be alternatively used for data set normalization [ 5 ]. In UnVa scaling, the variance of each variable is unified using the standard deviation, while in Par scaling, the square root of the standard deviation is used for normalization, whereas Ctr scaling emphasizes the analysis of data fluctuations and the large-fold change in the data [ 82 ]. For further noise reduction, smoothing algorithms, such as Savitzky-Golay or Gaussian smoothing, are applied to the spectra [ 52 ].…”

Klasifikasi Madu Berdasarkan Jenis Lebah (Apis dorsata versus Apis mellifera) Menggunakan Spektroskopi Ultraviolet dan Kemometrika

“…Metode kromatografi di antaranya adalah GC (gas chromatography) dan HPLC (high-performance liquid chromatography). Beberapa metode digabungkan sekaligus untuk uji keaslian madu, seperti metode GC-MS untuk membedakan madu Apis cerana dan Apis mellifera (Wang et al 2019) dan metode LC-MS untuk uji keaslian beberapa madu komersial dengan asal botani dan geografis yang berbeda (Cabanero et al 2006). Sebagian besar metode infrared spectroscopy untuk uji keaslian madu meliputi near infrared (NIR) dan mid infrared (MIR) spectroscopy.…”