Authentication of chaste honey adulterated with high fructose corn syrup by HS-SPME-GC-MS coupled with chemometrics

Wei, Quanzeng; Sun, Jian; Guo, Jiaqi; Li, Xiaofei; Zhang, Xiao-Hua; Xiao, Fugang

doi:10.1016/j.lwt.2023.114509

Cited by 7 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Likewise, the adulteration of chaste honey samples with syrups was examined with the use of HS–SPME and various statistical tools. PCA emerged as the primary method for their discrimination (Wei et al., 2023).…”

Section: Evolution Of the Food Volatilomics Researchmentioning

confidence: 99%

Volatilomics as a tool to ascertain food adulteration, authenticity, and origin

Kaldeli,

Zakidou,

Paraskevopoulou

2024

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

Over recent years, there has been an increase in the number of reported cases of food fraud incidents, whereas at the same time, consumers demand authentic products of high quality. The emerging volatilomics technology could be the key to the analysis and characterization of the quality of different foodstuffs. This field of omics has aroused the interest of scientists due to its noninvasive, rapid, and cost‐profitable nature. This review aims to monitor the available scientific information on the use of volatilomics technology, correlate it to the relevant food categories, and demonstrate its importance in the food adulteration, authenticity, and origin areas. A comprehensive literature search was performed using various scientific search engines and “volatilomics,” “volatiles,” “food authenticity,” “adulteration,” “origin,” “fingerprint,” “chemometrics,” and variations thereof as keywords, without chronological restriction. One hundred thirty‐seven relevant publications were retrieved, covering 11 different food categories (meat and meat products, fruits and fruit products, honey, coffee, tea, herbal products, olive oil, dairy products, spices, cereals, and others), the majority of which focused on the food geographical origin. The findings show that volatilomics typically involves various methods responsible for the extraction and consequential identification of volatile compounds, whereas, with the aid of data analysis, it can handle large amounts of data, enabling the origin classification of samples or even the detection of adulteration practices. Nonetheless, a greater number of specific research studies are needed to unlock the full potential of volatilomics.

show abstract

Section: Evolution Of the Food Volatilomics Researchmentioning

confidence: 99%

Volatilomics as a tool to ascertain food adulteration, authenticity, and origin

Kaldeli,

Zakidou,

Paraskevopoulou

2024

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

show abstract

“…Prior studies have investigated the quality of chaste honey by focusing on the analysis of basic physicochemical indexes, total phenolic compounds, and volatile components, as well as developing prediction models of chaste honey quality. − These studies are beneficial for the discernment of diverse monofloral honey. However, controlling the quality of chaste honey remains challenging due to the lack of specific, unique, and accurate indicators of chaste honey quality.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Formation of Chemical Markers in Chaste Honey by Comparative Metabolomics: From Nectar to Mature Honey

Yan,

Mu,

Yuan

et al. 2024

J. Agric. Food Chem.

View full text Add to dashboard Cite

Identification of chemical markers is important to ensure the authenticity of monofloral honey; however, the formation of chemical markers in honey has received little attention. Herein, using comparative metabolomics, we first identified chemical markers in chaste honey and then explored their formation and accumulation from nectar to mature honey. We identified agnuside and p-hydroxybenzoic acid glucosides as chemical markers for chaste honey. Besides, we developed an UHPLC−MS/MS method for quantifying these markers and found that their levels varied significantly across sample sources. We compared the presence of these compounds in chaste nectar and mature honey. The outcomes underscore that these characteristic compounds are not simply delivered from nectar to mature honey, and activities of honeybees (collecting and processing) play a pivotal role in their formation and accumulation. These observations shed light on how mature honey can form its unique qualities with a rich assortment of natural bioactive compounds, potentially supporting health benefits.

show abstract

“…Current methods involve advanced analytical tools coupled with chemometrics [13]. Physicochemical [14][15][16][17], elemental [18], isotopic [19], chromatographic, and hyphenated mass spectrometry methods [20,21], NMR [22,23], DNA-based [24,25], electronic sensing [26,27], and spectroscopic [28][29][30][31][32] techniques are nowadays the most used for predicting botanical and geographical origin, or detecting adulterants [33]. Usually, botanical issues are investigated using chromatographic techniques and physicochemical analyses [12,14,34].…”

Section: Introductionmentioning

confidence: 99%

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions

Mara,

Migliorini,

Ciulu

et al. 2024

Foods

View full text Add to dashboard Cite

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors.

show abstract

Authentication of chaste honey adulterated with high fructose corn syrup by HS-SPME-GC-MS coupled with chemometrics

Cited by 7 publications

References 21 publications

Volatilomics as a tool to ascertain food adulteration, authenticity, and origin

Volatilomics as a tool to ascertain food adulteration, authenticity, and origin

Exploring the Formation of Chemical Markers in Chaste Honey by Comparative Metabolomics: From Nectar to Mature Honey

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions

Contact Info

Product

Resources

About