An AI-powered e-nose system using a density-based clustering method for identifying adulteration in specialty coffees

Lee, Chung-Hong; Rianto, Babam

doi:10.1016/j.microc.2023.109844

Cited by 5 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, e-nose technology combined with PCA was used for coffee species classification, which proved to be less time-consuming, less expensive, and more precise in comparison to other techniques, such as SPME/GC-MS (Knysak, 2017). E-nose technology combined with ML techniques and density-based spatial clustering of applications with noise clustering models was also the preferred method for authenticating microclimate specialty coffee beans and for addressing adulteration issues such as blends of specialty Arabica with Robusta or Luwak with non-Luwak coffee beans (Lee & Rianto, 2024). In addition, roasted ground coffee has been primarily adulterated to elicit economic benefits.…”

Section: Coffeementioning

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: Coffeementioning

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

“…Coffee production in 2022 saw an increase of 4.2%, totaling 175.6 million bags. Considering this demand, producers were expected to increase coffee production in the 2023 campaign by 1.7% [2]. The coffee beans of the Arabica variety surpass those of the Robusta variety in terms of quality, with Arabica presenting greater aromatic complexity [3].…”

Section: Introductionmentioning

confidence: 99%

Electronic Prediction of Chemical Contaminants in Aroma of Brewed Roasted Coffee and Quantification of Acrylamide Levels

Cascos,

Montero-Fernández,

Marcía-Fuentes

et al. 2024

Foods

View full text Add to dashboard Cite

The aim of this research was to apply an electronic device as indirect predictive technology to evaluate toxic chemical compounds in roasted espresso coffee. Fresh coffee beans were subjected to different thermal treatments and analyzed to determine volatile organic compounds, content of acrylamide and 5-hydroxymethylfurfural, sensory characteristics and electronic nose data. In total, 70 different volatile compounds were detected and grouped into 15 chemical families. The greatest percentage of these compounds were furans, pyrazines, pyridines and aldehydes. The positive aroma detected had the intensity of coffee odor and a roasted aroma, whereas the negative aroma was related to a burnt smell. A linear relationship between the toxic substances and the sensory defect was established. A high sensory defect implied a lower content of acrylamide and a higher content of 5-hydroxymethylfurfural. Finally, electronic signals were also correlated with the sensory defect. This relationship allowed us to predict the presence of these contaminants in the roasted coffee beverage with an indirect method by using this electronic device. Thus, this device may be useful to indirectly evaluate the chemical contaminants in coffee beverages according to their sensory characteristics.

show abstract

Machine Learning Driven Atom‐Thin Materials for Fragrance Sensing

Liu,

Sun,

Bao

et al. 2024

Small

View full text Add to dashboard Cite

Fragrance plays a crucial role in the daily lives. Its importance spans various sectors, from therapeutic purposes to personal care, making the understanding and accurate identification of fragrances essential. To fully harness the potential of fragrances, efficient and precise fragrance sensing and identification are necessary. However, current fragrance sensors face several limitations, particularly in detecting and differentiating complex scent profiles with high accuracy. To address these challenges, the use of atom‐thin materials in fragrance sensors has emerged as a groundbreaking approach. These atom‐thin sensors, characterized by their enhanced sensitivity and selectivity, offer significant improvements over traditional sensing technology. Moreover, the integration of Machine Learning (ML) into fragrance sensing has opened new opportunities in the field. ML algorithms applied to fragrance sensing facilitate advancements in four key domains: accurate fragrance identification, precise discrimination between different fragrances, improved detection thresholds for subtle scents, and prediction of fragrance properties. This comprehensive review delves into the synergistic use of atom‐thin materials and ML in fragrance sensing, providing an in‐depth analysis of how these technologies are revolutionizing the field, offering insights into their current applications and future potential in enhancing the understanding and utilization of fragrances.

show abstract

An AI-powered e-nose system using a density-based clustering method for identifying adulteration in specialty coffees

Cited by 5 publications

References 30 publications

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

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

Electronic Prediction of Chemical Contaminants in Aroma of Brewed Roasted Coffee and Quantification of Acrylamide Levels

Machine Learning Driven Atom‐Thin Materials for Fragrance Sensing

Contact Info

Product

Resources

About