Analysis and classification of coffee beans using single coffee bean mass spectrometry with machine learning strategy

Tsai, Jia-Jen; Chang, Chia-Wei; Huang, De-Yi; Lin, Te-Sheng; Chen, Yu-Chie

doi:10.1016/j.foodchem.2023.136610

Cited by 4 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…with InceptionResnetV2, the classification for 17 classes achieved an accuracy of 53.35%, and for 3 classes, it achieved 92.52%. [41] The study classifies palm civet coffee beans based on their mass spectra.…”

Section: Reference Objectives and Scenario Of Applicationmentioning

confidence: 99%

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

de C. Motta,

Vuillerme,

Pham

et al. 2024

Preprint

View full text Add to dashboard Cite

In the realm of agribusiness, transformative shifts are underway, propelled by the growing demands and expanding scales of grain production. This evolution calls for a critical reevaluation of the existing paradigms in coffee production and marketing paradigms, with a specific focus on integrating Artificial Intelligence (AI). This work aims to review, synthesize, and summarize the available data regarding how Machine Learning (ML) has been used to detect and classify characteristics in coffee beans and leaves. For this purpose, a comprehensive literature review of the most significant research contributions describing the application of AI for advanced classification techniques in coffee agriculture has been carried out. Our analysis suggests that implementing AI technologies allows the classification of coffee, encompassing various attributes such as maturity, roast intensity, disease identification, flavor profiles, and overall quality. More largely, this technological advancement holds the potential to revolutionize coffee farming by providing producers and agricultural specialists with sophisticated tools to enhance production efficiency, minimize costs, and improve the accuracy and confidence of their decision-making processes.

show abstract

Section: Reference Objectives and Scenario Of Applicationmentioning

confidence: 99%

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

de C. Motta,

Vuillerme,

Pham

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…El café es un producto de alta rotación, en el que las elecciones de los consumidores se ven encaminadas por varios factores, uno de los cuales es el precio, que varía constantemente según el sabor, aroma y métodos de procesamiento (Tsai et al, 2023). No obstante, la promoción es determinante, pues la percepción del valor sobre el café se ve influenciada por aspectos culturales y sociales, afectando no solo su elección, sino también su frecuencia y ritmo de consumo (Queirolo Bobadilla, 2010).…”

unclassified

Análisis de la cadena de valor del café liofilizado a través de la metodología Delphi y propuestas competitivas

Carpio Espino,

Indacochea Torres,

Noriega Araníbar

2024

Ing. ind. (Lima)

View full text Add to dashboard Cite

Actualmente la cadena de valor del café liofilizado peruano cuenta con una participación discreta respecto a sus competidores en el mercado internacional. El objetivo principal de la investigación fue el análisis de la actual cadena de valor del café liofilizado peruano, identificando ventajas comparativas y competitivas en los mercados internacionales. La metodología que se empleó fue de enfoque mixto y con diseño cuasi experimental. A partir del mapeo actual de la cadena de valor y el diseño actual de la cadena de suministro del café liofilizado peruano, se permitió analizar con mayor precisión los principales problemas que presentaban los eslabones que conformaban la cadena de valor, para poder diseñar un modelo propuesto que abarque desde la producción primaria del café hasta la comercialización del café liofilizado peruano, que sea capaz de aportar en la competitividad del café en los mercados internacionales.

show abstract

Rapid classification of coffee origin by combining mass spectrometry analysis of coffee aroma with deep learning

Yang,

Ai,

Zhu

et al. 2024

Food Chemistry

View full text Add to dashboard Cite

Analysis and classification of coffee beans using single coffee bean mass spectrometry with machine learning strategy

Cited by 4 publications

References 32 publications

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

Análisis de la cadena de valor del café liofilizado a través de la metodología Delphi y propuestas competitivas

Rapid classification of coffee origin by combining mass spectrometry analysis of coffee aroma with deep learning

Contact Info

Product

Resources

About