Evaluation of chemical properties of intact green coffee beans using near‐infrared spectroscopy

2020 ◽  
Author(s):  
Leandro Macedo ◽  
Cintia Araújo ◽  
Wallaf Vimercati ◽  
Paulo Ricardo Hein ◽  
Carlos José Pimenta ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Chemical Properties ◽  
Green Coffee ◽  
Green Coffee Beans ◽  
Coffee Beans

Evaluation of green coffee beans quality using near infrared spectroscopy: A quantitative approach

2012 ◽  
Vol 135 (3) ◽  
pp. 1828-1835 ◽  
Author(s):  
João Rodrigo Santos ◽  
Mafalda C. Sarraguça ◽  
António O.S.S. Rangel ◽  
João A. Lopes
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Quantitative Approach ◽  
Green Coffee ◽  
Green Coffee Beans ◽  
Coffee Beans

scholarly journals Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy

Foods ◽  
2017 ◽  
Vol 6 (5) ◽  
pp. 38 ◽  
Author(s):  
Adnan Adnan ◽  
Dieter von Hörsten ◽  
Elke Pawelzik ◽  
and Daniel Mörlein
Keyword(s):  
Infrared Spectroscopy ◽  
Moisture Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Green Coffee ◽  
Green Coffee Beans ◽  
Coffee Beans ◽  
Rapid Prediction

scholarly journals Nondestructive Classification Analysis of Green Coffee Beans by Using Near-Infrared Spectroscopy

Foods ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 82 ◽  
Author(s):  
Naoya Okubo ◽  
Yohei Kurata
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Green Coffee ◽  
Classification Rate ◽  
Classification Analysis ◽  
Green Coffee Beans ◽  
Coffee Beans ◽  
Good Classification ◽  
Production Area

Near-infrared spectroscopy (NIRS) is a powerful tool for the nondestructive evaluation of organic materials, and it has found widespread use in a variety of industries. In the food industry, it is important to know the district in which a particular food was produced. Therefore, in this study, we focused on determining the production area (five areas and three districts) of green coffee beans using classification analysis and NIRS. Soft independent modeling of class analogy (SIMCA) was applied as the classification method. Samples of green coffee beans produced in seven locations—Cuba, Ethiopia, Indonesia (Bari, Java, and Sumatra), Tanzania, and Yemen—were analyzed. These regions were selected since green coffee beans from these locations are commonly sold in Japan supermarkets. A good classification result was obtained with SIMCA for the seven green bean samples, although some samples were partly classified into several categories. Then, the model distance values of SIMCA were calculated and compared. A few model distance values were ~10; such small values may be the reason for misclassification. However, over a 73% correct classification rate could be achieved for the different kinds of green coffee beans using NIRS.

Prediction of specialty coffee cup quality based on near infrared spectra of green coffee beans

Talanta ◽  
2016 ◽  
Vol 150 ◽  
pp. 367-374 ◽  
Author(s):  
Kassaye Tolessa ◽  
Michael Rademaker ◽  
Bernard De Baets ◽  
Pascal Boeckx
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Green Coffee ◽  
Green Coffee Beans ◽  
Near Infrared Spectra ◽  
Coffee Beans ◽  
Specialty Coffee ◽  
Cup Quality

Use of Near-Infrared Spectroscopy for Coffee Beans Quality Assessment

2015 ◽  
pp. 933-942 ◽  
Author(s):  
Ricardo N.M.J. Páscoa ◽  
Mafalda C. Sarraguça ◽  
Luis M. Magalhães ◽  
João R. Santos ◽  
António O.S.S. Rangel ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Quality Assessment ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Coffee Beans

scholarly journals Examination of wheat rheological characteristics by near-infrared spectroscopy

2012 ◽  
pp. 99-104
Author(s):  
Éva Kónya ◽  
Zoltán Győri
Keyword(s):  
Infrared Spectroscopy ◽  
Wheat Flour ◽  
Near Infrared Spectroscopy ◽  
Food Industry ◽  
Near Infrared ◽  
Chemical Properties ◽  
Rheological Characteristics ◽  
Physical And Chemical ◽  
Calibration Equations ◽  
And Chemical Properties

Near-infrared spectroscopy has many advantages that make it a widely used analitical method in the different areas, like agricultural and food industry as well. In wheat quality control rheological characteristics of dough made from wheat flour are as important as physical and chemical properties too. In this work we examined rheological properties of wheat flour samples by alveograph, and spectral data of the same samples were collected by FOSS Infratec 1241 instrument. Modified partial least squares analyses on NIR spectra were developed for two alveograph parameter (P/L és W) to get calibration equations.

scholarly journals Reliable Discrimination of Green Coffee Beans Species: A Comparison of UV-Vis-Based Determination of Caffeine and Chlorogenic Acid with Non-Targeted Near-Infrared Spectroscopy

Foods ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 788
Author(s):  
Adnan Adnan ◽  
Marcel Naumann ◽  
Daniel Mörlein ◽  
Elke Pawelzik
Keyword(s):  
Chlorogenic Acid ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Coffea Canephora ◽  
Green Coffee ◽  
Linear Discriminant ◽  
Green Coffee Beans ◽  
Coffee Beans ◽  
Vis Spectroscopy

Species adulteration is a common problem in the coffee trade. Several attempts have been made to differentiate among species. However, finding an applicable methodology that would consider the various aspects of adulteration remains a challenge. This study investigated an ultraviolet–visible (UV-Vis) spectroscopy-based determination of caffeine and chlorogenic acid contents, as well as the applicability of non-targeted near-infrared (NIR) spectroscopy, to discriminate between green coffee beans of the Coffea arabica (Arabica) and Coffea canephora (Robusta) species from Java Island, Indonesia. The discrimination was conducted by measuring the caffeine and chlorogenic acid content in the beans using UV-Vis spectroscopy. The data related to both compounds was processed using linear discriminant analysis (LDA). Information about the diffuse reflectance (log 1/R) spectra of intact beans was determined by NIR spectroscopy and analyzed using multivariate analysis. UV-Vis spectroscopy attained an accuracy of 97% in comparison to NIR spectroscopy’s accuracy by selected wavelengths of LDA (95%). The study suggests that both methods are applicable to discriminate reliably among species.

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