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.

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

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

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

DISCRIMINATIVE ANALYSIS OF FUNCTIONAL NEAR-INFRARED SPECTROSCOPY SIGNALS FOR DEVELOPMENT OF NEUROIMAGING BIOMARKERS OF ELDERLY DEPRESSION

2010 ◽  
Vol 03 (01) ◽  
pp. 69-74 ◽  
Author(s):  
YE ZHU ◽  
TIANZI JIANG ◽  
YUAN ZHOU ◽  
LISHA ZHAO
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Psychiatric Patients ◽  
Clinical Tool ◽  
Functional Near Infrared Spectroscopy ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Discriminative Model ◽  
Neuroimaging Biomarkers

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technology which is suitable for psychiatric patients. Several fNIRS studies have found abnormal brain activations during cognitive tasks in elderly depression. In this paper, we proposed a discriminative model of multivariate pattern classification based on fNIRS signals to distinguish elderly depressed patients from healthy controls. This model used the brain activation patterns during a verbal fluency task as features of classification. Then Pseudo-Fisher Linear Discriminant Analysis was performed on the feature space to generate discriminative model. Using leave-one-out (LOO) cross-validation, our results showed a correct classification rate of 88%. The discriminative model showed its ability to identify people with elderly depression and suggested that fNIRS may be an efficient clinical tool for diagnosis of depression. This study may provide the first step for the development of neuroimaging biomarkers based on fNIRS in psychiatric disorders.

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

scholarly journals Near infrared spectroscopy in the supply chain monitoring of Annurca apple

2019 ◽  
Vol 27 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Marina Buccheri ◽  
Maurizio Grassi ◽  
Fabio Lovati ◽  
Milena Petriccione ◽  
Pietro Rega ◽  
...  
Keyword(s):  
Supply Chain ◽  
Infrared Spectroscopy ◽  
Shelf Life ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Correct Classification ◽  
Geographical Indication ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Campania Region

Annurca is the most cultivated apple variety in the Campania region (Italy). It is an Italian protected geographical indication product and its management must follow a strict product specification which requires a typical postharvest treatment: the fruit must be subjected to a reddening process in air (‘melaio’) that improves the red colour and the flavour of the fruit but is very expensive and time consuming. For this reason there is sometimes a tendency to skip the ‘melaio’ process, but in this case the fruit cannot be labelled as ‘Melannurca Campana PGI’. The purpose of this work was to discriminate ‘melaio’ treated fruit from untreated fruit using near infrared spectroscopy. A further objective of the work was the non-destructive evaluation of the apple storage conditions which can affect the product quality. Fruit of Annurca ‘Rossa del Sud’ subjected or not subjected to the reddening treatment in ‘melaio’ were stored at 0.5℃ in air (Air) or in controlled atmosphere (1%O2, 0.7% CO2) for eight-month duration. Following storage, fruit were analysed for standard maturity indices (flesh firmness, soluble solids, acidity) and the near infrared spectrum of each fruit was collected. The spectral data, subjected to various pre-treatments, were used to calculate a calibration model by applying partial least squares-discriminant analysis. The best model allowed discrimination of fruit immediately after storage under different conditions, but with 0 days of shelf life, to be classified with a 93.3% correct classification rate for the prediction set. However, after seven days of shelf life at 20℃, post-storage, correct classification rate dropped to 70%, but it was always possible to discriminate the two treatments (96.6% correct classification rate). The results of this preliminary work suggest a possible use of the portable near infrared instrument in the monitoring of the Annurca (protected geographical indication) supply chain.

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 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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