Partial least square with discriminant analysis and near infrared spectroscopy for evaluation of geographic and genotypic origin of arabica coffee

Marquetti, Izabele; Link, Jade Varaschim; Lemes, André Luis Guimarães; Scholz, Maria Brígida dos Santos; Valderrama, Patrícia; Bona, Evandro

doi:10.1016/j.compag.2015.12.018

Cited by 98 publications

(58 citation statements)

References 22 publications

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“…[16] The second derivative can reduce the correlation between variables in hyperspectral images greatly. [17] SNV is used to correct spectral errors caused by particle scattering in samples. [18] The purpose of mean-centered is to eliminate the effects of dimensional differences and self-variation.…”

Section: Data Extraction and Preprocessingmentioning

confidence: 99%

Visual detection of apple bruises using AdaBoost algorithm and hyperspectral imaging

Zhang

2018

International Journal of Food Properties

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Hyperspectral imaging technique (400-1000 nm) was used for rapid and nondestructive recognition of bruises of apples. A total of 324 hyperspectral images were collected from 108 Fuji apples and the average spectral reflectance was extracted from the region of interest (ROI) of each image. The classification results of AdaBoost for the data pretreated by various existing methods were compared. Then, the correlation-based feature selection (CFS) algorithm was used to obtain characteristic wavelengths for reducing data redundancy. After pretreating with multiplicative scatter correction (MSC) and CFS, the average accuracy of the selected wavelengths was 97.63%. Then, an image processing algorithm based on the characteristic wavelengths selected before was proposed for the visual discrimination of bruises. This algorithm performed independent component analysis (ICA) transformation of the selected wavelengths, and chose the third component image of the ICA transform, then used adaptive threshold segmentation to obtain the bruise region of apples. The results showed that hyperspectral imaging technology could discriminate apple bruise, and this study can help to develop an online apple bruises detection system. ARTICLE HISTORY

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Section: Data Extraction and Preprocessingmentioning

confidence: 99%

Visual detection of apple bruises using AdaBoost algorithm and hyperspectral imaging

Zhang

2018

International Journal of Food Properties

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“…3 The latter species represents 70% of world coffee production 2 due to finer flavor and aroma, and therefore is more consumed than Robusta. 4 Coffee is one of the most consumed beverages in the world 5,6 and an important raw material of the international trade. 4 Thus, due to its importance, the chemical composition of coffee beans has been extensively studied.…”

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confidence: 99%

Irrigation and Light Access Effects on Coffea arabica L. Leaves by FTIR‑Chemometric Analysis

Sanchez¹,

Pauli²,

Scheel³

et al. 2017

J. Braz. Chem. Soc.

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Coffee bean chemical compositions has been extensively studied. However, there is a small amount of research on other parts of the coffee plant, including leaves. Fourier transform infrared (FTIR) spectral profiles of Coffea arabica L. cv. IAPAR 59 leaf extracts from a simplex-centroid design were studied by principal component analysis (PCA) to evaluate the effect of solvent extractor on its metabolites. PCA indicated that the extractor solvents containing ethanol were the most suitable for this study. FTIR spectra in conjunction with orthogonal signal correction and partial least squares-discrimination analysis (OSC-PLS-DA) were used to classify and discriminate the leaves of irrigated and non-irrigated plants by bands related to carbohydrates, amino acids and lipids. Leaves receiving different intensities of solar radiation were also discriminated by bands corresponding to caffeine, carbohydrates and lipids. FTIR spectral profile analyzed with chemometric tools showed to be a useful, powerful and simple procedure to discriminate coffee leaves collected from different microclimate conditions. Keywords:Coffea arabica leaves, simplex-centroid design, FTIR spectroscopy, chemometric analysis, environmental conditions IntroductionThe coffee plant is a woody, perennial, evergreen and dicotyledon species that belongs to the Rubiaceae family. 1,2 Among several species of the genus Coffea, two are considered economically important, i.e. Coffea canephora Pierre (Robusta coffee) and Coffea arabica L. (Arabica coffee). 3 The latter species represents 70% of world coffee production 2 due to finer flavor and aroma, and therefore is more consumed than Robusta. 4 Coffee is one of the most consumed beverages in the world 5,6 and an important raw material of the international trade. 4 Thus, due to its importance, the chemical composition of coffee beans has been extensively studied. However, there is relatively little research carried out on metabolites in other coffee plant parts, including the leaves.7-11 Phenolic compounds found in the coffee leaves 7 have been shown to be potentially beneficial for health, although the influence of consumption of these compounds on the human body requires further research. Other metabolite groups are found in coffee leaves, such as alkaloids, 12 several carbohydrates 13,14 and lipids 9 among others.Plant metabolites are susceptible to environmental changes and their responses can be evaluated by metabolomics analysis. 15 In periods of drought or excessive sun exposure, environmental stress can affect physiological processes in plants owing to their necessity to adapt to the new situation. In excess, these conditions can affect metabolic activities such as photosynthesis and growth rate modifying carbohydrate levels and protein synthesis. [16][17][18][19][20] In coffee plants, these changes may influence the metabolic quantities and consequently, the coffee bean quality. 9,21 Information about chemical modifications in coffee Sanchez et al. 169 Vol. 29, No. 1, 2018 leaves subjecte...

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“…Topographical factors such as altitude and slope exposure (Avelino et al, 2005;Bosselmann et al, 2009;Decazy et al, 2003), climate, specifically both precipitation (da Silva et al, 2005;Decazy et al, 2003) and temperature (Bertrand et al, 2012), and soil (Marquetti et al, 2016) are known to be able to determine the characteristics of coffee flavor profiles. Interaction of these factors gives the coffee a unique identity (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Interaction of these factors gives the coffee a unique identity (i.e. profile) that defines the final quality as they jointly determine the chemical composition of the coffee beans (Marquetti et al, 2016). Coffee sensory quality has been evaluated by criteria such as flavor, taste, bean size, colour, shape, roast potential, processing method, and crop year, among others (Choi, Choi, Park, Lim, & Kwon, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Coffee sensory quality has been evaluated by criteria such as flavor, taste, bean size, colour, shape, roast potential, processing method, and crop year, among others (Choi, Choi, Park, Lim, & Kwon, 2010). However, among these parameters, flavor and taste are the most important criteria (Vasconcelos et al, 2007) and they are potentially influenced by environmental factors such as soil and climate, as well as genetic factors such as species (Marquetti et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Smallholder coffee terroirs of Rwanda : linking local to global trade through geographical characterization

Mukashema¹

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Partial least square with discriminant analysis and near infrared spectroscopy for evaluation of geographic and genotypic origin of arabica coffee

Cited by 98 publications

References 22 publications

Visual detection of apple bruises using AdaBoost algorithm and hyperspectral imaging

Visual detection of apple bruises using AdaBoost algorithm and hyperspectral imaging

Irrigation and Light Access Effects on Coffea arabica L. Leaves by FTIR‑Chemometric Analysis

Smallholder coffee terroirs of Rwanda : linking local to global trade through geographical characterization

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