Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach

Castro, Wilson; De-la-Torre, Miguel; Ávila-George, Himer; Torres-Jiménez, José; Guivin, Alex; Acevedo-Juárez, Brenda

doi:10.1016/j.saa.2021.120815

Cited by 18 publications

(5 citation statements)

References 72 publications

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“…Related to comestible seeds and with the objective of quality control, as was this study with the Brazilian nut, some studies confirm the authentication of walnut [53] and agroforestry-grown coffee samples [54] and the discrimination of cacao nibs [55], indicate that the use of NIR spectra is a promising tool.…”

Section: Nir Spectroscopysupporting

confidence: 59%

Provenance Identification of Leaves and Nuts of Bertholletia excelsa Bonpl by Near-Infrared Spectroscopy and Color Parameters for Sustainable Extraction

Nisgoski,

dos Santos,

Vieira

et al. 2023

Sustainability

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The Brazil nut tree is considered symbolic of the Brazilian Amazon in function of its great importance, being one of the most significant extractivist products and a subsistence practice of the Indigenous people in many municipalities in Pará state. One of the main problems in different communities is related to the marketing process since it is not possible to distinguish the origin of the nuts and this causes inconvenience. The study evaluated the potential of VIS/NIR spectroscopy to identify the origin of leaves and nuts from Brazil nut trees growing in two indigenous villages, in the Xipaya Indigenous Lands, Pará state. Analysis was performed based on CIEL*a*b* parameters and using VIS (360–740 nm) and near-infrared spectra (1000–2500 nm). The samples were differentiated according to means tests, principal component analysis (PCA), and classification analysis based on k-NN. Color parameters and spectra were similar in both communities. Classification models based on k-NN produced adequate results for the distinction of villages in all evaluated situations, with accuracy of 98.54% for leaves, 89% and 90.91% for nuts with and without shell, respectively. Near infrared can be applied in forests as a technique for previous provenance identification and contribute to the subsistence and sustainable practice of extraction.

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Section: Nir Spectroscopysupporting

confidence: 59%

Provenance Identification of Leaves and Nuts of Bertholletia excelsa Bonpl by Near-Infrared Spectroscopy and Color Parameters for Sustainable Extraction

Nisgoski,

dos Santos,

Vieira

et al. 2023

Sustainability

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“…HSI principally relies on chemometric analysis for evaluating sample characteristics, and the relations between functional groups and wavebands play a key role in identifying chemical constituents. In the NIR range, molecular vibrations mainly contain overtones and combinations of O–H, N–H, and C–H vibrational bands; in addition, the overtones of stretching vibrations are stronger than those of bending or rocking modes …”

Section: Resultsmentioning

confidence: 99%

Machine Learning and Hyperspectral Imaging-Aided Forecast for the Share of Biogenic and Fossil Carbon in Solid Waste

Lan

et al. 2023

ACS Sustainable Chem. Eng.

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Traditional methods for analyzing the biogenic and fossil carbon shares in solid waste are time-consuming and labor-intensive. A novel approach was developed to directly classify the carbon group and predict carbon content using the hyperspectral imaging (HSI) spectra of solid waste in conjunction with state-of-the-art tree-based machine learning models, including random forest (RF), extreme gradient boost, and light gradient boost machine (LGBM). All of the classifiers and regressors were able to achieve an accuracy above 0.95 and an R 2 of 0.96 in the test set, respectively. In addition, two model interpretation approaches, the Shapley additive explanation and model explainer, were applied. The results showed that the predictions of the developed models were based on a reasonable understanding of the overtone and shake of the functional groups (C–H, N–H, and O–H). Furthermore, the developed models were validated by an external test set, which did not overlap with the data used for model construction. The RF and LGBM showed robust performance with a 0.790 accuracy for carbon group classification and a 0.806 R 2 for carbon content prediction. Overall, the optimal models provided a rapid method for characterizing the biogenic carbon share in solid waste based on raw HSI spectra without preprocessing.

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“…The authors built an SVM regression model that allows the determination of the percentage of cocoa shells from 0 to 20% with errors around 2%, independently of the geographical origin, harvesting period, or roasting process. Differentiation among cocoa varieties, clones, and geographical origin was also performed by treating NIR spectra with several chemometric tools [ 32 , 33 , 34 , 35 , 36 , 37 ]. Among these studies, it is worth mentioning the work by Cruz-Tirado et al because, unlike most reported studies, they analyzed cocoa bean hybrids directly, i.e., without a previous sample grounding treatment [ 33 ].…”

Section: Fingerprinting-based Authenticationmentioning

confidence: 99%

Authentication of Cocoa Products Based on Profiling and Fingerprinting Approaches: Assessment of Geographical, Varietal, Agricultural and Processing Features

Sentellas,

Saurina

2023

Foods

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Cocoa and its derivative products, especially chocolate, are highly appreciated by consumers for their exceptional organoleptic qualities, thus being often considered delicacies. They are also regarded as superfoods due to their nutritional and health properties. Cocoa is susceptible to adulteration to obtain illicit economic benefits, so strategies capable of authenticating its attributes are needed. Features such as cocoa variety, origin, fair trade, and organic production are increasingly important in our society, so they need to be guaranteed. Most of the methods dealing with food authentication rely on profiling and fingerprinting approaches. The compositional profiles of natural components –such as polyphenols, biogenic amines, amino acids, volatile organic compounds, and fatty acids– are the source of information to address these issues. As for fingerprinting, analytical techniques, such as chromatography, infrared, Raman, and mass spectrometry, generate rich fingerprints containing dozens of features to be used for discrimination purposes. In the two cases, the data generated are complex, so chemometric methods are usually applied to extract the underlying information. In this review, we present the state of the art of cocoa and chocolate authentication, highlighting the pros and cons of the different approaches. Besides, the relevance of the proposed methods in quality control and the novel trends for sample analysis are also discussed.

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Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach

Cited by 18 publications

References 72 publications

Provenance Identification of Leaves and Nuts of Bertholletia excelsa Bonpl by Near-Infrared Spectroscopy and Color Parameters for Sustainable Extraction

Provenance Identification of Leaves and Nuts of Bertholletia excelsa Bonpl by Near-Infrared Spectroscopy and Color Parameters for Sustainable Extraction

Machine Learning and Hyperspectral Imaging-Aided Forecast for the Share of Biogenic and Fossil Carbon in Solid Waste

Authentication of Cocoa Products Based on Profiling and Fingerprinting Approaches: Assessment of Geographical, Varietal, Agricultural and Processing Features

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