Application of portable near infrared spectroscopy for classifying and quantifying cocoa bean quality parameters

Anyidoho, Elliot K.; Teye, Ernest; Agbemafle, Robert; Amuah, Charles Lloyd Yeboah; Boadu, Vida Gyimah

doi:10.1111/jfpp.15445

Cited by 14 publications

(6 citation statements)

References 49 publications

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“… Anyidoho et al (2022) . used NIR/Chemometrics for rapid determination of fermentation duration, fermentation index and moisture content of cocoa beans.…”

Section: Applications To Food Quality Characterization and Adulterationsmentioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019–2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the “true green analytical chemistry” which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

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“… Anyidoho et al (2022) . used NIR/Chemometrics for rapid determination of fermentation duration, fermentation index and moisture content of cocoa beans.…”

Section: Applications To Food Quality Characterization and Adulterationsmentioning

confidence: 99%

Portable NIR spectroscopy: the route to green analytical chemistry

Gullifa,

Barone,

Papa

et al. 2023

Front. Chem.

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“…Although these devices do not have the spectroscopic quality of the more expensive benchmark instruments, combined with highly effective procedures for data pre-treatment and chemometric methods for data analysis, they provide high performance regarding correct classification and authentication. For example, the product quality, highly dependent on the harvesting and production (fermentation and roasting) processes, was assessed by Anydoho and co-workers using portable NIR spectroscopy [ 39 , 40 ]. NIR spectra recorded in the range 900 to 1700 nm were preprocessed using various treatments, including first and second derivative, multiplicative scatter correction, mean centering, and standard normal variable, with multiplicative scatter correction being the most suitable option.…”

Section: Fingerprinting-based Authenticationmentioning

confidence: 99%

“…The resulting data were analyzed with classification methods such as PLS-DA, random forest (RF), and K-nearest neighbors (KNN) differentiated organic and conventional harvesting processes with calibration and prediction rates around 90% [ 39 ]. Similarly, fermentation features strongly related to the quality of the cocoa beans were satisfactorily predicted using NIR and multivariate analyses (LDA, SVM, and PLS) [ 40 ].…”

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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“…The data obtained with NIR were used to build the regression model, which used the information from the data matrix X (full spectrum) and the response matrix Y (pH and total acidity values) to obtain new variables, which were called latent variables, components or factors. The absorbance values from the full spectrum were used as variables (Anyidoho et al, 2021;Hayati et al, 2021;Santos et al, 2021).…”

Section: Partial Least Squares (Pls)mentioning

confidence: 99%

Identification of cocoa bean quality by near infrared spectroscopy and multivariate modeling

Batista¹,

Oliveira²,

Pereira

et al. 2021

RSD

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Cocoa is a commodity responsible for the income of millions of people and the manufacture of several important products for the food, pharmaceutical, and cosmetic industries. Its quality is associated with several factors involved in the processing steps, mainly in fermentation and drying. The objective of this study was to evaluate the application of near-infrared spectroscopic data associated with multivariate analysis to classify cocoa beans according to their quality and predict attributes such as pH and total acidity by PLS-DA and PLS, respectively. The pH values (4.4-6.7) and total acidity (6.12-29.9) were determined by conventional methods. The PLS-DA proved to be effective in differentiating the classes of cocoa samples with superior and inferior quality, presenting in the validation 100% and 71.43% correct cocoa bean classification with inferior Quality and Higher Quality, respectively. The models obtained by PLS presented satisfactory parameters, being classified as having moderate practical utility and excellent predictive capacity for pH and moderate practical utility and reasonable predictive capacity for total acidity. Thus, the potential of the NIRS technology associated with chemometrics was found and showed efficiency in the classification and prediction of attributes in cocoa beans.

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Application of portable near infrared spectroscopy for classifying and quantifying cocoa bean quality parameters

Cited by 14 publications

References 49 publications

Portable NIR spectroscopy: the route to green analytical chemistry

Portable NIR spectroscopy: the route to green analytical chemistry

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

Identification of cocoa bean quality by near infrared spectroscopy and multivariate modeling

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