Rapid determination of the shell content in cocoa products using FT-NIR spectroscopy and chemometrics

Drees, Alissa; Brockelt, Johannes; Cvancar, L.; Fischer, Markus

doi:10.1016/j.talanta.2023.124310

Cited by 21 publications

(10 citation statements)

References 44 publications

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“…can be mixed to increase the mass of production at a lower cost. The shell case deserves special attention since, during the manufacturing process, it is impossible to remove this residue from the cocoa paste completely, and a small fraction is always present [ 15 ]. Indeed, the Codex Alimentarius Commission establishes a maximum of 5% shell and germ bud content in the non-fat dry matter obtained in cocoa manufacturing.…”

Section: General Approach To Cocoa Authenticationmentioning

confidence: 99%

“…14% of the samples differs from the informed value. More specifically, the cocoa adulteration with peanut or carob flour or cocoa shell was also evaluated by untargeted analysis using IR spectra combined with different chemometric tools, namely PLS, SVM, and MCR-ALS [ 15 , 29 , 30 , 31 ]. In one of the examples, the percentage of cocoa shells was estimated using a simple and fast FT-NIR method combined with chemometrics [ 15 ].…”

Section: Fingerprinting-based Authenticationmentioning

confidence: 99%

“…More specifically, the cocoa adulteration with peanut or carob flour or cocoa shell was also evaluated by untargeted analysis using IR spectra combined with different chemometric tools, namely PLS, SVM, and MCR-ALS [ 15 , 29 , 30 , 31 ]. In one of the examples, the percentage of cocoa shells was estimated using a simple and fast FT-NIR method combined with chemometrics [ 15 ]. 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.…”

Section: Fingerprinting-based Authenticationmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: General Approach To Cocoa Authenticationmentioning

confidence: 99%

Section: Fingerprinting-based Authenticationmentioning

confidence: 99%

Section: Fingerprinting-based Authenticationmentioning

confidence: 99%

See 1 more Smart Citation

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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“…They can provide accurate and reliable test results, but complex professional operations, time‐consuming laboratory procedures, and expensive chemicals limit the rapid detection of large volumes of samples due to L. tsaoko ’s diversity of compounds, which is not only via chemical analysis to indicate a comprehensive representation of its complex chemical information. In recent years, many researchers have applied spectroscopic techniques to the quality analysis and evaluation of edible mushrooms and traditional Chinese medicine (Drees et al., 2023; Li et al., 2022; Yan et al., 2022). Among them, Fourier transform‐near infrared (FT‐NIR) spectroscopy is widely used in qualitative and quantitative studies because of its fast, nondestructive, stable, and green detection advantages.…”

Section: Introductionmentioning

confidence: 99%

A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT‐NIR combined with chemometrics and image recognition

He,

Yang,

Wang

2024

Journal of Food Science

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Lanxangia tsaoko’s accurate classifications of different origins and fruit shapes are significant for research in L. tsaoko difference between origin and species as well as for variety breeding, cultivation, and market management. In this work, Fourier transform‐near infrared (FT‐NIR) spectroscopy was transformed into two‐dimensional and three‐dimensional correlation spectroscopies to further investigate the spectral characteristics of L. tsaoko. Before building the classification model, the raw FT‐NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares‐discriminant analysis (PLS‐DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS‐DA and SVM models can achieve 100% classification in origin classification, but what is difficult to avoid is the complex process of model optimization. The ResNet image recognition model classifies the origin and shape of L. tsaoko with 100% accuracy, and without the need for complex preprocessing and feature extraction, the model facilitates the realization of fast, accurate, and efficient identification.

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“…Cocoa (Theobroma cacao L.) holds great significance in Indonesia as it is the world's largest producer of cocoa, contributing significantly to the country's foreign exchange. Indonesia consistently produces around 550 thousand tons of cocoa beans each year, making it a consistent player in the global cocoa market [1]. Additionally, smallholder cocoa cultivation and cocoa farmers dominates the cocoa sector, covering approximately 2,028,596 hectares or 94% of the total cocoa area in 2020.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Prediction of Moisture and Fat Content in Cocoa Beans Using Near-Infrared Spectroscopy and Multivariate Regression Models

Hayati,

Maulina,

Munawar

2024

IJDNE

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Presented paper aimed to apply the near infrared spectroscopy (NIRS) for rapid, nondestructive and simultaneous prediction of moisture and fat content on intact cocoa beans. Near infrared spectral data were acquired using a portable NIRS instrument (PSD NIRS i16, Universitas Syiah Kuala) in the wavelength range of 1000-2500 nm. Actual moisture and fat contents were measured and determined by means of thermo-gravimetry and soxhlet methods respectively. Spectral data were enhanced and corrected using standard normal variate (SNV) and de-trending order 2 (Dt-2) methods. Prediction models, used to determine the moisture and fat contents were established using partial least square regression (PLSR) and principal component regression (PCR). The results showed that moisture content and fat content can be predicted non-destructively and simultaneously using NIRS with maximum prediction index (RPD) are 2.16 for moisture content and 2.39 for fat content respectively which can be categorized as good model performances. Moreover, SNV found to be the ebst spectra correction method in predicting both moisture and fat contents.

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Rapid determination of the shell content in cocoa products using FT-NIR spectroscopy and chemometrics

Cited by 21 publications

References 44 publications

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

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

A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT‐NIR combined with chemometrics and image recognition

Non-Destructive Prediction of Moisture and Fat Content in Cocoa Beans Using Near-Infrared Spectroscopy and Multivariate Regression Models

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