Editorial Feature

Applying the Electronic Nose to Cocoa Quality Control

Continue reading to learn more about how we can apply the electronic nose to cocoa quality control.

cocoa nibs on the table.

Image Credit: femeindrei/Shutterstock.com

Cocoa beans are the key ingredient in chocolate and various cocoa-based products, and their quality significantly impacts the flavor and aroma of the final product. As the global demand for high-quality cocoa-based products increases, there is a growing need for quick and reliable methods to assess cocoa bean quality, especially the degree of fermentation. The electronic nose (E-nose) is emerging as a promising tool that can be applied for cocoa quality control.

What is an Electronic Nose, and How Does it Work?

An electronic nose is a chemical sensor array designed to mimic the functioning of the human olfactory system (sense of smell) through an array of gas sensors. When volatile organic compounds (VOCs) interact with the sensors, changes in electrical signals occur, creating a unique "fingerprint" that can be analyzed using various statistical and machine learning algorithms to identify, classify, and quantify odors and aromas.

Electronic noses provide a rapid, affordable, and portable alternative to more complex analytical chemistry techniques for aroma analysis. Unlike conventional equipment like gas chromatography-mass spectrometry (GC-MS), E-noses do not require extensive sample preparation, skilled operators, or laboratory facilities. Additionally, they can analyze samples in real-time, facilitating continuous monitoring of production processes.

These advantages make them well-suited for routine quality testing by smallholder farmers and manufacturers.

Electronic Noses for Cocoa Quality Assessment

Cocoa beans develop their distinctive flavor via various VOCs produced during Strecker degradation and Maillard reactions. Electronic noses accurately classify cocoa samples by variety, geographical origin, and quality grade by detecting differences in these VOC profiles.

A study found that coupling an E-nose with chemometric tools, such as support vector machine (SVM), extreme machine learning (ELM) and linear discriminant analysis (LDA), provides 89-95% accuracy in assessing the quality of cocoa beans.

Cocoa Quality Control During Fermentation

Adequate fermentation of freshly harvested cocoa beans is essential for developing flavor precursors and desirable sensory qualities. However, the process is notoriously difficult to control and standardize. Small variations in fermenting conditions can yield extremely different results. This forces chocolate makers to adjust their product formulations batch-by-batch to accommodate inconsistencies.

Electronic noses give cocoa farmers a practical way to monitor VOC profile changes during fermentation so adjustments can be made in real-time to optimize results.

Portable E-nose setups integrated with data acquisition and analysis software provide easy-to-interpret results on fermentation progress. Farmers could use such systems to track critical parameters like temperature, pH, and volatile compounds to determine ideal fermentation times for their batch of cocoa beans.

This level of control improves batch-to-batch consistency for enhanced efficiency during subsequent chocolate manufacturing stages.

Cocoa Quality Control During Refining and Conching

After fermentation, cocoa nibs undergo refining for particle size and viscosity, followed by conching, where agitation and heating over several hours fully develop flavor, aroma, and a smooth, liquid-like texture. However, excessive processing can remove vital volatile compounds or make undesirable flavors more prominent.

Studies have explored using E-noses to continuously analyze VOC profiles during refining and conching to pinpoint optimal processing times. A study published in LWT applied a kernel distribution model to differentiate between under-processed, optimally processed, and over-processed samples based on sensor data. Its findings indicated that E-nose accurately identified optimal conching points during cocoa processing compared to labor-intensive GC-MS testing.

By providing chocolate makers with real-time feedback on flavor development instead of relying on rough time estimates, E-noses reduce the likelihood of over-processing while avoiding prematurely stopping runs before achieving the full potential of a batch. Optimizing these processes enhances quality consistency and improves production efficiency.

Bowl with cocoa nibs on the table.

Image Credit: WS-Studio/Shutterstock.com

Multi-Dimensional Quality Assessment

While aroma profiles represent cocoa's most obvious electronic nose application, sensor technology developments now permit assessing various auxiliary bean attributes fundamental to quality.

Various quality issues originate from supply chain points lacking oversight, such as improper drying of beans or using contaminated equipment on small shareholder farms. However, the same aroma profiling strategies supporting electronic noses for flavor monitoring also facilitate creating detector arrays targeting volatiles marking these defects.

For example, leftover pulp sugars undergoing spontaneous fermentation yield ethanol and acetic acid signatures indicative of incomplete fermentation. Storage mold growth releases microbial volatile organic compounds like alcohols, aldehydes, and ketones. Even the atypical aroma of stale or over-aged cocoa butter within under-dried beans is identifiable by electronic nose sensors. Altogether, electronic noses deliver comprehensive quality assessments from a single rapid sampling, finally offering a path to tight supply chain control.

Challenges and Limitations

While showing immense promise, some challenges remain in effectively implementing electronic noses for continuous monitoring in complex industrial environments.

Fluctuating background conditions like temperature, humidity, and airflow can interfere with measurements if not properly controlled. In addition, E-nose data can sometimes be difficult to interpret without calibration against established analysis methods like GC-MS.

Lastly, adopting new technology inherently presents economic barriers - particularly acute for under-resourced small farming operations that stand to benefit tremendously from enhanced quality control. Reducing costs through scaled production and emphasizing user-friendly design will improve accessibility and support widespread E-nose implementation.


Adopting electronic noses for cocoa assessment has far-reaching implications for upgrading quality across the entire cocoa-chocolate value chain while promoting sustainability through waste reduction. As the technology matures, E-noses will become standard equipment, enhancing the traceability and transparency of quality verification procedures for all stakeholders.

See More: How to Easily Ensure Ingredient Quality

References and Further Reading

Florez, A., Durán, C., & Carrillo, J. (2020, November). Data processing from electrical signals acquired by an E-nose system used for quality control of cocoa. In Journal of Physics: Conference Series (Vol. 1704, No. 1, p. 012013). IOP Publishing. https://doi.org/10.1088/1742-6596/1704/1/012013

Hidayat, S. N., Rusman, A., Julian, T., Triyana, K., Veloso, A. C., & Peres, A. M. (2019). Electronic nose coupled with linear and nonlinear supervised learning methods for rapid discriminating quality grades of superior java cocoa beans. International Journal of Intelligent Engineering and Systems12(6), 167-176. https://www.inass.org/2019/2019123116.pdf

Nazli, N. A., Najib, M. S., Daud, S. M., Mohammad, M., Baharum, Z., & Ishak, M. Y. (2020). Intelligent Classification of Cocoa Bean using E-nose. MEKATRONIKA2(2), 28-35. https://doi.org/10.15282/mekatronika.v2i2.6747

Tan, J., & Kerr, W. L. (2019). Characterizing cocoa refining by electronic nose using a Kernel distribution model. Lwt104, 1-7. https://doi.org/10.1016/j.lwt.2019.01.028

Tan, J., Balasubramanian, B., Sukha, D., Ramkissoon, S., & Umaharan, P. (2019). Sensing fermentation degree of cocoa (Theobroma cacao L.) beans by machine learning classification models based electronic nose system. Journal of Food Process Engineering42(6), e13175. https://doi.org/10.1111/jfpe.13175

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Owais Ali

Written by

Owais Ali

NEBOSH certified Mechanical Engineer with 3 years of experience as a technical writer and editor. Owais is interested in occupational health and safety, computer hardware, industrial and mobile robotics. During his academic career, Owais worked on several research projects regarding mobile robots, notably the Autonomous Fire Fighting Mobile Robot. The designed mobile robot could navigate, detect and extinguish fire autonomously. Arduino Uno was used as the microcontroller to control the flame sensors' input and output of the flame extinguisher. Apart from his professional life, Owais is an avid book reader and a huge computer technology enthusiast and likes to keep himself updated regarding developments in the computer industry.


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