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In 2024, the global chocolate market was valued at approximately $131 billion USD. It is projected to reach approximately $173 billion by 2030, implying a steady growth rate of roughly 4% [1]. This growth is driven by sustained consumer demand and is expected to continue rising.

Spectroscopy is increasingly utilized in chocolate manufacturing for quality control (QC), thanks to its ability to assess the composition of chocolate, providing a «fingerprint» spectrum that reveals its chemical details. Specifically, Raman can be used in QC to distinguish between types of chocolate, detect adulteration, measure crystallization and texture, and monitor the manufacturing process.

This Application Note outlines techniques for effectively collecting Raman spectra from various chocolates, providing a foundation for quality assessment and adulteration detection.

Raman spectra of chocolate bars with different cocoa content, including white chocolate (Lindt, Switzerland), were measured at a low (5%) laser power and long (120 s) integration time (Figure 1).

Specific chocolate sample types used in this study are summarized in Table 1. Laser power was adjusted based on the chocolate’s cocoa content (ranging from white to 100%), not to exceed 15%, unless molten chocolate measurement was desired.

Table 1. Samples and important Raman peaks.
Sample % Cocoa
Chocolate (various types) 100%
85%
70%
Milk (~30%)
White (20%)
Figure 1. Representative Raman spectra of 100% cocoa and white chocolate (no math treatment).
Table 2. List of important peaks shown in Figure 1.*
Relation Wavenumber (cm-1)
Cocoa-related 1060–1080
~1300
1420–1480
~1670, ~1780
Sugar-related <750, 848
1460

 * See reference [2] for more details on band assignments.

Figure 2. The i-Raman NxG 1064 and mounted fiber-optic probe from Metrohm were used in this study.

Raman data collection with an i-Raman NxG 1064 laboratory Raman system (Figure 2) was optimized by adjusting integration time and laser power (Table 3) to determine the best conditions to maximize signal strength and minimize risk of sample melting.

 

Table 3. System settings used for the analysis of different chocolate types by Raman spectroscopy.
Excitation wavelength 1064 nm
Laser power 5–15%
Integration time >60 s
Accessories Standard Sampling Probe
Probe Holder with distance regulator
Software SpecSuite 
Figure 3. A chocolate sample on the probe holder.

All chocolate samples were analyzed by placing a piece of chocolate on the stage with a Raman probe securely locked above the sample (Figure 3). The optimal working distance was determined by adjusting the probe’s z-axis position while continuously monitoring the intensity of the Raman signal.

Once the optimal focal distance is found, a distance regulator helps the operator position the probe on the sample to ensure consistent and reliable measurement.

Laser intensity and melting

Figure 4a. Close-up view of Raman peaks from 100% chocolate measured with 1064 nm laser at 5%, 10%, and 15% laser power.

Chocolate melts between 30–36  °C. 100% chocolate was used to establish the laser-induced melting threshold due to its darker color. The darker sample absorbs more laser light and, as a result, melts at lower powers and shorter exposure times.

Optimizing laser power is critical to prevent thermal damage or structural changes during measurement. Raman spectra collected at 5%, 10%, and 15% laser power revealed notable shifts in cocoa-related peaks (Figure 4a), with visible melting at 10%. Lighter-colored chocolates tolerated higher powers, generally up to 15%.

Figure 4b. White chocolate's gauche chain extension region measured with 10% and 25% laser power, demonstrating the difference between laser powers.

However, melting is not the sole indicator of heat-induced structural changes. Even white chocolate exhibited subtle crystallinity shifts in the 1060–1100 cm⁻¹ range when laser power exceeded 10% (Figure 4b). These results highlight that chocolate can undergo thermal alteration at relatively low laser powers, emphasizing the need for careful power selection during quality assurance and quality control (QA/QC). Fluorescence rejection methods combined with lower-power 785 nm excitation offer potential solutions.

Raman spectra of test sample

Chocolate generally consists of three primary components—cacao solids, cocoa butter, and sugar—in significantly different proportions, depending on the chocolate type. For instance, 100% chocolate contains no added sugar, whereas white chocolate lacks any cacao solids, but does contain cocoa butter. The other varieties fall between these extremes, with different cocoa and sugar content (Table 4).

Table 4. Cocoa and sugar content of different chocolates.
Chocolate Type Cocoa (%)* Sugar (g)**
White 20 16
Milk 31 17
70% 70 9
85% 85 4
100% 100 0

*Cocoa solid % based on the manufacturer [3].
**Based on the total sugar content from nutrient tables.

Figure 5. Raman spectra of 100% cocoa, 85% cocoa, 70% cocoa, milk chocolate, white chocolate, and sugar. Data collection method: laser power 5%, integration time 120 s, average 3.

The major sugar peaks are clearly observed in white, milk, and 70% chocolates (Figure 5). However, in 85% chocolate, the only noticeable sugar-related spectral feature appears at 1460 cm⁻¹. This suggests that Raman effectively determines sugar content for QC measurements. Cocoa-related ingredients exhibit characteristic Raman bands around 1300 cm⁻¹ and 1420–1480 cm⁻¹. Confining a Partial Least Squares (PLS) model to these spectral regions resulted in the most accurate predictive model for cocoa content analysis.

PLS model performance and predictive accuracy

Figure 6a. PLS calibration model and model statistics of cacao solids and cocoa butter.

PLS models built from key peaks between 1200–1600 cm⁻¹ in the Raman spectra of various chocolate types show strong agreement between predicted and measured cocoa content, with low standard error. This confirms Raman spectroscopy’s effectiveness for routine cocoa content analysis (Figure 6a). Adding data points would enhance confidence in predictions at higher cocoa levels.

Figure 6b. PLS calibration model and model statistics of sugar content in different samples.

The sugar content model demonstrates even greater predictive accuracy, attributed to distinct sugar peaks and the lack of temperature-related variation in sugar measurements. Sugar data may also refine predictions of cocoa-related content, as sugar’s Raman intensity varies proportionally with cocoa content (Figure 6b). Both sugar and cocoa content are important QC parameters measurable via Raman spectroscopy.

This study highlights the capability of Raman spectroscopy for rapid, nondestructive measurements of chocolate quality indicators. PLS models demonstrate high predictive accuracy for both cocoa-related materials and sugar content. Increasing the number of samples and testing a broader range of chocolates would further improve the robustness and accuracy of the model.

Overall, Raman spectroscopy, combined with chemometric modeling, offers a reliable QC method for routine and real-time chocolate analysis.

  1. marknteladvisors. Chocolate Market Size, Share, Analysis and Industry Trend to 2030.
  2. Esmonde-White, K.; Lewis, M.; Lewis, I. R. Direct Measurement of Chocolate Components Using Dispersive Raman Spectroscopy at 1000 Nm Excitation. Appl Spectrosc 2023, 77 (3), 320–326. https://doi.org/10.1177/00037028221147941.
  3. Chocolates, Truffles, and Delicious Gifts: Buy Online | Lindt Shop Intl. https://www.chocolate.lindt.com/ (accessed 2025-08-17).
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