Multi-block classification of chocolate and cocoa samples into sensory poles

Near-infrared spectroscopy for comprehensive analysis of dark chocolate composition

Fat, sugar, theobromine, and caffeine are important compounds in chocolates that influence the physico-chemical and sensory characteristics of the products. These parameters commonly are determined with conventional, time-consuming, environmentally pollutant methods. In this study, near infrared spectroscopy (NIRS) coupled with partial least square regression (PLSR) was used to predict the quantity of these compounds. Fat-free cocoa content was calculated based on the alkaloid concentration, while measured fat content was also used for the cocoa content. Prediction models have also been developed for these parameters. The determination coefficient (R2) of the models were as follows: R2cv fat 0.98, theobromine 0.94, caffeine 0.76, sucrose 0.92, cocoa 0.98. The root mean square error (RMSE) was found to be relatively low compared to the determined values (RMSECV fat 1.08 %, theobromine 0.42 mg/g, caffeine 0.1 mg/g, sucrose 3.42 %, cocoa content 1.93 %). Therefore, NIRS can be suitable for the analysis of the quality of dark chocolate.

Discriminating three lab scale dark chocolate bars from fine Cameroon cocoa hybrids using sensorial evaluation and organic acid content

Previous studies have shown a correlation between chocolate sensory profile and certain (bio)chemical components. The aim of this study was to examine the sensorial profile and organic acid content of three lab scale chocolate brands produced from different cocoa genotypes. The sensorial evaluation was examined by a team of 12 panelists and evaluation of aroma volatiles was done by means of HS-SPME-GC-MS. On the other hand, organic acids were assessed using a high-performance ion chromatography coupled with an electrochemical detector (HPIC-ED). Results showed a variability in sensorial profile: SCA12×ICS40 chocolate (vanilla/sweet, spicy, and floral), ICS40 × SCA12 chocolate (fruity, bitter, and dry) and SNK16 × T60/887 chocolate (chocolate, honey-like, woody, sweet). Moreover, some aroma volatiles like (2-methyl, 3-methyl, iso) butanal (ICS), terpenes (SCA), and ketones (acetophenone and 2-nonanone) (SNK) allowed to discriminate dark chocolate sample according to their raw cocoa genetic group. Besides, the organic acid content differed from one chocolate brand to another, and it was obtained a high content of oxalic acid and a low lactic acid content which are good indicators of chocolate quality. Results of the current study highly recommend knowing the variety of cocoa beans with high content of some volatiles and high oxalic and low acetic acid and lactic contents to produce high aromatic (special flavor) chocolates.

Exploring the Impact of Fermentation Time and Climate on Quality of Cocoa Bean-Derived Chocolate: Sensorial Profile and Volatilome Analysis

The market for fine-flavor cocoa provides significant benefits to farmers. However, identifying the sensory qualities of chocolate under specific environmental conditions and measuring how its chemical compounds may be affected by climate differences and postharvesting practices remain a challenge. This study investigates how fermentation time and agroclimatic conditions in Colombia's fine cocoa-producing region of Arauca influence the sensory profile and volatile compound composition (volatilome) of chocolate derived from cocoa beans. Sensory evaluation was conducted on chocolates fermented for 48, 72, 96, and 120 h, revealing that fermentation time critically affects the development of fine-flavor attributes, particularly fruitiness and nuttiness. The optimal fermentation period to enhance these attributes was identified at 96 h, a duration consistently associated with peak fruitiness under all studied climatic conditions. Analysis of 44 volatile compounds identified several key aroma markers, such as acetoin, 1-methoxy-2-propyl acetate, and various pyrazines, which correlate with desirable sensory attributes. These compounds exhibited varying amounts depending on fermentation time and specific agroclimatic conditions, with a 96 h fermentation yielding chocolates with a higher quantity of volatile compounds associated with preferred attributes. Our findings highlight the complex interaction between fermentation processes and agroclimatic factors in determining cocoa quality, providing new insights into optimizing the flavor profiles of chocolate.

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

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.

A Novel Multi-Preprocessing Integration Method for the Qualitative and Quantitative Assessment of Wild Medicinal Plants: Gentiana rigescens as an Example

Until now, the over-exploitation of wild resources has increased growing concern over the quality of wild medicinal plants. This led to the necessity of developing a rapid method for the evaluation of wild medicinal plants. In this study, the content of total secoiridoids (gentiopicroside, swertiamarin, and sweroside) of Gentiana rigescens from 37 different regions in southwest China were analyzed by high performance liquid chromatography (HPLC). Furthermore, Fourier transform infrared (FT-IR) was adopted to trace the geographical origin (331 individuals) and predict the content of total secoiridoids (273 individuals). In the traditional FT-IR analysis, only one scatter correction technique could be selected from a series of preprocessing candidates to decrease the impact of the light correcting effect. Nevertheless, different scatter correction techniques may carry complementary information so that using the single scatter correction technique is sub-optimal. Hence, the emerging ensemble approach to preprocessing fusion, sequential preprocessing through orthogonalization (SPORT), was carried out to fuse the complementary information linked to different preprocessing methods. The results suggested that, compared with the best results obtained on the scatter correction modeling, SPORT increased the accuracy of the test set by 12.8% in qualitative analysis and decreased the RMSEP by 66.7% in quantitative analysis.

Multi-platform integration based on NIR and UV-Vis spectroscopies for the geographical traceability of the fruits of Amomum tsao-ko

Due to the world-wide concern relating to herb quality and safety, there is a momentum to authenticate the geographical origin of herb with multi-platform techniques. This study attempted to assess multi-platform information as a practical strategy for the geographical traceability of the fruits of Amomum tsao-ko. To this aim, one hundred and eighty dried fruits of A. tsao-ko from five geographical regions were analyzed by near infrared (NIR) and ultraviolet visible (UV-Vis) spectroscopy. On this basis, two variable dimension reduction strategies, including principal component analysis (PCA) and sequential and orthogonalized partial-least squares (SO-PLS), and two variables selection strategies, including variable importance in projection (VIP) and sequential and orthogonalized covariance selection (SO-CovSel), were performed to extract the feature information in the two blocks. Partial least squares discriminant analysis (PLS-DA) classification algorithm combined with fused matrices was used to identify the geographical origins. The results of PLS-DA models indicated that SO-PLS and SO-CovSel, taking advantage of the sequential modeling coupled to orthogonalization, could not only identify the common information presented in the two blocks but also provide more concise methods without any loss of classification ability, which could be employed in authenticating the geographical regions of the fruits of A. tsao-ko, effectively.

Comprehensive strategy for pesticide residue analysis in cocoa beans through qualitative and quantitative approach

Multiresidue quantitative and qualitative screening method for the analysis of pesticide residues in dried cocoa beans was validated and applied to imported and domestic cocoa beans samples. The quantitative method comprises of 15 pesticides while the screening method covers 110 pesticides of different chemical classes. The method was based on modified QuEChERS (Quick Easy Cheap Efficient Rugged Safe) extraction and detection using triple quadrupole (QQQ-MS) and ion mobility quadrupole time of flight mass spectrometry (IMS-QTOF). The method was quantitatively validated in terms of linearity, limit of quantification (LOQ), specificity, selectivity, accuracy, and precision. On the other hand, screening detection limits were established for 110 pesticides. Finally, the optimized strategy was successfully applied for the routine analysis of pesticide residues in 137 cocoa bean samples and 32% of the total samples were found positive for ametryn, chlorpyrifos, isoprocarb, and metalaxyl.

Comparison of the Effects of Conching Parameters on the Contents of Three Dominant Flavan3-ols, Rheological Properties and Sensory Quality in Chocolate Milk Mass Based on Liquor from Unroasted Cocoa Beans

The content of polyphenols in chocolate depends on many factors related to the properties of raw material and manufacturing parameters. The trend toward developing chocolates made from unroasted cocoa beans encourages research in this area. In addition, modern customers attach great importance to how the food they consume benefits their bodies. One such benefit that consumers value is the preservation of natural antioxidant compounds in food products (e.g., polyphenols). Therefore, in our study we attempted to determine the relationship between variable parameters at the conching stage (i.e., temperature and time of) and the content of dominant polyphenols (i.e.,catechins, epicatechins, and procyanidin B2) in chocolate milk mass (CMM) obtained from unroasted cocoa beans. Increasing the conching temperature from 50 to 60 °C decreased the content of three basic flavan-3-ols. The highest number of these compounds was determined when the process was carried out at 50 °C. However, the time that caused the least degradation of these compounds differed. For catechin, it was 2 h; for epicatechin it was 1 h; and for procyanidin it was 3 h. The influence of both the temperature and conching time on the rheological properties of chocolate milk mass was demonstrated. At 50 °C, the viscosity and the yield stress of the conched mass showed its highest value.

Assessing the flavor of cocoa liquor and chocolate through instrumental and sensory analysis: a critical review

The performance of appropriate instrumental and/or sensory analyses is essential to gain insights into the flavor profile of cocoa products. This three-part review is compiled of an overview of the most commonly used instrumental techniques to study cocoa liquor and chocolate flavor, their perception by a trained panel and the potential relationship between them. Each part is the result of a thorough literature study, principally focusing on the assumptions, features and limitations of these techniques. Reviewing of the literature revealed that cocoa matrix effects and methodology restraints were not always considered when instrumentally analyzing cocoa flavor. With respect to sensory analyses, various studies lacked reporting of accomplished trainings and performance of panelists. Moreover, a discrepancy was noticed in the descriptive flavor lexicon employed. Finally, when linking instrumental and sensory data, linear modeling is regularly applied, which might not always be appropriate. This review paper addresses the challenges associated with flavor assessment, intending to incite researchers to critically study cocoa flavor and apply standardized protocols and procedures.

Sequential Data Fusion Techniques for the Authentication of the P.G.I. Senise (“Crusco”) Bell Pepper

Bell pepper is the common name of the berry obtained from some varieties of the Capsicum annuum species. This agro-food is appreciated all over the world and represents one of the key ingredients of several traditional dishes. It is used as a fresh product, or dried and ground as a seasoning (e.g., paprika). Specific varieties of sweet pepper present organoleptic peculiarities and they have been awarded by quality marks as a further confirmation of their unicity (e.g., Piment d’Espelette, Pimiento de Herbón, Peperone di Senise). Due to the market value of this aliment, it can be subjected to frauds, such as adulterations and sophistication. The present study lays on these considerations and aims at developing a spectroscopy-based approach for authenticating Senise bell pepper and for detecting its adulteration with common paprika. In order to achieve this goal, 60 pure samples of bell pepper from Senise were analyzed by mid- and near-infrared spectroscopies. Then, in order to mimic the adulteration, 40 mixtures of Senise bell pepper and paprika were prepared and analyzed (by the same spectroscopic techniques). Eventually, two different multi-block classification approaches (sequential and orthogonalized partial least squares linear discriminant analysis and sequential and orthogonalized covariance selection linear discriminant analysis) were used to discriminate between pure and adulterated Senise bell pepper samples. Both proposed procedures achieved extremely successful results in external validation, correctly classifying all the (thirty-five) test samples, indicating that both approaches represent a winning solution for the investigated classification problem.