Analytical MethodsEstimating cocoa bean parameters by FT-NIRS and chemometrics analysis
Introduction
Ghana is the second leading producer of cocoa bean worldwide and her cocoa beans continue to enjoy high premium price in the world market because of its high quality. It is also the preferred choice for all chocolate and beverage producers of high reputation (COCOBOD, 2013). The consumption of cocoa bean derived products has very beneficial importance such as: it reduces coronary artery disease, it is a myocardial stimulant, diuretic, coronary dilator and muscle relaxant (Di Castelnuovo et al., 2012, Kim and Keeney, 2006, Kris-Etherton and Keen, 2002).
The post harvest activity of cocoa beans involves two main processes, namely fermentation and drying. After harvesting, the seeds housed in the mucilaginous pulp of the pod are taken out, fermented and dried. These two activities are particularly crucial in determining the quality of the cocoa bean flavour and aroma. For instance, a good flavour in chocolate is closely attributed to good fermentation and drying. Errors committed during these two processes cannot be corrected in subsequent processing (Minifie, 1989). Partially fermented or unfermented cocoa beans would result in bitter and astringent cocoa derived products with no chocolate flavour (Jalil & Ismail, 2008). Also, flavour and aroma precursors are produced during fermentation which reduces the astringency, and bitterness of the beans. Drying is also essential as it eliminates the growth of moulds that impart unpleasant flavours on the beans. Fermentation and drying therefore, work synergistically for high quality. Quality cocoa beans are examined by cut test score or sensory evaluation. However, cut-test and sensory analysis are subjective and often not consistent because of human error arising from fatigue or mood of the assessor. For instance, cut test and sensory evaluation indicated variability in their results and not consistent (Ilangantileke, Wahyudi, & Bailon, 1991). Furthermore, the analytical methods normally used to examine cocoa beans are: expensive, time consuming, destructive, involves chemical usage, and very often tedious particularly when analysing a lot of samples.
Near infrared spectroscopy (NIRS) is an advanced analytical tool. It is fast, simple, non destructive and does not involve chemical use and elaborate sample preparation. Coupled with the recent advancement in computers and chemometrics, NIRS has been applied in various sectors namely agriculture, pharmaceutical, petrochemical, medical, polymer and food industries. NIRS has been used to determine phytochemicals and other food quality parameters namely xanthenes and polyphenols in bakery products (Bedini et al., 2013), fats, caffeine, theobromine, and epicatechin in unfermented criollo cocoa (Álvarez et al., 2012), polyphenol contents in green tea (Chen, Zhao, Liu, Cai, & Liu, 2008). However, upon a thorough literature search there is no information on the investigation of NIRS and chemometric techniques for simultaneous estimation of cocoa bean categories, pH and fermentation index of cocoa beans.
Fermentation index (FI) and pH are essential attributes of cocoa bean quality. FI is a good marker to determine the degree of fermented cocoa beans (Pettipher, 1986). Also, FI correlated significantly with reducing sugars, free amino acids, pH, and cocoa bean cotyledon colour cocoa beans (Ilangantileke et al., 1991). FI > 1 means the cocoa bean mass was adequately fermented (Nazaruddin, Seng, Hassan, & Said, 2006). Moreover, the pH < 4.5 is not accepted by cocoa bean processers because, it leads to low flavour precursors, and over acidic derived products, while pH of 5–6 is considered good for flavour development (Saltini, Akkerman, & Frosch, 2013). Consequently, FI and pH could be used to assess the quality of cocoa beans and check fraudulent activities in the cocoa industry.
The objective of this present study was to develop a model for non-destructive and rapid estimation of cocoa bean category (fermented, partially fermented and unfermented), pH and fermentation index (FI) by FT-NIR spectroscopy together with linear and nonlinear algorithms. In this work, partial least square discriminant analysis (PLSDA) and back propagation artificial neural network (BPANN) were attempted to identify the quality categories, while different partial least squares algorithms (PLS, iPLS & SiPLS), back propagation artificial neural network regression (BPANNR) and efficient variable selection technique by synergy interval back propagation artificial neural network regression (Si-BPANNR) were also used to develop a prediction model for the estimation of pH and FI. The combination of synergy interval selection and BPANNR as a new technique was attempted comparatively with the others. Theoretical and experimental evidence have shown that spectral bands selection can significantly improve the performance of the model (Chen, Zhao, Liu, Cai & Liu, 2008, Nørgaard et al., 2000).
Section snippets
Samples preparation
The samples used in this study were acquired from Ghana, and comprised three main cocoa bean categories: fermented (FM = 80 samples), partly fermented (PF = 25 samples) and unfermented (UFM = 25 samples). The cocoa bean samples were powdered separately by grinding with a small multi-purpose grinder (QE-100, Zhejiang YiLi Tool Co., Ltd., China) for 15 s and sieved with a 400 μm mesh. The grinder was allowed to cool down after successive grindings to reduce loss of volatile compounds. The samples were
Spectra examination and principal component analysis (PCA)
Fig. 1A shows the raw spectra of the original data after FT-NIRS measurement, and it revealed major peaks that are caused by the stretch of the hydrogen groups of C–H, O–H, and N–H in the cocoa bean samples. From Fig. 1B, it could be observed that Smooth-1der method has a unique characteristic, and could have an influence on the model. The application of pre-processing method is extremely necessary in spectral analysis because, data acquired from spectrometer contains back-ground information
Conclusions
This study has sufficiently demonstrated that cocoa bean of different quality categories can be non-destructively identified and some quality parameters such as pH and FI simultaneously measured by FT-NIRS together with appropriate nonlinear multivariate analysis. The overall results show that BPANN model could be used to identify different cocoa beans quality category. Si-BPANNR model revealed its superiority and can be used for the simultaneous prediction of pH and FI in cocoa beans. This
Acknowledgements
The authors wish to acknowledge the financial assistance provided by University of Cape Coast (AS/86A/V6/1735) and National Natural Science Foundation of China (No. 31071549). We are also grateful to Quality Control Company of the Ghana Cocoa board for their support.
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