Cocoa bean and cocoa bean products quality evaluation by NIR spectroscopy and chemometrics: A review

Journal Pre-proofs Cocoa bean and cocoa bean products quality evaluation by NIR spectroscopy and chemometrics: A Review Ernest Teye, Elliot Anyidoho, Robert Agbemafle, Livingstone K. SamAmoah, Chris Elliott PII: DOI: Reference:

S1350-4495(19)30549-3 https://doi.org/10.1016/j.infrared.2019.103127 INFPHY 103127

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Infrared Physics & Technology

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Please cite this article as: E. Teye, E. Anyidoho, R. Agbemafle, L.K. Sam-Amoah, C. Elliott, Cocoa bean and cocoa bean products quality evaluation by NIR spectroscopy and chemometrics: A Review, Infrared Physics & Technology (2019), doi: https://doi.org/10.1016/j.infrared.2019.103127

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Cocoa bean and cocoa bean products quality evaluation by NIR spectroscopy and chemometrics: A Review *Ernest Teye1, Elliot Anyidoho1, Robert Agbemafle2, Livingstone K. Sam-Amoah1, Chris Elliott3 1University

of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture,

Department of Agricultural Engineering, Cape Coast, Ghana 2University

of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture,

Department of Laboratory Technology, Cape Coast, Ghana 3Queen’s

University Belfast, Institute for Global Food Security, Belfast, UK

Tel :+233-243170302/+233-206969565 *Email: [email protected] / [email protected] Abstract Cocoa bean is an international commodity largely produced in developing countries and mostly consumed worldwide in several forms. During the last few decades, rapid detection of cocoa beans and cocoa bean products quality has gained centre stage with many kinds of research conducted. However, no reviews about the application of NIR spectroscopy for cocoa beans and cocoa bean products (CACBP) have been reported. Therefore this review presents application of NIR spectroscopy and chemometrics in the field of the postharvest value chain of cocoa: cocoa beans and cocoa bean products, focusing on the prediction of categorization, authentication, chemical composition, and sensory attributes. In addition geographical classification, fraud and safety are also covered. The information presented in this review clearly shows that NIR spectroscopy has its usefulness in the entire postharvest cocoa bean industry. After analyzing the literature, it was found out that, NIR spectroscopy technology could be successful for qualitative and quantitative examination of CACBP. However, more work needs to be done to move this technology from the laboratory applications to real onsite usage especially among cocoa producing countries in the developing world for optimum global benefits in the face of concerns regarding cocoa bean integrity. This requires the use of extensive samples covering a wide range of cocoa beans from West African. Keywords: Cocoa beans, Cocoa bean products, NIR spectroscopy, Chemometrics, Examination 1

1.0 Introduction Cocoa (Theobroma cacao) after its discovery in the Americans is now among the topmost commercially cultivated cash crop for many tropical and sub-tropical countries. More importantly, it represents a major exportable agricultural commodity for several countries in West Africa. Notable among these countries in decreasing order of world production are; Cote D’Ivoire, Ghana, Indonesia, Brazil, Nigeria, Cameroon, and Ecuador. West Africa produces a majority of the total global tonnage and it is among the main source of foreign exchange earnings. It also employs more than 48% of the rural poor who engages in the production and postharvest activities such as harvesting, storage, pod breaking, fermentation, and drying. These activities are vital steps in the overall quality of confectionery products. The cocoa beans derived from the pod are used for many tasty dishes and it is consumed by majority of the world’s population. It is the key raw material in the manufacturing of chocolate. Cocoa bean products have become a daily necessity and a famous consumer products because of their numerous health benefits, notable among them are; the high potential of decreasing the risk of cancers, stroke, diabetes, and improving the vascular system [1-4]. Cocoa bean products are also very nutritious foods for all categories of people, they are rich in protein, vitamins, minerals carbohydrates, quality fats and rich source of polyphenols with comparatively more antioxidant activity than teas and red wines [3]. These aforementioned quality properties of cocoa beans make it a super-food with high premium value and often high quality is demanded. Furthermore, cocoa bean is the main raw material for chocolate and confectionery products [5] and their consumption has increased in the world because of its numerous health benefits. Quality is not a single well-defined attribute but comprises many properties or characteristics [6]. Cocoa bean quality attribute like any other agriculture produce differ from one producing country to the other due to various factors such as climate, soil condition, pre-harvesting and post-harvest activities [2, 7]. Thus, it has resulted in various classes of cocoa beans in the international market. These differences in quality are often recognized and appreciated by food processors and consumers. Good quality has, therefore, become an important factor that determines the price. For instance, Ghana and Nigeria cocoa beans received a higher response for strong chocolate flavor than others [8]. More so, research results revealed differences in epicatechin content, antioxidant capacity and phenolic content of 2

cocoa beans from different countries [9] and other researchers also found out that even chocolate samples show differences according to the geographic origin [10]. On the other hand, these quality attribute that differentiates different classes of cocoa beans have resulted in what is known as food fraud; the mislabeling, and adulteration for self-fish financial gain or reward. To make matters worse, the analytical techniques to detect this kind of fraud and other cocoa bean qualities is quite expensive, slow and cumbersome, living most primary producers little or no option to fingerprint their unique qualities. Moreover, the analytical techniques employed for examining cocoa bean before trading in the international market and onward processing have various challenges; they are it is slow, tedious, cumbersome, time wasting, sophisticated, destructive and involves using analytical chemicals that are expensive, poisonous and unfriendly to the laboratory technician and the environment. Furthermore, these quality analytical methods require highly trained personnel and laborious isolation and purification steps, coupled with the fact that most cocoa beans are produced by the rural poor in most developing countries. These make quality measurements extremely difficult. Also, cocoa beans exported are frequently characterized by great heterogeneity with regard to their quality attributes and a reliable, rapid and inexpensive quality assessment method would be of great importance to producers, purchasers, and, processors [11]. Normally, the visual inspections that are often applied to mitigate the afore-mentioned bottlenecks are also subjective to the influence of human errors. For this matter, rapid evaluation of cocoa bean quality is of paramount interest and many researchers have dedicated themselves into the study of using NIR spectroscopy. NIR spectroscopy technique coupled with the current advancement in computer science and chemometrics offers a great opportunity for qualitative and quantitative techniques for the cocoa bean industry. This technique could be friendly to both cocoa bean producers (mostly in the developing countries; Cote d’Ivoire, Ghana, Nigeria) and processors (mostly in the developed countries; Germany, Netherland, Japan) alike. NIR spectroscopy is a type of vibration spectroscopy that employs photon energy corresponding to the wavelength range of 750 to 2,500 nm or 13,300 to 4000 cm-1) [12]. It is well known that when radiation interacts with biological materials it is either absorbed, transmitted or reflected, which is captured by utilizing different measurement modes of the equipment. NIR spectrum is located between the infrared and the 3

visible region and made up of broad bands associated with the overtones and combinations of vibration modes of OH, NH, CH, and SH stretching vibrations as well as stretching-bending combination, involving these groups [13]. The advancement in new instrumentation and computer algorithms is advantageous to the complex NIR data and has contributed to making the technique more powerful and easy to use. Therefore, vital quality parameters of interest in biological materials could be measured by analyzing their NIR spectra data set by using chemometrics. Chemometrics comprising several multivariate statistical algorithms (eg Principal component analysis; PCA, Linear discrimiant analsysis; LDA, Partial least square; PLS, Support vector machine; SVM, Synergy interval partial least square; Si-PLS, etc) have necessitated the rapid analysis of NIR spectra for the actual concentration of important components in the sample matrix. It has facilitated the simultaneous detection of various qualitative and quantitative parameters at a single scan. Upon literature search, there have been reviews on the application of NIR spectroscopy for monitoring foods and beverages [14]. Other authors also reviewed the application of NIR spectroscopy in analyzing meat [15, 16], tea quality and nutrition [17], and fish quality [18]. However, little or no review paper up until now is available in the literature on the applications of NIR spectroscopic technique for cocoa and cocoa bean products quality evaluation. The review paper therefore outlines and highlights the application of NIR spectroscopy for qualitative and quantitative analysis of cocoa beans. More so, the commonly applied multivariate algorithms were discussed. This review will particularly be helpful as it will provide the background information and the way forward for practical application of NIR spectroscopy for cocoa bean examination in developoing countries where most of the beans are produced. NIR spectroscopic scan on any biological material like cocoa could provide a global fingerprint for cocoa beans for any quality attribute of interest. 2.0 Procedure for NIR spectroscopy and chemometrics 2.1 Spectral acquisition The general procedure normally used for qualitative and quantitative measurement by using NIR spectroscopy is described in the following sections. The are two main procedures are: scanning to obtain spectra and the second step involves the use of chemometrics to bring out the meaning in the spectral dataset. Figure 1.0 shows the typical spectral profile of chocolate mass, ground 4

cocoa beans, and whole cocoa beans. It could be seen that each spectral presents some similarities irrespective of the form. The various peaks seen in this figure arose from overlapping absorptions which correspond mainly to overtones and combinations of vibrational modes involving some useful chemical bonds (C-H, O-H, N-H, and S-H). These absorption bands are indications of major unique constituents.

(a)

5

(b)

6

(c) Figure 1.0 Raw spectra of chococlate mass (a), ground cocoa beans (b) and whole cocoa beans [19-21]. 2.2 Chemometrics Chemometrics is the science of relating measurement made on chemical systems or processes to the state of the system via application of mathematical or statistical methods [22]. All the copious researches revealed that the spectra of the samples are obtained by spectrometer either on semi-destructive or whole cocoa beans and cocoa bean products. This is followed by four (4) main steps: i. pretreatment/preprocessing of spectra, ii. model development or building, iii. model testing and transfer. All these aforementioned mentioned steps used chemometrics for NIR spectroscopy analysis leading to final method development. 2.2.1

Pretreatments

7

After spectral collection, the spectral data is transferred into pretreatment techniques for preprocessing. This initial preprocessing technique is very vital as it removes noise and correct signal weaknesses as a result of background and physical characteristics of the sample which results in uncontrolled variations in the baseline [23]. It is also known to remove any irrelevant information which cannot be handled properly by the multivariate calibration models [24]. Various preprocessing techniques have been used for cocoa bean and cocoa bean product spectra data. Notably among them are: standard normal variant (SNV), mean centering (MC), multiplicative scatter correction (MSC), derivative methods (1st and 2nd), detrend (DT), direct orthogonal signal correction (DOSC) and a combination of others such as Smooth plus 1st derivative. Figure 2.0 showed the spectral profile of the most used preprocessing techniques applied on cocoa beans. It can be observed that the original spectral of the cocoa beans changed when different preprocessing techniques were employed. However, it must be known that different preprocessing techniques showed their own superiority for different investigated challenges. For instance, first derivative combined with Synergy partial least square regression (Si-PLSR) improved the prediction of total fat in cocoa beans with R = 0.97 and RMSEP =0.015% in prediction set [25]. However, it must be emphasized that one preprocessing technique cannot suit all the challenges for optimum effect. Therefore various preprocessing techniques could be attempted for optimum results as cocoa and cocoa beans product are very heterogeneous with uniques differences.

8

(a)

9

(b)

10

(c)

11

(d) Figure 2.0 The preprocessed effect of spectral fingerprint/signature of cocoa beans samples (A) 1-der, (B) SNV, (C) MSC and (D) Detrend [20, 26]. 2.2.2

Multivariate data analyses

The model development step involves the use of multivariate calibration algorithms that correlate matrix X and matrix Y. Where the matrix X is composed of known information (it could be chemical data or quality class) and the matrix Y composed of spectral data of the wavelength range used. According to Alishahi et al [27], the more complex the relationship exist between the two matrices appear, the more difficult the calibration model building and precision are. However, other researchers have proposed that more complex relationships require the use of a non-linear model which tends to give better calibration models with higher reliability due to its non-linearity [27, 28]. From our search, we found that various calibration models have been attempted without any clear cut favourite as their choice depends on the prediction challenge in question. In this research, the multivariate techniques we found can be grouped into qualitative 12

models and quantitative models. The qualitative models used for cocoa beans analysis included: K-nearest neighbour (KNN), linear discriminant analysis (LDA), fishers discriminant analysis (FDA), partial least square discriminant analysis (PLS-DA), support vector machine (SVM), back propagation neural network (BPNN). While the quantitative models were partial least square (PLS), interval partial least square (iPLS), synergy partial least square (SiPLS) support vector regression (SVR). Interesting other hybrid techniques were also obtained; synergy interval support vector machine (Si-SVM). Among the qualitative techniques used for cocoa bean and cocoa bean products, Support vector machine model was found to be superior for classification of cocoa beans from different locations. Its supperiority is attributed to the fact that SVM model embodies structural risk minimization principle where upper bound is lowered on the expected risk [29]. It is also well know that cocoa bean and cocoa product contains unique chemical properties that makes it complex to obtrain optimal results with linear algorithms. However, SVM is a transformational techniques that works for two categories, if the category is linearly separated SVM finds optimal hyperplane boundary which separates both classes of the training set and unknown sample while if the classes are separated by non-linear boundary it finds the boundary by mapping the non-separted data into a higher dimentsional space and causes the class to be separated [30]. More so for quantitative model, different PLS models have been used, however synergy interval partial least square proved very useful with comapratively optimal results. This could be explained that, the normal PLS model is build using the full spectrum which contains both useful and redundant information and these reduendant information inevitably reduce the overall performance of the model. However, for Synergy interval partial least square (SiPLS), multiple useful spectral sub-intervals are selected while irrelevant information are eliminated hence, this lead to good results[20, 31]. For cocoa samples, it was realized that the spectral sub-intervals selected by SiPLS model could be related to the complex compositional differences thereby improving the quantitative prediction with little or no influence on model performance by redundant information.

13

Figure 3.0 Schematic representation of the procedure for NIR spectroscopy 2.2.3

Model evaluation

Finally, after building or developing the model with the appropriate preprocessing and multivariate calibration tool, it is then tested or evaluated for its reliability and robustness. Normally the strength of the model was tested with the reference data and its predictability was measured by a coefficient of determination (R2) or correlation coefficient (R). The higher the value obtained in the calibration models, the more stable and reliable the models were. With regards to cocoa and beans products, some researchers used the following parameters to judge the strength of the model, the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP), and the correlation coefficient (R) among others [32]. These parameters were calculated by equations 1-3. Their results showed that non-linear model worked better than linear ones with correlation coefficient ranging from 90-100 percent.

RMSECV 

RMSEP 



n i 1

( y \ i  yi ) 2 n



n i

(1)

( yi  y i ) 2

(2)

n 14

 1 

n

R

i 1 n

( y i  yi ) 2

(3)

( yi  y ) 2 i 1

Where: n = the number of samples yi = the reference measurement results for sample i, ŷ\i = the estimated result for sample i when the model is constructed with sample i removed ŷi= the estimated results of the model for the sample i, ȳ = the mean of reference measurement results for all samples 3.0 Applications of NIR spectroscopy to cocoa and cocoa bean products quality evaluation Cocoa bean and bean product like any other food commodity are made up of abundant hydrogen organic groupings, thus making it very possible for utilizing NIR spectroscopy for qualitative, and quantitative or simultaneous analysis of several quality parameters. In this regard, the cocoa bean industry is expected to benefit from the recent interest in NIR spectroscopy as an advanced rapid and simple analytical technique. More importantly, with the development of computers and chemometrics, the application of the NIR spectroscopy method for cocoa bean examinations has shown great potential and would become very necessary because of its numerous advantages and the peculiality of the cocoa bean industry. Most recent applications of NIR spectroscopy to cocoa industry are grouped under cocoa bean and cocoa bean product listed in table 1. 3.1 Qualitative evaluation of cocoa beans Many studies have confirmed the potential of NIR spectroscopy for qualitative analysis of cocoa beans such as identification, classification, characterization, authentication and differentiation of cocoa beans from different varieties, country of origin, as an important parameter. For example, cocoa beans from some countries enjoys high premium price than others, while certain varieties are also very unique and of high value. Practically, there are wide differences in cocoa bean quality due to the fact that different countries grow different varieties under different Agrogeographical zones. Furthermore, fermentation and drying methods (the main initial processing methods) vary from country to country. These factors normally lead to variations of cocoa bean quality. According to Miller et al [33], Ghana cocoa beans are a good example of well 15

fermented, flavored cocoa beans compared to others, which are considered less fermented and of lower quality because of their bitterness and low cocoa flavor. Aculey et al [34] and Teye et al, [35] used NIR spectroscopy to characterize and differentiate cocoa beans from different cocoa growing regions of Ghana respectively. Teye and co-workers found the MC+ SVM model to be the best tool for optimum classification for different geographical origin [35]. While Teye et al also found SNV+SVM as an appropriate model for cocoa bean varietal identification [36]. Also DOSC+SVM model was found to accurately classify fermented, and unfermented cocoa beans as well as blends of fermented with unfermented at 100% prediction rate [20]. Kutsabedzie et al also used NIR to discriminant three cocoa bean quality grades in terms of degrees of fermentation and they observed 94% accuracy by using SVM and ELM with SNV preprocessing treatment [37]. All these studies revealed that NIR spectroscopy coupled with the right chemometric tool can be used for qualitative measurements of cocoa beans. More importantly, these measurements will go a long way in detecting cocoa bean fraud due to mislabelling and adulteration with low-cost raw materials for undue price advantage. 3.2 Quantitative determination of cocoa beans Quantitative measurements of cocoa bean quality attributes involve the determination of the amount of the parameter in questions. Various researchers have used NIR spectroscopy to measure quality parameters such as fat content, caffeine, polyphenols, moisture content, volatile and nonvolatile compounds in cocoa beans. In current time, Krahmer et al used the first derivative and other pretreatments plus PCA-PLS to determine biochemical quality parameters, fermentation time and pH value with promising results [38] while Teye and Huang also used MSC and the combined strength of Si-PLS and SVMR to form Si-SVMR for the current determination of total fat content [26]. Si-SVMR combination was the first time such hybrid multivariate algorithm has been used for enhancing the quality determination of cocoa bean parameter of interest. However, all the aforementioned authors used ground beans. On the other hand, Sunoj and co-workers nondestructively used NIR spectroscopy to determine fermentation index, pH, and total polyphenols content in raw cocoa beans [21]. Barbin et al, also extensively determined the compositional characteristics (protein, moisture, fat ash, and colour) of different cocoa bean varieties for both whole bean and ground beans with comparatively (wet analytical methods) favourable results [39] and this has confirmed earlier studies done by Kaffka and 16

coworkers in 1980 that NIR spectroscopy and chemometrics presents a reliable technique for measuring proximate analysis of cocoa beans [40]. Other authors also used NIR spectroscopy to determine fermentation levels through ammonia nitrogen quantification and concluded that, this technology could be used by chocolate manufacturers as a routine method to sort cocoa beans samples according to their level of fermentation [41]. This would prove helpful to breeders (for rapid screening & selection) and cocoa bean processors who prefer cocoa beans of high-fat content. More so, Hue et al used NIR spectroscopy to estimate levels of targeted compounds in the case of total flavan-3-ols [41]. Total fungi count, a very important indicator of cocoa bean quality in world trade [42], was also investigated by NIR spectroscopy [37]. The authors did not directly scan the cocoa beans but prepared solutions from cocoa bean samples. Their findings indicated that NIR spectroscopy combined with Si-GAPLS may be employed for in-situ quantification of total fungi count in cocoa beans. 3.3 Determination of the composition of cocoa bean products Cocoa beans processors such as chocolate manufacture also demand an efficient, reliable and fast analytical method for product quality assurance. This is because the demand for cocoa bean product has increased with tightened supply and steadily rise in price [43]. Application of NIR spectroscopy could be very helpful and hence, it has attracted the attention of some researchers. The earliest was the prediction of chocolate quality from NIR spectroscopic measurement of raw cocoa beans. The results proved promising for replacing the difficult and demanding sensory analysis method [44] and the chemometrics used were MSC-PCA and canonical correlation analysis (CCA). Furthermore, Moros et al [45] used diffuse reflectance NIR spectroscopy together with artificial neural networks (ANN) to predict main nutritional parameters such as fat, protein, energetic value and cocoa content in chocolate samples with considerable success. Rapid fraud detection of cocoa powder with carob flour as a cheap substitute has been investigated with 100% classification accuracy by Quelal-Vasconez and co-worker [46]. This form of adulteration has easily and rapidly been detected accurately by PCA-PLS-DA algorithm. Furthermore, this same studies revealed that NIR spectroscopy coupled with chemometrics could be used for simultaneous prediction of cocoa bean product quality as; the data allowed the researchers to conclude that NIRS technology combined with multivariate analysis can identify and determine the amount of natural cocoa powder present in a mixture adulterated with carob flour [46]. 17

Furthermore, Quelal-Vasconez et al also used NIR spectroscopy for fast detection of cocoa shell in cocoa powders with PLSR for quantification of adulterant (R = 0.967) and PLSDA (classification rate = 92.5%) and concluded that: NIR spectroscopy technology is an important tool for cocoa producers and clients, who will be able to discriminate among samples in or out specifications, avoiding the use of destructive techniques that require a complex preparation of the sample or techniques that imply an important expense for the company [47]. FT-NIR spectroscopy was applied to analyze chocolate quality parameters such as fat, sucrose, lactose with considerable success. However the authors revealed that prediction values of moisture, viscosity and yield are less reliable [48]. The less reliability could be because the authors used only PLS algorithm. On the other hand, Vesela et al, measured fat, nitrogen and moisture content in cocoa powders by NIR spectroscopy coupled with principal component transform PLS and recommended the use of second derivative preprocessing [49]. Others are the determination of sucrose content in chocolate mass with high correlation coefficient above 0.98 using multiple techniques; PLS, MLR and GA-MLR [19]. With the good predictability observed in that work, it could be concluded that NIR spectroscopy can be used for chocolate analysis. A study conducted by new food magazine [50] confirmed that NIR spectroscopy is a viable tool for quick determination of fat, protein, sugar, water content in chocolate with PCA, PCR and PLSR. 3.4. Fraud and Safety prediction of cocoa beans Fraud and safety are important parameters in the cocoa bean industry and rapid novel determination of these quality parameters by NIR spectroscopy would be an advantage and bring relief to cocoa bean value chain players especially quality assurance officers. Notwithstanding fungi infestation which leads to mycotoxins build-up in cocoa beans causing safety concerns are due to insufficient drying, poor storage and improper handling of the feremented beans. The potential of NIR spectroscopy for predicting total fungi count in cocoa beans was attempted by Kutsnsdzie and co-workers [51] with well elaborated chemometric techniques. The best model was ACO-PLS with 0.9698 and 0.398 for R and RMSEP (CFU/mL) respectively in the prediction set. However, the fungal infestation of the cocoa beans was simulated in the laboratory and the NIR spectral data sets were collected indirectly from a neat prepared solution. Furthermore, though it was a novel attempt, no real samples were included in the model development or model testing [51]. Notwithstanding, the study provided feasibility of semi18

destructive, rapid and affordable detection compared with DNA-based polymerase chain reactions (PCR) and enzyme-linked immunosorbent assay (ELISA) techniques. Future studies should focus on direct scanning of the cocoa beans but the challenge here is that most of these safety traits are beyond the limit of detection for NIR spectroscopy. Adulteration a type of food fraud practiced illegally since ancient times but recently has become more sophisticated could happen in the cocoa industry. Cocoa bean is a high value international commodity with a long supply chain and susceptible to or a target for adulteration. The main reasons for this incidence are motivated by undue profit, adulterants easily mixed with difficulty in detection and normally, expensive techniques such as HPLC among others have been employed. NIR spectroscopy techniques, therefore, comes at the right time with a high possibility of replacing the already known cumbersome and expensive methods. For instance, rapid qualitative and quantitative detection of cocoa powder with carob flour using NIR spectroscopy and PCA, PLS-DA and PLS-R have been reported [46]. The diffuse reflectance spectra (1100-2500 nm) of the samples was used to simultaneously differentiate unadulterated cocoa powder from adulterated ones. The model achieved 100% classification accuracy using PLS-DA while PLSR model obtained R2 = 0.974 and RMSEP = 3.2 using the external validation set [46]. The discrimination between fermented, unfermented and adulterated cocoa beans and quantification of adulterant was also achieved with 100% accuracy by SVM and 0.98 regression coefficient by Si-PLSR using the prediction set with NIR spectroscopy [20]. Furthermore, a study reported the use of NIR spectroscopy to detect cocoa shell in cocoa powders by PLS-DA and PLSR with a good determination coefficient of 0.967 and root mean square error of 2.43 in prediction set respectively [47]. The aforementioned application of NIR spectroscopy for the detection of potential adulteration of cocoa bean and cocoa bean powder (shell, carob, unfermented cocoa beans) showed some similarities such as: spectral range of 1100-2500nm / 4000-10000 cm-1, PLS-DA classification model and PLS quantification model while the other used SVM classification model and hybrid Si-PLS model for quantification. The optimum results obtained by these researchers have proven that NIR spectroscopy could offer an alternative method for rapid detection of cocoa beans and cocoa bean products adulterations and fraud.

19

3.5. Sensory analysis Sensory analysis normally performed by the humans comes with its own challenge of subjectivity and often not consistent as it is influenced by uncontrolled external factors. Studies carried out to establish a relationship between the sensory properties of the cocoa bean and cocoa bean products to NIR spectroscopy are very scanty. That is not to say it is not feasible as other agro-foods have been studied and shown the possibility of using NIR spectroscopy for predicting sensory properties. On the other hand, our search found only one application of NIR spectroscopy for predicting sensory attributes of chocolate, and this was studied about three decades ago. The authors concluded that in view of the uncertainty inherent in all sensory data, their results by NIR spectroscopy holds great promise for the replacement of the difficult and demanding sensory analysis [44]. The study by Davis and coworkers further revealed that NIR spectra of raw and roasted cocoa beans, chocolate mass and finished chocolate corresponded with the chocolate samples in the sensory study and hence NIR spectral data of raw cocoa beans can be used to correlate with and replace sensory analysis of the finished chocolate [44]. Principal component analysis and Canonical correlation analysis were used for this sensory analysis to establish the correlation. However, since that research by Davis and co-workers, no other application has been found yet compare to the prediction of sensory properties of coffee such as acidity, mouthfeel, bitterness, and aftertaste studied by using NIR spectroscopy [52].

20

Table 1.0 Application of NIR spectrocopy technology in cocoa bean and cocoa bean products Application

Task

Categorization

Discriminating of

of cocoa beans

geographical location of

Wavelength

Chemometrics analysis

Performance/Rate %

range

Preprocessing

algorithms

R cal

R pre

400 - 1000 cm-1

MC, MSC,

KNN, LDA,

100

100

[35]

Detrend, 2-Der

BPANN,

cocoa beans Clustering fermentation

Reference

SVM 800 - 2498 nm

SNV-MC

PCA-PLS

-

-

[34]

400 - 1000 cm-1

SNV

LDA, SVM

100%

100%

[36]

900 - 2500 nm

PCA-SNV

LDA, KNN,

94%

94%

[37]

degree of cocoa beans Identifying cocoa bean varieties Classifying cocoa beans quality grades

Chemical

Ammonia nitrogen

compositions

content

of cocoa beans

Characterization of changes in flavan-3-ol

SVM, ELM

400 - 2500 nm

PCA

m-PLS

0.975

0.938

[41]

400-2500 nm

SNV, detrend,

PLS

0.95-0.98

0.93-0.96

[53]

SG, 2-Der, PCA

derivatives (epicatechin & total flavan-3-ols)

21

Procyanidins content;

400-2500 nm

SNV,

m-PLS

0.983

0.98

[54]

PCA-PLS

0.88-0.94

0.87-0.98

[38]

PLS

0.76-0.88

0.76-0.88

[21]

SG, MSC, MC,

PLSDA,

0.98-1

0.98-0.997

[26]

fermentation index and

SNV, 2-Der,

BPANN, &

fermentation groups

PCA

PLS, iPLS,

0.984

0.971

[25]

monomers, epicatechin,

detrending,

catechin and oligomers of

1-Der

flavan-3-ol monomers Biochemical quality

3600 - 12500

MSc, SNV,

parameters; phenol,

cm-1

1-Der, min &

organic acid, epicatechin,

max-normal

lactic acid, theobromine Fermentation index,pH,

3600-12500 cm-1

total polyphenols

Vector normal, MSC, 1-Der, straight line subtraction, min & max-normal

Measuring pH,

4000-10000 cm-1

SiPLS, BPANNR, SiBPANNR Predicting total fat content 4000-10000 cm-1

MC, MSC, 1-

Si-PLS,

Der, SG

SVMR

22

To determine fat, caffeine, 780-2500 nm

SNV, 2-Der,

m-PLS

0.88-96

0.88-0.98

[55]

theorbromine and

Savitzky-Golay

PLS

0.9-0.98

0.80-0.99

[39]

PLS

0.8190.943

0.824-0.943

[56]

1 & 2-Der

PLSR

0.98-0.89

0.90-0.57

[48]

PC transform

PLSR

0.94-0.96

0.94-0.98

[49]

epicatechin in unfermented criollo Protein, moisture, fat, ash,

400-2498 nm

carbohydrates, and colour

MSC, SNV, 1 & 2-Der

(L*, a*, b*) of whole cocoa beans and ground cocoa beans Measuring protein, pH,

4020-10000 cm-1

acidity, fat, shell content,

SNV, MSC, SG, 1-Der

moisture, total phenolics, caffeine and theobromine Chemical

Sucrose, lactose, fat,

910-2500

composition of

moisture, viscosity and

nm/11,000-4000

cocoa bean

yield of chocolate

cm-1

Quantification of fat,

1100-2500 nm

products

nitrogen and moisture content of cocoa powder

23

Predicting nutritional

714-2857 nm

PCA, 1st & 2nd

ANNS,

parameters;

(14000-3500 cm-

der (1& 2-Der)

PLSR

carbohydrates, fat,

1)

400-12500 cm-1

EMSC, PCA

1100-2500 nm

-

-

[45]

PLSR

-

0.989-0.998

[50]

PCA, MC,

PLSR, MLR,

0.997

0.998

[19]

Savitzky &

GA-MLR

SVM, PLS,

100%, &

100% &

[20]

Si-PLS

0.99

0.98

2-Der, SG, OSC,

PLS-DA, &

100%, &

100%, &

PCA

PLSR

0.980

0.974

protein, energy value anc cocoa content in chocolate samples Analysis of fat, protein, sugar and water content in chocolate base Sucrose content in chocolate mass

Golay, SNV, MSC Fraud and

Identifying and

safety

quantifying adulteration of

400-1000 cm-1

SNV, DOSC

fermented cocoa beans Fraud detection (qualitative &

1100-2500 nm

quantitative) of carob flour in cocoa powder

24

[46]

Detection of cocoa shell in 1100-2500 nm

EMSC, SNV, 2-

PLS-DA,

100%, &

100%, &

[47]

cocoa powders

Der, SG, OSC,

PLSR

0.99

0.96

PLS, Si-PLS,

0.95

0.97

[51]

-

0.86

[44]

PCA Measurement of total

1000-2500 nm

SNV, MSC

fungal count in cocoa

Si-GAPLS,

beans

CARS-PLS

Sensory

Sensory analysis of

quality

chocolate quality

1100-2500 nm

PCA, MSC

25

CVA

4.0 General Discussion The spectral profile of cocoa bean and cocoa bean products revealed some useful functional groups that are responsible for specific chemical consitituent that either alone or in association can be use for qualitative and quantitative determinations. A critical observation of the spectral plots revealed major peaks centred in the range of 9000 to 5000 cm-1 which could provide useful information for measurements. This range has several functional groups such as carbonyl group, C-H stretch, C-H deformation, S-H, N-H, CH2 and CH3 corresponding to phytochemicals such as proteins, alkaloids, fats, polyphenols, volatile and non-volatile acid present in cocoa beans and cocoa bean products [35] and these provide the major backbone for cocoa quality classifications and quantifications. More specifically, efficient variable selection by Si-PLS technique [25] resulted in the selection of optimum spectral intervals ranging between 4902-5199 cm-1, 55045801 cm-1, 6106-6403 cm-1, 7610-7909 cm-1. The first range is associated with O-H asymmetric stretching and C=O second overtone, followed by C-H first overtone corresponding to oil content in cocoa beans. Also the peak found around 5556 cm-1 and 5708 cm-1, are related to oleic acid and cocoa bean oil contains 35% of oleic acid [57]. Cocoa bean is know to contain about 60% saturated fatty acid and 35-43% unsaturated fatty acids [58] and the range around 7600-8000 cm1

that corresponds to saurated and unsaturated triyglycerides found in cocoa beans could be

useful when analysing spectral data. Other useful peaks could be identified around 8235 cm-1 associated with –CH=CH second overtone, at 7030 cm-1 associated with C-H first overtone, and 5798 / 5421 cm-1 associated with C-H first overtone, while 5176 and 4880 cm-1 are also associated with O-H combination and N-H bending and these spectral peaks typically corresponds to protein, fat, moisture and some aromatic compounds in cocoa beans [20, 49]. Therefore a critical examination of NIR spectral fingerprint of cocoa bean and cocoa bean products provides vital background information for qualitative and quantitative analysis. With the help of chemometric techniques and computer programmes, efficient variable selection is made possible. This further goes a long way to improve results, as other redundant or unuseful information that could influence results are remove while modelling. Furthermore, it has therefore become very important that combining the best spectral preprocessing techniques and efficient variable selection that eliminates unuseful information improves results to a large considerably. Hence quality attribute of interest could be measurement accurately, rapidly and nondestructive in an environmentaly cost efficent manner. 26

5.0 Future trends This manuscript has demonstrated that a lot of technical material is available for the use of NIR spectroscopy for cocoa beans and cocoa bean products authentication and prediction of quality attributes of interest. This is particularly important for maintaining the global high premium value enjoyed by the cocoa beans and cocoa bean products. It is known that the success of NIR spectroscopic technique depends on the training of models which encompass wide range of cocoa bean samples with large natural variability as possible. This make the results robust. Also, the use of larger samples from West Africa countries (especially Ivory Coast, Ghana, and Nigeria) will improve the global efficency of the techniques as this countries contribute about 70% of total global cocoa bean output. However, there are many repetitive studies with similar goals which resulted in more compositional analysis without the substantive use of a wide range of cocoa beans from West Africa. This implies that the models developed could be less robust for a global challenge. Therefore, including a wide range of samples will help make future models robust to deliver user-friendly and inexpensive analytical techniques favourable to different geographical locations where cocoa beans are produced. It was also noted that, worldwide geographical classification of cocoa beans to check global cocoa bean integrity is lacking. Therefore, studies are needed in this area to ascertain rapid detection of mislabelling (a type of food fraud) for economic gains as some countries enjoy high premium prices. Research has shown that cocoa beans from certain countries have high-quality characteristic properties than others and consequuently enjoys premuim price in the world market. Furthermore, with the recent development of miniaturized NIR spectrometer, cocoa beans, and cocoa bean products are yet to receive their fair share in research. This research project is particularly needed for rapid examination of cocoa beans from the primary processing of cocoa beans such as pod breaking, fermentation, drying, and bagging. These primary processing are done in developing countries challenged with laboratory infrastructure. It must be emphasized that errors made during this primary processing cannot be reversed or corrected in the secondary or tertiary processing stages. Hence, continuous monitoring by using nondestrictive, rapid, affordable and reliable technology such as NIR spectroscopy holds a brighter future for the cocoa bean industry and would be beneficial for the entire cocoa bean value chain. 27

6.0 Conclusion The acceptable quality cocoa beans in the world market are crucial to the sustainability of the cocoa bean industry. This would be beneficial to farmers and their dependents who rely on cocoa for income and as well as to both processors who increase cocoa bean value and consumers as a whole who enjoy cocoa bean related products. NIR spectroscopy provides the best option for measuring quality in the cocoa industry. The following conclusions can be drawn from this review paper: Near Infrared Spectroscopy technique coupled with chemometrics has shown to be a powerful tool for the accurate, reliable, rapid and environmentally friendly analysis of cocoa beans and cocoa bean products. Among the multivariate algorithms used by various researchers, the PLS model was extensively used with good results. Also, support vector machine (SVM) performed better for categorization of cocoa beans (grades, varieties, quality, and locations) than the others. Generally, another combination of multivariate algorithms such as Si-SVMR, Si-PLS and Si-BPANNR were found to be favourable for the quantitative determination of various quality attributes. It can therefore, be concluded that NIR Spectroscopy coupled with the appropriate multivariate model has high potential to aid quality control and quality assurance in the cocoa bean industry. However, more work needs to be done to move this technology from the laboratory applications to real usage in developing countries for optimum global benefits. This requires the use of extensive commercial samples covering a wide range of cocoa samples from producing regions. Global standardization of this technique would also be very helpful in the cocoa industry. Developing portable and affordable instruments is urgently needed particularly in West Africa. It would make onsite measurement application in developing countries possible and aid global traceability and production of high quality cocoa beans. Acknowledgments This work was supported by the Department of Agricultural Engineering, School of Agriculture, College of Agriculture and Natural Sciences University of Cape Coast, and Institute for Global Food Security, Queen’s University Belfast. The proofreading by Mrs. Winifred Akpene Teye is highly acknowledged.

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LIST OF ABBREVIATION ACOP-PLS = Ant colony optimization partial least squares ANN = Artificial neural networks BPANN = Back propagation artificial neural network BPANNR = Back propagation artificial neural network regression CARS-PLS = Competitive adaptive reweighted sampling partial least squares 31

CCA = Canonical correlation analysis CVA = Canonical variate analysis 1-Der = First derivative 2-Der = Second derivatative DOSC = Direct orthogonal signal correction DT = Detrend ELM = Extreme learning machine EMSC = Extended multiple signal correction, FDA =Fishers discriminant analysis GA-MLR = Genetic algorithm multiple linear regression iPLS = interval partial least squares KNN =K-nearest neighbour LDA =Linear discrimiant analsysis MC =Mean centering MLR = Mutiple linear regression MLR =Multi-linear regresion m-PLS = modified partial least squares MSC = Multipicative scatter correction NIRS = Near infrared spectroscopy PCA = Principal component analysis; PCA, PLS-DA =Partial least square discriminant analysis PLS-R = Partial least square regression R = correlation coefficient RMSECV = the root mean square error of cross-validation RMSEP = the root mean square error of prediction SG =Savitzky-Golay Si-GAPLS = Synergy interval genetic algorithm partial least square Si-PLS= Synergy interval partial least least 32

Si-SVMR = Synergy interval support vector machine regression SNV =Standard normal variant SVM = Support vector machine

1. Near Infrared Spectroscopy technique coupled with chemometrics could provide a powerful tool for the accurate, reliable, rapid and environmentally friendly analysis of cocoa beans and cocoa bean products 2. Global standardization of Portable NIR spectroscopic technique would also be very helpful in the cocoa industry. 3. Developing portable and affordable NIR instruments is urgently needed

Dear Sir, The authors state that there is no interest to declare

33