Estimating cocoa bean parameters by FT-NIRS and chemometrics analysis

Identification of Variety and Prediction of Chemical Composition in Cocoa Beans (Theobroma cacao L.) by FT-MIR Spectroscopy and Chemometrics

Cocoa is rich in polyphenols and alkaloids that act as antioxidants, anticarcinogens, and anti-inflammatories. Analytical methods commonly used to determine the proximal chemical composition of cocoa, total phenols, and antioxidant capacity are laborious, costly, and destructive. It is important to develop fast, simple, and inexpensive methods to facilitate their evaluation. Chemometric models were developed to identify the variety and predict the chemical composition (moisture, protein, fat, ash, pH, acidity, and phenolic compounds) and antioxidant capacity (ABTS and DPPH) of three cocoa varieties. SIMCA model showed 99% reliability. Quantitative models were developed using the PLS algorithm and favorable statistical results were obtained for all models: 0.93 < R2c < 0.98 (R2c: calibration determination coefficient); 0.03 < SEC < 4.34 (SEC: standard error of calibration). Independent validation of the quantitative models confirmed their good predictive ability: 0.93 < R2v < 0.97 (R2v: validation determination coefficient); 0.04 < SEP < 3.59 (SEP: standard error of prediction); 0.08 < % error < 10.35). SIMCA model and quantitative models were applied to five external cocoa samples, obtaining their chemical composition using only 100 mg of sample in less than 15 min. FT-MIR spectroscopy coupled with chemometrics is a viable alternative to conventional methods for quality control of cocoa beans without using reagents, and with the minimum sample preparation and quantity.

Quality Evaluation of Fair-Trade Cocoa Beans from Different Origins Using Portable Near-Infrared Spectroscopy (NIRS)

Determining cocoa bean quality is crucial for many players in the international supply chain. However, actual methods rely on a cut test protocol, which is limited by its subjective nature, or on time-consuming, expensive and destructive wet-chemistry laboratory procedures. In this context, the application of near infrared (NIR) spectroscopy, particularly with the recent developments of portable NIR spectrometers, may represent a valuable solution for providing a cocoa beans' quality profile, in a rapid, non-destructive, and reliable way. Monitored parameters in this work were dry matter (DM), ash, shell, fat, protein, total polyphenols, fermentation index (FI), titratable acidity (TA) and pH. Different chemometric analyses were performed on the spectral data and calibration models were developed using modified partial least squares regression. Prediction equations were validated using a fivefold cross-validation and a comparison between the different prediction performances for the portable and benchtop NIR spectrometers was provided. The NIRS benchtop instrument provided better performance of quantification considering the whole than the portable device, showing excellent prediction capability in protein and DM quantification. On the other hand, the NIRS portable device, although showing lower but valuable performance of prediction, can represent an appealing alternative to benchtop instruments for food business operators, being applicable in the field.

Geographical Discrimination of Ground Amazon Cocoa by Near-Infrared Spectroscopy: Influence of Sample Preparation

This work presents the application of the NIR technique associated with exploratory analysis of spectral data by main principal components for the discrimination of Amazon cocoa ground seeds. Cocoa samples from different geographic regions of the state of Pará, Brazil (Medicilândia, Tucumã, and Tomé-Açu), were evaluated. The samples collected from each region were divided into four groups distinguished by the treatment applied to the samples, which were fermented (1-with fat and 2-fat-free) and unfermented (3-with moisture and 4-dried). Each set of samples was analyzed separately to identify the influence of moisture, fermentation, and fat on the geographical differentiation of the three regions. From the results obtained, it can be observed that it was not possible to differentiate the samples of seeds not fermented by geographic origin. However, fermentation was crucial for efficient discrimination, providing more defined clusters for each geographic region. The presence of fat in the seeds was a determinant to obtain the best model of geographic discrimination.

A comprehensive insight on modern green analyses for quality control determination and processing monitoring in coffee and cocoa seeds

Green analysis is defined as the analysis of chemicals in a manner where sample extraction and analysis are performed with least amounts of steps, low hazardous materials, while maintaining efficiency in terms of analytes detection. Coffee and cocoa represent two of the most popular and valued beverages worldwide in addition to their several products i.e., cocoa butter, chocolates. This study presents a comprehensive overview of green methods used to evaluate cocoa and coffee seeds quality compared to other conventional techniques highlighting advantages and or limitations of each. Green techniques discussed in this review include solid phase microextraction, spectroscopic techniques i.e., infra-red (IR) spectroscopy and nuclear magnetic resonance (NMR) besides, e-tongue and e-nose for detection of flavor. The employment of multivariate data analysis in data interpretation is also highlighted in the context of identifying key components pertinent to specific variety, processing method, and or geographical origin.

Electrochemical sensing of nitrofurazone on Ru(bpy)32+ functionalized polyoxometalate combined with graphene modified electrode

Nitrofurazone is forbidden to be used in aquaculture, but it is often used illegally because of its good bactericidal effect, and its content in animals is extremely low and difficult to detect directly. Hence, a functionalized polyoxometalate combined with graphene modified electrodes through layer-by-layer assembly has achieved a sensitive detection of nitrofurazone in a pH = 6 Na₂HPO₄-citrate buffer solution and its detection limit as low as 0.08952 μM. Nitrofurazone has accelerated its electron transfer through [Ru-PMo₁₂/PDDA-GO]₃ modified electrode, thus realizing its direct detection at low levels through actual samples. This study provides a new perspective for the direct detection of nitrofurazone by electrochemical methods, which is of great significance for the supervision of nitrofurazone and the improvement of the quality and safety of aquatic products.

NIR Spectroscopy Oranges Origin Identification Framework Based on Machine Learning

Research on the identification model of orange origin based on machine learning in Near infrared (NIR) spectroscopy. According to the characteristics of NIR spectral data, a complete general framework for origin identification is proposed. It includes steps such as data preprocessing, feature selection, model building and cross validation. Compare multiple preprocessing algorithms and multiple machine learning algorithms under the framework. Based on NIR spectroscopy to identify the origin of orange, a good identification result was obtained. Improve the accuracy of orange origin identification and obtained the best origin identification accuracy of 92.8%.

Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques

The global market for organic cocoa beans continues to show sturdy growth. A low-cost handheld NIR spectrometer (900-1700 nm) combined with multivariate classification algorithms was used for rapid differentiation analysis of organic cocoa beans' integrity. In this research, organic and conventionally cultivated cocoa beans were collected from different locations in Ghana and scanned nondestructively with a handheld spectrometer. Different preprocessing treatments were employed. Principal component analysis (PCA) and classification analysis, RF (random forest), KNN (K-nearest neighbours), LDA (linear discriminant analysis), and PLS-DA (partial least squares-discriminant analysis) were performed comparatively to build classification models. The performance of the models was evaluated by accuracy, specificity, sensitivity, and efficiency. Second derivative preprocessing together with PLS-DA algorithm was superior to the rest of the algorithms with a classification accuracy of 100.00% in both the calibration set and prediction set. Second derivative algorithm was found to be the best preprocessing tool. The identification rates for the calibration set and prediction set were 96.15% and 98.08%, respectively, for RF, 91.35% and 92.31% for KNN, and 90.38% and 98.08% for LDA. Generally, the results showed that a handheld NIR spectrometer coupled with an appropriate multivariate algorithm could be used in situ for the differentiation of organic cocoa beans from conventional ones to ensure food integrity along the cocoa bean value chain.

High-Throughput Quantitation of Key Cocoa Tastants by Means of Ultra-High-Performance Liquid Chromatography Tandem Mass Spectrometry and Application to a Global Sample Set

Historically often described as the food of gods, cocoa-based products exhibit a pleasant aroma as well as a desirable astringent, bitter, and sour taste, which results in a high consumer preference. The key taste components of cocoa were identified and characterized by combining sensory analysis, fractionation, and structure elucidation. Cocoa astringency is driven by N-phenylpropenoyl-l-amino acids, polyphenol glycosides, and flavan-3-ols, while the latter compound class also contributes to bitterness. The key principle for cocoa bitterness was shown to be the combination of alkaloids and 2,5-diketopiperazines. To understand the influence of plant genetics, breeding, and processing on the sensory profile of cocoa products, high-throughput sensometabolite quantitation must be performed throughout all of these steps. In this work, we present a rapid, sensitive, and robust quantitation method on a single ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) platform, requiring minimal workup for any sample type from farm to fork. This method was applied to a global set of 75 cocoa bean samples from all over the world before and after using a uniform roasting protocol. Within this world map, geographical origin did not predetermine cocoa taste profiles, whereas simulated processing by roasting was confirmed to be crucial in profile development. This method will open avenues for further studies to ultimately enable chocolate producers to control and optimize the taste properties of products as well as to monitor raw material selection and processing.

Rapid and practical qualitative and quantitative evaluation of non-fumigated ginger and sulfur-fumigated ginger via Fourier-transform infrared spectroscopy and chemometric methods

A strategy was developed to distinguish and quantitate nonfumigated ginger (NS-ginger) and sulfur-fumigated ginger (S-ginger), based on Fourier transform near infrared spectroscopy (FT-NIR) and chemometrics. FT-NIR provided a reliable method to qualitatively assess ginger samples and batches of S-ginger (41) and NS-ginger (39) were discriminated using principal component analysis and orthogonal partial least squares discriminant analysis of FT-NIR data. To generate quantitative methods based on partial least squares (PLS) and counter propagation artificial neural network (CP-ANN) from the FT-NIR, major gingerols were quantified using high performance liquid chromatography (HPLC) and the data used as a reference. Finally, PLS and CP-ANN were deployed to predict concentrations of target compounds in S- and NS-ginger. The results indicated that FT-NIR can provide an alternative to HPLC for prediction of active components in ginger samples and was able to work directly on solid samples.

Robust prediction performance of inner quality attributes in intact cocoa beans using near infrared spectroscopy and multivariate analysis

Fast and simultaneous determination of inner quality parameters, such as fat and moisture contents, need to be predicted in cocoa products processing. This study aimed to employ the near-infrared reflectance spectroscopy (NIRS) in predicting the quality mentioned above parameters in intact cocoa beans. Near-infrared spectral data, in a wavelength ranging from 1000 to 2500 nm, were acquired for a total of 110 bulk cocoa bean samples. Actual fat and moisture contents were measured with standard laboratory procedures using the Soxhlet and Gravimetry methods, respectively. Two regression approaches, namely principal component regression (PCR) and partial least square regression (PLSR), were used to develop the prediction models. Furthermore, four different spectra correction methods, namely multiple scatter correction (MSC), de-trending (DT), standard normal variate (SNV), and orthogonal signal correction (OSC), were employed to enhance prediction accuracy and robustness. The results showed that PLSR was better than PCR for both quality parameters prediction. Spectra corrections improved prediction accuracy and robustness, while OSC was the best correction method for fat and moisture content prediction. The maximum correlation of determination (R²) and residual predictive deviation (RPD) index for fat content were 0.86 and 3.16, while for moisture content prediction, the R² coefficient and RPD index were 0.92 and 3.43, respectively. Therefore, NIRS combined with proper spectra correction method can be used to rapidly and simultaneously predict inner quality parameters of intact cocoa beans.

Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.

Total lipid prediction in single intact cocoa beans by hyperspectral chemical imaging

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Quantitative calibrations were built from shelled and in-shell single cocoa beans by HSI.

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The fat content of commercial batches of cocoa beans varies by up to 15% within batches.

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HSI prediction of the total lipid content was successful for shelled and unshelled beans.

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Segregation using HSI fat calibration enhanced cocoa bean fat content by 6%.

This work aimed to explore the possibility of predicting total fat content in whole dried cocoa beans at a single bean level using hyperspectral imaging (HSI).

170 beans randomly selected from 17 batches were individually analysed by HSI and by reference methodology for fat quantification. Both whole (i.e. in-shell) beans and shelled seeds (cotyledons) were analysed. Partial Least Square (PLS) regression models showed good performance for single shelled beans (R² = 0.84, external prediction error of 2.4%). For both in-shell beans a slightly lower prediction error of 4.0% and R² = 0.52 was achieved, but fat content estimation is still of interest given its wide range. Beans were manually segregated, demonstrating an increase by up to 6% in the fat content of sub-fractions.

HSI was shown to be a valuable technique for rapid, non-contact prediction of fat content in cocoa beans even from scans of unshelled beans, enabling significant practical benefits to the food industry for quality control purposes and for obtaining a more consistent raw material.

Nondestructive authentication of the regional and geographical origin of cocoa beans by using a handheld NIR spectrometer and multivariate algorithm

Traceability in the cocoa bean trade is vital to ensuring quality. In this study, a handheld near-infrared (NIR) spectrometer was attempted for rapid and nondestructive regional and geographical classification of cocoa beans from different locations. Cocoa bean samples collected from seven cocoa-producing regions in Ghana (Eastern, Ashanti, Volta, Western South, Western North, Central, and Brong Ahafo) and four cocoa-producing countries in Africa (Uganda, Ivory Coast, Nigeria, and Ghana) were used. Among the preprocessing techniques employed, multiplicative scatter correction (MSC) performed better. The correct classification rate for the seven cocoa-producing regions in Ghana was 100% for LDA and SVM models in the training set and testing set. For classification of cocoa beans based on the country of origin, LDA and SVM also gave 100% classification rate both in the training set and testing set. The results give strong indications that hand-held spectroscopy coupled with chemometrics could be employed to provide the quick, accurate, and nondestructive classification of cocoa beans according to different locations. This technique could improve the work of quality control inspectors both from industry and regulatory perspectives for effective and quick detection of cocoa bean fraud.

Data analysis on near infrared spectroscopy as a part of technology adoption for cocoa farmer in Aceh Province, Indonesia

Presented manuscript described data analysis on near infrared spectroscopy used as adopted and portable technology for cocoa farmers in Aceh Province, Indonesia. The near infrared spectroscopy (NIRS) assisted farmers in post-harvest handling especially for cocoa quality evaluation. This technology was used to determine moisture content (MC) and fat content (FC) of intact cocoa bean samples rapidly and simultaneously. Near infrared spectra data were acquired as absorbance spectrum in wavelength range from 1000 to 2500 nm with co-added of 32 scans for a total of 72 intact bulk cocoa bean samples. Spectra data can be used to predict MC and FC of intact cocoa beans by establishing prediction models and validate with actual MC and FC measured by means of standard laboratory procedures. Prediction performances were evaluated using several statistical indicators: coefficient correlation (r), coefficient of determination (R²), root mean square error (RMSE) and residual predictive deviation (RPD) index. Near infrared spectra data can be enhanced using spectra pre-treatment methods to improve prediction performances. Moreover, prediction models can be developed using principal component regression (PCR), partial least squares regression (PLSR) and other regression approaches. Ideal prediction models should have r and R² above 0.75, RPD index above 2.0 and RMSE lower than its standard deviation (SD). Dataset were available as raw MS Excel format and The Unscrambler files as *.unsb extension.

Matrix effect on quantification of sugars and mannitol developed during the postharvest of cocoa: an alternative method for traceability of aroma precursors by liquid chromatography with an evaporative detector

The profile of reducing sugars developed during each stage of the postharvest of cocoa beans is an important quality index; these sugars are found to be one of the main precursors of flavor and neoformed compounds. However, its quantification after extraction from different parts of the bean is a challenge due to the complexity of the matrix. The primary aim of this study was to track the formation of reducing sugars and mannitol in samples obtained from various steps of the fermentation and drying processes of cocoa beans by validating a rapid matrix-corrected chromatographic method utilizing a corona-charged aerosol detector for improved sensitivity. The analytes were extracted from ground cocoa beans by solid phase extraction without a defatting step (20 mg raw fermented and 10 mg dried). The experimental variables influencing the effective detection were evaluated at different temperatures and signal filtering levels. Method validation studies showed an average recovery between 77.8 and 120% for fermented cocoa and between 79.6 and 117.7% for dried cocoa. A linear response was achieved for fructose, glucose, sucrose, and mannitol for a concentration range of 0.1-40 mg/L, and maltose showed linearity in the range of 0.1-70 mg/L. Regression coefficients (R) were 0.9991, 0.9993, 0.9992, 0.9995 and 0.9994, respectively. This method was successfully applied to a clone mix of cocoa from Antioquia, Colombia, to confirm the hydrolysis reaction of sucrose into glucose and fructose during fermentation and drying. A quality indicator of an efficient postharvest process in this study was determined to be a glucose percentage of 0.66% w/w and a fructose percentage of 1.46% w/w, which were higher than the values reported by other studies.

Innovative and rapid analysis for rice authenticity using hand-held NIR spectrometry and chemometrics

Rice is the second most important food staple worldwide and the demand will continue to increase with the growth of the world population. As reports grow that frauds is prevalent in many supply chains there is the need for an effective and rapid technique for monitoring the authenticity and quality of rice. This study investigated the novel application of hand-held NIR spectrometry coupled to chemometric for the estimation of rice authenticity and quality in real time. A total of 520 rice samples from different quality grades (high quality, mid quality and low quality) and different countries (Ghana, Thailand, and Vietnam) of origin were used. Among the pre-processing methods used multiplicative scatter correction (MSC) was found to be superior. Principal component analysis (PCA) was used to extract relevant information from the spectral data set and the results showed that rice samples of different categories could be clearly clustered under the first three PCs using the MSC preprocessing method. The performance of K-nearest neighbor (KNN) revealed that for authentication of rice quality grades, the classification rate gave 91.62% and 91.81% in training set and prediction set respectively while identification rate based on different country of origin was 90.84% and 90.64% in both training set and prediction set respectively. For the differentiation of local rice from the imported, KNN and SVM all had 100% in both the training set and prediction set. These gives very strong evidence that hand-held spectrometry coupled with MSC-PCA-KNN could successfully be used to provide rapid and nondestructive classification of rice samples according to different quality grades, geographical origin and imported versus locally produced rice. This technique could enhance the work of quality control inspectors both from industry and regulatory perspectives for the rapid detection of rice integrity and fraud issues.

Hyperspectral imaging for non-destructive prediction of fermentation index, polyphenol content and antioxidant activity in single cocoa beans

The aim of the current work was to use hyperspectral imaging (HSI) in the spectral range 1000-2500 nm to quantitatively predict fermentation index (FI), total polyphenols (TP) and antioxidant activity (AA) of individual dry fermented cocoa beans scanned on a single seed basis, in a non-destructive manner. Seventeen cocoa bean batches were obtained and 10 cocoa beans were used from each batch. PLS regression models were built on 170 samples. The developed HSI predictive models were able to quantify three quality-related parameters with sufficient performance for screening purposes, with external validation R² of 0.50 (RMSEP = 0.27, RPD = 1.40), 0.70 (RMSEP = 34.1 mg ferulic acid g^-1, RPD = 1.77) and 0.74 (60.0 mmol Trolog kg^-1, RPD = 1.91) for FI, TP and AA, respectively. The calibrations were subsequently applied at a single bean and pixel level, so that the distribution was visualised within and between single seeds (chemical images). HSI is thus suggested as a promising approach to estimate cocoa bean composition rapidly and non-destructively, thus offering a valid tool for food inspection and quality control.

Non-destructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy

BACKGROUND

The viability of seeds is important for determining their quality. A high-quality seed is one that has a high capability of germination that is necessary to ensure high productivity. Hence, developing technology for the detection of seed viability is a high priority in agriculture. Fourier transform near-infrared (FT-NIR) spectroscopy is one of the most popular devices among other vibrational spectroscopies. This study aims to use FT-NIR spectroscopy to determine the viability of soybean seeds.

RESULTS

Viable and artificial ageing seeds as non-viable soybeans were used in this research. The FT-NIR spectra of soybean seeds were collected and analysed using a partial least-squares discriminant analysis (PLS-DA) to classify viable and non-viable soybean seeds. Moreover, the variable importance in projection (VIP) method for variable selection combined with the PLS-DA was employed. The most effective wavelengths were selected by the VIP method, which selected 146 optimal variables from the full set of 1557 variables.

CONCLUSIONS

The results demonstrated that the FT-NIR spectral analysis with the PLS-DA method that uses all variables or the selected variables showed good performance based on the high value of prediction accuracy for soybean viability with an accuracy close to 100%. Hence, FT-NIR techniques with a chemometric analysis have the potential for rapidly measuring soybean seed viability. © 2017 Society of Chemical Industry.

Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution

Total fungi count (TFC) is a quality indicator of cocoa beans when unmonitored leads to quality and safety problems. Fourier transform near infrared spectroscopy (FT-NIRS) combined with chemometric algorithms like partial least square (PLS); synergy interval-PLS (Si-PLS); synergy interval-genetic algorithm-PLS (Si-GAPLS); Ant colony optimization - PLS (ACO-PLS) and competitive-adaptive reweighted sampling-PLS (CARS-PLS) was employed to predict TFC in cocoa beans neat solution. Model results were evaluated using the correlation coefficients of the prediction (Rp) and calibration (Rc); root mean square error of prediction (RMSEP), and the ratio of sample standard deviation to RMSEP (RPD). The developed models performance yielded 0.951≤Rp≤0.975; and 3.15≤RPD≤4.32. The models' prediction stability improved in the order of PLS<CARS-PLS<ACO-PLS<Si-PLS<Si-GAPLS. FT-NIRS combined with Si-GAPLS may be employed for in-situ and noninvasive quantification of TFC in cocoa beans for quality and safety monitoring.

Origin-based polyphenolic fingerprinting of Theobroma cacao in unfermented and fermented beans

A comprehensive analysis of cocoa polyphenols from unfermented and fermented cocoa beans from a wide range of geographic origins was carried out to catalogue systematic differences based on their origin as well as fermentation status. This study identifies previously unknown compounds with the goal to ascertain, which of these are responsible for the largest differences between bean types. UHPLC coupled with ultra-high resolution time-of-flight mass spectrometry was employed to identify and relatively quantify various oligomeric proanthocyanidins and their glycosides amongst several other unreported compounds. A series of biomarkers allowing a clear distinction between unfermented and fermented cocoa beans and for beans of different origins were identified. The large sample set employed allowed comparison of statistically significant variations of key cocoa constituents.

A feasibility study of non-targeted adulterant screening based on NIRM spectral library of soybean meal to guarantee quality: The example of non-protein nitrogen

The quality and safety of soybean meal is a key matter for the livestock breeding and food industries, since it is one of the most important and widely used protein feed raw materials. As driven by commercial interests, new illegal adulterants which are unknown to consumers and regulators emerge constantly. In order to make up for the inadequacy of traditional detection methods, a novel non-targeted adulterant screening method based on a near-infrared microscopy spectral library of soybean meal is proposed. This study focused on the feasibility of non-targeted screening methods for the detection of adulteration in soybean meal. Six types of non-protein nitrogen were taken as examples and partial least squares discriminant analysis was employed to verify the feasibility of this novel method. The results showed that the non-targeted screening method could screen out adulterations in soybean meal with satisfactory results.