Classification of Organic and Conventional Cocoa Beans Using Laser-Induced Fluorescence Spectroscopy Combined with Chemometric Techniques.

The craving for organic cocoa beans has resulted in fraudulent practices such as mislabeling, adulteration, all known as food fraud, prompting the international cocoa market to call for the authenticity of organic cocoa beans before export. In this study, we proposed robust models using laser-induced fluorescence (LIF) and chemometric techniques for rapid classification of cocoa beans as either organic or conventional. The LIF measurements were conducted on cocoa beans harvested from organic and conventional farms. From the results, conventional cocoa beans exhibited a higher fluorescence intensity compared to organic ones. In addition, a general peak wavelength shift was observed when the cocoa beans were excited using a 445 nm laser source. These results highlight distinct characteristics that can be used to differentiate between organic and conventional cocoa beans. Identical compounds were found in the fluorescence spectra of both the organic and conventional ones. With preprocessed fluorescence spectra data and utilizing principal component analysis, classification models such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Network (NN) and Random Forest (RF) models were employed. LDA and NN models yielded 100.0% classification accuracy for both training and validation sets, while 99.0% classification accuracy was achieved in the training and validation sets using SVM and RF models. The results demonstrate that employing a combination of LIF and either LDA or NN can be a reliable and efficient technique to classify authentic cocoa beans as either organic or conventional. This technique can play a vital role in maintaining integrity and preventing fraudulent practices in the cocoa bean supply chain.

Effects of Predrying and Spontaneous Fermentation Treatments on Nib Acidification, Fermentation Quality, and Flavour Attributes of Ghanaian Cocoa (Theobroma cacao) Beans.

Cocoa bean acidification, fermentation, and flavour quality are intricately shaped by pulp preconditioning and fermentation treatments. This study investigates the impact of predrying and subsequent fermentation on key parameters such as pH, titratable acidity, fermentation quality (% purity), fermentation index (FI), and overall flavour quality (global quality (GQ)) of cocoa beans. Extended predrying periods and fermentation durations demonstrated a significant enhancement in bean acidification, reflected in the rise of nib pH (6.61-7.33) and the decline in nib acidity (0.023-0.013 meg NaOH/100 g). Notably, the cut test underscored the substantial improvement in % purity, reaching 75.6-99.7% for beans predried at 2-8 hours followed by a 6-day of fermentation. FI increased significantly from 1.026 to a peak of 1.067, followed by a decline to 0.098 in the control, 6 hours, and 8 hours of predried beans, respectively. Sensory evaluation showed substantial improvement in the GQ (40.1-44.6) of beans predried at 2-8 hours and fermented for 6 days, compared to the control (38.3). In addition, a significantly higher preference was shown for cocoa liquor made from the beans predried for 4-6 hours and fermented for 6 days. Principal component analysis clustered samples according to the predrying time, fermentation duration, and quality parameters measured. Optimal conditions for enhanced nib acidification, fermentation quality, and flavour attributes were identified at 6-hour predrying and 6-day fermentation using the response surface methodology. The study highlights the potential of predrying as a pulp preconditioning technique for enhancing fermentative and final bean quality.

Novel authentication of African geographical coffee types (bean, roasted, powdered) by handheld NIR spectroscopic method.

African coffee is among the best traded coffee types worldwide, and rapid identification of its geographical origin is very important when trading the commodity. The study was important because it used NIR techniques to geographically differentiate between various types of coffee and provide a supply chain traceability method to avoid fraud. In this study, geographic differentiation of African coffee types (bean, roasted, and powder) was achieved using handheld near-infrared spectroscopy and multivariant data processing. Five African countries were used as the origins for the collection of Robusta coffee. The samples were individually scanned at a wavelength of 740-1070 nm, and their spectra profiles were preprocessed with mean centering (MC), multiplicative scatter correction (MSC), and standard normal variate (SNV). Support vector machines (SVM), linear discriminant analysis (LDA), neural networks (NN), random forests (RF), and partial least square discriminate analysis (PLS-DA) were then used to develop a prediction model for African coffee types. The performance of the model was assessed using accuracy and F1-score. Proximate chemical composition was also conducted on the raw and roasted coffee types. The best classification algorithms were developed for the following coffee types: raw bean coffee, SD-PLSDA, and MC + SD-PLSDA. These models had an accuracy of 0.87 and an F1-score of 0.88. SNV + SD-SVM and MSC + SD-NN both had accuracy and F1 scores of 0.97 for roasted coffee beans and 0.96 for roasted coffee powder, respectively. The results revealed that efficient quality assurance may be achieved by using handheld NIR spectroscopy combined with chemometrics to differentiate between different African coffee types according to their geographical origins.

Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms.

Rapid and nondestructive measurement of moisture content in crude palm oil is essential for promoting the shelf-stability and quality. In this research, micro NIR spectrometer coupled with a multivariate calibration model was used to collect and analyse fingerprinted information from palm oil samples at different moisture contents. Several preprocessing methods such as standard normal variant (SNV), multiplicative scatter correction (MSC), Savitzky-Golay first derivative (SGD1), Savitzky-Golay second derivative (SGD2) together with partial least square (PLS) regression techniques, full PLS, interval PLS (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS (GAPLS), and successive projection algorithm PLS (SPA-PLS) were comparatively employed to construct an optimum quantitative prediction model for moisture content in crude palm oil. The models were evaluated according to the coefficient of determination and root mean square error in calibration (Rc and RMSEC) and prediction (Rp and RMSEC) set, respectively. The model SGD1 + SiPLS was the optimal novel algorithm obtained among the others with the performance of Rc = 0.968 and RMSEC = 0.468 in the calibration set and Rp = 0.956 and RMSEP = 0.361 in the prediction set. The results showed that rapid and nondestructive determination of moisture content in palm oil is feasible and this would go a long way to facilitating quality control of crude palm oil.

Pesticide Residues and Unauthorized Dyes as Adulteration Markers in Chilli Pepper and Tomato.

To assess the contamination of processed chilli pepper and tomatoes, a report over the past four decades since the establishment of the Rapid Alert System for Food and Feed (RASFF) was retrieved and analysed. Out of the 887 notification reports assessed for eligibility, 446 were found regarding chilli pepper and tomato contamination. This study identified India as the country of origin with the highest number of reported cases relating to chilli pepper contamination. Italy and Türkiye were the countries with the highest number of reported cases regarding the exportation of adulterated tomatoes to other countries according to the RASFF report. Unauthorized dyes such as Sudan I, III, IV, orange II, rhodamine B, and para red were reported to have been detected in either chilli pepper or tomato in the supply chain. Almost all unauthorized dyes in this study were found to be more than the range (0.5 to 1 mg/kg) of the detection limit of Sudan dye and other related dyes using analytical methods set by the European Union. Unapproved pesticides by the European Union (EU) found in this study were acetamiprid, chlorothalonil, chlorpyrifos, dimethoate, methomyl, monocrotophos, omethoate, oxamyl, and thiophanate methyl. The present study indicates the persistence of chilli pepper and tomato contamination with harmful dyes and pesticide residues despite the ban on the use of certain chemicals in the food chain.

Rapid authentication of coffee bean varieties of different forms by using a pocket-sized spectrometer and multivariate data modelling.

Coffee is the most consumed beverage and the second most valuable traded commodity in the world. In this current study, a pocket-sized spectrometer and multivariate analysis were used for rapid authentication of coffee varieties (Arabica and Robusta) in three states to check mislabelling (food fraud). Two main coffee varieties were collected from different locations in Africa. The samples were scanned in the 740-1070 nm wavelength and the spectral data were pre-treated with several methods: mean centering (MC), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD) and standard normal variate (SNV) independently while partial least squares discriminate analysis (PLS-DA), K-nearest neighbour (KNN) and support vector machine (SVM) were used to comparatively build the prediction models for coffee beans (raw, roasted and powdered). The performances of the models were evaluated by using accuracy and efficiency. Among the classification methods developed, the best results were obtained for the following: raw coffee bean SD-SVM had an accuracy of 0.92 and efficiency of 0.82. For roasted coffee beans, SD-KNN had an accuracy of 0.92 and efficiency of 0.87, while for roasted powdered coffee, FD-KNN showed an accuracy of 0.97 and efficiency of 0.97. These finding reveals that for a more accurate differentiation of coffee beans, the roasted powder offers the best results. The obtained results showed that a pocket-sized spectrometer coupled with chemometrics could be employed to provide accurate and rapid authentication of different categories of coffee bean varieties.

A feasibility study on the use of a pocket-sized NIR spectrometer and multivariate algorithm to distinguish expired drugs from unexpired ones.

An onsite technique for determining drug integrity in sub-Saharan Africa is needed to ensure drug integrity and enhance public health. This current study presents the application of handheld NIR spectroscopic and multivariate techniques for the accurate identification of unexpired drugs from expired ones. A total of 150 drugs comprising 75 drug samples each of antimalarial (40 unexpired and 35 expired) and antibiotics (40 unexpired and 35 expired) were used in the study. Principal component (PC) analysis was used to extract relevant information from the spectral fingerprint and pre-processed using different techniques comparatively to observe the best cluster trends. The performance of three multivariate algorithms: RF, SVM, and PLS-DA were compared after optimization by cross-validation. The results revealed that SVM and PLS-DA were superior with an identification rate for both antimalarial and antibiotic authenticity prediction above 98% at 5 PCs in both the prediction set and calibration set. For simultaneous prediction of expired and unexpired drugs, we achieved a 100% identification rate. Generally, the results show that handheld NIR spectrometers coupled with smartphone devices could successfully be used to identify unexpired antimalarial and antibiotic drugs from expired antimalarial and antibiotic drugs for effective quality assurance in poor-resource countries. This offers positive feasibility for an affordable and user-friendly approach to reducing drug fraud in Africa.

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.

Microbial contamination in palm oil selected from markets in major cities of Ghana.

The study assessed the microbiological contamination of palm oil sold in the major cities of Ghana's oil-producing regions. Seventy samples (10 samples from each region) were randomly collected in sterile bottles and transported aseptically to the laboratory for analysis. AOAC standard methods and procedures were used to isolate and identify bacteria and fungi based on their cultural, morphological, and biochemical characteristics. The results were analysed using One-Way ANOVA with 5% significance level, using GraphPad Prism, version 5.0 for windows, and the results presented in graph and tables. The quality of oils was moderately good with total Coliform counts of 2.0×101 ± 6.03 CFU/g and 1.72×103 ± 6.66 CFU/g. Microbial counts from the selected regions were statistically different at P < 0.05. Findings established the absence of yeast and moulds in the oils in addition to extremely pathogenic Coliforms such as Salmonella and Shigella species. Staphylococcus aureus, Staphylococcus epidermidis, Escherichia coli, and Pseudomonas aeruginosa were highlighted as dominant coliforms found in the oils after the assay. The overall findings suggest that the oil from the Greater Accra region was of best quality and safest for consumption. Oil samples from the Central and Ashanti regions were of relatively poor quality recording the highest dominant coliforms. Nonetheless, the presence of the isolated potentially harmful microorganisms in the palm oil samples points to hygienic issues and poses a relative health hazard to consumers.

Food Fingerprinting: Using a Two-Tiered approach to Monitor and Mitigate Food Fraud in Rice.

BACKGROUND: Rice is an important staple food that is consumed around the world. Like many foods, the price of rice varies considerably, from very inexpensive for a low-quality product to premium pricing for highly prized varieties from specific locations. Therefore, like other foods it is vulnerable to economically motivated adulteration through substitution or misrepresentation of inferior-quality rice for more expensive varieties. OBJECTIVE: In this article we describe results of a research project focused on addressing potential food fraud issues related to rice supplies in China, India, Vietnam, and Ghana. Rice fraud manifests differently in each country; therefore, tailored solutions were required. METHOD: Here we describe a two-tiered testing regime of rapid screening using portable Near Infrared technology supported by second tier testing using mass spectrometry-based analysis of suspicious samples. RESULTS: Portable Near Infrared spectroscopy models and laboratory-based Gas Chromatography-Mass Spectrometry methods were developed to differentiate between: high-value Basmati rice varieties and their potential adulterants; six Geographic Indicated protected rice varieties from specific regions within China; various qualities of rice in Ghana and Vietnam; and locally produced and imported rice in Ghana. Furthermore, an Inductively Coupled Plasma-Mass Spectrometry method was developed to support the Chinese rice varieties methods as well as a Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry method for quality differentiation in Vietnam. CONCLUSIONS/HIGHLIGHTS: This two-tier approach can provide a substantially increased level of testing through rapid screening outside of the laboratory with the reassurance of corroborating mass spectrometry-based laboratory analysis to support decision making.

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.

Delivering the Nutritional Needs by Food to Food Fortification of Staples Using Underutilized Plant Species in Africa.

Sub-Saharan Africa (SSA) is among the poorest region in the world, and undernourishment continues to be a great challenge although this region is endowed with a lot of underutilized plant species (UUPS), which are rich in nutrients, especially micronutrients that are unavailable in staple foods. The potential for fortifying major staple foods with UUPS could be the remedy. This study seeks to provide an overview of the fortification of staple foods with UUPS in Africa and suggest the way forward for effective nutritional and health benefits. The review revealed that fortification of major staple foods has been investigated: maize with grain amaranth, soybean, and moringa; sweet potato with cowpea, sorghum, bambara groundnut, peanut, and moringa; cassava with African yam bean, breadfruit, pigeon pea, bambara groundnut, moringa, and cowpea; and sorghum with pearl millet and green peas. The others were yam with cowpea, plantain, and moringa, while rice was also fortified with baobab pulp and locust pulp. All these studies were found to be acceptable with dense nutritional properties. Specifically, micronutrients such as magnesium, phosphorous, zinc, potassium, and iron were increased while others showed rise in fibre and protein levels. The fortification of staple foods with UUPS has been shown to be promising; however, more designed feeding trials are required to verify the impact on reducing undernutrition and hidden hunger. To do this, it is recommended that rice fortified with UUPS should be targeted as rice is increasingly becoming the leading and important staple food in Africa.

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.

Rapid and nondestructive fraud detection of palm oil adulteration with Sudan dyes using portable NIR spectroscopic techniques.

Non-destructive, simple and fast techniques for identifying authentic palm oil and those adulterated with Sudan dyes using portable NIR spectroscopy would be very beneficial to West Africa countries and the world at large. In this study, a portable NIR spectroscopy coupled with multivariate models were developed for detecting palm oil adulteration. A total of 520 samples of palm oil were used comprising; 40 authentic samples together with 480 adulterated samples containing Sudan dyes (I, II, III, IV of 120 samples each). Multiplicative scatter correction (MSC) preprocessing technique plus Principal component analysis (PCA) was used to extract relevant spectral information which gave visible cluster trends for authentic samples and adulterated ones. The performance of Linear discriminant analysis (LDA) and Support vector machine (SVM) were compared, and SVM showed superiority over LDA. The optimised results by cross-validation revealed that MSC-PCA + SVM gave an identification rate above 95% for both calibration and prediction sets. The overall results show that portable NIR spectroscopy together with MSC-PCA + SVM model could be used successfully to identify authentic palm oils from adulterated ones. This would be useful for quality control officers and consumers to manage and control Sudan dyes adulteration in red palm oil.

Estimating cocoa bean parameters by FT-NIRS and chemometrics analysis.

Rapid analysis of cocoa beans is an important activity for quality assurance and control investigations. In this study, Fourier transform near infrared spectroscopy (FT-NIRS) and chemometric techniques were attempted to estimate cocoa bean quality categories, pH and fermentation index (FI). The performances of the models were optimised by cross-validation and examined by identification rate (%), correlation coefficient (Rpre) and root mean square error of prediction (RMSEP) in the prediction set. The optimal identification model by back propagation artificial neural network (BPANN) was 99.73% at 5 principal components. The efficient variable selection model derived by synergy interval back propagation artificial neural network regression (Si-BPANNR) was superior for pH and FI estimation. Si-BPANNR model for pH was Rpre=0.98 and RMSEP=0.06, while for FI was Rpre=0.98 and RMSEP=0.05. The results demonstrated that FT-NIRS together with BPANN and Si-BPANNR model could successfully be used for cocoa beans examination.

Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification.

Quick, accurate and reliable technique for discrimination of cocoa beans according to geographical origin is essential for quality control and traceability management. This current study presents the application of Near Infrared Spectroscopy technique and multivariate classification for the differentiation of Ghana cocoa beans. A total of 194 cocoa bean samples from seven cocoa growing regions were used. Principal component analysis (PCA) was used to extract relevant information from the spectral data and this gave visible cluster trends. The performance of four multivariate classification methods: Linear discriminant analysis (LDA), K-nearest neighbors (KNN), Back propagation artificial neural network (BPANN) and Support vector machine (SVM) were compared. The performances of the models were optimized by cross validation. The results revealed that; SVM model was superior to all the mathematical methods with a discrimination rate of 100% in both the training and prediction set after preprocessing with Mean centering (MC). BPANN had a discrimination rate of 99.23% for the training set and 96.88% for prediction set. While LDA model had 96.15% and 90.63% for the training and prediction sets respectively. KNN model had 75.01% for the training set and 72.31% for prediction set. The non-linear classification methods used were superior to the linear ones. Generally, the results revealed that NIR Spectroscopy coupled with SVM model could be used successfully to discriminate cocoa beans according to their geographical origins for effective quality assurance.