Determination of the geographical origin of green coffee beans using NIR spectroscopy and multivariate data analysis

Geographical origin differentiation of Philippine Robusta coffee (C. canephora) using X-ray fluorescence-based elemental profiling with chemometrics and machine learning

The increasing demand for authenticity and traceability in high-value crops underscores the need for reliable methods to verify the geographical origin of single-origin coffee and prevent fraud. This study explores a rapid and cost-effective approach utilizing Energy-Dispersive X-ray Fluorescence (EDXRF) elemental profiling combined with chemometrics and machine learning techniques. The concentrations of ten elements (K, P, Ca, S, Cl, Fe, Cu, Mn, Sr, Zn) were analyzed in 43 green Robusta coffee samples from four Philippine provinces to assess origin differentiation. Principal Component Analysis (PCA) revealed distinct clustering patterns, while Linear Discriminant Analysis (LDA) achieved 79 % classification accuracy. Random Forest (RF) improved accuracy to 84 %, highlighting its potential for geographical classification. This study serves as a proof of concept for employing XRF-based elemental profiling to differentiate Robusta coffee by origin, providing baseline data to support the development of authenticity and traceability systems within the Philippine coffee industry.

Spectral Differentiation of Esophageal Precancerous Lesion Staging and an Improved Feature Wavelength Selection Method Based on Enhanced Fox Algorithm

Near-infrared (NIR) spectroscopy, known for its non-destructive, rapid, and precise nature, captures spectral responses to chemical bond changes in cancerous tissues. This provides a promising approach for accurate cancer staging and identifying spectral differences between cancerous and healthy tissues. In this study, NIR data from esophageal lesions excised via endoscopic submucosal dissection were analyzed using partial least squares discriminant analysis (PLS-DA) to classify normal tissues, low-grade, and high-grade intraepithelial neoplasia, confirming its feasibility for staging diagnosis. To enhance wavelength selection, the FOX algorithm, a swarm intelligence optimization method, is improved with two modifications: a nonlinear time-varying sigmoid transfer function and mirror selection. These enhancements are combined to form an improved FOX algorithm (iFOX) for wavelength selection. iFOX effectively enhances the algorithm's stability while enhancing classification performance.

Factors influencing metabolite profiles in global Arabica green coffee beans: Impact of continent, altitude, post-harvest processing, and variety

The metabolites of green coffee beans can be influenced by various factors, including species, variety, geographical origin, and post-harvest processing methods. However, previous studies often focused on limited factors separately and were not comprehensive in scope, utilizing only green coffee beans from a restricted area. To fill the gap, we simultaneously analyzed 176 global green coffee beans (C. arabica) from various continents, altitudes, post-harvest processing methods, and varieties to comprehensively investigate the primary factors influencing coffee quality, using metabolomics approach with GC-MS, and machine learning analysis. Partial least squares-discriminant analysis (PLS-DA) revealed that coffee bean characteristics were differently affected by each factor, highlighting 56 key metabolites that varied by each factor, while simultaneously identifying metabolites associated with sub-level variables within each factor. According to the F1 score of the Random Forest model (continent: 91.5 %, altitude: 74.2 %, processing method: 81.4 %, variety: 64.7 %), the continent had the greatest effect on coffee metabolite profiles, followed by the post-harvest processing, altitude, and variety. Additionally, comprehensive heatmap visualizations, incorporating the four factors, are presented, which can be utilized as valuable information for manufacturing customized coffee beans aligned with consumer preferences. These findings provide comprehensive insights into the association between various factors affecting coffee quality and coffee metabolites.

Comparative phytochemical profiling and authentication of four Artemisia species using integrated GC-MS, HPTLC and NIR spectroscopy approach

Genus Artemisia has diverse phytochemistry and a long history in traditional medicine with several species still having unexplored potential. Hence, comparative profiling of Artemisia species in Egypt (A. annua, A. herba-alba, A. monosperma and A. judaica) and their authentication is of great interest. An integrated approach of GC-MS, HPTLC-image analysis and near-infrared (NIR) spectroscopy was implemented for their fingerprinting, discrimination and authentication. GC-MS analysis revealed the phytochemical profile of their volatile oils identifying compounds spanning monoterpenes, sesquiterpenes, diterpenes and non-terpenoid compounds. The major chemical components were highlighted as camphor, β-caryophyllene and germacrene D in A. annua, camphene, cis-pinocarveol, trans-chrysanthenyl acetate and cis-chrysanthenyl acetate in A. herba-alba, α-pinene, β-pinene, α-terpinolene and (-)-spathulenol in A. monosperma, finally, camphor, piperitone and trans-ethyl cinnamate in A. judaica. HPTLC-image analysis allowed tracking chemical markers in their total alcoholic extracts. Artemisinin was detected only in A. annua while scopoletin was identified as a major characteristic coumarin in Artemisia species. Phenolic acids and flavonoids were also discovered in the different species. Finally, NIR spectroscopy allowed profiling and authentication of their powders revealing prominent spectral characteristics correlated to the chemical markers identified by GC-MS and HPTLC. Then, multivariate analysis facilitated classification and discrimination of the species. Additionally, PLS regression analysis was utilized for quality control of powdered A. annua, being an important industrial crop, by detecting its adulteration with other species in limits of detection less than 1.5%. This combined approach aided in the rapid comparative profiling of the Artemisia species as a mean for their fingerprinting and authentication.

Competing technologies: determining the geographical origin of strawberries (Fragaria × ananassa) using laboratory based near-infrared spectroscopy compared to a simple portable device

The application and development of fast and simple screening methods for the authentication of foods has increased continuously in recent years. A widely used analytical technique is Fourier transform near-infrared spectroscopy (FT-NIR). Despite the simple application of FT-NIR analysis, the analyses are usually carried out on benchtop devices in the laboratory. However small, inexpensive and mobile NIR devices could be used on-site. Despite the simple use of FT-NIR analysis, the examinations are usually carried out on a stationary benchtop device in a laboratory. However, in order to be able to perform the application directly on site, the application of small, cost-effective and mobile NIR devices for food analysis is crucial. In this study, both, a benchtop NIR instrument and a handheld NIR device with a lower resolution and analyzed wavenumber range were applied for the differentiation of strawberries from different geographical origins. Distinguishing German and non-German strawberries using linear discriminant analysis (LDA) yielded an accuracy of 91.9% and 84.0% using the benchtop and the handheld devices, respectively. Relevant variables could be assigned to lipids, carbohydrates and proteins. Overall, our study demonstrated for the first time that analyzing the geographical origin of strawberries using NIR spectroscopy is also possible by means of a handheld device.

The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades

During food quality control, NIR technology enables the rapid and non-destructive determination of the typical quality characteristics of food categories, their origin, and the detection of potential counterfeits. Over the past 20 years, the NIR results for a variety of food groups-including meat and meat products, milk and milk products, baked goods, pasta, honey, vegetables, fruits, and luxury items like coffee, tea, and chocolate-have been compiled. This review aims to give a broad overview of the NIRS processes that have been used thus far to assist researchers employing non-destructive techniques in comparing their findings with earlier data and determining new research directions.

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.

Preparation of Double-Layer Composite Coffee Filtration Nonwovens

The coffee industry is developing rapidly in the world, and the use of coffee filtration nonwovens (CFNs) is becoming more and more extensive; however, there is a lack of standards and research for its production and trade, and the quality of related products on the market is uneven at present. Here, eight double-layer composite coffee filtration nonwovens (D-LCCFNs) were prepared by using 5 g/m2 and 10 g/m2 polypropylene (PP) melt-blown nonwovens (MNs), 20 g/m2 PP spunbonded nonwovens and 20 g/m2 viscose/ES fiber chemically bonded nonwovens, and the physical properties, morphology and the filtration effect of coffee and purified water for the prepared samples were tested. It was found that the surface density of the microfiber layer (MNs) in the D-LCCFNs was negatively correlated with the coffee filtration rate; when the microfiber layer in the D-LCCFNs was in direct contact with the coffee, the liquid started to drip later, and the filtration rate of the coffee was slower; the filtration rate of the samples with the viscose/ES chemically bonded nonwovens was very fast. However, the samples without viscose/ES fibers basically did not filter pure water much, but they could filter out the coffee liquid normally, and the samples' hydrophilicity increased significantly after filtering coffee.

Identifying the origin of Yemeni green coffee beans using near infrared spectroscopy: a promising tool for traceability and sustainability

Yemeni smallholder coffee farmers face several challenges, including the ongoing civil conflict, limited rainfall levels for irrigation, and a lack of post-harvest processing infrastructure. Decades of political instability have affected the quality, accessibility, and reputation of Yemeni coffee beans. Despite these challenges, Yemeni coffee is highly valued for its unique flavor profile and is considered one of the most valuable coffees in the world. Due to its exclusive nature and perceived value, it is also a prime target for food fraud and adulteration. This is the first study to identify the potential of Near Infrared Spectroscopy and chemometrics-more specifically, the discriminant analysis (PCA-LDA)-as a promising, fast, and cost-effective tool for the traceability of Yemeni coffee and sustainability of the Yemeni coffee sector. The NIR spectral signatures of whole green coffee beans from Yemeni regions (n = 124; Al Mahwit, Dhamar, Ibb, Sa'dah, and Sana'a) and other origins (n = 97) were discriminated with accuracy, sensitivity, and specificity ≥ 98% using PCA-LDA models. These results show that the chemical composition of green coffee and other factors captured on the spectral signatures can influence the discrimination of the geographical origin, a crucial component of coffee valuation in the international markets.

Effects of geographical origin and post-harvesting processing on the bioactive compounds and sensory quality of Brazilian specialty coffee beans

Specialty coffee beans are those produced, processed, and characterized following the highest quality standards, toward delivering a superior final product. Environmental, climatic, genetic, and processing factors greatly influence the green beans' chemical profile, which reflects on the quality and pricing. The present study focuses on the assessment of eight major health-beneficial bioactive compounds in green coffee beans aiming to underscore the influence of the geographical origin and post-harvesting processing on the quality of the final beverage. For that, we examined the non-volatile chemical profile of specialty Coffea arabica beans from Minas Gerais state, Brazil. It included samples from Cerrado (Savannah), and Matas de Minas and Sul de Minas (Atlantic Forest) regions, produced by two post-harvesting processing practices. Trigonelline, theobromine, theophylline, chlorogenic acid derivatives, caffeine, caffeic acid, ferulic acid, and p-coumaric acid were quantified in the green beans by high-performance liquid chromatography with diode array detection. Additionally, all samples were roasted and subjected to sensory analysis for coffee grading. Principal component analysis suggested that Cerrado samples tended to set apart from the other geographical locations. Those samples also exhibited higher levels of trigonelline as confirmed by two-way ANOVA analysis. Samples subjected to de-pulping processing showed improved chemical composition and sensory score. Those pulped coffees displayed 5.8% more chlorogenic acid derivatives, with an enhancement of 1.5% in the sensory score compared to unprocessed counterparts. Multivariate logistic regression analysis pointed out altitude, ferulic acid, p-coumaric acid, sweetness, and acidity as predictors distinguishing specialty coffee beans obtained by the two post-harvest processing. These findings demonstrate the influence of regional growth conditions and post-harvest treatments on the chemical and sensory quality of coffee. In summary, the present study underscores the value of integrating target metabolite analysis with statistical tools to augment the characterization of specialty coffee beans, offering novel insights for quality assessment with a focus on their bioactive compounds.

Untargeted metabolomics in Anectocillus roxburghii with habitat heterogeneity and the key abiotic factors affecting its active ingredients

Introduction

Anoectochilus roxburghii is a rare, endangered herb with diverse pharmacological properties. Understanding the main metabolite types and characteristics of wild A. roxburghii is important for efficiently utilizing resources and examining quality according to origin.

Methods

Samples were collected from the main production areas across five regions in Fujian Province, China. An untargeted metabolomics analysis was performed on the entire plants to explore their metabolic profiles. We utilized UPLC-MS/MS to specifically quantify eight targeted flavonoids in these samples. Subsequently, correlation analysis was conducted to investigate the relationships between the flavonoids content and both the biological characteristics and geographical features.

Results

A comprehensive analysis identified a total of 3,170 differential metabolites, with terpenoids and flavonoids being the most prevalent classes. A region-specific metabolite analysis revealed that the Yongchun (YC) region showed the highest diversity of unique metabolites, including tangeretin and oleanolic acid. Conversely, the Youxi (YX) region was found to have the smallest number of unique metabolites, with only one distinct compound identified. Further investigation through KEGG pathway enrichment analysis highlighted a significant enrichment in pathways related to flavonoid biosynthesis. Further examination of the flavonoid category showed that flavonols were the most differentially abundant. We quantified eight specific flavonoids, finding that, on average, the YX region exhibited higher levels of these compounds. Correlation analysis highlighted a significant association between flavonoids and habitat, especially temperature and humidity.

Discussion

Untargeted metabolomics via LC-MS was suitable for identifying region-specific metabolites and their influence via habitat heterogeneity. The results of this study serve as a new theoretical reference for unique markers exclusively present in a specific sample group.

The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee

This study aimed to establish a rapid and practical method for monitoring and predicting volatile compounds during coffee roasting using near-infrared (NIR) spectroscopy coupled with chemometrics. Washed Arabica coffee beans from Ethiopia and Congo were roasted to industry-validated light, medium, and dark degrees. Concurrent analysis of the samples was performed using gas chromatography-mass spectrometry (GC-MS) and NIR spectroscopy, generating datasets for partial least squares (PLS) regression analysis. The results showed that NIR spectroscopy successfully differentiated the differently roasted samples, similar to the discrimination achieved by GC-MS. This finding highlights the potential of NIR spectroscopy as a rapid tool for monitoring and standardizing the degree of coffee roasting in the industry. A PLS regression model was developed using Ethiopian samples to explore the feasibility of NIR spectroscopy to indirectly measure the volatiles that are important in classifying the roast degree. For PLSR, the data underwent autoscaling as a preprocessing step, and the optimal number of latent variables (LVs) was determined through cross-validation, utilizing the root mean squared error (RMSE). The model was further validated using Congo samples and successfully predicted (with R2 values > 0.75 and low error) over 20 volatile compounds, including furans, ketones, phenols, and pyridines. Overall, this study demonstrates the potential of NIR spectroscopy as a practical and rapid method to complement current techniques for monitoring and predicting volatile compounds during the coffee roasting process.

Evaluation of spontaneous fermentation impact on the physicochemical properties and sensory profile of green and roasted arabica coffee by digital technologies

There is a growing demand for specialty coffee with more pleasant and uniform sensory perception. Wet fermentation could modulate and confer additional aroma notes to final roasted coffee brew. This study aimed to assess differences in volatile compounds and the intensities of sensory descriptors between unfermented and spontaneously fermented coffee using digital technologies. Fermented (F) and unfermented (UF) coffee samples, harvested from two Australia local farms Mountain Top Estate (T) and Kahawa Estate (K), with four roasting levels (green, light-, medium-, and dark-) were analysed using near-infrared spectrometry (NIR), and a low-cost electronic nose (e-nose) along with some ground truth measurements such as headspace/gas chromatography-mass spectrometry (HS-SPME-GC-MS), and quantitative descriptive analysis (QDA ®). Regression machine learning (ML) modelling based on artificial neural networks (ANN) was conducted to predict volatile aromatic compounds and intensity of sensory descriptors using NIR and e-nose data as inputs. Green fermented coffee had significant perception of hay aroma and flavor. Roasted fermented coffee had higher intensities of coffee liquid color, crema height and color, aftertaste, aroma and flavor of dark chocolate and roasted, and butter flavor (p < 0.05). According to GC-MS detection, volatile aromatic compounds, including methylpyrazine, 2-ethyl-5-methylpyrazine, and 2-ethyl-6-methylpyrazine, were observed to discriminate fermented and unfermented roasted coffee. The four ML models developed using the NIR absorbance values and e-nose measurements as inputs were highly accurate in predicting (i) the peak area of volatile aromatic compounds (Model 1, R = 0.98; Model 3, R = 0.87) and (ii) intensities of sensory descriptors (Model 2 and Model 4; R = 0.91), respectively. The proposed efficient, reliable, and affordable method may potentially be used in the coffee industry and smallholders in the differentiation and development of specialty coffee, as well as in process monitoring and sensory quality assurance.

The Authentication of Gayo Arabica Green Coffee Beans with Different Cherry Processing Methods Using Portable LED-Based Fluorescence Spectroscopy and Chemometrics Analysis

Aceh is an important region for the production of high-quality Gayo arabica coffee in Indonesia. In this area, several coffee cherry processing methods are well implemented including the honey process (HP), wine process (WP), and natural process (NP). The most significant difference between the three coffee cherry processing methods is the fermentation process: HP is a process of pulped coffee bean fermentation, WP is coffee cherry fermentation, and NP is no fermentation. It is well known that the WP green coffee beans are better in quality and are sold at higher prices compared with the HP and NP green coffee beans. In this present study, we evaluated the utilization of fluorescence information to discriminate Gayo arabica green coffee beans from different cherry processing methods using portable fluorescence spectroscopy and chemometrics analysis. A total of 300 samples were used (n = 100 for HP, WP, and NP, respectively). Each sample consisted of three selected non-defective green coffee beans. Fluorescence spectral data from 348.5 nm to 866.5 nm were obtained by exciting the intact green coffee beans using a portable spectrometer equipped with four 365 nm LED lamps. The result showed that the fermented green coffee beans (HP and WP) were closely mapped and mostly clustered on the left side of PC1, with negative scores. The non-fermented (NP) green coffee beans were clustered mostly on the right of PC1 with positive scores. The results of the classification using partial least squares-discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and principal component analysis-linear discriminant analysis (PCA-LDA) are acceptable, with an accuracy of more than 80% reported. The highest accuracy of prediction of 96.67% was obtained by using the PCA-LDA model. Our recent results show the potential application of portable fluorescence spectroscopy using LED lamps to classify and authenticate the Gayo arabica green coffee beans according to their different cherry processing methods. This innovative method is more affordable and could be easy to implement (in terms of both affordability and practicability) in the coffee industry in Indonesia.

An integrated chemical characterization based on FT-NIR, and GC-MS for the comparative metabolite profiling of 3 species of the genus Amomum

BACKGROUND

The fruits and seeds of genus Amomum are well-known as medicinal plants and edible spices, and are used in countries such as China, India and Vietnam to treat malaria, gastrointestinal disorders and indigestion. The morphological differences between different species are relatively small, and technical characterization and identification techniques are needed.

RESULTS

Fourier transform near infrared spectroscopy (FT-NIR) and gas chromatography-mass spectrometry (GC-MS), combined with principal component analysis and two-dimensional correlation analysis were used to characterize the chemical differences of Amomum tsao-ko, Amomum koenigii, and Amomum paratsaoko. The targets and pathways for the treatment of diabetes mellitus in three species were predicted using network pharmacology and screened for the corresponding pharmacodynamic components as potential quality markers. The results of "component-target-pathway" network showed that (+)-Nerolidol, 2-Nonanol, α-Terpineol, α-Pinene, 2-Nonanone had high degree values and may be the main active components. Partial least squares-discriminant analysis (PLS-DA) was further used to select for differential metabolites and was identified as a potential quality marker, 11 in total. PLS-DA and residual network (ResNet) classification models were developed for the identification of 3 species of the genus Amomum, ResNet model is more suitable for the identification study of large volume samples.

SIGNIFICANCE

This study characterizes the differences between the three species in a visual way and also provides a reliable technique for their identification, while demonstrating the ability of FT-NIR spectroscopy for fast, easy and accurate species identification. The results of this study lay the foundation for quality evaluation studies of genus Amomum and provide new ideas for the development of new drugs for the treatment of diabetes mellitus.

A Geographical Origin Classification of Durian (cv. Monthong) Using Near-Infrared Diffuse Reflectance Spectroscopy

The objective of this research was to classify the geographical origin of durians (cv. Monthong) based on geographical identification (GI) and regions (R) using near infrared (NIR). The samples were scanned with an FT-NIR spectrometer (12,500 to 4000 cm-1). The NIR absorbance differences among samples that were collected from different parts of the fruit, including intact peel with thorns (I-form), cut-thorn peel (C-form), stem (S-form), and the applied synthetic minority over-sampling technique (SMOTE), were also investigated. Models were developed across several classification algorithms by the classification learner app in MATLAB. The models were optimized using a featured wavenumber selected by a genetic algorithm (GA). An effective model based on GI was developed using SMOTE-I-spectra with a neural network; accuracy was provided as 95.60% and 95.00% in cross-validation and training sets. The test model was provided with a testing set value of %accuracy, and 94.70% by the testing set was obtained. Likewise, the model based on the regions was developed from SMOTE-ICS-form spectra, with the ensemble classifier showing the best result. The best result, 88.00FF% accuracy by cross validation, 86.50% by training set, and 64.90% by testing set, indicates the classification model of East (E-region), Northeast (NE-region), and South (S-region) regions could be applied for rough screening. In summary, NIR spectroscopy could be used as a rapid and nondestructive method for the accurate GI classification of durians.

Authentication and discrimination of new Brazilian Canephora coffees with geographical indication using a miniaturized near-infrared spectrometer

New Brazilian Canephora coffees (Conilon and Robusta) of high added value from specific origins have been protected by geographical indication to guarantee their origin and quality. Recently, benchtop near-infrared (NIR) spectroscopy combined with chemometrics has demonstrated its usefulness to discriminate them. It was the first study, however, and therefore the possibility exists to develop a new portable NIR method for this purpose. This work assessed a miniaturized NIR as a cheaper spectrometer to discriminate and authenticate new Brazilian Canephora coffees with certified geographical origins and to differentiate them from specialty Arabica. Discriminant chemometric and class modeling techniques have been applied and have obtained good predictive ability on external test sets. In addition, models with similar classification purpose were compared with those obtained in previous research carried out with benchtop NIR for the same samples, obtaining comparable results. In this context, the portable method was used as a laboratory technique and has the advantage of being cheaper than benchtop NIR spectrometer. Furthermore, it brings a high possibility to be implemented in small coffee cooperatives, industries or control agencies in the future that do not have high economic resources.

Cationic Conjugated Polymer Fluorescence Resonance Energy Transfer for DNA Methylation Assessment to Discriminate the Geographical Origins of Lonicerae japonicae flos

The flavor and taste of Lonicerae japonicae flos (LJF) products are heavily influenced by geographical origin. Tracing the geographical origin is an important aspect of LJF quality assessment. Here, DNA methylation analysis coupled with chemometrics revealed that, in 10 CpG islands upstream of genes in the chlorogenic acid and iridoid biosynthetic pathways, DNA methylation differences appear close association with LJF geographical origin. DNA methylation status in these CpG islands was determined using the cationic conjugated polymer fluorescence resonance energy transfer method. As a result, LJFs from 39 geographical origins were classified into four groups corresponding to Northern China, Central Plain of China, Southeast China, and Western China, according to cluster analysis and principal component analysis. Our findings contribute to an understanding of the modulation of LJF taste and can assist in understanding how DNA methylation in LJF varies with geographical origin.

Rapid Determination of Nutmeg Shell Content in Ground Nutmeg Using FT-NIR Spectroscopy and Machine Learning

Nutmeg is a popular spice often used in ground form, which makes it highly susceptible to food fraud. Therefore, the aim of the present study was to detect adulteration of ground nutmeg with nutmeg shell via Fourier transform near-infrared (FT-NIR) spectroscopy. For this purpose, 36 authentic nutmeg samples and 10 nutmeg shell samples were analyzed pure and in mixtures with up to 50% shell content. The spectra plot as well as a principal component analysis showed a clear separation trend as a function of shell content. A support vector machine regression used for shell content prediction achieved an R2 of 0.944 in the range of 0-10%. The limit of detection of the prediction model was estimated to be 1.5% nutmeg shell. Based on random sub-sampling, the likelihood was found to be 2% that a pure nutmeg sample is predicted with a nutmeg shell content of >1%. The results confirm the suitability of FT-NIR spectroscopy for rapid detection and quantitation of the shell content in ground nutmeg.

Chemical analysis and NIR spectroscopy in the determination of the origin, variety and roast time of Mexican coffee

Coffee is a product whose quality and price are associated with its geographical, genetic and processing origin; therefore, the development of analytical techniques to authenticate the above mentioned is important to avoid adulteration. The objective of this study was to compare conventional analytical methods with NIR technology for the authentication of roasted and ground coffee samples from different producing regions in Mexico (origins) and different varieties. A second objective was to determine, under the same processing conditions, if roasting times can be differentiated by using this technology. A total of 120 samples of roasted and ground commercial coffee were obtained from the states of Chiapas, Oaxaca, Tabasco and Veracruz in Mexico, 30 locally available samples per state. Samples from Veracruz included three different varieties, grown on the same farm and processed under the same conditions. One of these varieties was selected to evaluate the chemical composition of samples roasted at 185 °C using four different roasting times (15, 17, 19 and 21 min). Samples from different producing regions showed significant differences (P < 0.05) in fat content (from 7.45 ± 0.42% in Tabasco to 18.40 ± 2.95% in Chiapas), which was associated with the altitude of coffee plantations (Pearson's r = 0.96). The results indicate that NIR technology generates sufficient useful information to authenticate roasted and ground coffee from different geographical origins in Mexico and different varieties from the same coffee plantation, with similar results to those obtained by conventional analytical methods.

Evaluation of Multivariate Filters on Vibrational Spectroscopic Fingerprints for the PLS-DA and SIMCA Classification of Argan Oils from Four Moroccan Regions

This study aimed to develop an analytical method to determine the geographical origin of Moroccan Argan oil through near-infrared (NIR) or mid-infrared (MIR) spectroscopic fingerprints. However, the classification may be problematic due to the spectral similarity of the components in the samples. Therefore, unsupervised and supervised classification methods-including principal component analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA)-were evaluated to distinguish between Argan oils from four regions. The spectra of 93 samples were acquired and preprocessed using both standard preprocessing methods and multivariate filters, such as External Parameter Orthogonalization, Generalized Least Squares Weighting and Orthogonal Signal Correction, to improve the models. Their accuracy, precision, sensitivity, and selectivity were used to evaluate the performance of the models. SIMCA and PLS-DA models generated after standard preprocessing failed to correctly classify all samples. However, successful models were produced after using multivariate filters. The NIR and MIR classification models show an equivalent accuracy. The PLS-DA models outperformed the SIMCA with 100% accuracy, specificity, sensitivity and precision. In conclusion, the studied multivariate filters are applicable on the spectroscopic fingerprints to geographically identify the Argan oils in routine monitoring, significantly reducing analysis costs and time.

Near-infrared reflectance spectroscopy accurately predicted isotope and elemental compositions for origin traceability of coffee

Stable isotope ratios and trace elements are well-established tools that act as signatures of the product's environmental conditions and agricultural processes; but they involve time, money, and environmentally destructive chemicals. In this study, we tested for the first time the potential of near-infrared reflectance spectroscopy (NIR) to estimate/predict isotope and elemental compositions for the origin verification of coffee. Green coffee samples from two continents, 4 countries, and 10 regions were analysed for five isotope ratios (δ¹³C, δ¹⁵N, δ¹⁸O, δ²H, and δ³⁴S) and 41 trace elements. NIR (1100-2400 nm) calibrations were developed using pre-processing with extended multiplicative scatter correction (EMSC) and mean centering and partial-least squares regression (PLS-R). Five elements (Mn, Mo, Rb, B, La) and three isotope ratios (δ¹³C, δ¹⁸O, δ²H) were moderately to well predicted by NIR (R²: 0.69 to 0.93). NIR indirectly measured these parameters by association with organic compounds in coffee. These parameters were related to altitude, temperature and rainfall differences across countries and regions and were previously found to be origin discriminators for coffee.

Development of PCA-MLP Model Based on Visible and Shortwave Near Infrared Spectroscopy for Authenticating Arabica Coffee Origins

Arabica coffee, one of Indonesia's economically important coffee commodities, is commonly subject to fraud due to mislabeling and adulteration. In many studies, spectroscopic techniques combined with chemometric methods have been massively employed in classification issues, such as principal component analysis (PCA) and discriminant analyses, compared to machine learning models. In this study, spectroscopy combined with PCA and a machine learning algorithm (artificial neural network, ANN) were developed to verify the authenticity of Arabica coffee collected from four geographical origins in Indonesia, including Temanggung, Toraja, Gayo, and Kintamani. Spectra from pure green coffee were collected from Vis-NIR and SWNIR spectrometers. Several preprocessing techniques were also applied to attain precise information from spectroscopic data. First, PCA compressed spectroscopic information and generated new variables called PCs scores, which would become inputs for the ANN model. The discrimination of Arabica coffee from different origins was conducted with a multilayer perceptron (MLP)-based ANN model. The accuracy attained ranged from 90% to 100% in the internal cross-validation, training, and testing sets. The error in the classification process did not exceed 10%. The generalization ability of the MLP combined with PCA was superior, suitable, and successful for verifying the origin of Arabica coffee.

Discrimination between Korean and Chinese Kimchi using inductively coupled plasma-optical emission spectroscopy and mass spectrometry: A multivariate analysis of Kimchi

Kimchi has been designated as one of the world's five healthiest foods, and it is a traditional Korean fermented food. The purpose of this study was to investigate whether the origin of Kimchi can be discriminated against by using inorganic elements to develop a more accurate method. The OPLS-DA showed that the R² and Q² values were 0.908 and 0.81, a high-quality model. We selected 24 elements (¹³³Cs, ²³⁸U, ⁸⁸Sr, K, ¹⁵⁷Gd, Na, Mg, ¹³⁹La, P, ¹⁴¹Pr, ⁷²Ge, ¹⁴⁶Nd, ¹⁴⁷Sm, ¹⁵³Eu, ⁵⁵Mn, ¹⁶⁵Ho, ¹⁶³Dy, ¹⁶⁶Er, Fe, ¹⁷²Yb, ¹⁶⁹Tm, ¹⁸⁵Re, ¹⁷⁵Lu, and ¹¹⁸Sn) with VIP 1 or higher in the OPLS-DA model. In ROC, the selected elements had an accuracy of 100%. The heatmap confirmed the contents of rare earth elements (REEs) in Korean and Chinese Kimchi, the accuracy of distinguishing classification in CDA is 100%. Such results will help to distinguish the origin of agricultural products through inorganic analysis.

Protected Geographical Indication Discrimination of Zhejiang and Non-Zhejiang Ophiopogonis japonicus by Near-Infrared (NIR) Spectroscopy Combined with Chemometrics: The Influence of Different Stoichiometric and Spectrogram Pretreatment Methods

This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples from five major production areas in China were analyzed by NIR spectroscopy, and divided into 2127 from Zhejiang and 1530 from other areas ('non-Zhejiang'). Principal component analysis (PCA) was selected to screen outliers and eliminate them. Monte Carlo cross validation (MCCV) was introduced to divide the training set and test set according to a ratio of 3:7. The raw spectra were preprocessed by nine single and partial combination methods such as the standard normal variable (SNV) and derivative, and then modeled by partial least squares regression (PLSR), a support vector machine (SVM), and soft independent modeling of class analogies (SIMCA). The effects of different pretreatment and chemometrics methods on the model are discussed. The results showed that the three pattern recognition methods were effective in geographical origin tracing, and selecting the appropriate preprocessing method could improve the traceability accuracy. The accuracy of PLSR after the standard normal variable was better, with R² reaching 0.9979, while that of the second derivative was the lowest with an R² of 0.9656. After the SNV pretreatment, the accuracy of the training set and test set of SVM reached the highest values, which were 99.73% and 98.40%, respectively. The accuracy of SIMCA pretreated with SNV and MSC was the highest for the origin traceability of Ophiopogon japonicus, which could reach 100%. The distance between the two classification models of SIMCA-SNV and SIMCA-MSC is greater than 3, indicating that the SIMCA model has good performance.

Monitoring Chemical Changes of Coffee Beans During Roasting Using Real-time NIR Spectroscopy and Chemometrics

Variations occurring in coffee beans during roasting are ascribable to several chemical-physical phenomena: to quickly track the whole process and to ensure its reproducibility, a process analytical technology (PAT) approach is needed.

In this study, a method combining in-line Fourier transform near-infrared (FT-NIR) spectroscopy and chemometric modelling was investigated to get real-time and practical knowledge about the roasting effects on coffee’s chemical-physical composition. In-line spectra were acquired by inserting a NIR probe into a laboratory coffee roaster, running twenty-four roasting experiments, planned spanning different coffee species (Arabica and Robusta), four roasting temperature settings (TS1–TS4) and times (650–1580 s).

Multivariate curve resolution-alternate least squares (MCR-ALS) was used to model the chemical-physical changes occurring during the roasting process, and information about maximum rate, acceleration and deceleration of the process was obtained, also highlighting potential effects due to the different roasting temperatures and coffee varieties.

The proposed approach provides the groundwork for direct real-time implementation of rapid, non-invasive automated monitoring of the roasting process at industrial scale.

Headspace GC/MS and fast GC e-nose combined with chemometric analysis to identify the varieties and geographical origins of ginger (Zingiber officinale Roscoe)

Food authenticity regarding different varieties and geographical origins is increasingly becoming a concern for consumers. In this study, headspace gas chromatography-mass spectrometry (HS-GC-MS) and fast gas chromatography electronic nose (fast GC e-nose) were used to successfully distinguish the varieties and geographical origins of dried gingers from seven major production areas in China. By chemometric analysis, a distinct separation between the two varieties of ginger was achieved based on HS-GC-MS. Furthermore, flavor information extracted by fast GC e-nose realized the discrimination of geographical origins, and some potential flavor components were selected as important factors for origin certification. Moreover, several pattern recognition algorithms were compared in varietal and regional identification, and random forest (RF) led to the highest accuracies for discrimination. Overall, a rapid and precise method combining multivariate chemometrics and algorithms was developed to determine varieties and geographical origins of ginger, and it could also be applied to other agricultural products.

Performance Optimization of a Developed Near-Infrared Spectrometer Using Calibration Transfer with a Variety of Transfer Samples for Geographical Origin Identification of Coffee Beans

This research aimed to improve the classification performance of a developed near-infrared (NIR) spectrometer when applied to the geographical origin identification of coffee bean samples. The modification was based on the utilization of a collection of spectral databases from several different agricultural samples, including corn, red beans, mung beans, black beans, soybeans, green and roasted coffee, adzuki beans, and paddy and white rice. These databases were established using a reference NIR instrument and the piecewise direct standardization (PDS) calibration transfer method. To evaluate the suitability of the transfer samples, the Davies-Bouldin index (DBI) was calculated. The outcomes that resulted in low DBI values were likely to produce better classification rates. The classification of coffee origins was based on the use of a supervised self-organizing map (SSOM). Without the spectral modification, SSOM classification using the developed NIR instrument resulted in predictive ability (% PA), model stability (% MS), and correctly classified instances (% CC) values of 61%, 58%, and 64%, respectively. After the transformation process was completed with the corn, red bean, mung bean, white rice, and green coffee NIR spectral data, the predictive performance of the SSOM models was found to have improved (67-79% CC). The best classification performance was observed with the use of corn, producing improved % PA, % MS, and % CC values at 71%, 67%, and 79%, respectively.

Simultaneously Verifying the Original Region of Green and Roasted Coffee Beans by Stable Isotopes and Elements Combined with Random Forest

Simultaneously verifying the original region of green and roasted coffee beans is very important for protecting legal interests of the stakeholder according to the chemical analyzing method. 131 green coffee bean samples are collected from six different original regions and pretreated with three degrees (green, middle, and dark roasted); five stable isotope ratios (δ13C, δ14N, δ18O, δ2H, and δ32S) and twelve elemental contents (Al, Cr, Ni, Zn, Ba, Cu, Na, Mn, Fe, Ca, K, and Mg) of green, middle, and dark roasted coffee bean samples (131×3) were analyzed. Fractionation of stable isotopes and variation of elemental contents were evaluated, only isotope hydrogen (2H) significantly fractionated, and elemental concentrations increased with a certain rate during the roasting process. One-way analysis of variance (ANOVA) was used to compare the stable isotope ratios and elemental concentrations of all coffee bean samples from six different original regions. Random forest (RF) was employed to build a discriminating model for simultaneously verifying the original regions of green and roasted coffee bean samples; this model provided 100% accuracy. Inclusion of this mathematical model for simultaneously verifying the original region of green and roasted coffee beans had powerful distinguishing capability and which will not be influenced by fractionation of hydrogen (2H) and variation of element contents during the roasted process.

Rapid On-site identification of geographical origin and storage age of tangerine peel by Near-infrared spectroscopy

The feasibility of identifying geographical origin and storage age of tangerine peel was explored by using a handheld near-infrared (NIR) spectrometer combined with machine learning. A handheld NIR spectrometer (900-1700 nm) was used to scan the outer surface of tangerine peel and collect the corresponding NIR diffuse reflectance spectra. Principal component analysis (PCA) combined with Mahalanobis distance were used to detect outliers. The accuracies of all models in the anomaly set were much lower than that in calibration set and test set, indicating that the outliers were effectively identified. After removing the outliers, in order to initially explore the clustering characteristics of tangerine peels, PCA was performed on tangerine peels from different origins and the same origin with different storage ages. The results showed that the tangerine peels from the same origin or the same storage age had the potential to cluster, indicating that the spectral data of the same origin or the same storage age had a certain similarity, which laid the foundation for subsequent modeling and identification. However, there were quite a few samples with different origins or different storage ages overlapped and could not be distinguished from each other. In order to achieve qualitative identification of origin and storage age, Savitzky-Golay convolution smoothing with first derivative (SGFD) and standard normal variate (SNV) were used to preprocess the raw spectra. Random forest (RF), K-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to establish the discriminant model. The results showed that SGFD-LDA could accurately distinguish the origin and storage age of tangerine peel at the same time. The origin identification accuracy was 96.99%. The storage age identification accuracy was 100% for Guangdong tangerine peel and 97.15% for Sichuan tangerine peel. This indicated that the near-infrared spectroscopy (NIRS) combine with machine learning can simultaneously and rapidly identify the origin and storage age of tangerine peel on site.

Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices

Nowadays, coffee, cocoa, and spices have broad applications in the food and pharmaceutical industries due to their organoleptic and nutraceutical properties, which have turned them into products of great commercial demand. Consequently, these products are susceptible to fraud and adulteration, especially those sold at high prices, such as saffron, vanilla, and turmeric. This situation represents a major problem for industries and consumers’ health. Implementing analytical techniques, i.e., Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis, can ensure the authenticity and quality of these products since these provide unique information on food matrices. The present review addresses FT-MIR spectroscopy and multivariate analysis application on coffee, cocoa, and spices authentication and quality control, revealing their potential use and elucidating areas of opportunity for future research.

Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview

Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, 1H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis.

NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends

Coffee is both a vastly consumed beverage and a chemically complex matrix. For a long time, an arduous chemical analysis was necessary to resolve coffee authentication issues. Despite their demonstrated efficacy, such techniques tend to rely on reference methods or resort to elaborate extraction steps. Near infrared spectroscopy (NIRS) and the aquaphotomics approach, on the other hand, reportedly offer a rapid, reliable, and holistic compositional overview of varying analytes but with little focus on low concentration mixtures of Robusta-to-Arabica coffee. Our study aimed for a comparative assessment of ground coffee adulteration using NIRS and liquid coffee adulteration using the aquaphotomics approach. The aim was to demonstrate the potential of monitoring ground and liquid coffee quality as they are commercially the most available coffee forms. Chemometrics spectra analysis proved capable of distinguishing between the studied samples and efficiently estimating the added Robusta concentrations. An accuracy of 100% was obtained for the varietal discrimination of pure Arabica and Robusta, both in ground and liquid form. Robusta-to-Arabica ratio was predicted with R2CV values of 0.99 and 0.9 in ground and liquid form respectively. Aquagrams results accentuated the peculiarities of the two coffee varieties and their respective blends by designating different water conformations depending on the coffee variety and assigning a particular water absorption spectral pattern (WASP) depending on the blending ratio. Marked spectral features attributed to high hydrogen bonded water characterized Arabica-rich coffee, while those with the higher Robusta content showed an abundance of free water structures. Collectively, the obtained results ascertain the adequacy of NIRS and aquaphotomics as promising alternative tools for the authentication of liquid coffee that can correlate the water-related fingerprint to the Robusta-to-Arabica ratio.

The Potential of Spectroscopic Techniques in Coffee Analysis—A Review

This review provides an overview of recent studies on the potential of spectroscopy techniques (mid-infrared, near infrared, Raman, and fluorescence spectroscopy) used in coffee analysis. It specifically covers their applications in coffee roasting supervision, adulterants and defective beans detection, prediction of specialty coffee quality and coffees’ sensory attributes, discrimination of coffee based on variety, species, and geographical origin, and prediction of coffees chemical composition. These are important aspects that significantly affect the overall quality of coffee and consequently its market price and finally quality of the brew. From the reviewed literature, spectroscopic methods could be used to evaluate coffee for different parameters along the production process as evidenced by reported robust prediction models. Nevertheless, some techniques have received little attention including Raman and fluorescence spectroscopy, which should be further studied considering their great potential in providing important information. There is more focus on the use of near infrared spectroscopy; however, few multivariate analysis techniques have been explored. With the growing demand for fast, robust, and accurate analytical methods for coffee quality assessment and its authentication, there are other areas to be studied and the field of coffee spectroscopy provides a vast opportunity for scientific investigation.

Near-Infrared Spectroscopy Applied to the Detection of Multiple Adulterants in Roasted and Ground Arabica Coffee

Roasted coffee has been the target of increasingly complex adulterations. Sensitive, non-destructive, rapid and multicomponent techniques for their detection are sought after. This work proposes the detection of several common adulterants (corn, barley, soybean, rice, coffee husks and robusta coffee) in roasted ground arabica coffee (from different geographic regions), combining near-infrared (NIR) spectroscopy and chemometrics (Principal Component Analysis—PCA). Adulterated samples were composed of one to six adulterants, ranging from 0.25 to 80% (w/w). The results showed that NIR spectroscopy was able to discriminate pure arabica coffee samples from adulterated ones (for all the concentrations tested), including robusta coffees or coffee husks, and independently of being single or multiple adulterations. The identification of the adulterant in the sample was only feasible for single or double adulterations and in concentrations ≥10%. NIR spectroscopy also showed potential for the geographical discrimination of arabica coffees (South and Central America).

Fingerprint Approaches Coupled with Chemometrics to Discriminate Geographic Origin of Imported Salmon in China's Consumer Market

Of the salmon sold in China’s consumer market, 92% was labelled as Norwegian salmon, but was in fact was mainly imported from Chile. The aim of this study was to establish an effective method for discriminating the geographic origin of imported salmon using two fingerprint approaches, Near-infrared (NIR) spectroscopy and mineral element fingerprint (MEF). In total, 80 salmon (40 from Norway and 40 from Chile) were tested, and data generated by NIR and MEF were analysed via various chemometrics. Four spectral preprocessing methods, including vector normalization (VN), Savitzky Golay (SG) smoothing, first derivative (FD) and second derivative (SD), were employed on the raw NIR data, and a partial least squares (PLS) model based on the FD + SG9 pretreatment could successfully differentiate Norwegian salmons from Chilean salmons, with a R² value of 98.5%. Analysis of variance (ANOVA) and multiple comparative analysis were employed on the contents of 16 mineral elements including Pb, Fe, Cu, Zn, Al, Sr, Ni, As, Cr, V, Se, Mn, K, Ca, Na and Mg. The results showed that Fe, Zn, Al, Ni, As, Cr, V, Se, Ca and Na could be used as characteristic elements to discriminate the geographical origin of the imported salmon, and the discrimination rate of the linear discriminant analysis (LDA) model, trained on the above 10 elements, could reach up to 98.8%. The results demonstrate that both NIR and MEF could be effective tools for the rapid discrimination of geographic origin of imported salmon in China’s consumer market.

Identification of cumin and fennel from different regions based on generative adversarial networks and near infrared spectroscopy

Cumin (Cuminum cyminum) and fennel (Foeniculum vulgare) are widely used seasonings and play a very important role in industries such as breeding, cosmetics, winemaking, drug discovery, and nano-synthetic materials. However, studies have shown that cumin and fennel from different regions not only differ greatly in the content of lipids, phenols and proteins but also the substances contained in their essential oils are also different. Therefore, realizing precise identification of cumin and fennel from different regions will greatly help in quality control, market fraud and production industrialization. In this experiment, cumin and fennel samples were collected from each region, a total of 480 NIR spectra were collected. We used deep learning and traditional machine learning algorithms combined with near infrared (NIR) spectroscopy to identify their origin. To obtain the model with the best generalization performance and classification accuracy, we used principal component analysis (PCA) to reduce spectral data dimensionality after Rubberband baseline correction, and then established classification models including quadratic discriminant analysis based on PCA (PCA-QDA) and multilayer perceptron based on PCA (PCA-MLP). We also directly input the spectral data after baseline correction into convolutional neural networks (CNN) and generative adversarial networks (GAN). The experimental results show that GAN is more accurate than the PCA-QDA, PCA-MLP and CNN models, and the classification accuracy reached 100%. In the cumin and fennel classification experiment in the same region, the four models achieve great classification results from three regions under the condition that all model parameters remain unchanged. The experimental results show that when the training data are limited and the dimension is high, the model obtained by GAN using competitive learning has more generalization ability and higher classification accuracy. It also provides a new method for solving the problem of limited training data in food research and medical diagnosis in the future.