Search Results (123)

Dendrobium officinale is a valuable medicinal and edible homologous health food. It has immunomodulatory, antioxidant, and metabolism-regulating properties. However, its adulteration is widespread, seriously compromising product quality and safety. Traditional adulteration detection methods are complex, costly, and time-consuming, making it urgent to establish a rapid and non-destructive detection approach. This study developed a rapid identification and quantification method for adulterated D. officinale. The method combined near-infrared (NIR) spectroscopy with data-driven soft independent modeling of class analogy (DD-SIMCA) and partial least squares regression (PLSR) models. PCA, PLS-DA, and OPLS-DA were first used to visualize sample clustering and group differences. DT, SVM, ANN, and NB were used for classification. DD-SIMCA and PLSR were used for one-class modeling and quantitative analysis. Raw spectral data were preprocessed using multiplicative scatter correction (MSC), the standard normal variate (SNV), the first derivative, and Savitzky–Golay smoothing. In the identification analysis, the DD-SIMCA model achieved 100% sensitivity and 100% specificity in the validation set. Its overall accuracy in the independent test set was 99.2%, demonstrating excellent discrimination performance. In addition, SVM combined with NIR also achieved good accuracy. In the quantitative analysis of adulteration, the PLSR model predicted different adulteration levels. Most calibration and validation sets showed R² values above 0.99 and RMSE values below 0.05, indicating excellent predictive performance. The results indicate that NIR combined with DD-SIMCA and PLSR can achieve rapid identification and accurate quantification of adulterated D. officinale samples. This approach provides strong support for quality control and regulatory supervision of high-value health foods. Full article

This study aimed to characterize the physicochemical and functional properties of bovine milk from four districts (Acraquia, Ahuaycha, Pampas, and Daniel Hernández) of the Pampas Valley, Tayacaja province, Huancavelica (Peru), and assess its geographical traceability using vibrational spectroscopy and chemometric tools. Milk samples were analyzed for composition (fat, protein, lactose, salts), fatty acid profile, total phenolic compounds (TPC), antioxidant capacity (AC), and spectral features using mid-infrared (MIR) and Raman spectroscopy. The results revealed significant compositional differences among localities, particularly in fat, protein, and salt content, with Daniel Hernández milk showing higher nutritional density. The fatty acid profile, although statistically similar across districts, highlighted a favorable nutritional composition dominated by oleic, palmitic, and stearic acids. TPC and AC values were homogeneous among districts, reflecting similar feeding and management practices. Molecular vibration analysis via MIR and Raman spectroscopy allowed for the identification of key biochemical differences, particularly in lipid and carbohydrate regions. SIMCA classification models, based on MIR spectral data, successfully discriminated samples by origin with Inter-Class Distance (ICD) values exceeding 3, confirming statistically significant separation. Discriminating power plots revealed that proteins (amide I), lactose (C–O, C–C), and lipid-associated bands (C=O, CH₂) were major contributors to class differentiation. These findings demonstrate the effectiveness of combining spectroscopic and chemometric approaches to trace the geographical origin of milk and provide scientific support for potential quality labeling systems. This methodology contributes to ensuring product authenticity, promoting regional value-added dairy production, and supporting sustainable rural development in high-Andean ecosystems. Full article

This study explores the application of Mid-Infrared (MIR) and Near-Infrared (NIR) spectroscopy combined with various multivariate calibration techniques to detect the presence of cannabis in tobacco samples and tobacco in herbal smoking products. Both MIR and NIR spectra were recorded for self-prepared samples, followed by data exploration using Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), and the calculation of binary classification models with Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares-Discriminant Analysis (PLS-DA). PCA demonstrated a clear differentiation between tobacco samples containing and not containing cannabis. On the other hand, based on PCA, only NIR was able to distinguish herbal smoking products adulterated and not adulterated with tobacco. HCA further clarified these results by revealing distinct clusters within the data. Modelling results indicated that MIR and NIR spectroscopy, particularly when paired with preprocessing techniques like Standard Normal Variate (SNV) and autoscaling, demonstrated high classification accuracy in SIMCA and PLS-DA, achieving correct classification rates of 90% to 100% for external test sets. Comparison of MIR and NIR revealed that NIR spectroscopy resulted in slightly more accurate models for the screening of tobacco samples for cannabis and herbal smoking products for tobacco. The developed approach could be useful for the initial screening of tobacco samples for cannabis, e.g., in a night life setting by law enforcement, but also for inspectors visiting shops selling tobacco and/or herbal smoking products. Full article

Ensuring the authenticity of meat products is a critical issue for consumer protection, regulatory compliance, and the integrity of local food systems. In this study, attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy combined with chemometric and machine learning models was applied to differentiate Maltese from non-Maltese pork. Spectral datasets were subjected to a range of preprocessing techniques, including Savitzky–Golay first and second derivatives, detrending, orthogonal signal correction (OSC), and standard normal variate (SNV). Linear methods such as principal component analysis–linear discriminant analysis (PCA-LDA), the soft independent modeling of class analogy (SIMCA), and partial least squares regression (PLSR) were compared against nonlinear approaches, namely support vector machine regression (SVMR) and artificial neural networks (ANNs). The results revealed that derivative preprocessing consistently enhanced spectral resolution and model robustness, with the fingerprint region (1800–600 cm⁻¹) yielding the highest discriminative power. While PCA-LDA, SIMCA, and PLSR achieved high accuracy, SVMR and ANN models provided a superior predictive performance, with accuracies exceeding 0.99 and lower misclassification rates under external validation. These findings highlight the potential of FTIR spectroscopy combined with nonlinear chemometrics as a rapid, non-destructive, and cost-effective strategy for meat authentication, supporting both consumer safety and sustainable food supply chains. Full article

Geographical origin authentication of agrifood products is essential for ensuring their quality, preventing fraud, and maintaining consumers’ trust. In this study, we used proton nuclear magnetic resonance (¹H NMR) and excitation–emission matrix (EEM) fluorescence spectroscopy combined with chemometric methods for the geographical origin characterization of olive drupes and leaves from different Tuscany subregions, where olive oil production is relevant. Single-block approaches were implemented for individual datasets, using principal component analysis (PCA) for data visualization and Soft Independent Modeling of Class Analogy (SIMCA) for sample classification. ¹H NMR spectroscopy provided detailed metabolomic profiles, identifying key compounds such as polyphenols and organic acids that contribute to geographical differentiation. EEM fluorescence spectroscopy, in combination with Parallel Factor Analysis (PARAFAC), revealed distinctive fluorescence signatures associated with polyphenolic content. A mid-level data fusion strategy, integrating the common dimensions (ComDim) method, was explored to improve the models’ performance. The results demonstrated that both spectroscopic techniques independently provided valuable insights in terms of geographical characterization, while data fusion further improved the model performances, particularly for olive drupes. Notably, this study represents the first attempt to apply EEM fluorescence for the geographical classification of olive drupes and leaves, highlighting its potential as a complementary tool in geographic origin authentication. The integration of advanced spectroscopic and chemometric methods offers a reliable approach for the differentiation of samples from closely related areas at a subregional level. Full article

The mineral composition of table salt can be indicative of its origin. This work evaluated the possibility of identifying the origin of salt from four countries: Brazil, Spain, France, and Portugal. Eight metals were quantified through flame atomic absorption/emission spectroscopy (FAAS). The possibility of using portable near-infrared spectroscopy (NIR) as a faster and lower-cost alternative for identifying salt provenance was also evaluated. The content of Ca, Mg, Fe, Mn, and Cu was identified as possible markers to differentiate the salt origin. One-class classifiers using FAAS data and DD-SIMCA could discriminate the salt origin with few misclassifications. For NIR spectroscopy, it was possible to highlight the importance of controlling the humidity and granulometry before the spectra acquisition. After drying and milling the samples, it was possible to discriminate between samples based on the interaction between the water of hydration and the presence of the cations in the sample. The Mg, Mn, and Cu are important in identifying the origin of salt using NIR spectra. The DD-SIMCA model using NIR spectra could classify the origin with the same performance as observed in FAAS. However, it is important to emphasize that NIR spectroscopy requires less sample preparation, is faster, and has low-cost instrumentation. Full article

Background: Equally with other indigenous green leafy vegetables, Solunum nigrum L. has been widely consumed by the VhaVenda tribe found in the Limpopo Province of South Africa since ancient times as a source of food diversification due to its higher-quality nutritional value, sustainability, food security, and medicinal benefits. It is mostly cultivated from seeds in seedling trays and transplanted in the open field, and at the maturity stage, marketing and distribution are mainly conducting through informal markets (i.e., street vendors). However, recently, it can be found in selected supermarkets and commercial grocery stores in South Africa. The leaves and young shoots of S. nigrum are cooked solely and/or as a supplementary vegetable with Brassica rapa L. subsp. chinensis (Chinese cabbage), Spinacia oleracea L. (spinach), Amaranthus graecizans L. (green amaranth), Solanum lycopersicum L. (tomato), and/or cooking oil for flavor. Objective: Contrary to other green leafy vegetables, few studies have been conducted on the metabolites released by S. nigrum and the influence of growing conditions on the metabolites thereof. Method: A ¹H-nuclear magnetic resonance tool was used to identify the untargeted metabolites released by S. nigrum, and spectra were phase-corrected and binned with MestReNova and statistically analyzed with SIMCA 18.0.2. Results: The findings showed that a total of 12 metabolites were detected between the growing conditions. Eleven similar metabolites, such as glycocholate, chlorogenate (human health benefits), caffeine for its bitter taste, choline, 3-Chlorotyrosine (antidiabetic, blood pressure), etc., and a few vital soluble sugars, were detected in S. nigrum samples grown in the open field and greenhouse-cultivated. Glucose was exclusively detected in the S. nigrum grown under greenhouse conditions. Full article

The main objective of this study was to evaluate the possibilities of visible-near-infrared spectroscopy for investigating water and nitrogen stress in tomato plants. Two varieties of tomato plants (Red Bounty and Manusa) were grown in a greenhouse. Plants were divided into three groups: control, reduced nitrogen fertilization, and reduced watering. Spectral measurements of tomato leaves were made on-site. A USB4000 spectrometer for 450–1100 nm and a handheld AlbaNIR for the 900–1650 nm region were used for the spectra acquisition. Twenty-four vegetative indices were calculated using the reflectance characteristics of plants. Soft Independent Modeling of Class Analogy (SIMCA) models were developed for classification. Additionally, aquagrams were calculated. Results show differences between the spectra of leaves from control and stressed plants for both tomato varieties. Aquagrams clearly show the differences in water structures in the three groups of plants. The performance of developed SIMCA models for discriminating plants according to growing conditions was very high. The total accuracy was between 86.89% and 97.09%. Several vegetation indices successfully differentiate control and stressed plants for both tomato varieties. The results show successful differentiation of the control and stressed tomato plants based on spectral characteristics of the plants’ leaves in the visible and near-infrared region. Full article

This study analyzes, quantifies, and maps, from a bibliometric perspective, scientific production, bioeconomy and computational simulations regarding avocado use in the timeframe of 2004–2023 in Scopus. To categorize and evaluate the contributions of authors, countries, institutions, and journals, Biblioshiny software in RStudio was used. Their collaborative networks were also visualized using VOSviewer. The analysis reveals an exponential increase in scientific output, especially from 2019 onwards, driven by the growing importance of sustainable avocado use in bioeconomy models. The main findings highlight the valorization of avocado waste for producing biofuels, cosmetics, pharmaceuticals, and food. In addition, the use of computational tools such as Aspen Plus, ArcGIS Pro, Unscrambler-X, SIMCA, and DOCK-6 to optimize conversion processes, model climate change effects, perform chemometrics, and conduct multivariate analyses, and molecular docking, respectively, is discussed. This knowledge highlights potential uses of avocado waste and computational modeling tools for stakeholders in the avocado industry, reinforcing their value chain through bioeconomy models and strengthening their competitiveness by promoting more efficient and sustainable processes. This work provides a comprehensive overview of the avocado-based bioeconomy, serving as a reference for future studies that integrate process simulation in the valorization of agro-industrial waste. Full article

Coffee quality, which ultimately is reflected in the beverage aroma, relies on several aspects requiring multiple approaches to check it, which can be expensive and/or time-consuming. Therefore, this study aimed to develop and calibrate an electronic nose (e-nose) coupled with chemometrics to approach coffee-related quality tasks. Twelve different metal oxide sensors were employed in the e-nose construction. The tasks were (i) the separation of Coffea arabica and Coffea canephora species, (ii) the distinction between roasting profiles (light, medium, and dark), and (iii) the separation of expired and non-expired coffees. Exploratory analysis with principal component analysis (PCA) pointed to a fair grouping of the tested samples according to their specification, indicating the potential of the volatiles in grouping the samples. Moreover, a supervised classification employing soft independent modeling of class analogies (SIMCA), partial least squares discriminant analysis (PLS-DA), and least squares support vector machine (LS-SVM) led to great results with accuracy above 90% for every task. The performance of each model varies with the specific task, except for the LS-SVM models, which presented a perfect classification for all tasks. Therefore, combining the e-nose with distinct classification models could be used for multiple-purpose classification tasks for producers as a low-cost, rapid, and effective alternative for quality assurance. Full article

This study explores the effects of varying exposure times of microelement fertilization on hydrochemical parameters, plant growth, and nutrient content in an aquaponic system cultivating Capsicum annuum L. (pepper) with Cyprinus carpio (Common carp L.). It also investigates the potential of visible–near-infrared (VIS-NIR) spectroscopy to differentiate between treated plants based on their spectral characteristics. The findings aim to enhance the understanding of microelement dynamics in aquaponics and optimize the use of VIS-NIR spectroscopy for nutrient and stress detection in crops. The effects of microelement exposure on the growth and health of Cyprinus carpio (Common carp L.) in an aquaponic system are investigated, demonstrating a 100% survival rate and optimal growth performance. The findings suggest that microelement treatments, when applied within safe limits, can enhance system productivity without compromising fish health. Concerning hydrochemical parameters, conductivity remained stable, with values ranging from 271.66 to 297.66 μS/cm, while pH and dissolved oxygen levels were within optimal ranges for aquaponic systems. Ammonia nitrogen levels decreased significantly in treated variants, suggesting improved water quality, while nitrate and orthophosphate reductions indicated an enhanced plant nutrient uptake. The findings underscore the importance of managing water chemistry to maintain a balanced and productive aquaponic system. The increase in root length observed in treatments 2 and 6 suggests that certain microelement exposure times may enhance root development, with treatment 6 showing the longest roots (58.33 cm). Despite this, treatment 2 had a lower biomass (61.2 g), indicating that root growth did not necessarily translate into increased plant weight, possibly due to energy being directed towards root development over fruit production. In contrast, treatment 6 showed both the greatest root length and the highest weight (133.4 g), suggesting a positive correlation between root development and fruit biomass. Yield data revealed that treatment 4 produced the highest yield (0.144 g), suggesting an optimal exposure time before nutrient imbalances negatively impact growth. These results highlight the complexity of microelement exposure in aquaponic systems, emphasizing the importance of fine-tuning exposure times to balance root growth, biomass, and yield for optimal plant development. The spectral characteristics of the visible–near-infrared region of pepper plants treated with microelements revealed subtle differences, particularly in the green (534–555 nm) and red edge (680–750 nm) regions. SIMCA models successfully classified control and treated plants with a misclassification rate of only 1.6%, highlighting the effectiveness of the spectral data for plant differentiation. Key wavelengths for distinguishing plant classes were 468 nm, 537 nm, 687 nm, 728 nm, and 969 nm, which were closely related to plant pigment content and nutrient status. These findings suggest that spectral analysis can be a valuable tool for the non-destructive assessment of plant health and nutrient status. Full article

Authenticating poppy seed oil is essential to ensure product quality and prevent economic and health-related fraud. This study developed a non-targeted approach using FT-IR spectroscopy and pattern recognition analysis to verify the authenticity of poppy seed oil. Thirty-nine poppy seed oil samples were sourced from online stores and local markets in Turkiye. Gas chromatography–Flame Ionization Detector (GC-FID) analysis revealed adulteration in 23% of the samples, characterized by unusual fatty acid composition. Spectra of the oil samples were captured with a portable 5-reflection FT-IR sensor. Soft Independent Model of Class Analogies (SIMCA) was used to create class algorithms, successfully detecting all instances of adulteration. Partial least square regression (PLSR) models were then developed to predict the predominant fatty acid composition, achieving strong external validation performance (R_CV = 0.96–0.99). The models exhibited low standard errors of prediction (SEP = 0.03–1.40%) and high predictive reliability (RPD = 2.9–6.1; RER = 8.4–13.1). This rapid, non-destructive method offers a reliable solution for authenticating poppy seed oil and predicting its fatty acid composition, presenting valuable applications for producers and regulatory authorities. This approach aids in regulatory compliance, protection of public health, and strengthening of consumer confidence by ensuring the authenticity of the product. Full article

Amaranthus species are rich in protein, fiber, minerals, and other nutrients and have various health benefits. The genus is taxonomically difficult due to the high phenotypic plasticity and the spontaneous interspecies introgression and hybridization between species. The purpose of this study is to evaluate the possibilities of near-infrared spectroscopy (NIRS) for the taxonomic differentiation of some of the species common in Bulgaria and estimate their polyphenolic compounds. Tested samples were collected from six Bulgarian floristic regions: Amaranthus albus L., A. blitum L., A. deflexus L., A. hybridus L., and A. retroflexus L. were studied. The NIR spectra of dried and ground leaf and stalk samples were measured by NIRQuest 512 (region 900–1700 nm) using a fiber-optic probe. Soft independent modeling of class analogy (SIMCA) was used to develop the classification models and PLS regression for the quantitative determination of their polyphenolic compounds and antioxidant potential. There were statistically significant differences in the measured values of polyphenolic compounds and antioxidant potential among the tested species. NIRS allowed an accurate determination of these parameters. The performance of developed SIMCA models for the discrimination of species was very high. The precision of determination varied from 98.2 to 100%, and the total accuracy was 98.34%. The results show successful differentiation of the taxonomic species. Full article

The detection and quantification of additives in tobacco products are critical for ensuring consumer safety and compliance with regulatory standards. Traditional analytical techniques, like gas chromatography–mass spectrometry (GC–MS), liquid chromatography–mass spectrometry (LC–MS), and others, although effective, suffer from drawbacks, including complex sample preparation, high costs, lengthy analysis times, and the requirement for skilled operators. This study addresses these challenges by evaluating the efficacy of mid-infrared (MIR) spectroscopy and near-IR (NIR) spectroscopy, coupled with multivariate analysis, as potential solutions for the detection and quantification of additives in tobacco products. So, a representative set of tobacco products was selected and spiked with the targeted additives, namely caffeine, menthol, glycerol, and cocoa. Multivariate analysis of MIR and NIR spectra consisted of principal component analysis (PCA), hierarchical clustering analysis (HCA), partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) to classify samples based on targeted additives. Based on the unsupervised techniques (PCA and HCA), a distinction could be made between spiked and non-spiked samples for all four targeted additives based on both MIR and NIR spectral data. During supervised analysis, SIMCA achieved 87–100% classification accuracy for the different additives and for both spectroscopic techniques. PLS-DA models showed classification rates of 80% to 100%, also demonstrating robust performance. Regression studies, using PLS, showed that it is possible to effectively estimate the concentration levels of the targeted molecules. The results also highlight the necessity of optimizing data pretreatment for accurate quantification of the target additives. Overall, NIR spectroscopy combined with SIMCA provided the most accurate and robust classification models for all target molecules, indicating that it is the most effective single technique for this type of analysis. MIR, on the other hand, showed the overall best performance for quantitative estimation. Full article

The modern analytical technique of Fourier-transform mid-infrared spectroscopy (FT-MIR) has found its place in routine wine quality control. It allows rapid and nondestructive analysis, with easy sample preparation and without the need for chemical pretreatment or expensive reagents. The objective of this research was to apply these advantages to fruit wines in order to create a tool for the authentication of fruit wines produced from different fruit species (chokeberry, blackberry, and raspberry). The aim of this work was to establish a chemometric model from FT-MIR spectra and to find a “fingerprint” of specific fruit wines, enabling the classification of fruit wines by plant species. Physicochemical analysis of 111 Croatian fruit wine samples (38 liqueur fruit wines and 73 fruit wines) revealed content levels of the following parameters: alcoholic strength (5.0–15.2% vol.), total dry extract (60.4–253.3 g/L), total sugars (1.2–229.9 g/L), pH (3.13–4.98), total acidity (4.2–18.3 g/L) and volatile acidity (0.2–1.5 g/L). For statistical data processing, spectral ranges between 926 and 1450 cm⁻¹ and between 1801 and 2951 cm⁻¹ were used. The first principal component (PC1) explained 70.4% of the observed variation, and the second component (PC2) explained 16.7%, clearly separating chokeberry fruit wines from blackberry and raspberry fruit wines. Soft Independent Modeling Class Analogy (SIMCA) was performed following the development of a PCA model showing that the chokeberry and blackberry wine samples form clearly separated clusters. Key discriminators for classifying chokeberry vs. blackberry wines were identified at 1157, 1304, and 1435 cm⁻¹, demonstrating high discrimination power (DP 26, 17, and 14, respectively). FT-MIR spectroscopy, in combination with chemometric methods, has shown promising potential for the authenticity assessment of fruit wines. Full article