Non-destructive Analysis Research Articles

Spatial and Temporal Visualization of Polymorphic Transformations in Pharmaceutical Tablets.

X-ray Diffraction Computed Tomography (XRD-CT) represents a cutting-edge method for non-destructive material analysis, offering the unique capability of providing molecular-level information with spatial resolution. In this study, we have applied XRD-CT to investigate pharmaceutically relevant tablets that have been subjected to a range of compression pressures typical in tablet manufacturing. By employing XRD-CT to pharmaceutical tablets, we reveal material changes without tablet destruction, thereby avoiding potential phase transformations during sample preparation that could lead to errors in the interpretation of the processes that have occurred. Utilizing a pressure-sensitive marker, glycolide, we have tracked changes within tablet structures induced by compression, pinpointing locations where glycolide undergoes pressure-induced transformation. Additionally, we conducted a follow-up study with analysis one month later, observing an in-situ hydrolysis reaction of glycolide within the tablets. Through the complementary use of electron diffraction, we have elucidated the structure of the hydrolysis product, further enhancing our understanding of temporal changes in the tablets.

A Novel Neural Network Architecture Dedicated for LIBS Spectrum Analysis With Its Application to Steel Pipe Classification

Abstract As a non-destructive rapid composition analysis method, Laser-induced breakdown spectroscopy (LIBS) has received widespread attention in recent years. However, its application in the steel industry has not achieved an effective breakthrough in its detection accuracy due to the existence of matrix effect. This paper introduces a neural network model for LIBS spectral line analysis, which can automatically construct the features and analyze the correlation between them, and this model has been applied in the steel sample classification of seamless steel pipe with good results.

Handheld NIR Spectroscopy Combined with a Hybrid LDA-SVM Model for Fast Classification of Retail Milk

The EU market offers different types of milk, distinguished by origin, production method, processing technology, fat content, and other characteristics, which are often detailed on product labels. In this context, ensuring the authenticity of milk is crucial for maintaining standards and preventing fraud. Various food authenticity techniques have been employed to achieve this. Among them, near-infrared (NIR) spectroscopy is valued for its non-destructive and rapid analysis capabilities. This study evaluates the effectiveness of a miniaturized NIR device combined with support vector machine (SVM) algorithms and LDA feature selection to discriminate between four commercial milk types: high-quality fresh milk, milk labeled as mountain product, extended shelf-life milk, and TSG hay milk. The results indicate that NIR spectroscopy can effectively classify milk based on the type of milk, relying on different production systems and heat treatments (pasteurization). This capability was greater in distinguishing high-quality mountain and hay milk from the other types, while resulting in less successful class assignment for extended shelf-life milk. This study demonstrated the potential of portable NIR spectroscopy for real-time and cost-effective milk authentication at the retail level.

Non-Destructive Estimation of Paper Fiber Using Macro Images: A Comparative Evaluation of Network Architectures and Patch Sizes for Patch-Based Classification

Over the years, research in the field of cultural heritage preservation and document analysis has exponentially grown. In this study, we propose an advanced approach for non-destructive estimation of paper fibers using macro images. Expanding on studies that implemented EfficientNet-B0, we explore the effectiveness of six other deep learning networks, including DenseNet-201, DarkNet-53, Inception-v3, Xception, Inception-ResNet-v2, and NASNet-Large, in conjunction with enlarged patch sizes. We experimentally classified three types of paper fibers, namely, kozo, mitsumata, and gampi. During the experiments, patch sizes of 500, 750, and 1000 pixels were evaluated and their impact on classification accuracy was analyzed. The experiments demonstrated that Inception-ResNet-v2 with 1000-pixel patches achieved the highest patch classification accuracy of 82.7%, whereas Xception with 750-pixel patches exhibited the best macro-image-based fiber estimation performance at 84.9%. Additionally, we assessed the efficacy of the method for images containing text, observing consistent improvements in the case of larger patch sizes. However, limitations exist in background patch availability for text-heavy images. This comprehensive evaluation of network architectures and patch sizes can significantly advance the field of non-destructive paper analysis, offering valuable insights into future developments in historical document examination and conservation science.

Characteristics of foundries and alloy composition of modern and contemporary Korean artistic bronze

This study investigated the changes in foundries and production techniques of modern and contemporary Korean artistic bronze constructions from the 1910s to the 2000s. To determine the characteristics of foundries and changes in alloy composition based on era, interviews with key sculptors and foundry technicians, analysis of archival materials related to casting, and nondestructive X-ray fluorescence analysis of 92 bronze sculptures by Manlin Choi, were conducted. This study found that foundries for artistic bronze underwent technical and organizational changes from the 1910s to the 2000s. Modern casting techniques and foreign technologies have been introduced since the late 1960s that have resulted in various production methods. The portable X-ray fluorescence analysis of Manlin Choi’s 92 bronze sculptures revealed distinct differences in alloy composition depending on the foundry, findings that have also been supported by records (contracts, receipts, payment confirmations). This study systematically organizes the changes in foundries and production techniques of modern and contemporary Korean artistic bronze, identifying correlations between alloy composition and foundries, and is expected to provide important data for the future conservation and scientific authentication of artistic bronze.

Farm to fork applications: how vibrational spectroscopy can be used along the whole value chain?

Vibrational spectroscopy is a nondestructive analysis technique that depends on the periodic variations in dipole moments and polarizabilities resulting from the molecular vibrations of molecules/atoms. These methods have important advantages over conventional analytical techniques, including (a) their simplicity in terms of implementation and operation, (b) their adaptability to on-line and on-farm applications, (c) making measurement in a few minutes, and (d) the absence of dangerous solvents throughout sample preparation or measurement. Food safety is a concept that requires the assurance that food is free from any physical, chemical, or biological hazards at all stages, from farm to fork. Continuous monitoring should be provided in order to guarantee the safety of the food. Regarding their advantages, vibrational spectroscopic methods, such as Fourier-transform infrared (FTIR), near-infrared (NIR), and Raman spectroscopy, are considered reliable and rapid techniques to track food safety- and food authenticity-related issues throughout the food chain. Furthermore, coupling spectral data with chemometric approaches also enables the discrimination of samples with different kinds of food safety-related hazards. This review deals with the recent application of vibrational spectroscopic techniques to monitor various hazards related to various foods, including crops, fruits, vegetables, milk, dairy products, meat, seafood, and poultry, throughout harvesting, transportation, processing, distribution, and storage.

Machine vision combined with deep learning-based approaches for food authentication: An integrative review and new insights.

Food fraud undermines consumer trust, creates economic risk, and jeopardizes human health. Therefore, it is essential to develop efficient technologies for rapid and reliable analysis of food quality and safety for food authentication. Machine vision-based methods have emerged as promising solutions for the rapid and nondestructive analysis of food authenticity and quality. The Industry 4.0 revolution has introduced new trends in this field, including the use of deep learning (DL), a subset of artificial intelligence, which demonstrates robust performance and generalization capabilities, effectively extracting features, and processing extensive data. This paper reviews recent advances in machine vision and various DL-based algorithms for food authentication, including DL and lightweight DL, used for food authenticity analysis such as adulteration identification, variety identification, freshness detection, and food quality identification by combining them with a machine vision system or with smartphones and portable devices. This review explores the limitations of machine vision and the challenges of DL, which include overfitting, interpretability, accessibility, data privacy, algorithmic bias, and design and deployment of lightweight DLs, and miniaturization of sensing devices. Finally, future developments and trends in this field are discussed, including the development of real-time detection systems that incorporate a combination of machine vision and DL methods and the expansion of databases. Overall, the combination of vision-based techniques and DL is expected to enable faster, more affordable, and more accurate food authentication methods.

Theoretical precipitation modeling and multidimensional characterization of sulfide inclusions in tellurium-containing steels

This study investigates the precipitation characteristics of sulfide inclusions during the solidification of alloy steels under the combined influence of sulfur (S), manganese (Mn), and tellurium (Te). A multidimensional characterization and comparison of sulfide inclusions in Te-containing steels with varying sulfur content and cooling conditions were performed using X-ray Micro-CT, Raman spectroscopy, and field emission scanning electron microscopy (FE-SEM). Both 2D and 3D analyses confirmed that increasing the S content in Te-containing steels leads to a higher overall number of sulfide inclusions. In particular, the rise in the proportion of small-sized inclusions was analyzed from the perspectives of interfacial dynamics and equilibrium thermodynamics. Regarding inclusion morphology, 2D characterization exhibited significant inaccuracies in assessing large inclusions in high-S, Te-containing steels, while X-ray Micro-CT proved advantageous for evaluating both the spatial distribution and morphology of inclusions. In addition, X-ray Micro-CT was used for the first time in this study to perform a non-destructive analysis of the layered MnS-MnTe structure, expanding the method's application in the metallurgical field. A predictive model based on inter-element activity interaction coefficients was employed to determine the first-order activity interaction coefficient of Te for Mn. Furthermore, a thermodynamic model for sulfide precipitation in Te-containing steels was developed. This study lays a theoretical foundation for better control of sulfide inclusions in future Te-containing specialty steels.

Fast non-destructive measurement of intramuscular fat in Australian beef and lamb using nuclear magnetic resonance (NMR) technologies

Nuclear magnetic resonance (NMR) is an excellent technique for non-destructive analysis of meat because it has high accuracy, a linear response, and insignificant drift over time, which removes the need for recalibration. Furthermore, single-side NMR devices have open geometries that enable measurements of subsections of larger samples without taking sub-samples. Here we demonstrated long-term reproducibility in a benchtop device and the utility of a single-sided NMR device. We validated long-term reproducibility of NMR measurements of lamb intramuscular fat (IMF) in a commercial processor boning room years after the original model was created. It was hypothesised that the NMR IMF% model would retain precision and accuracy on independent validation. The root mean squared (RMS) error of prediction of lamb IMF was 0.79 %. The R2 between reference measurements, predicted IMF% was 0.74, the slope of the chemical IMF% vs NMR predictions was 0.989, and the bias was 0.53 % IMF. In the second example, we showed that IMF% measurements of high value beef striploins could be measured off a commercial processing belt and returned without damaging the product. It was hypothesised that a commercial prototype single-sided NMR system would predict IMF% in beef M. longissimus thoracis et lumborum. Here the RMS error of the correlation was 1.58 % IMF and R2 was 0.97. We believe the long-term stability, high accuracy, and nondestructive nature make unilateral NMR devices ideal for applications in the red meat industry where intramuscular fat contributes to product value.

Non-Destructive Analysis for Machine-Picked Tea Leaf Composition Using Near-Infrared Spectroscopy Combined Chemometric Methods

This paper aimed to predict the mechanical composition of machine-picked fresh tea leaves (MPFTLs) using near-infrared spectroscopy (NIRS) rapidly and non-destructively. Samples of MPFTL with different mechanical composition ratios were collected and subjected to NIRS analysis. Subsequently, various preprocessing methods were employed to eliminate extraneous noise information. Next, characteristic spectral information was extracted using the backward interval partial least squares (biPLS) method, which was subsequently subjected to principal component analysis (PCA). Finally, a predictive model was constructed by applying the back propagation artificial neural network (BP-ANN) method, which was tested by external samples to assess its predictive efficacy, and the results were expressed as root mean square error and determination coefficient of prediction (Rp2). The optimal spectral pretreatment method was the following: (standard normal variate (SNV) + second derivative (SD)). Four characteristic spectral subintervals of ([2, 3, 7, 10]) were screened out, and the cumulative contribution rate of 95.20%, attributable to the first three principal components, was determined. When the tanh transfer function was applied to construct the BP-ANN-NIRS model, the results demonstrated optimal performance, exhibiting a root mean square error and a determination coefficient of prediction (Rp2) of 0.976 and 0.027, respectively. The absolute values of prediction deviation for all prediction set samples were found to be less than 0.04. The results of the best BP-ANN model for external samples were found to be in close agreement with those of the prediction set model. NIRS technology has successfully achieved the forecasting of the mechanical composition of machine-picked fresh tea leaves rapidly and accurately, providing a fair and convenient new method for purchasing fresh tea raw materials by machines, according to their quality, and promoting the sustainable high-quality and healthy development of the tea industry.

Ultra-broadband emission from Mg7Ga2GeO12:Ni2+,Cr3+ phosphor spanning the NIR I–III regions for spectroscopy applications

Ultra-broadband near-infrared (NIR) lighting sources covering NIR Ⅰ to III regions are pivotal components for NIR spectroscopy devices. However, achieving ultra-broadband and efficient emission covering the entire NIR Ⅰ to III regions remains a significant challenge. To address this, we selected Mg7Ga2GeO12 as host material to realize ultra-broadband NIR emission. Density functional theory calculations on formation energies confirmed the feasibility of doping both Ni2+ and Cr3+ within Mg7Ga2GeO12. Band structure calculations reveal a significantly reduced band gap upon the doping of Ni2+. Following that, we synthesized Mg7Ga2GeO12:Ni2+ through conventional solid-state-reaction method. This phosphor exhibits broadband NIR emission with a full-width-at-half-maximum of 377 nm in the range of NIR II ∼ III (1100–2100 nm). To further enhance the emission efficiency, we co-doped Mg7Ga2GeO12:Ni2+ phosphor with Cr3+. This co-doping strategy not only dramatically enhances the Ni2+ emission intensity, but also extends the lower limit of emission band to 600 nm. However, a spectral gap around 1150 nm remains between Cr3+ and Ni2+ emission centers. To bridge this spectral gap, we employed LiScSiO4:Cr3+ phosphor alongside Mg7Ga2GeO12:Ni2+,Cr3+ phosphor to fabricate a phosphor converted LED (pc-LED) with ultra-broadband NIR emission covering entire NIR Ⅰ to III regions. By utilizing this pc-LED as the lighting source for NIR spectral detection, multiple organic functional group signals can be identified simultaneously in NIR II and III bands from 1000 to 2100 nm. The performance of such an NIR pc-LED not only confirms the application potential of Mg7Ga2GeO12:Ni2+,Cr3+ phosphor in non-destructive spectral analysis but also underscores the feasibility of combining two phosphors to achieve ultra-broadband NIR pc-LEDs, rendering them suitable to be lighting sources for portable spectrometers.

Development of a New Non-Destructive Analysis Method in Cultural Heritage with Artificial Intelligence

Cultural assets are all movable and immovable assets that have been the subject of social life in historical periods, have unique scientific and cultural value, and are located above ground, underground or underwater. Today, the fact that most of the analyses conducted to understand the technologies of these assets require sampling and that non-destructive methods that allow analysis without taking samples are costly is a problem for cultural heritage workers. In this study, which was prepared to find solutions to national and international problems, it is aimed to develop a non-destructive, cost-minimizing and easy-to-use analysis method. Since this article aimed to develop methodology, the materials were prepared for preliminary research purposes. Therefore, it was limited to four primary colors. These four primary colors were red and yellow ochre, green earth, Egyptian blue and ultramarine blue. These pigments were used with different binders. The produced paints were photographed in natural and artificial light at different light intensities and brought to a 256 × 256 pixel size, and then trained on support vector machine, convolutional neural network, densely connected convolutional network, residual network 50 and visual geometry group 19 models. It was asked whether the trained VGG19 model could classify the paints used in archaeological and artistic works analyzed with instrumental methods in the literature with their real identities. As a result of the test, the model was able to classify paints in artworks from photographs non-destructively with a 99% success rate, similar to the result of the McNemar test.

Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.

Exploring Carolingian and Romanesque cycles: a study of the detached wall paintings from the Church of St. Johann in Müstair

Twenty-six fragment of wall paintings detached from the upper register of the Carolingian and Romanesque cycles of the church of St. John in Müstair (Switzerland) have been part of the Swiss National Museum collection since the beginning of the twentieth century. As some of these objects were in a critical state of conservation, a research project was launched to assess and document their conditions, and to gain knowledge about painting materials and execution techniques, in view of planning a conservation project. In a first phase, non-destructive analyses were carried out, using handheld XRF and portable FTIR spectroscopy; followed by the targeted collection of samples for detailed analysis in the laboratory. In parallel, similar investigations were carried out on the imprints left on the walls of the church’s attic. The results allowed the identification of the pigment palette and the execution technique. Besides earth pigments such as ochres, a lime-based white, probably bianco di San Giovanni, and limited amounts of lapis lazuli were used in both painting cycles. For the Carolingian cycle, Egyptian blue and mixtures of several lead-based pigments were also identified. In several instances, the deterioration of lead-based pigments resulted in shades of grey to black severely modifying the original appearance of the wall paintings. Additionally, both cycles have suffered from pigment alteration due to fire. The obtained results, together with iconographic considerations, allowed for an extended interpretation of possible original colour palette.Graphical

Dose mapping of a 252Cf based non-destructive on-line elemental analysis device using Monte Carlo simulation and verification with experimental results

Environmental dose values are critical for human health in non-destructive analysis systems. The International Commission on Radiological Protection (ICRP) has defined specific limitations for permissible dose values. This study assesses the dose distribution of a prototype analysis device utilizing Californium-252 (252Cf) radioactive material through both Monte Carlo simulations and laboratory experiments. Conducted at the Istanbul Technical University (ITU) Energy Institute, the experimental investigation measured gamma-ray and neutron dose values surrounding the device. The objective was to compare dose maps generated by the Monte Carlo-based GEANT4 code with experimental data. Ten dose maps were created, and fundamental statistical analyses were performed. Results indicate that the dose values on the operator’s working surface of the prototype device are within ICRP limits, not exceeding 1 millisievert (mSv) annually, with an effective dose rate of 0.949±0.052 mSv per year. These findings underscore the device’s potential for safe and effective use in radiation safety and environmental dose assessment.

Burnt Cervidae bones and sedimentary environments at Luotuodun, Jiangsu Province, China: New insights for Neolithic human behavior

The Luotuodun archaeological site is located in Yixing City, Jiangsu Province, in the transition zone from the Yili Mountains to the coastal plain. It is of Neolithic Age and dates from approximately 7000 to 5000 years ago. A large number of animal skeleton fossils have been unearthed at the site, many of which, especially those belonging to species of Cervidae (possibly Sika, Cervis nippon), are characterized by the presence of black residue on the surface. There are three types of residue, one is black film-like coating, which is suggestive of exposure to fire, one is also black, but crystalline in nature, while the other is grey-white, particulate deposit. With the aim of exploring the nature of these residues and their possible origins, we applied a number of analytical techniques, including Raman Spectroscopy (RS), Fourier Transform Infrared Spectroscopy (FTIR), Energy dispersive spectroscopy (EDS) and Electron Microscopy (EM) on six bone fossils belonging to different parts of Cervidae skeleton unearthed from Luotuodun to evaluate the possible influence of fire and/or depositional processes in their formation. The black film-like coating, widespread on the surface of these bones, is highly reflective and appears white under the Raman microscope. The RS peak indicates that the film-like substance is rich in carbonaceous (graphitic carbon) components, suggesting that the bones have been exposed to combustion. FTIR measurements on these bones show that the structure of hydroxyapatite carbonate (Ca10(PO4)6−x(CO3)x(OH)2) has changed, consistent with their exposure to very high temperatures (500–600 °C), and implying that they were burnt, either for cooking or ritual behavior by ancient humans. In the case of the crystallized black particles, these are primarily confined to localities on the surface of bone fractures. EDS results, as confirmed by the RS analysis, show that the black crystals are comprised of an iron-rich, phosphatic mineral, compatible with vivianite (Fe3(PO4)2·(H2O)8). The substantial vivianite content is consistent with the fossil bones being submerged and deposited in waterlogged soils. The results of EDS show that the composition of the grey-white particle is hydroxyapatite carbonate (Ca10(PO4)6−x(CO3)x(OH)2). Non-destructive geochemical analysis of the surface residues of these Luotuodan bones provides valuable evidence for reconstructing the taphonomy of the Cervidae bones, and reveals that they were utilized by ancient humans during the Luotuodun culture period in SE China.

Validation of non-destructive morphology-based selection of cerebral cortical organoids by paired morphological and single-cell RNA-seq analyses

Organoids, self-organized cell aggregates, contribute significantly to developing disease models and cell-based therapies. Organoid-to-organoid variations, however, are inevitable despite the use of the latest differentiation protocols. Here, we focused on the morphology of organoids formed in a cerebral organoid differentiation culture and assessed their cellular compositions by single-cell RNA sequencing analysis. The data revealed that organoids primarily composed of non-neuronal cells, such as those from the neural crest and choroid plexus, showed unique morphological features. Moreover, we demonstrate that non-destructive morphological analysis can accurately distinguish organoids composed of cerebral cortical tissues from other cerebral tissues, thus enhancing experimental accuracy and reliability to ensure the safety of cell-based therapies.

Non-Destructive Analysis of Tetracycline Drugs by the Digital Colormetric Method Using a Smartphone and the Photometrix Pro® Software

Non-Destructive Analysis of Tetracycline Drugs by the Digital Colormetric Method Using a Smartphone and the Photometrix Pro® Software

Process analytical technology based quality assurance of API concentration and fiber diameter of electrospun amorphous solid dispersions

In this study, a novel quality assurance system was developed utilizing Process analytical technology (PAT) tools and artificial intelligence (AI). Our goal was to monitor the critical quality attributes (CQAs) like drug concentration, morphology and fiber diameter of electrospun amorphous solid dispersion (ASD) formulations with fast at-line techniques. Doxycycline-hyclate (DOX), a tetracycline-type antibiotic was used as a model drug with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) as the matrix excipient. The water-based formulations were electrospun with high-speed electrospinning (HSES). Raman and NIR sensors and machine vision-based color measurement techniques were employed to accurately determine the drug concentration. Given that morphology can influence the solubility of the drug, a convolutional neural network (CNN)-based AI model was developed to examine this property and detect manufacturing defects. Additionally, the diameter of electrospun fibrous samples was measured using camera images and a trained AI model, enabling rapid analysis of fiber diameter with results similar to that of scanning electron microscopy (SEM). These methods and models demonstrate potential in-line analytical tools, offering rapid, cheap and non-destructive analysis of ASD formulations.

Analytical Techniques for the Authenticity Evaluation of Chokeberry, Blackberry and Raspberry Fruit Wines: Exploring FT-MIR Analysis and Chemometrics

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.