Chemometric Approach Research Articles

Identification of Codonopsis Radix from different sources by integrating high‐performance liquid chromatography fingerprinting with chemometric approaches and Lobetyolin determination

AbstractCodonopsis Radix (CR) is often used as both a medicine and a food ingredient. However, identifying CR can be difficult due to various factors such as variety, sulfur fumigation, and storage time. Consequently, a comprehensive strategy based on fingerprinting and chemometric approaches was developed to distinguish different CRs. First, the fingerprints of CR were acquired by high‐performance liquid chromatography (HPLC). Secondly, various chemical pattern recognition methods were employed to identify CR. Additionally, 14 and 16 major compounds were identified as marker compounds to differentiate between different varieties and sulfur‐fumigated samples, respectively. Furthermore, the amount of Lobetyolin present in different samples was determined, showing that the content of Lobetyolin varied significantly across the samples. Codonopsis pilosula Nannf. var. modesta (Nannf.) L.T.Shen had the highest content of Lobetyolin, followed by Codonopsis tangshen Oliv., and Codonopsis pilosula (Franch.) Nannf. In addition, the content of Lobetyolin was highest when it was non‐sulfur‐fumigated and stored for 0 years. Conclusively, HPLC fingerprint in conjunction with chemical pattern recognition and component content determination, can be employed to differentiate different varieties and sulfur‐fumigated CR. Additionally, it is a reliable, comprehensive, simple, and rapid method for the identification of CR.

Controlling Fluorescence Wavelength in the Synthesis of TGA-Capped CdTe Quantum Dots

Quantum dots (QDs) are semiconductor materials, with a size range between 1–10 nm, showcasing unique size-dependent physical and chemical properties. Such properties have potentiated their use in areas like medical imaging and biosensing. Herein, we present an open-air approach for synthesis of QDs, reducing the need for controllable atmospheric conditions. Furthermore, we present a predictive mathematical model for maximum emission wavelength (λmax) control. Through a straightforward microwave-based aqueous synthesis of TGA-CdTe QDs, we investigated the influence of time, temperature, and Te:Cd and TGA:Cd molar ratios on λmax, using a chemometric experimental design approach. CdTe-QDs were characterized by UV-Vis and fluorescence spectroscopies. Additionally, Fourier-Transform Infrared spectroscopy, X-ray photoelectron spectroscopy, Transmission Electron Microscopy, and Energy Dispersive X-ray were conducted. Stable QDs with fluorescence ranging from green to red (527.6 nm to 629.2 nm) were obtained. A statistical analysis of the results revealed that time and temperature were the most significant factors influencing λmax. After fine-tuning the variables, a mathematical model with 97.7% of prediction accurately forecasted experimental conditions for synthesizing TGA-CdTe QDs at predefined λmax. Stability tests demonstrated that the QDs retained their optical characteristics for over a month at 4 °C, facilitating diverse applications.

Evaluation of Physicochemical Properties of Ipsapirone Derivatives Based on Chromatographic and Chemometric Approaches.

Drug discovery is a challenging process, with many compounds failing to progress due to unmet pharmacokinetic criteria. Lipophilicity is an important physicochemical parameter that affects various pharmacokinetic processes, including absorption, metabolism, and excretion. This study evaluated the lipophilic properties of a library of ipsapirone derivatives that were previously synthesized to affect dopamine and serotonin receptors. Lipophilicity indices were determined using computational and chromatographic approaches. In addition, the affinity to human serum albumin (HSA) and phospholipids was assessed using biomimetic chromatography protocols. Quantitative Structure-Retention Relationship (QSRR) methodologies were used to determine the impact of theoretical descriptors on experimentally determined properties. A multiple linear regression (MLR) model was calculated to identify the most important features, and genetic algorithms (GAs) were used to assist in the selection of features. The resultant models showed commendable predictive accuracy, minimal error, and good concordance correlation coefficient values of 0.876, 0.149, and 0.930 for the validation group, respectively.

Discrimination of untreated and sodium sulphite treated bean sprouts by Fourier transform infrared spectroscopy and chemometrics

Sprouts of black beans (Phaseolus vulgaris L.), soybeans (Glycine max L.) and mung beans (Vigna radiata L.) are widely consumed foods containing abundant nutrients with biological activities. They are commonly treated with sulphites for the preservation and extension of shelf-life. However, our previous investigation found that immersing the bean sprouts in sulphite might convert the active components into sulphur-containing derivatives, which can affect both the quality and safety of the sprouts. This study explores the use of FTIR in conjunction with chemometric techniques to differentiate between non-immersed (NI) and sodium sulphite immersed (SI) black bean, soybean and mung bean sprouts. A total of 168 batches of raw spectra were obtained from NI and SI-bean sprouts using FTIR spectroscopy. Four pre-processing techniques, three modelling assessment techniques and four model evaluation indices were examined for differences in performance. The results show that the multiplicative scatter correction is the most effective pre-processing method. Among the models, the accuracy rate of the three models was as follows: radial basis function neural network (95%) > convolutional neural network (91%) > random forest (82%). The overall findings indicate that FTIR spectroscopy, in conjunction with appropriate chemometric approaches, has a high potential for rapidly determining the difference between NI and SI-bean sprouts.

Classification of buckwheat honey produced in Kazakhstan according to their biochemical ingredients and bioactivities by chemometric approach

Herein, nineteen buckwheat honey samples collected from 19 stations of different ecological zones of Kazakhstan were analysed for their pollen density, physicochemical properties, chemical composition, antioxidant, anticholinesterase, tyrosinase inhibitory, and urease inhibitory activities with chemometric approaches. Twelve phenolic compounds and fumaric acid were identified using HPLC-DAD, and mainly fumaric, p-hydroxybenzoic, p-coumaric, trans-2-hydroxy cinnamic acids, and chrysin were detected in all samples. The honey samples collected from the Northern zone exhibited best antioxidant activity in lipid peroxidation inhibitory (IC50:8.65 ± 0.50 mg/mL), DPPH• (IC50:17.07 ± 1.49 mg/mL), ABTS•+ (IC50:8.90 ± 0.65 mg/mL), CUPRAC (A0.50:7.51 ± 0.30 mg/mL) and metal chelating assay (IC50:10.39 ± 0.71 mg/mL). In contrast, South-eastern zone samples indicated better acetylcholinesterase (55.57 ± 0.83%), butyrylcholinesterase (49.59 ± 1.09%), tyrosinase (44.40 ± 1.21%), and moderate urease (24.57 ± 0.33%) inhibitory activities at 20 mg/mL. The chemometric classification of nineteen buckwheat honey was performed using PCA and HCA techniques. Both were supported by correlation analysis. Thirteen compounds contributed significantly to the clustering of buckwheat honey based on geographical origin.

Assessing metal(loid)s-Induced long-term spatiotemporal health risks in Coastal Regions, Bay of Bengal: A chemometric study.

Despite sporadic and irregular studies on heavy metal(loid)s health risks in water, fish, and soil in the coastal areas of the Bay of Bengal, no chemometric approaches have been applied to assess the human health risks comprehensively. This review aims to employ chemometric analysis to evaluate the long-term spatiotemporal health risks of metal(loid)s e.g., Fe, Mn, Zn, Cd, As, Cr, Pb, Cu, and Ni in coastal water, fish, and soils from 2003 to 2023. Across coastal parts, studies on metal(loid)s were distributed with 40% in the southeast, 28% in the south-central, and 32% in the southwest regions. The southeastern area exhibited the highest contamination levels, primarily due to elevated Zn content (156.8 to 147.2 mg/L for Mn in water, 15.3 to 13.2 mg/kg for Cu in fish, and 50.6 to 46.4 mg/kg for Ni in soil), except for a few sites in the south-central region. Health risks associated with the ingestion of Fe, As, and Cd (water), Ni, Cr, and Pb (fish), and Cd, Cr, and Pb (soil) were identified, with non-carcinogenic risks existing exclusively through this route. Moreover, As, Cr, and Ni pose cancer risks for adults and children via ingestion in the southeastern region. Overall non-carcinogenic risks emphasized a significantly higher risk for children compared to adults, with six, two-, and six-times higher health risks through ingestion of water, fish, and soils along the southeastern coast. The study offers innovative sustainable management strategies and remediation policies aimed at reducing metal(loid)s contamination in various environmental media along coastal Bangladesh.

Grading of Agarwood Based on Their Chemical Profiles Using GC-MS Incorporating Chemometric Approaches

Grading of Agarwood Based on Their Chemical Profiles Using GC-MS Incorporating Chemometric Approaches

Optimization of HS-SPME combined with GC–MS for key marker volatile organic compound analysis in Kappaphycus alvarezii with a chemometric approach

Optimization of HS-SPME combined with GC–MS for key marker volatile organic compound analysis in Kappaphycus alvarezii with a chemometric approach

Chemometric-assisted eMIP-modified screen-printed sensor for robust herbicide MCPA determination

The paper describes the development and application of a screen-printed electrode cell with a graphite-ink working electrode modified by a molecularly imprinted electropolymerized polypyrrole for the voltammetric determination of the herbicide 4‑chloro-2-methylphenoxyacetic acid (MCPA). The method exploits the direct measurement of the analyte by applying the differential pulse voltammetry (DPV) technique, taking advantage of the irreversible oxidation peak at about +1.0 V vs. Ag/AgCl pseudo reference electrode. The presence of the molecularly imprinted polypyrrole enhances the sensor's selectivity and sensitivity. A chemometric approach has been crucial for quantitative analysis because of the peak's broad and not well-defined shape. Firstly, a proper pretreatment of the voltammetric signals is identified, proving the most effective is the first-derivative function transformation of the signal. The Partial Least Square regression (PLS) is the tool applied for MCPA quantification. A preliminary PLS model has been developed and validated in dihydrogen phosphate solution at pH 5.5, aiming to optimize the data treatment approach. Then, the same approach is used to develop a PLS model analyzing tap water samples fortified with MCPA and other pesticides as possible interferents to simulate contaminated natural waters. The model correctly predicted the analyte concentration in the range of 2.5–75 μM, assuring the reliability and robustness of the sensor for the possible quantification of MCPA in wastewater samples.

Detection and quantification of cocoa powder adulteration using Vis-NIR spectroscopy with chemometrics approach

The rising demand for cocoa powder has resulted in an upsurge in market prices, leading to the emergence of adulteration practices aimed at achieving economic benefits. This study aimed to detect and quantify cocoa powder adulteration using visible and near-infrared spectroscopy (Vis-NIRS). The adulterants used in this study were powdered carob, cocoa shell, foxtail millet, soybean, and whole wheat. The NIRS data could not be resolved using Savitzky-Golay smoothing. Nevertheless, the application of a random forest and support vector machine successfully classified the samples with 100% accuracy. Quantification of adulteration using partial least squares (PLS), Lasso, Ridge, elastic Net, and RF regressions provided R2 higher than 0.96 and root mean square error <2.6. Coupling PLS with the Boruta algorithm produced the most reliable regression model (R2 = 1, RMSE = 0.0000). Finally, an online application was prepared to facilitate the determination of adulterants in the cocoa powder.

DEVELOPMENT OF A FAST LIQUID CHROMATOGRAPHY METHOD WITH A CHEMOMETRIC APPROACH BASED ON BOX-BEHNKEN DESIGN FOR THE DETERMINATION OF ANTIDEPRESSANTS IN PHARMACEUTICAL FORMULATIONS

Objective: The objective of this work was to develop a liquid chromatographic method for the quantification of antidepressants, namely duloxetine (DXN), fluoxetine (FXN), citalopram (CIT), paroxetine (PXN), and sertraline (SRN), by a chemometric approach based on Box-Behnken design. Material and Method: After initial experiments to determine significant parameters, a Box-Behnken design consisting of 17 experiment sets was carried out. All separations were conducted using an Agilent Poroshell 120 EC-C18 analytical column (75 mm × 4.6 mm × 2.7 µm). Result and Discussion: The optimum levels of pH, acetonitrile ratio, and flow rate were determined with the desirability function as 2.7, 38%, and 1.1 ml/min, respectively. The differences (&amp;lt;8%) between predicted optimum responses and experimentally obtained results proved the model's suitability. Limits of detection and limits of quantification values were in the ranges of 0.17-0.29 µg/ml and 0.53-0.89 µg/ml, respectively. The feasibility of the technique was proven by analyzing PXN and DXN formulations.

Optimization of a Validated Liquid Chromatographic Method with Chemometric Approach for Estimation of Eugenol from Seed Powder Extract and Perform Anti-oxidant Activity

Background:: Developed and validated a new reverse phase high-performance liquid chromatographic (RP-HPLC) method, and it is prompt, precise, sensitive and robust for the esti-mation of eugenol in seed powder extract of Myristica fragrans. Objective:: The chemometric approach was utilized to obtain a rugged and definitive chromato-graphic method for the purpose. Methods:: Method variables such as acetonitrile (%) and flow rate were investigated for robust-ness and optimization by using a face-centered cubic design (FCCD). The Design Expert 12.0.1.0 software has been employed for this optimization. Further, the effects of factors were monitored on the concentrations of eugenol recovered from seed powder extract. Chromatograms have been developed by using an optimized mobile phase mixture containing methanol-water-acetonitrile (10:40:50, v/v/v) and Symmetry® C18 column (5 μm, 3.9 ×150 mm). The mobile phase was de-rived at a flow rate of 1 mL/min, and estimation of eugenol was performed at ʎmax 272 nm. Results:: Validation of the method has been carried out to reveal its selectivity, linearity, preci-sion, accuracy, LOD, and LOQ. Linear calibration plot for eugenol was held over the concentra-tion across 6.25 and 100 μg/mL (R2 = 0.999). The coefficient of variation was less than 1%, and accurate recovery of eugenol was observed between 96.80 and 99.56 %. The LOD and LOQ have been established to be 1.97 and 6.25 μg/mL, respectively. Intraday and Inter-day coefficients of variation have 1.81-1.91 and 1.92-1.57, respectively. Antioxidant activity (AA) by DPPH assay of seed powder extract in five different solvents was performed, and % AA activity was calculat-ed against ascorbic acid. Conclusion:: The validated method has been found to be highly robust and will be applied for the analysis of eugenol in formulation. The highest % AA has been reported in hexane solvent.

Using clustering as pre-processing in the framework of signal unmixing for exhaustive exploration of archaeological artefacts in Raman imaging

Analytical chemistry on archaeological material is an essential part of modern archaeological investigations and from year to year, instrumental improvement has made it possible to generate data at a high spatial and temporal frequency. In particular, Raman spectral imaging can be successfully applied in archaeological research by its simplicity of implementation to study past human societies through the analysis of their material remains. This technique makes it possible to simultaneously obtain spatial and spectral information by preserving sample integrity. However, because of the inherent complexity of the samples in Archaeology (e.g. seniority, fragility, lack or full absence of any information about its composition), chemical interpretation can be difficult at first glance. Indeed, specific problems of spectral selectivity related to unexpected chemical compounds could appear due to their state of conservation. Furthermore, detecting minor compounds becomes challenging as major components impose their contributions in the acquired spectra. Therefore, a relevant chemometric approach has been introduced in this context to characterize distinct spectral sources in a Raman imaging dataset of an archaeological specimen – a mosaic fragment. The fragment was unearthed during the Ruscino archaeological dig on the outskirts of Perpignan, France. It dates back to the oppidum period. The aim is to extract selective spectral information from pixel clustering analysis in order to enhance the initial optimisation step within the Multivariate Curve Resolution and Alternating Least-Squares (MCR-ALS) algorithm, a well-known signal unmixing technique. The underlying principle of the MCR-ALS is that the acquired spectra can be expressed as linear combinations of pure spectra of all individual components present in the chemical system under study. Sometimes it can be difficult to obtain the desired results through the algorithm, particularly if initial estimates of spectral or concentration profiles are inaccurate due to complex signals, noise or lack of selectivity, resulting in rank deficiency (i.e. a poor estimation of the total number of pure signals). For this reason, an innovative threshold-based clustering algorithm, combined with multiple Orthogonal Projection Approaches (OPA), has been developed to improve matrix rank investigation and thus the initialisation step of the MCR-ALS approach before optimisation. The effective analysis of Raman imaging data for an archaeological mosaic played a crucial role in uncovering significant chemical information about a particular biogenic material. This insight sheds light on the origins of mortar manufacture during the oppidum period.

Chemometric guided isolation of new triterpenoid saponins as acetylcholinesterase inhibitors from seeds of Achyranthes bidentata Blume

Achyranthes bidentata Blume (Amaranthaceae) is an annual or perennial herb widely used as ethnomedicine in Traditional Chinese Medicine for treating fever, cold, ulcers, mensural pain, dementia, and osteoporosis. In the current study, UPLC-IM-Q-TOF-MS/MS-based chemometric approach was adopted for the tentative identification of fifty-six compounds in the extract and fractions of A.bidentata seeds. Further, the chemometric-guided isolation led to the isolation of two previously undescribed oleanane-type triterpenoid saponins, named achyranosides A-B (27 and 30), along with three known compounds (31, 44, and 23) from water fraction of A. bidentata seeds. The structures of new compounds were elucidated based on the detailed analysis of NMR, HR-ESI-MS, FT-IR spectral data, and GC-FID techniques. The isolated compounds in vitro acetylcholinesterase inhibitory activity revealed the promising activity of chikusetsusaponin IVa (23) (IC50 = 63.7 μM) with mixed type of AChE inhibition in enzyme kinetic studies. Additionally, in silico binding free energy of isolated compounds disclosed the greater stability of enzyme-ligand complex owing to underlying multiple H-bond interactions. Overall, the study demonstrates the effectiveness of a chemometric-guided approach for the phytochemical exploration and isolation of new oleanane-type triterpenoid saponins from A. bidentata seeds.

Pressurized liquid extraction of glucosinolates from Camelina sativa (L.) Crantz by-products: Process optimization and biological activities of green extract

The cultivation of Camelina sativa (L.) Crantz is rapidly increasing due to oil production resulting in a substantial volume of by-products, which still have an interesting composition in secondary metabolites, especially glucosinolates. Therefore, a green extraction procedure of glucosinolates by Pressurised Liquid Extraction was developed and optimized using a chemometric approach. Furthermore, the glucosinolates were purified by solid phase extraction, and a preliminary study on bioaccessibility and bioavailability study was carried out to evaluate the resistance of the glucosinolates to the digestive process. The application of pressurised liquid extraction to the recovery of glucosinolates from camelina sativa by-product, is a green, automatic, and rapid method, representing a valid alternative to conventional extraction method to obtain ingredients for food industries.

Impact of semolina-barley mixture on the volatolomic profile of dough and pasta: characterization by a multivariate chemometric approach.

Barley flour, known to be rich in various phytochemicals, has been demonstrated to improve the technological and nutritional properties of pasta; however, its volatile profile, on which its aromatic properties depend, also plays an important role in the acceptance of barley-enriched pasta. In the present work, volatile organic compounds (VOCs) of semolina doughs enriched with different percentages of barley and of the related pasta were characterized by solid phase micro-extraction (HS-SPME) coupled to gas-chromatography/mass spectrometry (GC-MS), and evaluated using a multivariate statistical approach, including principal component analysis (PCA), cluster heatmaps, Pearson's and Spearman's correlations, and partial least squares correlation (PLSC). The effects of single raw materials, and their interactions, were studied to establish their importance in the volatile profile of the samples, and the correlation between the dough VOCs and the processed product VOCs was assessed. The presence of barley flour markedly affected the volatile profile in comparison with the dough obtained with only durum wheat. For alcohols, esters, terpenes, and some aldehydes there was a clear correlation with the percentage of barley. For some of the VOCs, on the other hand, a strong dependence on the ingredients interaction effect due to the mixing stage has been demonstrated. The heatmaps allowed a good graphical visualization of the relationship between molecules and barley percentage, offering the possibility to select the best one according to the desired volatolomic footprint. Pasta with 40% of barley was demonstrated to give pasta with the most complex volatile profile. © 2024 Society of Chemical Industry.

Four chemometric models enhanced by Latin hypercube sampling design for quantification of anti-COVID drugs: sustainability profiling through multiple greenness, carbon footprint, blueness, and whiteness metrics

Montelukast sodium (MLK) and Levocetirizine dihydrochloride (LCZ) are widely prescribed medications with promising therapeutic potential against COVID-19. However, existing analytical methods for their quantification are unsustainable, relying on toxic solvents and expensive instrumentation. Herein, we pioneer a green, cost-effective chemometrics approach for MLK and LCZ analysis using UV spectroscopy and intelligent multivariate calibration. Following a multilevel multifactor experimental design, UV spectral data was acquired for 25 synthetic mixtures and modeled via classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), and genetic algorithm-PLS (GA-PLS) techniques. Latin hypercube sampling (LHS) strategically constructed an optimal validation set of 13 mixtures for unbiased predictive performance assessment. Following optimization of the models regarding latent variables (LVs) and wavelength region, the optimum root mean square error of cross-validation (RMSECV) was attained at 2 LVs for the 210–400 nm spectral range (191 data points). The GA-PLS model demonstrated superb accuracy, with recovery percentages (R%) from 98 to 102% for both analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.0943, 0.1872) and (0.1926, 0.1779) for MLK and LCZ, respectively, as well bias-corrected mean square error of prediction (BCMSEP) of -0.0029 and 0.0176, relative root mean square error of prediction (RRMSEP) reaching 0.7516 and 0.6585, and limits of detection (LOD) reaching 0.0813 and 0.2273 for MLK and LCZ respectively. Practical pharmaceutical sample analysis was successfully confirmed via standard additions. We further conducted pioneering multidimensional sustainability evaluations using state-of-the-art greenness, blueness, and whiteness tools. The method demonstrated favorable environmental metrics across all assessment tools. The obtained Green National Environmental Method Index (NEMI), and Complementary Green Analytical Procedure Index (ComplexGAPI) quadrants affirmed green analytical principles. Additionally, the method had a high Analytical Greenness Metric (AGREE) score (0.90) and a low carbon footprint (0.021), indicating environmental friendliness. We also applied blueness and whiteness assessments using the high Blue Applicability Grade Index (BAGI) and Red–Green–Blue 12 (RGB 12) algorithms. The high BAGI (90) and RGB 12 (90.8) scores confirmed the method's strong applicability, cost-effectiveness, and sustainability. This work puts forward an optimal, economically viable green chemistry paradigm for pharmaceutical quality control aligned with sustainable development goals.

Ongoing Multivariate Chemometric Approaches in Bioactive Compounds and Functional Properties of Foods—A Systematic Review

In this review, papers published in the chemometrics field were selected in order to gather information and conduct a systematic review regarding food science and technology; more precisely, regarding the domain of bioactive compounds and the functional properties of foods. More than 50 papers covering different food samples, experimental techniques and chemometric techniques were selected and presented, focusing on the chemometric methods used and their outcomes. This study is one way to approach an overview of the current publications related to this subject matter. The application of the multivariate chemometrics approach to the study of bioactive compounds and the functional properties of foods can open up even more in coming years, since it is fast-growing and highly competitive research area.

Tri-chromic sustainable tools of derivative/discrete Fourier transform HPLC convoluted peaks: Application on LCZ696, an antihypertensive supramolecular complex, with its in process and degradation impurity analysis

The aim of this manuscript is to discuss the use of chemometrics approaches in handling the response data of HPLC to sustainable analytical chemistry procedure development. This was performed on FDA approved LCZ696, an antihypertensive supramolecular complex of sacubitril (SAC) and valsartan (VAL), with the in process and degradation impurity (BMP). Structure similarity between SAC and its in process and degradation impurity (BMP) makes it difficult for their simultaneous determination and need too much time and solvent consumption for their separation. It was found that convolution of the resulting derivative curves using 8-points sin xi polynomials (discrete Fourier functions) after derivative treatment of the chromatographic response data was beneficial in handling one of the most common chromatographic problems which is overlapping of the chromatographic peaks without complete resolution, thus, reducing time of analysis and solvent consumption. The chemometric data handling succeeded in analyzing LCZ696, with its closely related in process impurity in less than 4.5 min. reaching impurity detection limit of 0.5 % in presence of the parent drug, SAC, while the direct chromatographic measurement showed higher LOD and LOQ of BMP with poor recovery, outside the accepted range 98–102 %, ranging from 80.56 to 104.41. Moreover, the proposed chemometric methodology will be compared with other chromatographic methods in terms of greenness, blueness and whiteness (tri-chromic assessment) using Analytical Eco Scale, AGREE, Blue Applicability Grade Index (BAGI) and the up-to-date Chlortox scale in addition to the white RGB 12 model in order to ascertain the influence of chemometric methods in minimizing the amount of time spent conducting the analysis and reducing solvent, energy and waste consumption.

Identification of black and white antique photographs via non-destructive microscopic and spectroscopic techniques coupled with chemometric approaches

In this study, we investigated three types of black and white antique photographs, namely salt print, albumen photograph, and silver gelatin photograph, utilizing multiple techniques. Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy and spectroscopic imaging, confocal light and laser microscopy, X-ray Fluorescence (XRF), and Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDS) were employed to characterize these antique photographs, providing structural and chemical information. Chemometric methods including Principal Component Analysis (PCA) and k-means clustering were utilized to analyze the ATR-FTIR spectroscopic imaging data obtained from the surface and cross-sectional samples of the photographs, respectively. Successful differentiation of the three antique photographs and chemical characterization of different layers within each photograph were achieved. These results demonstrate a promising approach for the non-destructive and efficient identification of various photographs with distinct manufacturing processes.