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Application of Spectroscopy and Chemometrics for Authentication of Foods and Drugs (Volume II)

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A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Analytical Chemistry".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 13375

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Special Issue Editors

Prof. Dr. Abdul Rohman

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Guest Editor

Department of Pharmaceutical Chemistry, Universitas Gadjah Mada, Yogyakarta, Indonesia
Interests: analysis of halal products authentication; analytical chemistry; chemometrics; molecular spectroscopy
Special Issues, Collections and Topics in MDPI journals

Dr. Florentinus Dika Octa Riswanto

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Guest Editor

Faculty of Pharmacy, Universitas Sanata Dharma, Yogyakarta, Indonesia
Interests: chemometrics; pharmaceutical chemistry; spectroscopy; food analysis; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research on food and drug analysis has become increasingly popular in the last decade. The discovery and development of natural food and drug products increases the existence of scientific challenges in the field of analytical chemistry due the complexity of the matrices, as well as other ingredients or excipients of the products. Hence, authentication studies are commonly applied as a quality control aspect for dietary and pharmaceutical products. Authentication analysis can be applied at several stages of a product‘s evaluation, including material preparation, manufacturing or formulation processes, as well as finished and marketed products. Spectroscopy, an analytical technique, is commonly applied for the qualitative and quantitative analysis of foods and drugs. The combination of spectroscopy and chemometrics techniques enables the generation of a predictive model for an authentication study. With the help of an appropriate machine learning algorithm, experimental data resulting from the spectroscopic analysis can be exploited for further authentication purposes.

The present Special Issue aims to provide a comprehensive overview of the field of food and drug authentication with the employment of spectroscopy and chemometrics techniques. This Special Issue welcomes original studies and review articles on the authentication, pattern recognition modelling, and other applications of spectroscopic and chemometrics in the analysis of dietary and pharmaceutical products.

Prof. Dr. Abdul Rohman
Dr. Florentinus Dika Octa Riswanto
Guest Editors

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Keywords

authentication
chemometrics
spectroscopy
foods
drugs
pharmaceutical chemistry
food analysis

Published Papers (11 papers)

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Research

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22 pages, 3438 KiB

Open AccessArticle

Full Characterisation of Heroin Samples Using Infrared Spectroscopy and Multivariate Calibration

by Eric Deconinck, Sybrien Lievens, Michael Canfyn, Peter Van Campenhout, Loic Debehault, Lies Gremaux and Margot Balcaen

Molecules 2024, 29(5), 1116; https://doi.org/10.3390/molecules29051116 - 1 Mar 2024

Viewed by 742

Abstract

The analysis of heroin samples, before use in the protected environment of user centra, could be a supplementary service in the context of harm reduction. Infrared spectroscopy hyphenated with multivariate calibration could be a valuable asset in this context, and therefore 125 heroin samples were collected directly from users and analysed with classical chromatographic techniques. Further, Mid-Infrared spectra were collected for all samples, to be used in Partial Least Squares (PLS) modelling, in order to obtain qualitative and quantitative models based on real live samples. The approach showed that it was possible to identify and quantify heroin in the samples based on the collected spectral data and PLS modelling. These models were able to identify heroin correctly for 96% of the samples of the external test set with precision, specificity and sensitivity values of 100.0, 75.0 and 95.5%, respectively. For regression, a root mean squared error of prediction (RMSEP) of 0.04 was obtained, pointing at good predictive properties. Furthermore, during mass spectrometric screening, 10 different adulterants and impurities were encountered. Using the spectral data to model the presence of each of these resulted in performant models for seven of them. All models showed promising correct-classification rates (between 92 and 96%) and good values for sensitivity, specificity and precision. For codeine and morphine, the models were not satisfactory, probably due to the low concentration of these impurities as a consequence of acetylation. For methacetin, the approach failed. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Chemometrics for Authentication of Foods and Drugs (Volume II))

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23 pages, 4184 KiB

Open AccessArticle

The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning

by Zhiliang Kang, Rongsheng Fan, Chunyi Zhan, Youli Wu, Yi Lin, Kunyu Li, Rui Qing and Lijia Xu

Molecules 2024, 29(3), 682; https://doi.org/10.3390/molecules29030682 - 1 Feb 2024

Cited by 1 | Viewed by 754

Abstract

A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475–1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Chemometrics for Authentication of Foods and Drugs (Volume II))

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14 pages, 1742 KiB

Open AccessArticle

Comparative Study of Augmented Classical Least Squares Models for UV Assay of Co-Formulated Antiemetics Together with Related Impurities

by Muneera S. M. Al-Saleem, Hany W. Darwish, Ibrahim A. Naguib and Mohammed E. Draz

Molecules 2023, 28(20), 7044; https://doi.org/10.3390/molecules28207044 - 12 Oct 2023

Viewed by 674

Abstract

The classical least squares (CLS) model and three augmented CLS models are adopted and validated for the analysis of pyridoxine HCl (PYR), cyclizine HCl (CYC), and meclizine HCl (MEC) in a quinary mixture with two related impurities: the CYC main impurity, Benzhydrol (BEH), which has carcinogenic and hepatotoxic effects, and the MEC official impurity, 4-Chlorobenzophenone (BEP). The proposed augmented CLS models are orthogonal signal correction CLS (OSC-CLS), direct orthogonal signal correction CLS (DOSC-CLS), and net analyte processing CLS (NAP-CLS). These models were applied to quantify the three active constituents in their raw materials and their corresponding dosage forms using their UV spectra. To evaluate the CLS-based models sensibly, we design a comparative study involving two sets: the training set to construct models and the validation set to assess the prediction abilities of these models. A five-level, five-factor calibration design was established to produce 25 mixtures for the calibration set. In addition, 16 experiments were performed for a test set distributed equally between the in-space and out-space samples. The primary criterion for comparing the models’ performance was the validation set’s root mean square error of prediction (RMSEP) value. Finally, augmented CLS models showed acceptable results for assaying the three analytes. The results were compared statistically with the reported HPLC methods; however, the DOSC-CLS model proved the best for assaying the dosage forms. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Chemometrics for Authentication of Foods and Drugs (Volume II))

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19 pages, 6429 KiB

Open AccessArticle

Identification of Turtle-Shell Growth Year Using Hyperspectral Imaging Combined with an Enhanced Spatial–Spectral Attention 3DCNN and a Transformer

by Tingting Wang, Zhenyu Xu, Huiqiang Hu, Huaxing Xu, Yuping Zhao and Xiaobo Mao

Molecules 2023, 28(17), 6427; https://doi.org/10.3390/molecules28176427 - 4 Sep 2023

Viewed by 1004

Abstract

Turtle shell (Chinemys reecesii) is a prized traditional Chinese dietary therapy, and the growth year of turtle shell has a significant impact on its quality attributes. In this study, a hyperspectral imaging (HSI) technique combined with a proposed deep learning (DL) network algorithm was investigated for the objective determination of the growth year of turtle shells. The acquisition of hyperspectral images was carried out in the near-infrared range (948.72–2512.97 nm) from samples spanning five different growth years. To fully exploit the spatial and spectral information while reducing redundancy in hyperspectral data simultaneously, three modules were developed. First, the spectral–spatial attention (SSA) module was developed to better protect the spectral correlation among spectral bands and capture fine-grained spatial information of hyperspectral images. Second, the 3D convolutional neural network (CNN), more suitable for the extracted 3D feature map, was employed to facilitate the joint spatial–spectral feature representation. Thirdly, to overcome the constraints of convolution kernels as well as better capture long-range correlation between spectral bands, the transformer encoder (TE) module was further designed. These modules were harmoniously orchestrated, driven by the need to effectively leverage both spatial and spectral information within hyperspectral data. They collectively enhance the model’s capacity to extract joint spatial and spectral features to discern growth years accurately. Experimental studies demonstrated that the proposed model (named SSA–3DTE) achieved superior classification accuracy, with 98.94% on average for five-category classification, outperforming traditional machine learning methods using only spectral information and representative deep learning methods. Also, ablation experiments confirmed the effectiveness of each module to improve performance. The encouraging results of this study revealed the potentiality of HSI combined with the DL algorithm as an efficient and non-destructive method for the quality control of turtle shells. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Chemometrics for Authentication of Foods and Drugs (Volume II))

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21 pages, 1603 KiB

Open AccessArticle

Assessing Fermentation Broth Quality of Pineapple Vinegar Production with a Near-Infrared Fiber-Optic Probe Coupled with Stability Competitive Adaptive Reweighted Sampling

by Sumaporn Kasemsumran, Antika Boondaeng, Sunee Jungtheerapanich, Kraireuk Ngowsuwan, Waraporn Apiwatanapiwat, Phornphimon Janchai and Pilanee Vaithanomsat

Molecules 2023, 28(17), 6239; https://doi.org/10.3390/molecules28176239 - 25 Aug 2023

Viewed by 1182

Abstract

In this study, the performance of a near-infrared (NIR) fiber-optic probe coupled with stability competitive adaptive reweighted sampling (SCARS) was investigated for the analysis of acetic acid, ethanol, total soluble solids, caffeic acid, gallic acid, and tannic acid in the broth of pineapple vinegar during fermentation. The NIR spectra of the broth samples in the region of 11,536–3956 cm⁻¹ were collected during vinegar fermentation promoted by Acetobacter aceti. This continuous biological process led to changes in the concentrations of all analytes studied. SCARS provided optimized and stabilized NIR spectral variables for the construction of a partial least squares (PLS) model for each analyte using a small number of optimal variables (under 88 variables). The SCARS-PLS model outperformed the conventional PLS model, and achieved excellent accuracy in accordance with ISO 12099:2017 for the four prediction models of acetic acid, ethanol, caffeic acid, and gallic acid, with root-mean-square error of prediction values of 0.137%, 0.178%, 0.637 μg/mL and 0.640 μg/mL, respectively. In contrast, only an acetic acid content prediction model constructed via the conventional PLS method and using the whole spectral region (949 variables) could pass with acceptable accuracy. These results indicate that the NIR optical probe coupled with SCARS is an appropriate method for the continuous monitoring of multianalytes during vinegar fermentation, particularly acetic acid and ethanol contents, which are indicators of the finished fermentation of pineapple vinegar. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Chemometrics for Authentication of Foods and Drugs (Volume II))

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13 pages, 1781 KiB

Open AccessArticle

Hybrid Raman and Laser-Induced Breakdown Spectroscopy for Food Authentication Applications

by Sungho Shin, Iyll-Joon Doh, Kennedy Okeyo, Euiwon Bae, J. Paul Robinson and Bartek Rajwa

Molecules 2023, 28(16), 6087; https://doi.org/10.3390/molecules28166087 - 16 Aug 2023

Cited by 1 | Viewed by 1170

Abstract

The issue of food fraud has become a significant global concern as it affects both the quality and safety of food products, ultimately resulting in the loss of customer trust and brand loyalty. To address this problem, we have developed an innovative approach that can tackle various types of food fraud, including adulteration, substitution, and dilution. Our methodology utilizes an integrated system that combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Although both techniques emerged as valuable tools for food analysis, they have until now been used separately, and their combined potential in food fraud has not been thoroughly tested. The aim of our study was to demonstrate the potential benefits of integrating Raman and LIBS modalities in a portable system for improved product classification and subsequent authentication. In pursuit of this objective, we designed and tested a compact, hybrid Raman/LIBS system, which exhibited distinct advantages over the individual modalities. Our findings illustrate that the combination of these two modalities can achieve higher accuracy in product classification, leading to more effective and reliable product authentication. Overall, our research highlights the potential of hybrid systems for practical applications in a variety of industries. The integration and design were mainly focused on the detection and characterization of both elemental and molecular elements in various food products. Two different sets of solid food samples (sixteen Alpine-style cheeses and seven brands of Arabica coffee beans) were chosen for the authentication analysis. Class detection and classification were accomplished through the use of multivariate feature selection and machine-learning procedures. The accuracy of classification was observed to improve by approximately 10% when utilizing the hybrid Raman/LIBS spectra, as opposed to the analysis of spectra from the individual methods. This clearly demonstrates that the hybrid system can significantly improve food authentication accuracy while maintaining the portability of the combined system. Thus, the successful implementation of a hybrid Raman-LIBS technique is expected to contribute to the development of novel portable devices for food authentication in food as well as other various industries. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Chemometrics for Authentication of Foods and Drugs (Volume II))

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15 pages, 3540 KiB

Open AccessArticle

Analysis of Pork in Beef Sausages Using LC-Orbitrap HRMS Untargeted Metabolomics Combined with Chemometrics for Halal Authentication Study

by Anjar Windarsih, Nor Kartini Abu Bakar, Dachriyanus, Nancy Dewi Yuliana, Florentinus Dika Octa Riswanto and Abdul Rohman

Molecules 2023, 28(16), 5964; https://doi.org/10.3390/molecules28165964 - 9 Aug 2023

Cited by 2 | Viewed by 1352

Abstract

Beef sausage (BS) is one of the most favored meat products due to its nutrition and good taste. However, for economic purposes, BS is often adulterated with pork by unethical players. Pork consumption is strictly prohibited for religions including Islam and Judaism. Therefore, advanced detection methods are highly required to warrant the halal authenticity of BS. This research aimed to develop a liquid chromatography–high-resolution mass spectrometry (LC–HRMS) method to determine the halal authenticity of BS using an untargeted metabolomics approach. LC–HRMS was capable of detecting various metabolites in BS and BS containing pork. The presence of pork in BS could be differentiated using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) with high accuracy. PLS-DA perfectly classified authentic BS and BS containing pork in all concentration levels of pork with R²X = (0.821), R²Y(= 0.984), and Q² = (0.795). The level of pork in BS was successfully predicted through partial least squares (PLS) and orthogonal PLS (OPLS) chemometrics. Both models gave high R² (>0.99) actual and predicted values as well as few errors, indicating good accuracy and precision. Identification of discriminating metabolites’ potential as biomarker candidates through variable importance for projections (VIP) value revealed metabolites of 2-arachidonyl-sn-glycero-3-phosphoethanolamine, 3-hydroxyoctanoylcarnitine, 8Z,11Z,14Z-eicosatrienoic acid, D-(+)-galactose, oleamide, 3-hydroxyhexadecanoylcarnitine, arachidonic acid, and α-eleostearic acid as good indicators to detect pork. It can be concluded that LC–HRMS metabolomics combined with PCA, PLS-DA, PLS, and OPLS was successfully used to detect pork adulteration in beef sausages. The results imply that LC–HRMS untargeted metabolomics in combination with chemometrics is a promising alternative as an analytical technique to detect pork in sausage products. Further analysis of larger samples is required to warrant the reproducibility. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Chemometrics for Authentication of Foods and Drugs (Volume II))

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14 pages, 2837 KiB

Open AccessArticle

Discrimination of Milk Freshness Based on Synchronous Two-Dimensional Visible/Near-Infrared Correlation Spectroscopy Coupled with Chemometrics

by Dan Peng, Rui Xu, Qi Zhou, Jinxia Yue, Min Su, Shaoshuai Zheng and Jun Li

Molecules 2023, 28(15), 5728; https://doi.org/10.3390/molecules28155728 - 28 Jul 2023

Cited by 1 | Viewed by 831

Abstract

Milk is one of the preferred beverages in modern healthy diets, and its freshness is of great significance for product sales and applications. By combining the two-dimensional (2D) correlation spectroscopy technique and chemometrics, a new method based on visible/near-infrared (Vis/NIR) spectroscopy was proposed to discriminate the freshness of milk. To clarify the relationship be-tween the freshness of milk and the spectra, the changes in the physicochemical indicators of milk during storage were analyzed as well as the Vis/NIR spectra and the 2D-Vis/NIR correlation spectra. The threshold-value method, linear discriminant analysis (LDA) method, and support vector machine (SVM) method were used to construct the discriminant models of milk freshness, and the parameters of the SVM-based models were optimized by the grid search method and particle swarm optimization algorithm. The results showed that with the prolongation of storage time, the absorbance of the Vis/NIR spectra of milk gradually increased, and the intensity of autocorrelation peaks and cross peaks in synchronous 2D-Vis/NIR spectra also increased significantly. Compared with the SVM-based models using Vis/NIR spectra, the SVM-based model using 2D-Vis/NIR spectra had a >15% higher prediction accuracy. Under the same conditions, the prediction performances of the SVM-based models were better than those of the threshold-value-based or LDA-based models. In addition, the accuracy rate of the SVM-based model using the synchronous 2D-Vis/NIR autocorrelation spectra was >97%. This work indicates that the 2D-Vis/NIR correlation spectra coupled with chemometrics is a great pattern to rapidly discriminate the freshness of milk, which provides technical support for improving the evaluation system of milk quality and maintaining the safety of milk product quality. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Chemometrics for Authentication of Foods and Drugs (Volume II))

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8 pages, 2149 KiB

Open AccessCommunication

Rapid Detection of Benzo[a]pyrene in Extra Virgin Olive Oil Using Fluorescence Spectroscopy

by Emmanouil Orfanakis, Aggeliki Koumentaki, Aikaterini Zoumi, Aggelos Philippidis, Peter C. Samartzis and Michalis Velegrakis

Molecules 2023, 28(11), 4386; https://doi.org/10.3390/molecules28114386 - 27 May 2023

Cited by 3 | Viewed by 1973

Abstract

Extra virgin olive oil (EVOO) should be naturally free of polycyclic aromatic hydrocarbon (PAH) contamination. PAHs are carcinogenic and toxic, and may cause human health and safety problems. This work aims to detect benzo[a]pyrene residues in EVOO using an easily adaptive optical methodology. This approach, which is based on fluorescence spectroscopy, does not require any sample pretreatment or prior extraction of PAH content from the sample, and is reported for the first time herein. The detection of benzo[a]pyrene even at low concentrations in extra virgin olive oil samples demonstrates fluorescence spectroscopy’s capability to ensure food safety. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Chemometrics for Authentication of Foods and Drugs (Volume II))

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14 pages, 1818 KiB

Open AccessArticle

Differentiation of Medicinal Plants According to Solvents, Processing, Origin, and Season by Means of Multivariate Analysis of Spectroscopic and Liquid Chromatography Data

by Lenka Burdejova, Blanka Tobolkova, Martin Polovka and Jarmila Neugebauerova

Molecules 2023, 28(10), 4075; https://doi.org/10.3390/molecules28104075 - 13 May 2023

Cited by 2 | Viewed by 1762

Abstract

Effects of processing and extraction solvents on antioxidant properties and other characteristics were evaluated for ten medicinal plant species originating from two different localities and two production years. A combination of spectroscopic and liquid chromatography techniques possessed data for multivariate statistics. Water, 50% (v/v) ethanol, and dimethyl sulfoxide (DMSO) were compared to select the most suitable solvent for the isolation of functional components from the frozen/dried medicinal plants. DMSO and 50% (v/v) ethanol were evaluated as more efficient for phenolic compounds and colorants extraction, while water was more useful for element extraction. Drying and extraction of herbs with 50% (v/v) ethanol was the most appropriate treatment to ensure a high yield of most compounds. The satisfactory differentiation of herbs (61.8–100%) confirmed the significant effect of the processing, geographical, and seasonal factors on target functional component concentrations. Total phenolic and total flavonoid compounds content, total antioxidant activity expressed as TAA, yellowness, chroma, and browning index were identified as the most important markers for medicinal plant differentiation. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Chemometrics for Authentication of Foods and Drugs (Volume II))

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Review

Jump to: Research

17 pages, 2465 KiB

Open AccessReview

Research Progress of Rapid Non-Destructive Detection Technology in the Field of Apple Mold Heart Disease

by Yanlei Li, Zihao Yang, Wenxiu Wang, Xiangwu Wang, Chunzhi Zhang, Jun Dong, Mengyu Bai and Teng Hui

Molecules 2023, 28(24), 7966; https://doi.org/10.3390/molecules28247966 - 6 Dec 2023

Viewed by 883

Abstract

Apples are rich in vitamins and dietary fiber and are one of the essential fruits in people’s daily diet. China has always been a big apple consumer, and with the improvement of people’s life quality, nutrition, and health requirements, the demand for high-quality apples has increased year by year. Apple mold heart disease is one of the main diseases affecting apple quality. However, this disease cannot be easily detected from the surface, so it is difficult to detect mold heart disease. Therefore, this paper focuses on the analysis of seven non-destructive detection technologies, including near infrared spectroscopy technology, hyperspectral technology, Raman spectroscopy technology, electronic nose technology, acoustic technology, electrical technology, and magnetic technology, summarizes their application status in the detection of apple mold heart disease, and then analyzes their advantages and disadvantages. Combined with the current rapid development of artificial intelligence (AI) technology, this paper proposes the future development trends of using non-destructive technologies to detect apple mold heart disease. It is expected to provide basic theory and application references for the intelligent detection of apple mold heart disease. Full article

(This article belongs to the Special Issue Application of Spectroscopy and Chemometrics for Authentication of Foods and Drugs (Volume II))

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