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Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control

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A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: 27 August 2024 | Viewed by 7605

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Dr. Mourad Kharbach

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Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
Interests: spectroscopy; foods; statistics; chemometrics; material sciences; climate change; catalytic reactions; mixture analysis
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Dear Colleagues,

The field of food analysis and quality control has greatly benefited from the advancements in chromatographic and spectroscopic techniques. These techniques play a pivotal role in ensuring the safety, authenticity, and quality of food products. Traditional analytical methods, although widely used, often suffer from limitations such as time-consuming procedures, destructive sample preparation, and high costs.

In recent years, advanced chromatographic and spectroscopic techniques have emerged as powerful tools for food analysis and quality control. Chromatographic techniques, including gas chromatography (GC) and liquid chromatography (LC), provide excellent separation and identification capabilities, enabling the detection of various compounds in complex food matrices. These techniques offer enhanced sensitivity, selectivity, and efficiency, making them indispensable in food applications.

Complementing the chromatographic methods, spectroscopic techniques encompass a range of approaches such as infrared spectroscopy (IR), nuclear magnetic resonance (NMR), mass spectrometry (MS), and ultraviolet-visible (UV-Vis) spectroscopy. These techniques provide valuable insights into the chemical composition, structural characteristics, and functional properties of food components. They offer advantages such as non-destructive analysis, rapid detection, and minimal sample preparation requirements.

Furthermore, the integration of chemometric approaches with chromatographic and spectroscopic techniques has revolutionized data analysis in food analysis and quality control. Chemometrics enables the extraction of meaningful information from complex datasets, facilitates pattern recognition, and allows for the quantitative analysis of food samples. Statistical methods, multivariate analysis, and machine learning algorithms aid in the interpretation of chromatographic and spectroscopic data, enabling reliable classification, prediction, and quality assessment.

This Special Issue aims to highlight the recent advancements and applications of advanced chromatographic and spectroscopic techniques in food analysis and quality control. It focuses on research related to method development, validation, and the practical implementation of these techniques in various areas such as food safety, authenticity, nutritional analysis, and quality evaluation. Contributions that explore the integration of chemometrics with chromatography and spectroscopy are particularly encouraged.

By bringing together cutting-edge research and practical industrial applications, this Special Issue seeks to promote the adoption of advanced chromatographic and spectroscopic techniques for enhanced food analysis and quality control, ultimately contributing to the assurance of consumer safety and confidence in the food industry.

Dr. Mourad Kharbach
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

food control
food analysis
food safety
food classification
quality control
authenticity
gas chromatography
liquid chromatography
spectroscopic techniques
chemometric approaches
multivariate analysis

Published Papers (9 papers)

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Research

11 pages, 1198 KiB

Open AccessArticle

Determination of Multiclass Antibiotics in Fish Muscle Using a QuEChERS-UHPLC-MS/MS Method

by Yousra Aissaoui, Gabriel Jiménez-Skrzypek, Javier González-Sálamo, Malika Trabelsi-Ayadi, Ibtissem Ghorbel-Abid and Javier Hernández-Borges

Foods 2024, 13(7), 1081; https://doi.org/10.3390/foods13071081 - 01 Apr 2024

Viewed by 578

Abstract

The surging global demand for fish has increased aquaculture practices, where antibiotics have become indispensable to prevent diseases. However, the passive incorporation of these compounds into the diet may have adverse effects on human health. In this work, the QuEChERS method combined with ultra-high-performance liquid chromatography tandem mass spectrometry was applied for the determination of 10 multiclass antibiotics (5 quinolones, 2 sulfonamides, 2 diaminopyrimidines, and 1 macrolide) in muscle tissue of farmed fish (European sea bass and gilt-head sea bream). The applied method demonstrated acceptable recovery values, mostly between 70 and 120%, with limits of quantification of the method meeting the established EU maximum residue limits. The analysis of twenty fish samples in duplicate revealed that most antibiotics were not present, with the only exception of oxolinic acid and tilmicosin in European sea bass, which were below the limit of quantification of the method. Full article

(This article belongs to the Special Issue Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control)

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18 pages, 1795 KiB

Open AccessArticle

Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion

by Yang Li, Yankun Peng, Yongyu Li, Tianzhen Yin and Bingwei Wang

Foods 2024, 13(7), 1037; https://doi.org/10.3390/foods13071037 - 28 Mar 2024

Viewed by 583

Abstract

Soluble solids content (SSC) is one of the main quality indicators of apples, and it is important to improve the precision of online SSC detection of whole apple fruit. Therefore, the spectral pre-processing method of spectral-to-spectral ratio (S/S), as well as multiple characteristic wavelength member model fusion (MCMF) and characteristic wavelength and non-characteristic wavelength member model fusion (CNCMF) methods, were proposed for improving the detection performance of apple whole fruit SSC by diffuse reflection (DR), diffuse transmission (DT) and full transmission (FT) spectra. The modeling analysis showed that the S/S- partial least squares regression models for all three mode spectra had high prediction performance. After competitive adaptive reweighted sampling characteristic wavelength screening, the prediction performance of all three model spectra was improved. The particle swarm optimization–extreme learning machine models of MCMF and CNCMF had the most significant enhancement effect and could make all three mode spectra have high prediction performance. DR, DT, and FT spectra all had some prediction ability for apple whole fruit SSC, with FT spectra having the strongest prediction ability, followed by DT spectra. This study is of great significance and value for improving the accuracy of the online detection model of apple whole fruit SSC. Full article

(This article belongs to the Special Issue Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control)

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15 pages, 4453 KiB

Open AccessArticle

Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage

by Junyi Wang, Dandan Fu, Zhigang Hu, Yan Chen and Bin Li

Foods 2024, 13(5), 783; https://doi.org/10.3390/foods13050783 - 03 Mar 2024

Viewed by 677

Abstract

The hardness of passion fruit is a critical feature to consider when determining maturity during post-harvest storage. The capacity of near-infrared diffuse reflectance spectroscopy (NIRS) for non-destructive detection of outer and inner hardness of passion fruit epicarp was investigated in this work. The passion fruits’ spectra were obtained using a near-infrared spectrometer with a wavelength range of 10,000–4000 cm⁻¹. The hardness of passion fruit’s outer epicarp (F1) and inner epicarp (F2) was then measured using a texture analyzer. Moving average (MA) and mean-centering (MC) techniques were used to preprocess the collected spectral data. Competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) were used to pick feature wavelengths. Grid-search-optimized random forest (Grids-RF) models and genetic-algorithm-optimized support vector regression (GA-SVR) models were created as part of the modeling process. After MC preprocessing and CARS selection, MC-CARS-Grids-RF model with 7 feature wavelengths had the greatest prediction ability for F1. The mean square error of prediction set (RMSE_P) was 0.166 gN. Similarly, following MA preprocessing, the MA-Grids-RF model displayed the greatest predictive performance for F2, with an RMSE_P of 0.101 gN. When compared to models produced using the original spectra, the R²_P for models formed after preprocessing and wavelength selection improved. The findings showed that near-infrared spectroscopy may predict the hardness of passion fruit epicarp, which can be used to identify quality during post-harvest storage. Full article

(This article belongs to the Special Issue Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control)

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17 pages, 3680 KiB

Open AccessArticle

Development of a Green, Quick, and Efficient Method Based on Ultrasound-Assisted Extraction Followed by HPLC-DAD for the Analysis of Bioactive Glycoalkaloids in Potato Peel Waste

by Isabel Martínez-García, Carlos Gaona-Scheytt, Sonia Morante-Zarcero and Isabel Sierra

Foods 2024, 13(5), 651; https://doi.org/10.3390/foods13050651 - 21 Feb 2024

Viewed by 631

Abstract

α-Solanine and α-chaconine are the two most predominant glycoalkaloids (GAs) present in potato. Potato peel contains a high concentration of GAs, which are especially interesting for application in the pharmaceutical industry due to their different beneficial properties (such as anticarcinogenic, anti-inflammatory, antiallergic, antipyretic, antiviral, fungicide, and antibiotic activities, among others); so, potato peel waste can be valorized by extracting these biologically active compounds. For this, a green, quick, and efficient miniaturized analytical approach based on ultrasound-assisted extraction (UAE) combined with HPLC-DAD was developed to quantify α-solanine and α-chaconine in potato peel. Some parameters of the extraction were optimized, including the extraction method, the type of solvent, and the sample/solvent ratio, by a three-factor, three-level (3³) full factorial experimental design. The optimal extraction conditions were obtained with UAE using methanol as a solvent and a sample/solvent ratio of 1:10 (w/v, g/mL). The analytical greenness metric for sample preparation (AGREEprep) tool was used to assess the greenness of the methods used. The tool revealed an acceptable green analysis, with 0.61 points. The method was validated and applied to the evaluation of GAs in the peel of 15 commercial varieties of potato. The amount of glycoalkaloids found in the samples evaluated ranged from 143 to 1273 mg/kg and from 117 to 1742 mg/kg dry weight for α-solanine and α-chaconine, respectively. These results reveal the important variability that exists between potato varieties; so, their analysis is of great importance to select the most suitable ones for biovalorization (e.g., the Amandine and Rudolph varieties, with around 3000 mg/kg, in total, of both GAs). To provide higher stability to the peel during storage, freeze-drying or a medium-temperature drying process resulted preferable to avoid GA degradation. Overall, this study will contribute to the expansion of the future biovalorization of potato peel waste as well as provide a powerful analytical tool for GA analysis. Full article

(This article belongs to the Special Issue Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control)

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23 pages, 7896 KiB

Open AccessArticle

Degradation and Transformation Mechanisms of Zanthoxylum Alkylamides Exposed to UVB Light

by Rui Wang, Chaolong Rao, Qiuyan Liu and Xiong Liu

Foods 2023, 12(24), 4392; https://doi.org/10.3390/foods12244392 - 06 Dec 2023

Cited by 1 | Viewed by 754

Abstract

Zanthoxylum oleoresin, a concentrated extract derived from Zanthoxylum bungeanum, is rich in non-volatile, intensely flavorful substances and amide compounds, such as hydroxy-α-sanshool, hydroxy-β-sanshool, and hydroxy-ε-sanshool. The production process of Zanthoxylum oleoresin remains unstandardized, and there is still a lack of research on the precise classification and quantification of its key chemical constituents, as well as the stability of these compounds when produced using different extraction methods. This study utilized preparative liquid chromatography to extract and purify amide compounds from Zanthoxylum oleoresin, successfully isolating three sanshools: hydroxy-α-sanshool, hydroxy-β-sanshool, and hydroxy-ε-sanshool. The stability of three these sanshools under UVB irradiation in different solvents was explored in UVB-simulated sunlight conditions to investigate the degradation or transformation mechanism of Zanthoxylum alkylamides under UVB irradiation. The findings indicate a rapid decrease in the hydroxy-α-sanshool content under UVB ultraviolet light, aligning with the second-order kinetics. This study revealed alterations in the contents of hydroxy-α-sanshool, hydroxy-β-sanshool, and hydroxy-ε-sanshool and the formation of a new compound following exposure to UVB light. This new compound, along with the three sanshools, possesses a uniform m/z 264 and shares similar chemical structures. Further analysis also uncovered that these compounds are capable of undergoing isomerization reactions under UVB irradiation. This demonstrates that UVB irradiation of certain intensities can modify the concentrations and chemical structures of these Zanthoxylum alkylamides. These insights offer crucial guidance for future studies on the processing and preservation of Zanthoxylum alkylamides and their derivatives. Full article

(This article belongs to the Special Issue Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control)

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15 pages, 3248 KiB

Open AccessArticle

Simultaneous Quantitative Determination of Low-Concentration Preservatives and Heavy Metals in Tricholoma Matsutakes Based on SERS and FLU Spectral Data Fusion

by Yuanyin Jin, Chun Li, Zhengwei Huang and Ling Jiang

Foods 2023, 12(23), 4267; https://doi.org/10.3390/foods12234267 - 26 Nov 2023

Viewed by 795

Abstract

As an ingredient of great economic value, Tricholoma matsutake has received widespread attention. However, heavy metal residues and preservatives in it will affect the quality of Tricholoma matsutake and endanger the health of consumers. Here, we present a method for the simultaneous detection of low concentrations of potassium sorbate and lead in Tricholoma matsutakes based on surface-enhanced Raman spectroscopy (SERS) and fluorescence (FLU) spectroscopy to test the safety of consumption. Data fusion strategies combined with multiple machine learning methods, including partial least-squares regression (PLSR), deep forest (DF) and convolutional neural networks (CNN) are used for model training. The results show that combined with reasonable band selection, the CNN prediction model based on decision-level fusion achieves the best performance, the correlation coefficients (R²) were increased to 0.9963 and 0.9934, and the root mean square errors (RMSE) were reduced to 0.0712 g·kg⁻¹ and 0.0795 mg·kg⁻¹, respectively. The method proposed in this paper accurately predicts preservatives and heavy metals remaining in Tricholoma matsutake and provides a reference for other food safety testing. Full article

(This article belongs to the Special Issue Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control)

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11 pages, 565 KiB

Open AccessArticle

Simultaneous Determination of 12 Preservatives in Pastries Using Gas Chromatography–Mass Spectrometry

by Liyuan Wang, Zhengyan Hu, Jing Chen, Tianjiao Wang, Pinggu Wu and Ying Ying

Foods 2023, 12(20), 3819; https://doi.org/10.3390/foods12203819 - 18 Oct 2023

Viewed by 1166

Abstract

(1) Background: Preservatives may pose a potential threat to human health. To ensure food safety, this study has devised a method that concurrently detects a dozen preservatives (acetic acid, propionic acid, dehydroacetic acid, benzoic acid, sorbic acid, dimethyl fumarate, methyl parahydroxybenzoate, ethyl parahydroxybenzoate, propyl parahydroxybenzoate, isopropyl parahydroxybenzoate, butyl parahydroxybenzoate, and isobutyl parahydroxybenzoate) in pastry, utilizing gas chromatography–mass spectrometry. (2) Methods: The pastry samples were acidified with hydrochloric acid, extracted with acetonitrile via vortexing, purified by hexane and saturated with sodium chloride solution to remove lipids and impurities, and then concentrated via nitrogen blowing. The method was then quantitatively analyzed using GC-MS with the internal standard method after methanol re-dissolution. (3) Results: The results showed that the content of the 12 preservatives had good linearity within the range of 1.0–50 μg/mL, with correlation coefficients all greater than 0.99. The method detection limit was 0.04–2.00 mg/kg and the quantification limit was 0.12–6.67 mg/kg. The average recovery rates of the samples at three different spiked concentrations of low, medium, and high were 70.18–109.22%, and the relative standard deviations were 1.82–9.79% (n = 6). (4) Conclusions: This method requires a small amount of sample, has high sensitivity, and is simple and fast to operate, making it suitable for the simultaneous determination of 12 preservatives in pastry. This approach contributes to the effective surveillance and regulation of preservative usage in pastries, thereby safeguarding public well-being. Full article

(This article belongs to the Special Issue Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control)

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17 pages, 5334 KiB

Open AccessArticle

Research on Defect Detection in Kubo Peach Based on Hyperspectral Imaging Technology Combined with CARS-MIV-GA-SVM Method

by Lixiu Zhang, Pengcheng Nie, Shujuan Zhang, Liying Zhang and Tianyuan Sun

Foods 2023, 12(19), 3593; https://doi.org/10.3390/foods12193593 - 27 Sep 2023

Viewed by 939

Abstract

Due to the dark red surface of ripe fresh peaches, their internal injury defects cannot be detected using the naked eye and conventional images. The rapid and accurate detection of fresh peach defects can improve the efficiency of fresh peach classification. The goal of this paper was to develop a nondestructive approach to simultaneously detecting internal injury defects and external injuries in fresh peaches. First, we collected spectral data from 347 Kubo peach samples using hyperspectral imaging technology (900–1700 nm) and carried out pretreatment. Four methods (the competitive adaptive reweighting algorithm (CARS), the combination of CARS and the average influence value algorithm (CARS-MIV), the combination of CARS and the successive projections algorithm (CARS-SPA), and the combination of CARS and uninformative variable elimination (CARS-UVE)) were used to extract the characteristic wavelength. Based on the characteristic wavelength extracted using the above methods, a genetic algorithm optimization support vector machine (GA-SVM) model and a least-squares support vector machine (LS-SVM) model were used to establish classification models. The results show that the combination of CARS and other feature wavelength extraction methods can effectively improve the prediction accuracy of the model when the number of wavelengths is small. Among them, the discriminant accuracy of the CARS-MIV-GA-SVM model reaches 93.15%. In summary, hyperspectral imaging technology can accomplish the accurate detection of Kubo peaches defects, and provides feasible ideas for the automatic classification of Kubo peaches. Full article

(This article belongs to the Special Issue Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control)

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11 pages, 10138 KiB

Open AccessArticle

Investigation of ⁶⁰Co Irradiation on the Volatile Organic Compounds from Finger Citron (Citri Sarcodactylis Fructus) Using GC–IMS

by Yun Xiang, Chang Lei, Ge Hu, Wei Zhou, Ya Li and Dan Huang

Foods 2023, 12(19), 3543; https://doi.org/10.3390/foods12193543 - 23 Sep 2023

Cited by 2 | Viewed by 861

Abstract

In recent years, as the desire for a healthy lifestyle has become more widespread, consumers are gaining an increasing appreciation for safe, high-quality food. Researchers are constantly seeking new ways to protect foods from insect pests and fungi. This study used GC-IMS to analyze the volatile organic compounds and flavor characteristics of Finger Citron in response to different doses of ⁶⁰Co irradiation. The principal component analysis method was used to explore the overall differences in flavor spectra, and a total of 60 compounds were identified. The fingerprints of volatile organic compounds in the samples showed that the volatile organic compounds with doses of ⁶⁰Co irradiation in about 0 kGy and 5 kGy are similar, while the 10 kGy samples are quite different. The PCA results showed that the similarity between 0 kGy and 5 kGy was slightly higher, and the difference between 10 kGy and other samples was greater. Therefore, it was determined that ⁶⁰Co irradiation with a 10 kGy intensity has a significant influence on the content of volatile oils components, while ⁶⁰Co irradiation with a 5 kGy intensity has little effect. Irradiation technology is demonstrated as a promising method of food sterilization, but the irradiation dose and chemical composition must be taken into consideration. Full article

(This article belongs to the Special Issue Advanced Chromatographic and Spectroscopic Techniques in Food Analysis and Quality Control)

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