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Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches

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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: closed (31 January 2023) | Viewed by 29543

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Dr. Mourad Kharbach

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Guest Editor

Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
Interests: spectroscopy; foods; statistics; chemometrics; material sciences; climate change; catalytic reactions; mixture analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Samuli Urpelainen

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Guest Editor

Nano and Molecular Systems Research Unit, University of Oulu, FI-90014 Oulu, Finland
Interests: spectroscopy; material sciences; climate change; catalytic reactions; mixture analysis; statistics; chemometrics

Special Issue Information

Dear Colleagues,

Due to the globalization of the food market, food producers must satisfy consumers while fulfilling the required food safety and quality standards. Food quality analysis may include, among others, chemical characterization, physical properties, sensory evaluation, authentication, origin traceability, processing, harvest, storage, microbiological and toxic contamination. Food analysis is the process that controls all the sub-mentioned steps, in which traditional analytical techniques are widely used. However, conventional analytical techniques use destructive procedures, which are laborious, time-consuming, expensive and contaminating. In this context, advanced spectroscopic techniques, for instance, X-ray-based methods, hyperspectral and multispectral imaging, NMR, Raman, IR, mass, UV, visible and fluorescence, are non-destructive, fast, use less solvent, environmentally friendly and inexpensive. Statistical analysis including chemometric approaches (preprocessing, exploration, variable selection, classification, regression and data fusion) is crucial to extract and investigate the relevant information hidden in the spectra (fingerprints) or image data. In addition, the extracted information (spectral features) from one or multiple spectroscopic sources allows the construction of calibration models that may serve for qualitative and or quantitative analysis of studied foods. Advanced spectroscopic techniques and chemometric tools may have great application in food science and technology, as well as in achieving consumer confidence.

This Special Issue will address and publish the recent advanced spectroscopy techniques focused on their use in the food analysis, quality evaluation, safety assessment and practical industrial applications, with an emphasis on chemometric approaches.

Dr. Mourad Kharbach
Dr. Samuli Urpelainen
Guest Editors

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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 quality
food authenticity
food contaminants
foodomics
spectroscopy
chemometrics
multivariate analysis

Published Papers (12 papers)

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Editorial

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3 pages, 185 KiB

Open AccessEditorial

Special Issue “Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches”

by Mourad Kharbach and Samuli Urpelainen

Foods 2023, 12(15), 2831; https://doi.org/10.3390/foods12152831 - 26 Jul 2023

Viewed by 823

Abstract

The globalization of the food market has created a pressing need for food producers to meet the ever-increasing demands of consumers while ensuring adherence to stringent food safety and quality standards [...] Full article

(This article belongs to the Special Issue Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches)

Research

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16 pages, 2248 KiB

Open AccessArticle

Heavy Metal Detection in Fritillaria thunbergii Using Laser-Induced Breakdown Spectroscopy Coupled with Variable Selection Algorithm and Chemometrics

by Muhammad Hilal Kabir, Mahamed Lamine Guindo, Rongqin Chen, Xinmeng Luo, Wenwen Kong and Fei Liu

Foods 2023, 12(6), 1125; https://doi.org/10.3390/foods12061125 - 07 Mar 2023

Cited by 1 | Viewed by 1611

Abstract

Environmental and health risks associated with heavy metal pollution are serious. Human health can be adversely affected by the smallest amount of heavy metals. Modeling spectrum requires the careful selection of variables. Hence, simple variables that have a low level of interference and a high degree of precision are required for fast analysis and online detection. This study used laser-induced breakdown spectroscopy coupled with variable selection and chemometrics to simultaneously analyze heavy metals (Cd, Cu and Pb) in Fritillaria thunbergii. A total of three machine learning algorithms were utilized, including a gradient boosting machine (GBM), partial least squares regression (PLSR) and support vector regression (SVR). Three promising wavelength selection methods were evaluated for comparison, namely, a competitive adaptive reweighted sampling method (CARS), a random frog method (RF), and an uninformative variable elimination method (UVE). Compared to full wavelengths, the selected wavelengths produced excellent results. Overall, RC², RV², RP², RSMEC, RSMEV and RSMEP for the selected variables are as follows: 0.9967, 0.8899, 0.9403, 1.9853 mg kg⁻¹, 11.3934 mg kg⁻¹, 8.5354 mg kg⁻¹; 0.9933, 0.9316, 0.9665, 5.9332 mg kg⁻¹, 18.3779 mg kg⁻¹, 11.9356 mg kg⁻¹; 0.9992, 0.9736, 0.9686, 1.6707 mg kg⁻¹, 10.2323 mg kg⁻¹, 10.1224 mg kg⁻¹ were obtained for Cd Cu and Pb, respectively. Experimental results showed that all three methods could perform variable selection effectively, with GBM-UVE for Cd, SVR-RF for Pb, and GBM-CARS for Cu providing the best results. The results of the study suggest that LIBS coupled with wavelength selection can be used to detect heavy metals rapidly and accurately in Fritillaria by extracting only a few variables that contain useful information and eliminating non-informative variables. Full article

(This article belongs to the Special Issue Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches)

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12 pages, 1820 KiB

Open AccessArticle

Phenotypic Analysis of Fourier-Transform Infrared Milk Spectra in Dairy Goats

by Bartolo de Jesús Villar-Hernández, Nicolò Amalfitano, Alessio Cecchinato, Michele Pazzola, Giuseppe Massimo Vacca and Giovanni Bittante

Foods 2023, 12(4), 807; https://doi.org/10.3390/foods12040807 - 14 Feb 2023

Cited by 2 | Viewed by 1832

Abstract

The infrared spectrum of bovine milk is used to predict many interesting traits, whereas there have been few studies on goat milk in this regard. The objective of this study was to characterize the major sources of variation in the absorbance of the infrared spectrum in caprine milk samples. A total of 657 goats belonging to 6 breeds and reared on 20 farms under traditional and modern dairy systems were milk-sampled once. Fourier-transform infrared (FTIR) spectra were taken (2 replicates per sample, 1314 spectra), and each spectrum contained absorbance values at 1060 different wavenumbers (5000 to 930 × cm⁻¹), which were treated as a response variable and analyzed one at a time (i.e., 1060 runs). A mixed model, including the random effects of sample/goat, breed, flock, parity, stage of lactation, and the residual, was used. The pattern and variability of the FTIR spectrum of caprine milk was similar to those of bovine milk. The major sources of variation in the entire spectrum were as follows: sample/goat (33% of the total variance); flock (21%); breed (15%); lactation stage (11%); parity (9%); and the residual unexplained variation (10%). The entire spectrum was segmented into five relatively homogeneous regions. Two of them exhibited very large variations, especially the residual variation. These regions are known to be affected by the absorbance of water, although they also exhibited wide variations in the other sources of variation. The average repeatability of these two regions were 45% and 75%, whereas for the other three regions it was about 99%. The FTIR spectrum of caprine milk could probably be used to predict several traits and to authenticate the origin of goat milk. Full article

(This article belongs to the Special Issue Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches)

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13 pages, 3734 KiB

Open AccessArticle

Extracting Tissue Optical Properties and Detecting Bruised Tissue in Pears Quickly and Accurately Based on Spatial Frequency Domain Imaging and Machine Learning

by Shengqiang Xing, Jiaming Zhang, Yifeng Luo, Yang Yang and Xiaping Fu

Foods 2023, 12(2), 238; https://doi.org/10.3390/foods12020238 - 04 Jan 2023

Cited by 5 | Viewed by 1418

Abstract

Recently, Spatial Frequency Domain Imaging (SFDI) has gradually become an alternative method to extract tissue optical properties (OPs), as it provides a wide-field, no-contact acquisition. SFDI extracts OPs by least-square fitting (LSF) based on the diffuse approximation equation, but there are shortcomings in the speed and accuracy of extracting OPs. This study proposed a Long Short-term Memory Regressor (LSTMR) solution to extract tissue OPs. This method allows for fast and accurate extraction of tissue OPs. Firstly, the imaging system was developed, which is more compact and portable than conventional SFDI systems. Next, numerical simulation was performed using the Monte Carlo forward model to obtain the dataset, and then the mapping model was established using the dataset. Finally, the model was applied to detect the bruised tissue of ‘crown’ pears. The results show that the mean absolute errors of the absorption coefficient and the reduced scattering coefficient are no more than 0.32% and 0.21%, and the bruised tissue of ‘crown’ pears can be highlighted by the change of OPs. Compared with the LSF, the speed of extracting tissue OPs is improved by two orders of magnitude, and the accuracy is greatly improved. The study contributes to the rapid and accurate extraction of tissue OPs based on SFDI and has great potential in food safety assessment. Full article

(This article belongs to the Special Issue Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches)

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13 pages, 677 KiB

Open AccessArticle

Discriminant Analysis of Brazilian Stingless Bee Honey Reveals an Iron-Based Biogeographical Origin

by Flavia C. Lavinas, Brendo A. Gomes, Marcos V. T. Silva, Renata M. Nunes, Suzana G. Leitão, Mirian R. L. Moura, Rosineide C. Simas, Carla S. Carneiro and Igor A. Rodrigues

Foods 2023, 12(1), 180; https://doi.org/10.3390/foods12010180 - 01 Jan 2023

Cited by 4 | Viewed by 1851

Abstract

Stingless bee honey (SBH) is gaining attention due to its nutritional, sensorial, and medicinal characteristics. This study focuses on the combination of physicochemical properties, antioxidant capacity, mineral profile, and mass spectrometry-based fingerprints, using a chemometric approach to differentiate SBH (n = 18) from three different Brazilian biogeographical zones (Caatinga, Cerrado, and Atlantic Forest). The physicochemical properties of SBH varied, resulting in a wide range of water activity, moisture, total soluble solids, pH, and total and free acidity. The Caatinga honey showed the highest and the lowest contents of phenolics and flavonoids, respectively. The antioxidant free-radical scavenging assays demonstrated that the Brazilian SBH has a high antioxidant potential. The mineral profile of honey samples from the Atlantic Forest revealed higher contents of Ca and Fe while the Cerrado and Caatinga honey showed the highest P contents. Partial Least-Squares Discriminant Analysis (PLS-DA) analysis separated the samples into three groups based on the biogeographical zones of harvest. The main separation factors between groups were the m/z 326 ion and the Fe content. Univariate analysis confirmed that Fe content is important for SBH discrimination. The present results indicate that the origin of SBH can be determined on the basis of mineral profile, especially Fe content. Full article

(This article belongs to the Special Issue Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches)

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17 pages, 798 KiB

Open AccessArticle

Quality Evaluation of Fair-Trade Cocoa Beans from Different Origins Using Portable Near-Infrared Spectroscopy (NIRS)

by Matteo Forte, Sarah Currò, Davy Van de Walle, Koen Dewettinck, Massimo Mirisola, Luca Fasolato and Paolo Carletti

Foods 2023, 12(1), 4; https://doi.org/10.3390/foods12010004 - 20 Dec 2022

Cited by 3 | Viewed by 3118

Abstract

Determining cocoa bean quality is crucial for many players in the international supply chain. However, actual methods rely on a cut test protocol, which is limited by its subjective nature, or on time-consuming, expensive and destructive wet-chemistry laboratory procedures. In this context, the application of near infrared (NIR) spectroscopy, particularly with the recent developments of portable NIR spectrometers, may represent a valuable solution for providing a cocoa beans’ quality profile, in a rapid, non-destructive, and reliable way. Monitored parameters in this work were dry matter (DM), ash, shell, fat, protein, total polyphenols, fermentation index (FI), titratable acidity (TA) and pH. Different chemometric analyses were performed on the spectral data and calibration models were developed using modified partial least squares regression. Prediction equations were validated using a fivefold cross-validation and a comparison between the different prediction performances for the portable and benchtop NIR spectrometers was provided. The NIRS benchtop instrument provided better performance of quantification considering the whole than the portable device, showing excellent prediction capability in protein and DM quantification. On the other hand, the NIRS portable device, although showing lower but valuable performance of prediction, can represent an appealing alternative to benchtop instruments for food business operators, being applicable in the field. Full article

(This article belongs to the Special Issue Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches)

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16 pages, 3601 KiB

Open AccessArticle

Effect of Moisture Content Difference on the Analysis of Quality Attributes of Red Pepper (Capsicum annuum L.) Powder Using a Hyperspectral System

by Ji-Young Choi, Jeong-Seok Cho, Kee Jai Park, Jeong Hee Choi and Jeong Ho Lim

Foods 2022, 11(24), 4086; https://doi.org/10.3390/foods11244086 - 17 Dec 2022

Cited by 1 | Viewed by 1766

Abstract

The variety of characteristics of red pepper makes it difficult to analyze at the production field through hyperspectral imaging. The importance of pretreatment to adjust the moisture content (MC) in the process of predicting the quality attributes of red pepper powder through hyperspectral imaging was investigated. Hyperspectral images of four types of red pepper powder with different pungency levels and MC were acquired in the visible near-infrared (VIS-NIR) and short-wave infrared (SWIR) regions. Principal component analysis revealed that the powders were grouped according to their pungency level, color value, and MC (VIS-NIR, Principal Component 1 = 95%; SWIR, Principal Component 1 = 91%). The loading plot indicated that 580–610, 675–760, 870–975, 1020–1130, and 1430–1520 nm are the key wavelengths affected by the presence of O-H and C-H bonds present in red pigments, capsaicinoids, and water molecules. The R² of the partial least squares model for predicting capsaicinoid and free sugar in samples with a data MC difference of 0–2% was 0.9 or higher, and a difference of more than 2% in MC had a negative effect on prediction accuracy. The color value prediction accuracy was barely affected by the difference in MC. It was demonstrated that adjusting the MC is essential for capsaicinoid and free sugar analysis of red pepper. Full article

(This article belongs to the Special Issue Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches)

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14 pages, 6114 KiB

Open AccessArticle

Time-Resolved Laser-Induced Breakdown Spectroscopy for Accurate Qualitative and Quantitative Analysis of Brown Rice Flour Adulteration

by Honghua Ma, Shengqun Shi, Deng Zhang, Nan Deng, Zhenlin Hu, Jianguo Liu and Lianbo Guo

Foods 2022, 11(21), 3398; https://doi.org/10.3390/foods11213398 - 27 Oct 2022

Cited by 7 | Viewed by 1605

Abstract

To solve the adulteration problem of brown rice flour in the commodity market, a novel, accurate, and stable detection method based on time-resolved laser-induced breakdown spectroscopy (TR-LIBS) is proposed. Qualitative and quantitative analysis was used to detect five adulterants and seven different adulterant ratios in brown rice flour. Being able to excavate more information from plasma by obtaining time-resolved spectra, TR-LIBS has a stronger performance, which has been further verified by experiments. For the qualitative analysis of adulterants, the traditional machine learning models based on TR-LIBS, linear discriminant analysis (LDA), naïve Bayes (NB) and support vector machine (SVM) have significantly better classification accuracy than those based on traditional LIBS, increasing by 3–11%. The deep learning classification model based on TR-LIBS also achieved the same results, with an accuracy increase of more than 8%. For the quantitative analysis of the adulteration ratio, compared with traditional LIBS, the quantitative model based on TR-LIBS reduces the limit of detection (LOD) of five adulterants from about 8–51% to 4–19%, which effectively improves the quantitative detection performance. Moreover, t-SNE visualization proved that there were more obvious boundaries between different types of samples based on TR-LIBS. These results demonstrate the great prospect of TR-LIBS in the identification of brown rice flour adulteration. Full article

(This article belongs to the Special Issue Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches)

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11 pages, 1835 KiB

Open AccessArticle

Classification of Prunus Genus by Botanical Origin and Harvest Year Based on Carbohydrates Profile

by Marius Gheorghe Miricioiu, Roxana Elena Ionete, Diana Costinel and Oana Romina Botoran

Foods 2022, 11(18), 2838; https://doi.org/10.3390/foods11182838 - 14 Sep 2022

Cited by 2 | Viewed by 1366

Abstract

The ¹H-NMR carbohydrates profiling was used to discriminate fruits from Rosaceae family in terms of botanical origin and harvest year. The classification was possible by application of multivariate data analysis, such as principal component analysis (PCA), linear discriminant analysis (LDA) and Pearson analysis. Prior, a heat map was created based on ¹H-NMR signals which offered an overview of the content of individual carbohydrates in plum, apricot, cherry and sour cherry, highlighting the similarities. Although, the PCA results were almost satisfactory, based only on carbohydrates signals, the LDA reached 94.39% and 100% classification of fruits according to their botanical origin and growing season, respectively. Additionally, a potential association with the relevant climatic data was explored by applying the Pearson analysis. These findings are intended to create an efficient NMR-based solution capable of differentiating fruit juices based on their basic sugar profile. Full article

(This article belongs to the Special Issue Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches)

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14 pages, 2270 KiB

Open AccessArticle

Chemical Authentication and Speciation of Salvia Botanicals: An Investigation Utilizing GC/Q-ToF and Chemometrics

by Joseph Lee, Mei Wang, Jianping Zhao, Bharathi Avula, Amar G. Chittiboyina, Jing Li, Charles Wu and Ikhlas A. Khan

Foods 2022, 11(14), 2132; https://doi.org/10.3390/foods11142132 - 19 Jul 2022

Cited by 3 | Viewed by 1829

Abstract

Members of the genus Salvia are used as culinary herbs and are prized for their purported medicinal attributes. Since physiological effects can vary widely between species of Salvia, it is of great importance to accurately identify botanical material to ensure safety for consumers. In the present study, an in-depth chemical investigation is performed utilizing GC/Q-ToF combined with chemometrics. Twenty-four authentic plant samples representing five commonly used Salvia species, viz. S. apiana, S. divinorum, S. mellifera, S. miltiorrhiza, and S. officinalis, are analyzed using a GC/Q-ToF technique. High-resolution spectral data are employed to construct a sample class prediction (SCP) model followed by principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). This model demonstrates 100% accuracy for both prediction and recognition abilities. Additionally, the marker compounds present in each species are identified. Furthermore, to reduce the time required and increase the confidence level for compound identification and the classification of different Salvia species, a personal compound database and library (PCDL) containing marker and characteristic compounds is constructed. By combining GC/Q-ToF, chemometrics, and PCDL, the unambiguous identification of Salvia botanicals is achieved. This high-throughput method can be utilized for species specificity and to probe the overall quality of various Salvia-based products. Full article

(This article belongs to the Special Issue Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches)

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16 pages, 3738 KiB

Open AccessArticle

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

by Weixin Ye, Tianying Yan, Chu Zhang, Long Duan, Wei Chen, Hao Song, Yifan Zhang, Wei Xu and Pan Gao

Foods 2022, 11(11), 1609; https://doi.org/10.3390/foods11111609 - 30 May 2022

Cited by 27 | Viewed by 4169

Abstract

Rapid and accurate detection of pesticide residue levels can help to prevent the harm of pesticide residue. This study used visible/near-infrared (Vis-NIR) (376–1044 nm) and near-infrared (NIR) (915–1699 nm) hyperspectral imaging systems (HISs) to detect the level of pesticide residues. Three different varieties of grapes were sprayed with four levels of pesticides. Logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and residual neural network (ResNet) models were used to build classification models for pesticide residue levels. The saliency maps of CNN and ResNet were conducted to visualize the contribution of wavelengths. Overall, the results of NIR spectra performed better than those of Vis-NIR spectra. For Vis-NIR spectra, the best model was ResNet, with the accuracy of over 93%. For NIR spectra, LR was the best, with the accuracy of over 97%, but SVM, CNN, and ResNet also showed closed and fine results. The saliency map of CNN and ResNet presented similar and closed ranges of crucial wavelengths. Overall results indicated deep learning performed better than conventional machine learning. The study showed that the use of hyperspectral imaging technology combined with machine learning can effectively detect the level of pesticide residues in grapes. Full article

(This article belongs to the Special Issue Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches)

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Review

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46 pages, 2758 KiB

Open AccessEditor’s ChoiceReview

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

by Mourad Kharbach, Mohammed Alaoui Mansouri, Mohammed Taabouz and Huiwen Yu

Foods 2023, 12(14), 2753; https://doi.org/10.3390/foods12142753 - 19 Jul 2023

Cited by 14 | Viewed by 6119

Abstract

In today’s era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis. Full article

(This article belongs to the Special Issue Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches)

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