Innovative Chromatographic and Spectroscopic Analytical Methods for Profiling Chemicals in Foods

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 15618

Special Issue Editors

School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: spectroscopy; chemometrics; biomimetic sensors
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: spectroscopy; chemometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food safety is an important public health issue that affects the health of all human beings and the global economy. As an important part of food safety management, food testing can provide corresponding data support for the development of the latter work. With the rapid development of instrumental analysis, advanced technologies of some disciplines continue to penetrate into food analysis, forming an increasing number of analytical instruments and analytical methods. Chromatography is one of the most dynamic fields in food quality analysis in recent decades, which is essentially a method of physicochemical separation and analysis, and spectroscopic analysis refers to an analytical method that uses experimental methods and principles of spectroscopy to determine the chemical composition and structure of substances. Although these two types of methods have been widely used in food safety testing, the innovative applications of these methods at the theoretical and technical levels should also keep up with the trends in response to the increasing demand for modern food testing. Therefore, this Special Issue focuses on modern innovative chromatographic and spectroscopic analysis methods for food quality and safety analysis, which have practical significance and a guiding role in the development of food safety testing technology.


Dr. Hui Jiang
Dr. Huanhuan Li
Guest Editors

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Keywords

  • Column chromatography
  • Thin layer chromatography
  • Gas chromatography
  • High-performance liquid chromatography
  • Near-infrared spectroscopy
  • Infrared spectroscopy
  • Raman spectroscopy
  • Laser-induced breakdown spectroscopy
  • Hyperspectral imaging
  • Chemometrics

Published Papers (7 papers)

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Research

11 pages, 1861 KiB  
Article
Monitoring of Chlorpyrifos Residues in Corn Oil Based on Raman Spectral Deep-Learning Model
by Yingchao Xue and Hui Jiang
Foods 2023, 12(12), 2402; https://doi.org/10.3390/foods12122402 - 17 Jun 2023
Cited by 2 | Viewed by 1415
Abstract
This study presents a novel method for the quantitative detection of residual chlorpyrifos in corn oil through Raman spectroscopy using a combined long short-term memory network (LSTM) and convolutional neural network (CNN) architecture. The QE Pro Raman+ spectrometer was employed to collect Raman [...] Read more.
This study presents a novel method for the quantitative detection of residual chlorpyrifos in corn oil through Raman spectroscopy using a combined long short-term memory network (LSTM) and convolutional neural network (CNN) architecture. The QE Pro Raman+ spectrometer was employed to collect Raman spectra of corn oil samples with varying concentrations of chlorpyrifos residues. A deep-learning model based on LSTM combined with a CNN structure was designed to realize feature self-learning and model training of Raman spectra of corn oil samples. In the study, it was discovered that the LSTM-CNN model has superior generalization performance compared to both the LSTM and CNN models. The root-mean-square error of prediction (RMSEP) of the LSTM-CNN model is 12.3 mg·kg−1, the coefficient of determination (RP2) is 0.90, and the calculation of the relative prediction deviation (RPD) results in a value of 3.2. The study demonstrates that the deep-learning network based on an LSTM-CNN structure can achieve feature self-learning and multivariate model calibration for Raman spectra without preprocessing. The results of this study present an innovative approach for chemometric analysis using Raman spectroscopy. Full article
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16 pages, 3205 KiB  
Article
Differentiating Huangjiu with Varying Sugar Contents from Different Regions Based on Targeted Metabolomics Analyses of Volatile Carbonyl Compounds
by Junting Yu, Zhilei Zhou, Xibiao Xu, Huan Ren, Min Gong, Zhongwei Ji, Shuangping Liu, Zhiming Hu and Jian Mao
Foods 2023, 12(7), 1455; https://doi.org/10.3390/foods12071455 - 29 Mar 2023
Cited by 5 | Viewed by 1365
Abstract
Huangjiu is one of the oldest alcoholic beverages in the world. It is usually made by fermenting grains, and Qu is used as a saccharifying and fermenting agent. In this study, we identified differential carbonyl compounds in Huangjiu with varying sugar contents from [...] Read more.
Huangjiu is one of the oldest alcoholic beverages in the world. It is usually made by fermenting grains, and Qu is used as a saccharifying and fermenting agent. In this study, we identified differential carbonyl compounds in Huangjiu with varying sugar contents from different regions. First, we developed and validated a detection method for volatile carbonyl compounds in Huangjiu, and for optimal extraction, 5 mL of Huangjiu and 1.3 g/L of O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine hydrochloride (PFBHA) were incubated at 45 °C for 5 min before extracting the volatile carbonyl compounds at 45 °C for 35 min. Second, the targeted quantitative analysis of 50 carbonyl compounds in Huangjiu showed high levels of Strecker aldehydes and furans. Finally, orthogonal projections to latent structures discriminant analysis (OPLS-DA) was used to differentiate between Huangjiu with different sugar contents, raw materials, and region of origin. A total of 19 differential carbonyl compounds (VIP > 1, p < 0.05) were found in Huangjiu with different sugar contents (semidry and semisweet Huangjiu), and 20 differential carbonyl compounds (VIP > 1, p < 0.05) were found in different raw materials for Huangjiu production (rice and nonrice Huangjiu). A total of twenty-two and eight differential carbonyl compounds, with VIP > 1 and p < 0.05, were identified in semidry and semisweet Huangjiu from different regions (Zhejiang, Jiangsu, Shanghai, and Fujian), respectively. Full article
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15 pages, 1854 KiB  
Article
Non-Destructive Measurement of Egg’s Haugh Unit by Vis-NIR with iPLS-Lasso Selection
by Leiming Yuan, Xueping Fu, Xiaofeng Yang, Xiaojing Chen, Guangzao Huang, Xi Chen, Wen Shi and Limin Li
Foods 2023, 12(1), 184; https://doi.org/10.3390/foods12010184 - 1 Jan 2023
Cited by 2 | Viewed by 1668
Abstract
Egg freshness is of great importance to daily nutrition and food consumption. In this work, visible near-infrared (vis-NIR) spectroscopy combined with the sparsity of interval partial least square regression (iPLS) were carried out to measure the egg’s freshness by semi-transmittance spectral acquisition. A [...] Read more.
Egg freshness is of great importance to daily nutrition and food consumption. In this work, visible near-infrared (vis-NIR) spectroscopy combined with the sparsity of interval partial least square regression (iPLS) were carried out to measure the egg’s freshness by semi-transmittance spectral acquisition. A fiber spectrometer with a spectral range of 550-985 nm was embedded in the developed spectral scanner, which was designed with rich light irradiation mode from another two reflective surfaces. The semi-transmittance spectra were collected from the waist of eggs and monitored every two days. Haugh unit (HU) is a key indicator of egg’s freshness, and ranged 56–91 in 14 days after delivery. The profile of spectra was analyzed the relation to the changes of egg’s freshness. A series of iPLS models were constructed on the basis of spectral intervals at different divisions of the spectral region to predict the egg’s HU, and then the least absolute shrinkage and selection operator (Lasso) was used to sparse the number of iPLS member models acting as a role of model selection and fusion regression. By optimization of the number of spectral intervals in the range of 1 to 40, the 26th fusion model obtained the best performance with the minimum root mean of squared error of prediction (RMSEP) of 5.161, and performed the best among the general PLS model and other intervals-combined PLS models. This study provided a new, rapid, and reliable method for the non-destructive and in-site determination of egg’s freshness. Full article
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8 pages, 8133 KiB  
Article
Simple and Reliable Determination of the Histamine Content of Selected Greek Vegetables and Related Products in the Frame of “Low Histamine Diet”
by Apostolia Tsiasioti and Paraskevas D. Tzanavaras
Foods 2022, 11(20), 3234; https://doi.org/10.3390/foods11203234 - 17 Oct 2022
Cited by 4 | Viewed by 3580
Abstract
The determination of histamine in Greek foods that should potentially be avoided during a “low histamine diet” is reported herein. Cation exchange chromatography combined to selective post column derivatization proved to be an excellent tool for this type of analysis as well, offering [...] Read more.
The determination of histamine in Greek foods that should potentially be avoided during a “low histamine diet” is reported herein. Cation exchange chromatography combined to selective post column derivatization proved to be an excellent tool for this type of analysis as well, offering accurate results following minimal sample preparation. Tomato-, eggplant- and spinach-related products have been successfully analyzed and were all found to contain histamine. Higher amounts were quantified in eggplants, eggplant salads and spinach in the range of 15.4–34.2 mg kg−1 and lower in fresh tomatoes and related products (0.8–10.6 mg kg−1). The method is capable of determining as low as 0.5 mg kg−1 histamine without matrix effects, with percent recoveries ranging between 87 and 112% (tomatoes and related products), 95 and 119% (eggplants and related products) and 90 and 106% (fresh and frozen spinach). Full article
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12 pages, 10360 KiB  
Article
Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize
by Bo Wang, Jihong Deng and Hui Jiang
Foods 2022, 11(15), 2210; https://doi.org/10.3390/foods11152210 - 25 Jul 2022
Cited by 15 | Viewed by 2355
Abstract
This work provides a novel approach to monitor the aflatoxin B1 (AFB1) content in maize by near-infrared (NIR) spectra-based deep learning models that integrates Markov transition field (MTF) image coding and a convolutional neural network (CNN) strategy. According to the [...] Read more.
This work provides a novel approach to monitor the aflatoxin B1 (AFB1) content in maize by near-infrared (NIR) spectra-based deep learning models that integrates Markov transition field (MTF) image coding and a convolutional neural network (CNN) strategy. According to the data structure characteristics of near-infrared spectra, new structures of one-dimensional CNN (1D-CNN) and two-dimensional MTF-CNN (2D-MTF-CNN) were designed to construct a deep learning model for the monitoring of AFB1 in maize. The results obtained showed that compared with the 1D-CNN model, the performance of the 2D-MTF-CNN model had been significantly improved, and its root mean square error of prediction, coefficient of predictive determination, and relative percent deviation were 1.3591 μg·kg−1, 0.9955, and 14.9386, respectively. The results indicate that the MTF is an effective data encoding technique for converting one-dimensional spectra into two-dimensional images. It more intuitively reflects the intrinsic characteristics of the NIR spectra from a new perspective and provides richer spectral information for the construction of deep learning models, which can ensure the detection accuracy and generalization performance of deep learning quantitative detection models. This study provides a new analytical perspective for the chemometrics analysis of the NIR spectroscopy. Full article
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10 pages, 1928 KiB  
Article
Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM
by Yuhan Ding, Yuli Yan, Jun Li, Xu Chen and Hui Jiang
Foods 2022, 11(11), 1658; https://doi.org/10.3390/foods11111658 - 5 Jun 2022
Cited by 23 | Viewed by 2552
Abstract
In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm−1 using near-infrared spectroscopy. The spectral data were then [...] Read more.
In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm−1 using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky–Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor c and the kernel function parameter g in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%. Full article
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13 pages, 2066 KiB  
Article
High Precisive Prediction of Aflatoxin B1 in Pressing Peanut Oil Using Raman Spectra Combined with Multivariate Data Analysis
by Chengyun Zhu, Hui Jiang and Quansheng Chen
Foods 2022, 11(11), 1565; https://doi.org/10.3390/foods11111565 - 26 May 2022
Cited by 2 | Viewed by 2012
Abstract
This study proposes a label-free rapid detection method for aflatoxin B1 (AFB1) in pressing peanut oil based on Raman spectroscopy technology combined with appropriate chemometric methods. A DXR laser Raman spectrometer was used to acquire the Raman spectra of the [...] Read more.
This study proposes a label-free rapid detection method for aflatoxin B1 (AFB1) in pressing peanut oil based on Raman spectroscopy technology combined with appropriate chemometric methods. A DXR laser Raman spectrometer was used to acquire the Raman spectra of the pressed peanut oil samples, and the obtained spectra were preprocessed by wavelet transform (WT) combined with adaptive iteratively reweighted penalized least squares (airPLS). The competitive adaptive reweighted sampling (CARS) method was used to optimize the characteristic bands of the Raman spectra pretreated by the WT + airPLS, and a partial least squares (PLS) detection model for the AFB1 content was established based on the features optimized. The results obtained showed that the root mean square error of prediction (RMSEP) and determination coefficient of prediction (RP2) of the optimal CARS-PLS model in the prediction set were 22.6 µg/kg and 0.99, respectively. The results demonstrate that the Raman spectroscopy combined with appropriate chemometrics can be used to quickly detect the safety of edible oil with high precision. The overall results can provide a technical basis and method reference for the design and development of the portable Raman spectroscopy system for the quality and safety detection of edible oil storage, and also provide a green tool for fast on-site analysis for regulatory authorities of edible oil and production enterprises of edible oil. Full article
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