Vibrational Spectroscopy, Chemometrics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Engineering and Technology".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 17300

Special Issue Editors


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Guest Editor
Institute of Analytical Chemistry and Radiochemistry, CCB-Center for Chemistry and Biomedicine, Leopold-Franzens University, Innrain 80-82, 6020 Innsbruck, Austria
Interests: vibrational spectroscopy; near-infrared (NIR) spectroscopy; analytical chemistry; physical chemistry; chemometrics; natural product analysis
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute of Analytical Chemistry and Radiochemistry, CCB-Center for Chemistry and Biomedicine, Leopold-Franzens University, Innrain 80-82, 6020 Innsbruck, Austria
Interests: molecular spectroscopy; analytical chemistry; natural product analysis; physical chemistry; chemometrics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute of Analytical Chemistry and Radiochemistry, CCB-Center for Chemistry and Biomedicine, Leopold-Franzens University, Innrain 80-82, 6020 Innsbruck, Austria
Interests: vibrational spectroscopy; separation science; enrichment techniques; mass spectrometry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Quality of food is one of the most important and urgent problems of the modern world. With accelerating increases in world trade, emerging markets, and thriving internationality of food supplies, food safety has become increasingly difficult to maintain. The above factors have induced a long-term and global rise in demand for adequate methods of food quality control and monitoring.

Vibrational spectroscopy is one of the most used techniques for the assessment of the quality of foodstuffs. As an analytical tool, it is useful for sample characterization and qualitative and quantitative analysis. Rapid, on-site analysis performed by handheld spectrometers particularly suits the nature of the discussed field of application, given the complexity of food supply chains and their vulnerability to quality compromise. Progressing miniaturization and simplification of spectroscopic instrumentation, combined with cloud-based analysis of spectral data, brings the technique even closer to the ordinary consumer who gains the ability to perform food analysis on a daily basis. Rapid progress in sensor technology has been accompanied by continuing advancement of data-analytical procedures, which are ubiquitous tools enabling effective spectroscopic analysis. Novel approaches in chemometrics, machine learning, and deep learning (which has recently grown in importance and applicability) make the analysis more reliable and more widely applicable.

This Special Issue collects contributions reporting on the current progress achieved in vibrational spectroscopic methods of food analysis. This includes, but is not limited to, FT-IR, NIR, Raman spectroscopy, hyperspectral imaging techniques, the design of new experimental techniques, data-analytical approaches, and the development of new applications.

Dr. Justyna Grabska
Dr. Krzysztof B. Bec
Prof. Dr. Christian Huck
Guest Editors

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Keywords

  • vibrational spectroscopy
  • analytical chemistry
  • chemometrics
  • food quality
  • data analysis
  • hand-held/portable spectrometers

Published Papers (5 papers)

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Research

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19 pages, 3114 KiB  
Article
Development of PCA-MLP Model Based on Visible and Shortwave Near Infrared Spectroscopy for Authenticating Arabica Coffee Origins
by Agus Dharmawan, Rudiati Evi Masithoh and Hanim Zuhrotul Amanah
Foods 2023, 12(11), 2112; https://doi.org/10.3390/foods12112112 - 24 May 2023
Cited by 8 | Viewed by 1652
Abstract
Arabica coffee, one of Indonesia’s economically important coffee commodities, is commonly subject to fraud due to mislabeling and adulteration. In many studies, spectroscopic techniques combined with chemometric methods have been massively employed in classification issues, such as principal component analysis (PCA) and discriminant [...] Read more.
Arabica coffee, one of Indonesia’s economically important coffee commodities, is commonly subject to fraud due to mislabeling and adulteration. In many studies, spectroscopic techniques combined with chemometric methods have been massively employed in classification issues, such as principal component analysis (PCA) and discriminant analyses, compared to machine learning models. In this study, spectroscopy combined with PCA and a machine learning algorithm (artificial neural network, ANN) were developed to verify the authenticity of Arabica coffee collected from four geographical origins in Indonesia, including Temanggung, Toraja, Gayo, and Kintamani. Spectra from pure green coffee were collected from Vis–NIR and SWNIR spectrometers. Several preprocessing techniques were also applied to attain precise information from spectroscopic data. First, PCA compressed spectroscopic information and generated new variables called PCs scores, which would become inputs for the ANN model. The discrimination of Arabica coffee from different origins was conducted with a multilayer perceptron (MLP)-based ANN model. The accuracy attained ranged from 90% to 100% in the internal cross-validation, training, and testing sets. The error in the classification process did not exceed 10%. The generalization ability of the MLP combined with PCA was superior, suitable, and successful for verifying the origin of Arabica coffee. Full article
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13 pages, 4050 KiB  
Article
Discrimination of Minced Mutton Adulteration Based on Sized-Adaptive Online NIRS Information and 2D Conventional Neural Network
by Zongxiu Bai, Jianfeng Gu, Rongguang Zhu, Xuedong Yao, Lichao Kang and Jianbing Ge
Foods 2022, 11(19), 2977; https://doi.org/10.3390/foods11192977 - 23 Sep 2022
Cited by 1 | Viewed by 1294
Abstract
Single-probe near-infrared spectroscopy (NIRS) usually uses different spectral information for modelling, but there are few reports about its influence on model performance. Based on sized-adaptive online NIRS information and the 2D conventional neural network (CNN), minced samples of pure mutton, pork, duck, and [...] Read more.
Single-probe near-infrared spectroscopy (NIRS) usually uses different spectral information for modelling, but there are few reports about its influence on model performance. Based on sized-adaptive online NIRS information and the 2D conventional neural network (CNN), minced samples of pure mutton, pork, duck, and adulterated mutton with pork/duck were classified in this study. The influence of spectral information, convolution kernel sizes, and classifiers on model performance was separately explored. The results showed that spectral information had a great influence on model accuracy, of which the maximum difference could reach up to 12.06% for the same validation set. The convolution kernel sizes and classifiers had little effect on model accuracy but had significant influence on classification speed. For all datasets, the accuracy of the CNN model with mean spectral information per direction, extreme learning machine (ELM) classifier, and 7 × 7 convolution kernel was higher than 99.56%. Considering the rapidity and practicality, this study provides a fast and accurate method for online classification of adulterated mutton. Full article
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13 pages, 3170 KiB  
Article
Effects of Variations in the Chemical Composition of Individual Rice Grains on the Eating Quality of Hybrid Indica Rice Based on Near-Infrared Spectroscopy
by Weimin Cheng, Zhuopin Xu, Shuang Fan, Pengfei Zhang, Jiafa Xia, Hui Wang, Yafeng Ye, Binmei Liu, Qi Wang and Yuejin Wu
Foods 2022, 11(17), 2634; https://doi.org/10.3390/foods11172634 - 30 Aug 2022
Cited by 2 | Viewed by 2095
Abstract
The chemical composition of individual hybrid rice (F2) varieties varies owing to genetic differences between parental lines, and the effects of these differences on eating quality are unclear. In this study, based on a self-developed near-infrared spectroscopy platform, we explored these effects among [...] Read more.
The chemical composition of individual hybrid rice (F2) varieties varies owing to genetic differences between parental lines, and the effects of these differences on eating quality are unclear. In this study, based on a self-developed near-infrared spectroscopy platform, we explored these effects among a set of 143 hybrid indica rice varieties with different eating qualities. The single-grain amylose content (SGAC) and single-grain protein content (SGPC) models were established with coefficients of determination (R2) of 0.9064 and 0.8847, respectively, and the dispersion indicators (standard deviation, variance, extreme deviation, quartile deviation, and coefficient of variation) were proposed to analyze the variations in the SGAC and SGPC based on the predicted results. Our correlation analysis found that the higher the variation in the SGAC and SGPC, the lower the eating quality of the hybrid indica rice. Moreover, the addition of the dispersion indicators of the SGAC and SGPC improved the R2 of the eating quality model constructed using the composition-related physicochemical indicators (amylose content, protein content, alkali-spreading value, and gel consistency) from 0.657 to 0.850. Therefore, this new method proved to be useful for identifying high-eating-quality hybrid indica rice based on single near-infrared spectroscopy prior to processing and cooking. Full article
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14 pages, 2725 KiB  
Article
Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm
by Binbin Fan, Rongguang Zhu, Dongyu He, Shichang Wang, Xiaomin Cui and Xuedong Yao
Foods 2022, 11(15), 2278; https://doi.org/10.3390/foods11152278 - 30 Jul 2022
Cited by 11 | Viewed by 2007
Abstract
The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral [...] Read more.
The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an RP2 of 0.9304 and an RMSEP of 0.0458 g·g−1. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions. Full article
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Review

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53 pages, 4210 KiB  
Review
Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives
by Krzysztof B. Beć, Justyna Grabska and Christian W. Huck
Foods 2022, 11(10), 1465; https://doi.org/10.3390/foods11101465 - 18 May 2022
Cited by 74 | Viewed by 9176
Abstract
The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive [...] Read more.
The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products. Full article
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