Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
5.2
Journals
Foods
Special Issues
Recent Applications of Near-Infrared Spectroscopy in Food Analysis
share announcement

Recent Applications of Near-Infrared Spectroscopy in Food Analysis

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

Deadline for manuscript submissions: closed (16 December 2023) | Viewed by 8769

Special Issue Editors

Dr. Mohammad Nadimi

E-Mail Website
Guest Editor
Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Interests: spectroscopy; microstructural analysis; chemometrics; imaging; machine learning; deep learning; statistical analysis; lasers
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jitendra Paliwal

E-Mail Website
Guest Editor
Faculty of Agricultural and Food Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
Interests: image processing; infrared and Raman spectroscopy; near-infrared hyperspectral imaging; chemometrics; artificial neural networks

Special Issue Information

Dear Colleagues,

With the ever-increasing world population, food demands are expected to continue growing. Since production cannot be increased indefinitely, more emphasis is needed on mitigating spoilage via effective quality monitoring. Hence, the food industry must develop and adopt reliable, fast, inexpensive, and environmentally friendly technologies to monitor food quality and safety. Near-infrared spectroscopy combined with chemometrics is proving to be such a technology that offers a green, non-destructive, rapid, and affordable solution for food quality and safety analysis; however, it needs to be further developed through research to realize its full potential.

This Special Issue of Foods intends to serve as a platform for sharing research findings and insights in " Recent Applications of Near-Infrared Spectroscopy in Food Analysis".

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • The application of near-infrared spectroscopy in agri-food disease identification;
  • The application of near-infrared spectroscopy in agri-food toxin identification;
  • The application of near-infrared spectroscopy in agri-food grading and quality monitoring;
  • Novel data analysis and model development techniques using chemometrics and artificial intelligence for near-infrared spectra obtained from agri-food products;
  • The application of near-infrared spectroscopy in smart farming;
  • The integration of near-infrared spectroscopy with other optical imaging modalities for agri-food quality monitoring.

Dr. Mohammad Nadimi
Prof. Dr. Jitendra Paliwal
Guest Editors

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

  • near-infrared spectroscopy
  • chemometrics
  • food quality
  • food safety
  • toxins
  • food constitutes
  • food security
  • food grading
  • deep learning
  • agri-food products
  • post-harvest quality
  • grain storage

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 7469 KiB  
Article
Non-Destructive Assessment of Microstructural Changes in Kabuli Chickpeas during Storage
by Navnath S. Indore, Mudassir Chaudhry, Digvir S. Jayas, Jitendra Paliwal and Chithra Karunakaran
Foods 2024, 13(3), 433; https://doi.org/10.3390/foods13030433 - 29 Jan 2024
Viewed by 753
Abstract
The potential of hyperspectral imaging (HSI) and synchrotron phase-contrast micro computed tomography (SR-µCT) was evaluated to determine changes in chickpea quality during storage. Chickpea samples were stored for 16 wk at different combinations of moisture contents (MC of 9%, 11%, 13%, and 15% [...] Read more.
The potential of hyperspectral imaging (HSI) and synchrotron phase-contrast micro computed tomography (SR-µCT) was evaluated to determine changes in chickpea quality during storage. Chickpea samples were stored for 16 wk at different combinations of moisture contents (MC of 9%, 11%, 13%, and 15% wet basis) and temperatures (10 °C, 20 °C, and 30 °C). Hyperspectral imaging was utilized to investigate the overall quality deterioration, and SR-µCT was used to study the microstructural changes during storage. Principal component analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used as multivariate data analysis approaches for HSI data. Principal component analysis successfully grouped the samples based on relative humidity (RH) and storage temperatures, and the PLS-DA classification also resulted in reliable accuracy (between 80 and 99%) for RH-based and temperature-based classification. The SR-µCT results revealed that microstructural changes in kernels (9% and 15% MC) were dominant at higher temperatures (above 20 °C) as compared to lower temperatures (10 °C) during storage due to accelerated spoilage at higher temperatures (above 20 °C). Chickpeas which had internal irregularities like cracked endosperm and air spaces before storage were spoiled at lower moisture from 8 wk of storage. Full article
(This article belongs to the Special Issue Recent Applications of Near-Infrared Spectroscopy in Food Analysis)
►▼ Show Figures

Figure 1

15 pages, 3819 KiB  
Article
Quality Characterization of Fava Bean-Fortified Bread Using Hyperspectral Imaging
by Sunday J. Olakanmi, Digvir S. Jayas, Jitendra Paliwal, Muhammad Mudassir Arif Chaudhry and Catherine Rui Jin Findlay
Foods 2024, 13(2), 231; https://doi.org/10.3390/foods13020231 - 11 Jan 2024
Viewed by 808
Abstract
As the demand for alternative protein sources and nutritional improvement in baked goods grows, integrating legume-based ingredients, such as fava beans, into wheat flour presents an innovative alternative. This study investigates the potential of hyperspectral imaging (HSI) to predict the protein content (short-wave [...] Read more.
As the demand for alternative protein sources and nutritional improvement in baked goods grows, integrating legume-based ingredients, such as fava beans, into wheat flour presents an innovative alternative. This study investigates the potential of hyperspectral imaging (HSI) to predict the protein content (short-wave infrared (SWIR) range)) of fava bean-fortified bread and classify them based on their color characteristics (visible–near-infrared (Vis-NIR) range). Different multivariate analysis tools, such as principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and partial least square regression (PLSR), were utilized to assess the protein distribution and color quality parameters of bread samples. The result of the PLS-DA in the SWIR range yielded a classification accuracy of ˃99%, successfully classifying the samples based on their protein contents (low protein and high protein). The PLSR model showed an RMSEC of 0.086% and an RMSECV of 0.094%. Also, the external validation resulted in an RMSEP of 0.064%. The PLSR model possessed the capability to efficiently predict the protein content of the bread samples. The results suggest that HSI can be successfully used to classify bread samples based on their protein content and for the prediction of protein composition. Hyperspectral imaging can therefore be reliably implemented for the quality monitoring of baked goods in commercial bakeries. Full article
(This article belongs to the Special Issue Recent Applications of Near-Infrared Spectroscopy in Food Analysis)
►▼ Show Figures

Figure 1

19 pages, 2843 KiB  
Article
Assessment of Mechanical Damage and Germinability in Flaxseeds Using Hyperspectral Imaging
by Mohammad Nadimi, L. G. Divyanth, Muhammad Mudassir Arif Chaudhry, Taranveer Singh, Georgia Loewen and Jitendra Paliwal
Foods 2024, 13(1), 120; https://doi.org/10.3390/foods13010120 - 29 Dec 2023
Cited by 2 | Viewed by 867
Abstract
The high demand for flax as a nutritious edible oil source combined with increasingly restrictive import regulations for oilseeds mandates the exploration of novel quantity and quality assessment methods. One pervasive issue that compromises the viability of flaxseeds is the mechanical damage to [...] Read more.
The high demand for flax as a nutritious edible oil source combined with increasingly restrictive import regulations for oilseeds mandates the exploration of novel quantity and quality assessment methods. One pervasive issue that compromises the viability of flaxseeds is the mechanical damage to the seeds during harvest and post-harvest handling. Currently, mechanical damage in flax is assessed via visual inspection, a time-consuming, subjective, and insufficiently precise process. This study explores the potential of hyperspectral imaging (HSI) combined with chemometrics as a novel, rapid, and non-destructive method to characterize mechanical damage in flaxseeds and assess how mechanical stresses impact the germination of seeds. Flaxseed samples at three different moisture contents (MCs) (6%, 8%, and 11.5%) were subjected to four levels of mechanical stresses (0 mJ (i.e., control), 2 mJ, 4 mJ, and 6 mJ), followed by germination tests. Herein, we acquired hyperspectral images across visible to near-infrared (Vis-NIR) (450–1100 nm) and short-wave infrared (SWIR) (1000–2500 nm) ranges and used principal component analysis (PCA) for data exploration. Subsequently, mean spectra from the samples were used to develop partial least squares-discriminant analysis (PLS-DA) models utilizing key wavelengths to classify flaxseeds based on the extent of mechanical damage. The models developed using Vis-NIR and SWIR wavelengths demonstrated promising performance, achieving precision and recall rates >85% and overall accuracies of 90.70% and 93.18%, respectively. Partial least squares regression (PLSR) models were developed to predict germinability, resulting in R2-values of 0.78 and 0.82 for Vis-NIR and SWIR ranges, respectively. The study showed that HSI could be a potential alternative to conventional methods for fast, non-destructive, and reliable assessment of mechanical damage in flaxseeds. Full article
(This article belongs to the Special Issue Recent Applications of Near-Infrared Spectroscopy in Food Analysis)
►▼ Show Figures

Figure 1

11 pages, 1407 KiB  
Communication
Performance of a Handheld MicroNIR Instrument for Determining Protein Levels in Sorghum Grain Samples
by Kamaranga H. S. Peiris, Scott R. Bean, Xiaorong Wu, Sarah A. Sexton-Bowser and Tesfaye Tesso
Foods 2023, 12(16), 3101; https://doi.org/10.3390/foods12163101 - 18 Aug 2023
Viewed by 900
Abstract
Near infrared (NIR) spectroscopy is widely used for evaluating quality traits of cereal grains. For evaluating protein content of intact sorghum grains, parallel NIR calibrations were developed using an established benchtop instrumentation (Perten DA-7250) as a baseline to test the efficacy of an [...] Read more.
Near infrared (NIR) spectroscopy is widely used for evaluating quality traits of cereal grains. For evaluating protein content of intact sorghum grains, parallel NIR calibrations were developed using an established benchtop instrumentation (Perten DA-7250) as a baseline to test the efficacy of an adaptive handheld instrument (VIAVI MicroNIR OnSite-W). Spectra were collected from 59 grain samples using both instruments at the same time. Cross-validated calibration models were validated with 33 test samples. The selected calibration model for DA-7250 with a coefficient of determination (R2) = 0.98 and a root mean square error of cross validation (RMSECV) = 0.41% predicted the protein content of a test set with R2 = 0.94, root mean square error of prediction (RMSEP) = 0.52% with a ratio of performance to deviation (RPD) of 4.13. The selected model for the MicroNIR with R2 = 0.95 and RMSECV = 0.62% predicted the protein content of the test set with R2 = 0.87, RMSEP = 0.76% with an RPD of 2.74. In comparison, the performance of the DA-7250 was better than the MicroNIR, however, the performance of the MicroNIR was also acceptable for screening intact sorghum grain protein levels. Therefore, the MicroNIR instrument may be used as a potential tool for screening sorghum samples where benchtop instruments are not appropriate such as for screening samples in the field or as a less expensive option compared with benchtop instruments. Full article
(This article belongs to the Special Issue Recent Applications of Near-Infrared Spectroscopy in Food Analysis)
►▼ Show Figures

Figure 1

16 pages, 2418 KiB  
Article
Assessment of Maturity of Plum Samples Using Fourier Transform Near-Infrared Technique Combined with Chemometric Methods
by Marietta Fodor, Zsuzsa Jókai, Anna Matkovits and Eszter Benes
Foods 2023, 12(16), 3059; https://doi.org/10.3390/foods12163059 - 15 Aug 2023
Cited by 1 | Viewed by 1116
Abstract
The FT-NIR technique was used for rapid and non-destructive determination of plum ripeness. The dry matter (DM), titratable acidity (TA), total soluble solids (TSS) and calculated maturity index (MI: TSS/TA) were used as reference values. The PLS correlations were validated via five-fold cross-validation [...] Read more.
The FT-NIR technique was used for rapid and non-destructive determination of plum ripeness. The dry matter (DM), titratable acidity (TA), total soluble solids (TSS) and calculated maturity index (MI: TSS/TA) were used as reference values. The PLS correlations were validated via five-fold cross-validation (RMSECV for different parameters: DM: 0.66%, w/w; TA = 0.07%, w/w; TSS = 0.72%, w/w; MI = 1.39) and test set validation (RMSEP for different parameters: DM: 0.65%, w/w TA = 0.07%, w/w; TSS = 0.61%, w/w; MI = 1.50). Different classification algorithms were performed for TA, TSS and MI. Linear, quadratic and Mahalanobis discriminant analysis (LDA, QDA, MDA) were found to be the best sample detection methods. The accuracy of the classification methods was 100% for all investigated parameters and cultivars. Full article
(This article belongs to the Special Issue Recent Applications of Near-Infrared Spectroscopy in Food Analysis)
►▼ Show Figures

Graphical abstract

11 pages, 1748 KiB  
Article
Determination of Freshness of Mackerel (Scomber japonicus) Using Shortwave Infrared Hyperspectral Imaging
by Jeong-Seok Cho, Byungho Choi, Jeong-Ho Lim, Jeong Hee Choi, Dae-Yong Yun, Seul-Ki Park, Gyuseok Lee, Kee-Jai Park and Jihyun Lee
Foods 2023, 12(12), 2305; https://doi.org/10.3390/foods12122305 - 07 Jun 2023
Cited by 2 | Viewed by 1167
Abstract
Shortwave infrared (SWIR) hyperspectral imaging was applied to classify the freshness of mackerels. Total volatile basic nitrogen (TVB-N) and acid values, as chemical compounds related to the freshness of mackerels, were also analyzed to develop a prediction model of freshness by combining them [...] Read more.
Shortwave infrared (SWIR) hyperspectral imaging was applied to classify the freshness of mackerels. Total volatile basic nitrogen (TVB-N) and acid values, as chemical compounds related to the freshness of mackerels, were also analyzed to develop a prediction model of freshness by combining them with hyperspectral data. Fresh mackerels were divided into three groups according to storage periods (0, 24, and 48 h), and hyperspectral data were collected from the eyes and whole body, separately. The optimized classification accuracies were 81.68% using raw data from eyes and 90.14% using body data by multiple scatter correction (MSC) pretreatment. The prediction accuracy of TVB-N was 90.76%, and the acid value was 83.76%. These results indicate that hyperspectral imaging, as a nondestructive method, can be used to verify the freshness of mackerels and predict the chemical compounds related to the freshness. Full article
(This article belongs to the Special Issue Recent Applications of Near-Infrared Spectroscopy in Food Analysis)
►▼ Show Figures

Figure 1

14 pages, 4104 KiB  
Article
Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy
by Ali Khorramifar, Vali Rasooli Sharabiani, Hamed Karami, Asma Kisalaei, Jesús Lozano, Robert Rusinek and Marek Gancarz
Foods 2022, 11(24), 4077; https://doi.org/10.3390/foods11244077 - 16 Dec 2022
Cited by 5 | Viewed by 1886
Abstract
Potato is an important agricultural product, ranked as the fourth most common product in the human diet. Potato can be consumed in various forms. As customers expect safe and high-quality products, precise and rapid determination of the quality and composition of potatoes is [...] Read more.
Potato is an important agricultural product, ranked as the fourth most common product in the human diet. Potato can be consumed in various forms. As customers expect safe and high-quality products, precise and rapid determination of the quality and composition of potatoes is of crucial significance. The quality of potatoes may alter during the storage period due to various phenomena. Soluble solids content (SSC) and pH are among the quality parameters experiencing alteration during the storage process. This study is thus aimed to assess the variations in SSC and pH during the storage of potatoes using an electronic nose and Vis/NIR spectroscopic techniques with the help of prediction models including partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), support vector regression (SVR) and an artificial neural network (ANN). The variations in the SSC and pH are ascending and significant. The results also indicate that the SVR model in the electronic nose has the highest prediction accuracy for the SSC and pH (81, and 92%, respectively). The artificial neural network also managed to predict the SSC and pH at accuracies of 83 and 94%, respectively. SVR method shows the lowest accuracy in Vis/NIR spectroscopy while the PLS model exhibits the best performance in the prediction of the SSC and pH with respective precision of 89 and 93% through the median filter method. The accuracy of the ANN was 85 and 90% in the prediction of the SSC and pH, respectively. Full article
(This article belongs to the Special Issue Recent Applications of Near-Infrared Spectroscopy in Food Analysis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop