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Innovative of Chemometrics for Application of Food and Agricultural Products
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Innovative of Chemometrics for Application of Food and Agricultural Products

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

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 5286

Special Issue Editor

Prof. Dr. Chao Hui Feng

E-Mail Website
Guest Editor
1. School of Regional Innovation and Social Design Engineering, Faculty of Engineering, Kitami Institute of Technology, 165 Koen-Cho, Kitami, Hokkaido 090-8507, Japan
2. RIKEN Centre for Advanced Photonics, RIKEN, 519-1399 Aramaki-Aoba, Aoba-ku, Sendai 980-0845, Japan
Interests: meat science and food technology; development of food technology to exploit new foodstuffs and improve food quality and rapidly detect food safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food is a very crucial source that provides essential amino acids, carbohydrates, proteins, vitamins, minerals, and other nutritional compounds, supporting humans’ daily activity, energy, and nutritional requirements. There is an increasing concern about the quality and safety of food and agricultural products using innovative, effective, and quantitative inspection methods. Consequently, technologies that can significantly improve and non-invasively detect the quality of foodstuffs are urgently needed. Chemometrics analysis, as essential form of access to mine spectral data and explicate the relationship between measured parameters and spectral data, has been comprehensively utilized in spectral data analysis. In recent decades, mainly benchtop devices coupled with chemometrics analysis have been used for developing at-line methods for qualitative and quantitative control. Simple, rapid, and more accurate models are in high demand.

This Special Issue will focus on applying the latest novel non-invasive technologies in tandem with chemometrics analysis to ensure and maintain the safety and quality of agri-food products. All types of articles, such as original research studies, numerical studies, and comprehensive reviews on the current state of the art and emerging non-destructive technologies, are welcome.

Prof. Dr. Chao Hui Feng
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

  • hyperspectral imaging
  • terahertz spectroscopy
  • food safety
  • adulteration
  • food quality
  • green extraction
  • near-infrared spectroscopy
  • at-line methods

Published Papers (3 papers)

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Research

19 pages, 2106 KiB  
Article
Effects of Saccharomyces cerevisiae and Kluyveromyces marxianus on the Physicochemical, Microbial, and Flavor Changes of Sauce Meat during Storage
by Lili Ji, Shu Wang, Yanan Zhou, Qing Nie, Chunyan Zhou, Jiawen Ning, Chunping Ren, Chun Tang and Jiamin Zhang
Foods 2024, 13(3), 396; https://doi.org/10.3390/foods13030396 - 25 Jan 2024
Viewed by 1091
Abstract
Saccharomyces cerevisiae (S. cerevisiae) and Kluyveromyces marxianus (K. marxianus) are often used as fermenters in yogurt and alcohol, and have been less studied within meat products. The yeasts were added to sauce meat, and the uninoculated group served as [...] Read more.
Saccharomyces cerevisiae (S. cerevisiae) and Kluyveromyces marxianus (K. marxianus) are often used as fermenters in yogurt and alcohol, and have been less studied within meat products. The yeasts were added to sauce meat, and the uninoculated group served as a control in this study to examine and compare the changing patterns of physicochemical and flavor characteristics of S. cerevisiae and K. marxianus on sauce meat during storage. The changes in moisture content, aw, pH, thiobarbituric acid reactive substances (TBARS), and other flavor characteristics were measured in sauce meat during the first, second, fourth, and sixth months after production. The following factors were examined: moisture content, aw, pH, TBARS, peroxide value (POV), acid value (AV), soluble protein (SP), free amino acid (FAA), and volatile flavoring compounds. With VIP > 1 and p < 0.05 as the screening conditions, the partial least squares model (PLS-DA) was used to assess the distinctive flavor components in the sausages. The findings demonstrated that the three groups’ changes in sauce meat were comparable during the first two months of storage but differed significantly between the 4th and 6th months. The moisture content, water activity, and pH of the sauce meat decreased gradually with the storage time; TBARS, AV, and FAA increased significantly; SP decreased significantly from 2.61 to 1.72, while POV increased to 0.03 and then decreased to 0.02. The POV and TBARS values of the yeast-infected meat were substantially lower than those of the control group, and the POV and TBARS values of the meat inoculated with S. cerevisiae were particularly decreased (p < 0.05). The POV and TBARS values of SC (S. cerevisiae group) decreased by 49.09% and 40.15%, respectively, compared to CK (the control group) at the time of storage until June. The experimental group (KM: K. marxianus group) significantly increased the SP and FAA values of the sauce meat (p < 0.05) by 32.4% and 29.84% compared to the CK group, respectively. Esters and olefins as well as alcohols and esters were much greater in meat that had been supplemented with S. cerevisiae and K. marxianus than in meat from the control group. In conclusion, inoculating sauce meat with S. cerevisiae can significantly enhance the quality and flavor of sauce meat while it is being stored. Full article
(This article belongs to the Special Issue Innovative of Chemometrics for Application of Food and Agricultural Products)
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13 pages, 1766 KiB  
Article
Hyperspectral Imaging Combined with Chemometrics Analysis for Monitoring the Textural Properties of Modified Casing Sausages with Differentiated Additions of Orange Extracts
by Chao-Hui Feng, Hirofumi Arai and Francisco J. Rodríguez-Pulido
Foods 2023, 12(5), 1069; https://doi.org/10.3390/foods12051069 - 02 Mar 2023
Cited by 3 | Viewed by 1350
Abstract
The textural properties (hardness, springiness, gumminess, and adhesion) of 16-day stored sausages with different additions of orange extracts to the modified casing solution were estimated by response surface methodology (RSM) and a hyperspectral imaging system in the spectral range of 390–1100 nm. To [...] Read more.
The textural properties (hardness, springiness, gumminess, and adhesion) of 16-day stored sausages with different additions of orange extracts to the modified casing solution were estimated by response surface methodology (RSM) and a hyperspectral imaging system in the spectral range of 390–1100 nm. To improve the model performance, normalization, 1st derivative, 2nd derivative, standard normal variate (SNV), and multiplicative scatter correction (MSC) were applied for spectral pre-treatments. The raw, pretreated spectral data and textural attributes were fit to the partial least squares regression model. The RSM results show that the highest R2 value achieved at adhesion (77.57%) derived from a second-order polynomial model, and the interactive effects of soy lecithin and orange extracts on adhesion were significant (p < 0.05). The adhesion of the PLSR model developed from reflectance after SNV pretreatment possessed a higher calibration coefficient of determination (0.8744) than raw data (0.8591). The selected ten important wavelengths for gumminess and adhesion can simplify the model and can be used for convenient industrial applications. Full article
(This article belongs to the Special Issue Innovative of Chemometrics for Application of Food and Agricultural Products)
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15 pages, 2730 KiB  
Article
Evaluation of pH in Sausages Stuffed in a Modified Casing with Orange Extracts by Hyperspectral Imaging Coupled with Response Surface Methodology
by Chao-Hui Feng, Hirofumi Arai and Francisco J. Rodríguez-Pulido
Foods 2022, 11(18), 2797; https://doi.org/10.3390/foods11182797 - 10 Sep 2022
Cited by 4 | Viewed by 2076
Abstract
The pH values of sausages stuffed in natural hog casings with different modifications (soy lecithin, soy oil, orange extracts (OE) from waste orange peels, lactic acid in slush salt, and treatment time) after 16-day 4 °C storage were evaluated for the first time [...] Read more.
The pH values of sausages stuffed in natural hog casings with different modifications (soy lecithin, soy oil, orange extracts (OE) from waste orange peels, lactic acid in slush salt, and treatment time) after 16-day 4 °C storage were evaluated for the first time by hyperspectral imaging (350–1100 nm) coupled with response surface methodology (RSM). A partial least squares regression (PLSR) model was developed to relate the spectra to the pH of sausages. Spectral pretreatment, including first derivative, second derivative, multiplicative scatter correction (MSC), standard normal variate (SNV), normalization, and normalization, with different combinations was employed to improve model performance. RSM showed that only soy lecithin and OE interactively affected the pH of sausages (p < 0.05). The pH value decreased when the casing was treated with a higher concentration of soy lecithin with 0.26% OE. As the first and second derivatives are commonly used to eliminate the baseline shift, the PLSR model derived from absorbance pretreated by the first derivative in the full wavelengths showed a calibration coefficient of determination (R2) of 0.73 with a root mean square error of calibration of 0.4283. Twelve feature wavelengths were selected with a comparable R2 value compared with the full wavelengths. The prediction map enables the visualization of the pH evolution of sausages stuffed in the modified casings added with OE. Full article
(This article belongs to the Special Issue Innovative of Chemometrics for Application of Food and Agricultural Products)
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