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Lemes EM. Raman spectroscopy - a visit to the literature on plant, food, and agricultural studies. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024. [PMID: 39132989 DOI: 10.1002/jsfa.13803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 07/23/2024] [Accepted: 07/26/2024] [Indexed: 08/13/2024]
Abstract
Raman spectroscopy, a fast, non-invasive, and label-free optical technique, has significantly advanced plant and food studies and precision agriculture by providing detailed molecular insights into biological tissues. Utilizing the Raman scattering effect generates unique spectral fingerprints that comprehensively analyze tissue composition, concentration, and molecular structure. These fingerprints are obtained without chemical additives or extensive sample preparation, making Raman spectroscopy particularly suitable for in-field applications. Technological enhancements such as surface-enhanced Raman scattering, Fourier-transform-Raman spectroscopy, and chemometrics have increased Raman spectroscopy sensitivity and precision. These and other advancements enable real-time monitoring of compound translocation within plants and improve the detection of chemical and biological contaminants, essential for food safety and crop optimization. Integrating Raman spectroscopy into agronomic practices is transformative and marks a shift toward more sustainable farming activities. It assesses crop quality - as well as the quality of the food that originated from crop production - early plant stress detection and supports targeted breeding programs. Advanced data processing techniques and machine learning integration efficiently handle complex spectral data, providing a dynamic and detailed view of food conditions and plant health under varying environmental and biological stresses. As global agriculture faces the dual challenges of increasing productivity and sustainability, Raman spectroscopy stands out as an indispensable tool, enhancing farming practices' precision, food safety, and environmental compatibility. This review is intended to select and briefly comment on outstanding literature to give researchers, students, and consultants a reference for works of literature in Raman spectroscopy mainly focused on plant, food, and agronomic sciences. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Ernane Miranda Lemes
- Instituto de Ciências Agrárias (ICIAG), Universidade Federal de Uberlândia (UFU), Uberlândia, Brazil
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Wang S, Wu B, Todhanakasem T. Expanding the horizons of levan: from microbial biosynthesis to applications and advanced detection methods. World J Microbiol Biotechnol 2024; 40:214. [PMID: 38789837 DOI: 10.1007/s11274-024-04023-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024]
Abstract
Levan, a β-(2,6)-linked fructose polymer, exhibits diverse properties that impart versatility, rendering it a highly sought-after biopolymer with various industrial applications. Levan can be produced by various microorganisms using sucrose, food industry byproducts and agricultural wastes. Microbial levan represents the most potent cost-effective process for commercial-scale levan production. This study reviews the optimization of levan production by understanding its biosynthesis, physicochemical properties and the fermentation process. In addition, genetic and protein engineering for its increased production and emerging methods for its detection are introduced and discussed. All of these comprehensive studies could serve as powerful tools to optimize levan production and broaden its applications across various industries.
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Affiliation(s)
- Sijie Wang
- School of Food Industry, King Mongkut's Institute of Technology Ladkrabang, Bangkok, 10520, Thailand
| | - Bo Wu
- Biomass Energy Technology Research Center, Biogas Institute of Ministry of Agriculture and Rural Affairs, Renmin Rd. S 4-13, Chengdu, 610041, China
| | - Tatsaporn Todhanakasem
- School of Food Industry, King Mongkut's Institute of Technology Ladkrabang, Bangkok, 10520, Thailand.
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Jin N, Song J, Wang Y, Yang K, Zhang D. Biospectroscopic fingerprinting phytotoxicity towards environmental monitoring for food security and contaminated site remediation. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133515. [PMID: 38228003 DOI: 10.1016/j.jhazmat.2024.133515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/18/2023] [Accepted: 01/10/2024] [Indexed: 01/18/2024]
Abstract
Human activities have resulted in severe environmental pollution since the industrial revolution. Phytotoxicity-based environmental monitoring is well known due to its sedentary nature, abundance, and sensitivity to environmental changes, which are essential preconditions to avoiding potential environmental and ecological risks. However, conventional morphological and physiological methods for phytotoxicity assessment mainly focus on descriptive determination rather than mechanism analysis and face challenges of labour and time-consumption, lack of standardized protocol and difficulties in data interpretation. Molecular-based tests could reveal the toxicity mechanisms but fail in real-time and in-situ monitoring because of their endpoint manner and destructive operation in collecting cellular components. Herein, we systematically propose and lay out a biospectroscopic tool (e.g., infrared and Raman spectroscopy) coupled with multivariate data analysis as a relatively non-destructive and high-throughput approach to quantitatively measure phytotoxicity levels and qualitatively profile phytotoxicity mechanisms by classifying spectral fingerprints of biomolecules in plant tissues in response to environmental stresses. With established databases and multivariate analysis, this biospectroscopic fingerprinting approach allows ultrafast, in situ and on-site diagnosis of phytotoxicity. Overall, the proposed protocol and validation of biospectroscopic fingerprinting phytotoxicity can distinguish the representative biomarkers and interrogate the relevant mechanisms to quantify the stresses of interest, e.g., environmental pollutants. This state-of-the-art concept and design broaden the knowledge of phytotoxicity assessment, advance novel implementations of phytotoxicity assay, and offer vast potential for long-term field phytotoxicity monitoring trials in situ.
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Affiliation(s)
- Naifu Jin
- College of Water Sciences, Beijing Normal University, Beijing 100875, PR China
| | - Jiaxuan Song
- College of Water Sciences, Beijing Normal University, Beijing 100875, PR China
| | - Yingying Wang
- College of Water Sciences, Beijing Normal University, Beijing 100875, PR China
| | - Kai Yang
- College of Water Sciences, Beijing Normal University, Beijing 100875, PR China
| | - Dayi Zhang
- Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun 130021, PR China; College of New Energy and Environment, Jilin University, Changchun 130021, PR China; Key Laboratory of Regional Environment and Eco-restoration, Ministry of Education, Shenyang University, Shenyang 110044, PR China.
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Pielorz S, Kita A, Rytel E, Szostak R, Mazurek S. Application of vibrational and fluorescence spectroscopy to the compositional analysis of colored-flesh potatoes. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:1399-1407. [PMID: 37782467 DOI: 10.1002/jsfa.13021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 09/20/2023] [Accepted: 10/02/2023] [Indexed: 10/03/2023]
Abstract
BACKGROUND Determination of composition and physicochemical parameters of natural products requires dedicated, often laborious and expensive, analytical protocols. Different spectroscopic techniques, in conjunction with chemometrics, seem to have a considerable potential in direct analysis of raw plant material and foods, without any chemical treatment. RESULTS Fluorescence spectroscopy and three vibrational spectroscopy techniques were applied to determine total polyphenol content, antioxidant activity and macronutrient levels in red- and purple-fleshed potato varieties. Excitation-emission matrix fluorescence, Fourier transform Raman, attenuated total reflection Fourier transform infrared and near-infrared spectra were recorded for the freeze-dried samples. Combining spectral data and the results of reference analyses, partial least squares regression models were constructed for each parameter studied. For polyphenols and antioxidant activity, quantification errors found for validation samples amounted to 3.74-5.04% and 4.75-6.35%, respectively, whereas macronutrient analysis gave errors in the 3.45-4.55%, 3.09-5.30% and 5.10-8.58% ranges for starch, protein and sugar determinations, respectively. CONCLUSION The obtained results demonstrate that different spectroscopic techniques in combination with multivariate modeling allow simultaneous determination of various parameters of plant samples based on a single sample spectrum. They can effectively replace commonly used protocols of food product analysis requiring sample dissolving and extraction of the compounds of interest. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Sonia Pielorz
- Department of Chemistry, University of Wrocław, Wrocław, Poland
| | - Agnieszka Kita
- Department of Food Storage and Technology, Faculty of Biotechnology and Food Science, Wrocław University of Environmental and Life Sciences, Wrocław, Poland
| | - Elżbieta Rytel
- Department of Food Storage and Technology, Faculty of Biotechnology and Food Science, Wrocław University of Environmental and Life Sciences, Wrocław, Poland
| | - Roman Szostak
- Department of Chemistry, University of Wrocław, Wrocław, Poland
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Didar Z. Characterization of white chocolate enriched with co-encapsulated Lactobacillus acidophilus ( La-5) and rose hip shell fruit extract: Characterization, probiotic viability during storage, and in vitro gastrointestinal digestion. Food Sci Nutr 2024; 12:890-906. [PMID: 38370043 PMCID: PMC10867508 DOI: 10.1002/fsn3.3805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 02/20/2024] Open
Abstract
This research focused on the production of a new kind of probiotic chocolate containing co-encapsulated Lactobacillus acidophilus (La-5) bacteria and rose hip shell fruit extract. Several properties of chocolate samples, including rheological, textural, thermal properties, particle size distribution, color indices, total phenolic and anthocyanin magnitude, antioxidant potential, and Raman spectroscopy were performed. The prepared white chocolates were assessed for the survival of the probiotic cell and the stability of anthocyanins and phenolic components in different storage times (until 90 days) and different storage temperatures (at 4 and 25°C). Observations imply that both temperature and duration of storage had an impact on the extent of survival of probiotics as well as stability of total phenolic content (TPC) and anthocyanin content (p < .05). During in vitro gastrointestinal circumstances, the extent of survival of L. acidophilus, in two chocolate matrixes, was assessed. At the end of gastric and intestinal condition, the log of viable cells was 7 and 6, respectively. The magnitude of the bioaccessibility of anthocyanin and phenolic components was 81% and 78%, respectively. Sensory evaluation affirmed that there was no remarkable variation between samples in terms of overall acceptance.
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Affiliation(s)
- Zohreh Didar
- Department of Food Science and Technology, Neyshabur BranchIslamic Azad UniversityNeyshaburIran
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Liu K, Chen F, Shang L, Wang Y, Peng H, Liu B, Li B. Deep learning-based ultra-fast identification of Raman spectra with low signal-to-noise ratio. JOURNAL OF BIOPHOTONICS 2024; 17:e202300270. [PMID: 37651642 DOI: 10.1002/jbio.202300270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 09/02/2023]
Abstract
Ensuring the correct use of cell lines is crucial to obtaining reliable experimental results and avoiding unnecessary waste of resources. Raman spectroscopy has been confirmed to be able to identify cell lines, but the collection time is usually 10-30 s. In this study, we acquired Raman spectra of five cell lines with integration times of 0.1 and 8 s, respectively, and the average accuracy of using long-short memory neural network to identify the spectra of 0.1 s was 95%, and the average accuracy of identifying the spectra of 8 s was 99.8%. At the same time, we performed data enhancement of 0.1 s spectral data by real-valued non-volume preserving method, and the recognition average accuracy of long-short memory neural networks recognition of the enhanced spectral data was improved to 96.2%. With this method, we shorten the acquisition time of Raman spectra to 1/80 of the original one, which greatly improves the efficiency of cell identification.
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Affiliation(s)
- Kunxiang Liu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, PR China
- University of Chinese Academy of Sciences, Beijing, PR China
| | - Fuyuan Chen
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, PR China
- University of Chinese Academy of Sciences, Beijing, PR China
| | - Lindong Shang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, PR China
- University of Chinese Academy of Sciences, Beijing, PR China
| | - Yuntong Wang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, PR China
- University of Chinese Academy of Sciences, Beijing, PR China
| | - Hao Peng
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, PR China
- University of Chinese Academy of Sciences, Beijing, PR China
| | - Bo Liu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, PR China
- University of Chinese Academy of Sciences, Beijing, PR China
| | - Bei Li
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, PR China
- University of Chinese Academy of Sciences, Beijing, PR China
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Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy. Comput Struct Biotechnol J 2022; 21:802-811. [PMID: 36698976 PMCID: PMC9842960 DOI: 10.1016/j.csbj.2022.12.050] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/29/2022] [Accepted: 12/29/2022] [Indexed: 12/31/2022] Open
Abstract
Cell misuse and cross-contamination can affect the accuracy of cell research results and result in wasted time, manpower and material resources. Thus, cell line identification is important and necessary. At present, the commonly used cell line identification methods need cell staining and culturing. There is therefore a need to develop a new method for the rapid and automated identification of cell lines. Raman spectroscopy has become one of the emerging techniques in the field of microbial identification, with the advantages of being rapid and noninvasive and providing molecular information for biological samples, which is beneficial in the identification of cell lines. In this study, we built a library of Raman spectra for gastric mucosal epithelial cell lines GES-1 and gastric cancer cell lines, such as AGS, BGC-823, HGC-27, MKN-45, MKN-74 and SNU-16. Five spectral datasets were constructed using spectral data and included the full spectrum, fingerprint region, high-wavelength number region and Raman background of Raman spectra. A stacking ensemble learning model, SL-Raman, was built for different datasets, and gastric cancer cell identification was achieved. For the gastric cancer cells we studied, the differentiation accuracy of SL-Raman was 100% for one of the gastric cancer cells and 100% for six of the gastric cancer cells. Additionally, the separation accuracy for two gastric cancer cells with different degrees of differentiation was 100%. These results demonstrate that Raman spectroscopy combined with SL-Raman may be a new method for the rapid and accurate identification of gastric cancer. In addition, the accuracy of 94.38% for classifying Raman spectral background data using machine learning demonstrates that the Raman spectral background contains some useful spectral features. These data have been overlooked in previous studies.
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