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Qu M, Tian S, Yu H, Liu D, Zhang C, He Y, Cheng F. Single-kernel classification of deoxynivalenol and zearalenone contaminated maize based on visible light imaging under ultraviolet light excitation combined with polarized light imaging. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Application of Optical Quality Control Technologies in the Dairy Industry: An Overview. PHOTONICS 2021. [DOI: 10.3390/photonics8120551] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Sustainable development of the agricultural industry, in particular, the production of milk and feed for farm animals, requires accurate, fast, and non-invasive diagnostic tools. Currently, there is a rapid development of a number of analytical methods and approaches that meet these requirements. Infrared spectrometry in the near and mid-IR range is especially widespread. Progress has been made not only in the physical methods of carrying out measurements, but significant advances have also been achieved in the development of mathematical processing of the received signals. This review is devoted to the comparison of modern methods and devices used to control the quality of milk and feed for farm animals.
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Tian Y, Gao X, Qi WL, Wang Y, Wang X, Zhou J, Lu D, Chen B. Advances in differentiation and identification of foodborne bacteria using near infrared spectroscopy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:2558-2566. [PMID: 34095906 DOI: 10.1039/d1ay00124h] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Rapid and sensitive detection of foodborne bacteria is a growing concern for ensuring safe food supply and preventing human foodborne infections. It is difficult for conventional methods to meet these detection requirements because they are often tedious and time-consuming. In the recent years, near infrared (NIR) spectroscopy has been found to be a promising method for all sorts of analyses in microbiology due to its highly specific absorption signature and non-destructive measurements. In this review, we first briefly introduce the fundamental and basic operational procedure of NIR spectroscopy for foodborne bacteria detection. Then we summarize the main advances and contributions of this technique in the study of foodborne bacteria. Finally, we conclude that much work still remains to be done before NIR spectroscopy really becomes a viable alternative in the field of microbiological characterization.
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Affiliation(s)
- Yanlong Tian
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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Smirnova M, Miamin U, Kohler A, Valentovich L, Akhremchuk A, Sidarenka A, Dolgikh A, Shapaval V. Isolation and characterization of fast-growing green snow bacteria from coastal East Antarctica. Microbiologyopen 2021; 10:e1152. [PMID: 33377317 PMCID: PMC7887010 DOI: 10.1002/mbo3.1152] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 12/03/2020] [Accepted: 12/08/2020] [Indexed: 11/25/2022] Open
Abstract
Snow microorganisms play a significant role in climate change and affecting the snow melting rate in the Arctic and Antarctic regions. While research on algae inhabiting green and red snow has been performed extensively, bacteria dwelling in this biotope have been studied to a much lesser extent. In this study, we performed 16S rRNA gene amplicon sequencing of two green snow samples collected from the coastal area of the eastern part of Antarctica and conducted genotypic and phenotypic profiling of 45 fast-growing bacteria isolated from these samples. 16S rRNA gene amplicon sequencing of two green snow samples showed that bacteria inhabiting these samples are mostly represented by families Burkholderiaceae (46.31%), Flavobacteriaceae (22.98%), and Pseudomonadaceae (17.66%). Identification of 45 fast-growing bacteria isolated from green snow was performed using 16S rRNA gene sequencing. We demonstrated that they belong to the phyla Actinobacteria and Proteobacteria, and are represented by the genera Arthrobacter, Cryobacterium, Leifsonia, Salinibacterium, Paeniglutamicibacter, Rhodococcus, Polaromonas, Pseudomonas, and Psychrobacter. Nearly all bacterial isolates exhibited various growth temperatures from 4°C to 25°C, and some isolates were characterized by a high level of enzymatic activity. Phenotyping using Fourier transform infrared (FTIR) spectroscopy revealed a possible accumulation of intracellular polymer polyhydroxyalkanoates (PHA) or lipids in some isolates. The bacteria showed different lipids/PHA and protein profiles. It was shown that lipid/PHA and protein spectral regions are the most discriminative for differentiating the isolates.
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Affiliation(s)
- Margarita Smirnova
- Faculty of Science and TechnologyNorwegian University of Life SciencesÅsNorway
| | | | - Achim Kohler
- Faculty of Science and TechnologyNorwegian University of Life SciencesÅsNorway
| | - Leonid Valentovich
- Faculty of BiologyBelarusian State UniversityMinskBelarus
- Institute of MicrobiologyNational Academy of Sciences of BelarusMinskBelarus
| | - Artur Akhremchuk
- Institute of MicrobiologyNational Academy of Sciences of BelarusMinskBelarus
| | - Anastasiya Sidarenka
- Faculty of BiologyBelarusian State UniversityMinskBelarus
- Institute of MicrobiologyNational Academy of Sciences of BelarusMinskBelarus
| | - Andrey Dolgikh
- Institute of GeographyRussian Academy of SciencesMoscowRussia
| | - Volha Shapaval
- Faculty of Science and TechnologyNorwegian University of Life SciencesÅsNorway
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Zhu Y, Zhang J, Li M, Ren H, Zhu C, Yan L, Zhao G, Zhang Q. Near-infrared spectroscopy coupled with chemometrics algorithms for the quantitative determination of the germinability of Clostridium perfringens in four different matrices. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 232:117997. [PMID: 32062401 DOI: 10.1016/j.saa.2019.117997] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 12/21/2019] [Accepted: 12/25/2019] [Indexed: 06/10/2023]
Abstract
Clostridium perfringens (C. perfringens) has the ability to form metabolically-dormant spores that can survive food preservation processes and cause food spoilage and foodborne safety risks upon germination outgrowth. This study was conducted to investigate the effects of different AGFK concentrations (0, 50, 100, 200 mM/mL) on the spore germination of C. perfringens in four matrices, including Tris-HCl, FTG, milk, and chicken soup. C. perfringens spore germinability was investigated using near infrared spectroscopy (NIRS) combined with chemometrics. The spore germination rate (S), the OD600%, and the Ca2+-DPA% were measured using traditional spore germination methods. The results of spore germination assays showed that the optimum germination rate was obtained using 100 mM/L concentrations of AGFK in the FTG medium, and the S, OD600% and Ca2+-DPA% were 98.6%, 59.3% and 95%, respectively. The best prediction models for the S, OD600% and Ca2+-DPA% were obtained using SNV as the preprocessing method for the original spectra, with the competitive adaptive weighted resampling method (CARS) as the characteristic variables related to the selected spore germination methods from NIRS data. The results of the S showed that the optimum model was built by CARS-PLSR (RMSEV = 0.745, Rc = 0.897, RMSEP = 0.769, Rp = 0.883). For the OD600%, interval partial least squares regression (CARS-siPLS) was performed to optimize the models. The calibration yielded acceptable results (RMSEV = 0.218, Rc = 0.879, RMSEP = 0.257, Rp = 0.845). For the Ca2+-DPA%, the optimum model with CARS-siPLS yielded acceptable results (RMSEV = 44.7, Rc = 0.883, RMSEP = 50.2, Rp = 0.872). This indicated that quantitative determinations of the germinability of C. perfringens spores using NIR technology is feasible. A new method based on NIR was provided for rapid, automatic, and non-destructive determination of the germinability of C. perfringens spores.
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Affiliation(s)
- Yaodi Zhu
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
| | - Jiaye Zhang
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
| | - Miaoyun Li
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China.
| | - Hongrong Ren
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
| | - Chaozhi Zhu
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
| | - Longgnag Yan
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
| | - Gaiming Zhao
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
| | - Qiuhui Zhang
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
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Feasibility of Determination of Foodborne Microbe Contamination of Fresh-Cut Shredded Cabbage Using SW-NIR. AGRIENGINEERING 2019. [DOI: 10.3390/agriengineering1020018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Shredded cabbage is widely used in much ready-to-eat food. Therefore, rapid methods for detecting and monitoring the contamination of foodborne microbes is essential. Short wavelength near infrared (SW-NIR) spectroscopy was applied on two types of solutions, a drained solution from the outer surface of the shredded cabbage (SC) and a ground solution of shredded cabbage (GC) which were inoculated with a mixture of two bacterial suspensions, Escherichia coli and Salmonella typhimurium. NIR spectra of around 700 to 1100 nm were collected from the samples after 0, 4, and 8 h at 37 °C incubation, along with the growth of total bacteria, E. coli and S. typhimurium. The raw spectra were obtained from both sample types, clearly separated with the increase of incubation time. The first derivative, a Savitzky–Golay pretreatment, was applied on the GC spectra, while the second derivative was applied on the SC spectra before developing the calibration equation, using partial least squares regression (PLS). The obtained correlation (r) of the SC spectra was higher than the GC spectra, while the standard error of cross-validation (SECV) was lower. The ratio of prediction of deviation (RPD) of the SC spectra was higher than the GC spectra, especially in total bacteria, quite normal for the E. coli but relatively low for the S. typhimurium. The prediction results of microbial spoilage were more reliable on the SC than on the GC spectra. Total bacterial detection was best for quantitative measurement, as E. coli contamination could only be distinguished between high and low values. Conversely, S. typhimurium predictions were not optimal for either sample type. The SW-NIR shows the feasibility for detecting the existence of microbes in the solution obtained from SC, but for a more specific application for discrimination or quantitation is needed, proving further research in still required.
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Long Y, Li B, Liu H. Analysis of fluoroquinolones antibiotic residue in feed matrices using terahertz spectroscopy. APPLIED OPTICS 2018; 57:544-550. [PMID: 29400779 DOI: 10.1364/ao.57.000544] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 11/22/2017] [Indexed: 06/07/2023]
Abstract
As antibiotic residue becomes more and more serious all over the world, a rapid and effective detection method is needed to evaluate the antibiotic residue in feed matrices to ensure food safety for consumers. In this study, three different kinds of fluoroquinolones (norfloxacin, enrofloxacin, and ofloxacin) in feed matrices were analyzed using terahertz (THz) spectroscopy, respectively. Meanwhile, pure fluoroquinolones and pure feed matrices were also measured in the same way. Then, the absorption spectra of all of the samples were extracted in the transmission mode. Pure norfloxacin has two absorption peaks at 0.825 and 1.187 THz, and they could still be observed when mixing norfloxacin with feed matrices. Also, there was an obvious and strong absorption peak for ofloxacin at 1.044 THz. However, no obvious absorption peak for enrofloxacin was observed, and only a weak absorption peak was located at 0.8 THz. Then, the different models were established with different chemometrics to identify the fluoroquinolones in feed matrices and determined the fluoroquinolones content in the feed matrices. The least squares support vector machines, Naive Bayes, Mahalanobis distance, and back propagation neural network (BPNN) were used to build the identification model with a Savitzky-Golay filter and standardized normal variate pretreatments. The results show that the excellent classification model was acquired with the BPNN combined with no pretreatment. The optimal classification accuracy was 80.56% in the testing set. After that, multiple linear regression and stepwise regression were used to establish the quantitative detection model for different kinds of fluoroquinolones in feed matrices. The optimal correlation coefficients for norfloxacin, enrofloxacin, and ofloxacin in the prediction set were obtained with multiple linear regression that combined absorption peaks with wavelengths selected by stepwise regression, which were 0.867, 0.828, and 0.964, respectively. Overall, this research explored the potential of identifying the fluoroquinolones in feed matrices using THz spectroscopy without a complex pretreatment process and then quantitatively detecting the fluoroquinolones content in feed matrices. The results demonstrate that THz spectra could be used to identify fluoroquinolones in feed matrices and also detect their content quantitatively, which has great significance for the food safety industry.
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He HJ, Sun DW. Microbial evaluation of raw and processed food products by Visible/Infrared, Raman and Fluorescence spectroscopy. Trends Food Sci Technol 2015. [DOI: 10.1016/j.tifs.2015.10.004] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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