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Matenda RT, Rip D, Marais J, Williams PJ. Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124261. [PMID: 38608560 DOI: 10.1016/j.saa.2024.124261] [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: 09/28/2023] [Revised: 04/04/2024] [Accepted: 04/06/2024] [Indexed: 04/14/2024]
Abstract
Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.
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Affiliation(s)
- Rumbidzai T Matenda
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Diane Rip
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Jeannine Marais
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
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Liu Z, Li Y. Fungi Classification in Various Growth Stages Using Shortwave Infrared (SWIR) Spectroscopy and Machine Learning. J Fungi (Basel) 2022; 8:jof8090978. [PMID: 36135703 PMCID: PMC9501579 DOI: 10.3390/jof8090978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/07/2022] [Accepted: 09/17/2022] [Indexed: 11/23/2022] Open
Abstract
Dark septate endophytes (DSEs) fungi are beneficial to host plants with regard to abiotic stress. Here, we examined the capability of SWIR spectroscopy to classify fungus types and detected the growth stages of DSEs fungi in a timely, non-destructive and time-saving manner. The SWIR spectral data of five DSEs fungi in six growth stages were collected, and three pre-processing methods and sensitivity analysis (SA) variable selection methods were performed using a machine learning model. The results showed that the De-trending + first Derivative (DET_FST) processing spectra combined with the support vector machine (SVM) model yielded the best classification accuracy for fungi classification at different growth stages and growth stage detection on different fungus types. The mean accuracy of generic model for fungi classification and growth stage detection are 0.92 and 0.99 on the calibration set, respectively. Seven important bands, 1164, 1456, 2081, 2272, 2278, 2448 and 2481 nm, were found to be related to the SVM fungi classification. This study provides a rapid and efficient method for the classification of fungi in different growth stages and the detection of fungi growth stage of various types of fungi and could serve as a tool for fungi study.
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Green and sustainable technologies for the decontamination of fungi and mycotoxins in rice: A review. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.04.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Application of near-infrared hyperspectral (NIR) images combined with multivariate image analysis in the differentiation of two mycotoxicogenic Fusarium species associated with maize. Food Chem 2020; 344:128615. [PMID: 33223289 DOI: 10.1016/j.foodchem.2020.128615] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 11/06/2020] [Accepted: 11/08/2020] [Indexed: 01/06/2023]
Abstract
Maize (Zea mays L.) is one of the most versatile crops worldwide with high socioeconomic relevance. However, mycotoxins produced by pathogenic fungi are of constant concern in maize production, as they pose serious risks to human and animal health. Thus, the search for rapid detection and/or identification methods for mycotoxins and mycotoxin-producing fungi for application in food safety remain important. In this work, we implemented use of near infrared hyperspectral images (HSI-NIR) combined with pattern recognition analysis, partial-least-squares discriminant analysis (PLS-DA) of images, to develop a rapid method for identification of Fusarium verticillioides and F. graminearum. Validation of the HSI-NIR method and subsequent analysis was realized using 15 Fusarium spp. isolates. The method was efficient as a rapid, non-invasive, and non-destructive assessment was achieved with 100% accuracy, sensitivity, and specificity for both fungi.
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Yuan D, Jiang J, Qiao X, Qi X, Wang W. An application to analyzing and correcting for the effects of irregular topographies on NIR hyperspectral images to improve identification of moldy peanuts. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.109915] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Žuntar I, Putnik P, Bursać Kovačević D, Nutrizio M, Šupljika F, Poljanec A, Dubrović I, Barba FJ, Režek Jambrak A. Phenolic and Antioxidant Analysis of Olive Leaves Extracts ( Olea europaea L.) Obtained by High Voltage Electrical Discharges (HVED). Foods 2019; 8:foods8070248. [PMID: 31288471 PMCID: PMC6678916 DOI: 10.3390/foods8070248] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 07/04/2019] [Accepted: 07/05/2019] [Indexed: 02/08/2023] Open
Abstract
Background: The aim of this study was to evaluate high voltage electrical discharges (HVED) as a green technology, in order to establish the effectiveness of phenolic extraction from olive leaves against conventional extraction (CE). HVED parameters included different green solvents (water, ethanol), treatment times (3 and 9 min), gases (nitrogen, argon), and voltages (15, 20, 25 kV). Methods: Phenolic compounds were characterized by ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS), while antioxidant potency (total phenolic content and antioxidant capacity) were monitored spectrophotometrically. Data for Near infrared spectroscopy (NIR) spectroscopy, colorimetry, zeta potential, particle size, and conductivity were also reported. Results: The highest yield of phenolic compounds was obtained for the sample treated with argon/9 min/20 kV/50% (3.2 times higher as compared to CE). Obtained results suggested the usage of HVED technology in simultaneous extraction and nanoformulation, and production of stable emulsion systems. Antioxidant capacity (AOC) of obtained extracts showed no significant difference upon the HVED treatment. Conclusions: Ethanol with HVED destroys the linkage between phenolic compounds and components of the plant material to which they are bound. All extracts were compliant with legal requirements regarding content of contaminants, pesticide residues and toxic metals. In conclusion, HVED presents an excellent potential for phenolic compounds extraction for further use in functional food manufacturing.
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Affiliation(s)
- Irena Žuntar
- Faculty of Pharmacy and Biochemistry, University of Zagreb, 10000 Zagreb, Croatia
| | - Predrag Putnik
- Faculty of Food Technology and Biotechnology, University of Zagreb, 10000 Zagreb, Croatia
| | | | - Marinela Nutrizio
- Faculty of Food Technology and Biotechnology, University of Zagreb, 10000 Zagreb, Croatia
| | - Filip Šupljika
- Faculty of Food Technology and Biotechnology, University of Zagreb, 10000 Zagreb, Croatia
| | - Andreja Poljanec
- Faculty of Food Technology and Biotechnology, University of Zagreb, 10000 Zagreb, Croatia
| | - Igor Dubrović
- Teaching Institute for Public health of Primorje-Gorski Kotar County, 51000 Rijeka, Croatia
| | - Francisco J Barba
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vicent Andrés Estellés, s/n, Burjassot, 46100 València, Spain
| | - Anet Režek Jambrak
- Faculty of Food Technology and Biotechnology, University of Zagreb, 10000 Zagreb, Croatia.
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Williams PJ, Bezuidenhout C, Rose LJ. Differentiation of Maize Ear Rot Pathogens, on Growth Media, with Near Infrared Hyperspectral Imaging. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01490-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Zhou RQ, Jin JJ, Li QM, Su ZZ, Yu XJ, Tang Y, Luo SM, He Y, Li XL. Early Detection of Magnaporthe oryzae-Infected Barley Leaves and Lesion Visualization Based on Hyperspectral Imaging. FRONTIERS IN PLANT SCIENCE 2019; 9:1962. [PMID: 30697221 PMCID: PMC6341029 DOI: 10.3389/fpls.2018.01962] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 12/18/2018] [Indexed: 05/24/2023]
Abstract
Early detection of foliar diseases is vital to the management of plant disease, since these pathogens hinder crop productivity worldwide. This research applied hyperspectral imaging (HSI) technology to early detection of Magnaporthe oryzae-infected barley leaves at four consecutive infection periods. The averaged spectra were used to identify the infection periods of the samples. Additionally, principal component analysis (PCA), spectral unmixing analysis and spectral angle mapping (SAM) were adopted to locate the lesion sites. The results indicated that linear discriminant analysis (LDA) coupled with competitive adaptive reweighted sampling (CARS) achieved over 98% classification accuracy and successfully identified the infected samples 24 h after inoculation. Importantly, spectral unmixing analysis was able to reveal the lesion regions within 24 h after inoculation, and the resulting visualization of host-pathogen interactions was interpretable. Therefore, HSI combined with analysis by those methods would be a promising tool for both early infection period identification and lesion visualization, which would greatly improve plant disease management.
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Affiliation(s)
- Rui-Qing Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Juan-Juan Jin
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Qing-Mian Li
- Zhejiang Machinery Industry Information Institute, Hangzhou, China
| | - Zhen-Zhu Su
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Xin-Jie Yu
- Ningbo Institute of Technology, Zhejiang University, Ningbo, China
| | - Yu Tang
- College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Shao-Ming Luo
- College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Xiao-Li Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
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Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8040513] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Wang K, Pu H, Sun DW. Emerging Spectroscopic and Spectral Imaging Techniques for the Rapid Detection of Microorganisms: An Overview. Compr Rev Food Sci Food Saf 2018; 17:256-273. [DOI: 10.1111/1541-4337.12323] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 11/01/2017] [Accepted: 11/02/2017] [Indexed: 02/04/2023]
Affiliation(s)
- Kaiqiang Wang
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Acad. of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
| | - Hongbin Pu
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Acad. of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
| | - Da-Wen Sun
- School of Food Science and Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Acad. of Contemporary Food Engineering, South China Univ. of Technology; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods; Guangzhou Higher Education Mega Center; Guangzhou 510006 China
- Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, Univ. College Dublin; National Univ. of Ireland; Belfield Dublin 4 Ireland
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Jambrak AR, Šimunek M, Petrović M, Bedić H, Herceg Z, Juretić H. Aromatic profile and sensory characterisation of ultrasound treated cranberry juice and nectar. ULTRASONICS SONOCHEMISTRY 2017; 38:783-793. [PMID: 28012791 DOI: 10.1016/j.ultsonch.2016.11.027] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Revised: 11/22/2016] [Accepted: 11/22/2016] [Indexed: 06/06/2023]
Abstract
Ultrasonication is a nonthermal food processing technology that is used in several applications (extraction, pretreatment before drying, freezing, inactivation of microorganisms etc.). The objective of this study was to investigate the effect of high power ultrasound and pasteurisation on the aroma profile and sensory properties of cranberry juice and nectar. Samples were treated according to the experimental design, with high power sonicator at ultrasound frequency of 20kHz under various conditions (treatment time 3, 6 and 9min, sample temperature: 20, 40 and 60°C and amplitude 60, 90 and 120μm). The aromatic profiles of juices showed that, compared to the untreated samples of juices and nectars, the ultrasonic treatment led to the formation of new compounds or to the disappearance of compounds that were found in the untreated samples. Samples treated at the highest amplitude (120μm) were used for evaluation and comparison with untreated and pasteurised samples using electronic tongue study. Principle component analysis (PCA) confirmed the results of electronic tongue study, which showed that the ultrasound-treated and pasteurised juices had different scores compared to the untreated samples. Sensory evaluation showed that ultrasonically treated and pasteurised juices received lower scores in comparison with the untreated samples.
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Affiliation(s)
- Anet Režek Jambrak
- Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.
| | | | - Marinko Petrović
- Andrija Štampar Teaching Institute of Public Health, Mirogojska 16, 10000 Zagreb, Croatia
| | - Helena Bedić
- Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia
| | - Zoran Herceg
- Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia
| | - Hrvoje Juretić
- Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića 5, 10000 Zagreb, Croatia
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Kammies TL, Manley M, Gouws PA, Williams PJ. Differentiation of foodborne bacteria using NIR hyperspectral imaging and multivariate data analysis. Appl Microbiol Biotechnol 2016; 100:9305-9320. [DOI: 10.1007/s00253-016-7801-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Revised: 06/18/2016] [Accepted: 08/09/2016] [Indexed: 10/21/2022]
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14
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The Application of Vibrational Spectroscopy Techniques in the Qualitative Assessment of Material Traded as Ginseng. Molecules 2016; 21:472. [PMID: 27077839 PMCID: PMC6273312 DOI: 10.3390/molecules21040472] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 02/10/2016] [Accepted: 04/01/2016] [Indexed: 12/03/2022] Open
Abstract
The name “ginseng” is collectively used to describe several plant species, including Panax ginseng (Asian/Oriental ginseng), P. quinquefolius (American ginseng), P. pseudoginseng (Pseudoginseng) and Eleutherococcus senticosus (Siberian ginseng), each with different applications in traditional medicine practices. The use of a generic name may lead to the interchangeable use or substitution of raw materials which poses quality control challenges. Quality control methods such as vibrational spectroscopy-based techniques are here proposed as fast, non-destructive methods for the distinction of four ginseng species and the identification of raw materials in commercial ginseng products. Certified ginseng reference material and commercial products were analysed using hyperspectral imaging (HSI), mid-infrared (MIR) and near-infrared (NIR) spectroscopy. Principal component analysis (PCA) and (orthogonal) partial least squares discriminant analysis models (OPLS-DA) were developed using multivariate analysis software. UHPLC-MS was used to analyse methanol extracts of the reference raw materials and commercial products. The holistic analysis of ginseng raw materials revealed distinct chemical differences using HSI, MIR and NIR. For all methods, Eleutherococcussenticosus displayed the greatest variation from the three Panax species that displayed closer chemical similarity. Good discrimination models with high R2X and Q2 cum vales were developed. These models predicted that the majority of products contained either /P. ginseng or P. quinquefolius. Vibrational spectroscopy and HSI techniques in tandem with multivariate data analysis tools provide useful alternative methods in the authentication of ginseng raw materials and commercial products in a fast, easy, cost-effective and non-destructive manner.
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15
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Associations of Leaf Spectra with Genetic and Phylogenetic Variation in Oaks: Prospects for Remote Detection of Biodiversity. REMOTE SENSING 2016. [DOI: 10.3390/rs8030221] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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16
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Gredilla A, Fdez-Ortiz de Vallejuelo S, Elejoste N, de Diego A, Madariaga JM. Non-destructive Spectroscopy combined with chemometrics as a tool for Green Chemical Analysis of environmental samples: A review. Trends Analyt Chem 2016. [DOI: 10.1016/j.trac.2015.11.011] [Citation(s) in RCA: 82] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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Automatic Counting and Classification of Bacterial Colonies Using Hyperspectral Imaging. FOOD BIOPROCESS TECH 2015. [DOI: 10.1007/s11947-015-1555-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Keles H, Naylor A, Clegg F, Sammon C. The application of non-linear curve fitting routines to the analysis of mid-infrared images obtained from single polymeric microparticles. Analyst 2015; 139:2355-69. [PMID: 24665462 DOI: 10.1039/c3an01879b] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
For the first time, we report a series of time resolved images of a single PLGA microparticle undergoing hydrolysis at 70 °C that have been obtained using attenuated total reflectance-Fourier transform infrared spectroscopic (ATR-FTIR) imaging. A novel partially supervised non-linear curve fitting (NLCF) tool was developed to identify and fit peaks to the infrared spectrum obtained from each pixel within the 64 × 64 array. The output from the NLCF was evaluated by comparison with a traditional peak height (PH) data analysis approach and multivariate curve resolution alternating least squares (MCR-ALS) analysis for the same images, in order to understand the limitations and advantages of the NLCF methodology. The NLCF method was shown to facilitate consistent spatial resolution enhancement as defined using the step-edge approach on dry microparticle images when compared to images derived from both PH measurements and MCR-ALS. The NLCF method was shown to improve both the S/N and sharpness of images obtained during an evolving experiment, providing a better insight into the magnitude of hydration layers and particle dimension changes during hydrolysis. The NLCF approach facilitated the calculation of hydrolysis rate constants for both the glycolic (kG) and lactic (kL) acid segments of the PLGA copolymer. This represents a real advantage over MCR-ALS which could not distinguish between the two segments due to colinearity within the data. The NLCF approach made it possible to calculate the hydrolysis rate constants from a single pixel, unlike the peak height data analysis approach which suffered from poor S/N at each pixel. These findings show the potential value of applying NLCF to the study of real-time chemical processes at the micron scale, assisting in the understanding of the mechanisms of chemical processes that occur within microparticles and enhancing the value of the mid-IR ATR analysis.
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Affiliation(s)
- Hakan Keles
- Sheffield Hallam University, Materials and Engineering Research Institute, Sheffield, S1 1WB, UK.
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Recent applications of hyperspectral imaging in microbiology. Talanta 2015; 137:43-54. [DOI: 10.1016/j.talanta.2015.01.012] [Citation(s) in RCA: 96] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2014] [Revised: 01/05/2015] [Accepted: 01/09/2015] [Indexed: 11/19/2022]
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Min H, Cho BK. Spectroscopic Techniques for Nondestructive Detection of Fungi and Mycotoxins in Agricultural Materials: A Review. ACTA ACUST UNITED AC 2015. [DOI: 10.5307/jbe.2015.40.1.067] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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McMullin D, Mizaikoff B, Krska R. Advancements in IR spectroscopic approaches for the determination of fungal derived contaminations in food crops. Anal Bioanal Chem 2015; 407:653-60. [PMID: 25258282 PMCID: PMC4305099 DOI: 10.1007/s00216-014-8145-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2014] [Revised: 08/25/2014] [Accepted: 08/28/2014] [Indexed: 11/19/2022]
Abstract
Infrared spectroscopy is a rapid, nondestructive analytical technique that can be applied to the authentication and characterization of food samples in high throughput. In particular, near infrared spectroscopy is commonly utilized in the food quality control industry to monitor the physical attributes of numerous cereal grains for protein, carbohydrate, and lipid content. IR-based methods require little sample preparation, labor, or technical competence if multivariate data mining techniques are implemented; however, they do require extensive calibration. Economically important crops are infected by fungi that can severely reduce crop yields and quality and, in addition, produce mycotoxins. Owing to the health risks associated with mycotoxins in the food chain, regulatory limits have been set by both national and international institutions for specific mycotoxins and mycotoxin classes. This article discusses the progress and potential of IR-based methods as an alternative to existing chemical methods for the determination of fungal contamination in crops, as well as emerging spectroscopic methods.
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Affiliation(s)
- David McMullin
- Center for Analytical Chemistry, Department for Agrobiotechnology, University of Natural Resources and Applied Life Sciences Vienna, Konrad-Lorenz-Straße 20, 3430 Tulln, Austria
| | - Boris Mizaikoff
- Institute of Analytical and Bioanalytical Chemistry, University of Ulm, Albert-Einstein-Allee 11, 89075 Ulm, Germany
| | - Rudolf Krska
- Center for Analytical Chemistry, Department for Agrobiotechnology, University of Natural Resources and Applied Life Sciences Vienna, Konrad-Lorenz-Straße 20, 3430 Tulln, Austria
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Jiang X, Yang Z, Han L. A Markov random field based approach to the identification of meat and bone meal in feed by near-infrared spectroscopic imaging. Anal Bioanal Chem 2014; 406:4705-14. [DOI: 10.1007/s00216-014-7854-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2014] [Revised: 03/14/2014] [Accepted: 04/24/2014] [Indexed: 10/25/2022]
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Fox G, Manley M. Applications of single kernel conventional and hyperspectral imaging near infrared spectroscopy in cereals. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2014; 94:174-9. [PMID: 24038031 DOI: 10.1002/jsfa.6367] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2013] [Revised: 08/14/2013] [Accepted: 08/22/2013] [Indexed: 05/08/2023]
Abstract
Single kernel (SK) near infrared (NIR) reflectance and transmittance technologies have been developed during the last two decades for a range of cereal grain physical quality and chemical traits as well as detecting and predicting levels of toxins produced by fungi. Challenges during the development of single kernel near infrared (SK-NIR) spectroscopy applications are modifications of existing NIR technology to present single kernels for scanning as well as modifying reference methods for the trait of interest. Numerous applications have been developed, and cover almost all cereals although most have been for key traits including moisture, protein, starch and oil in the globally important food grains, i.e. maize, wheat, rice and barley. An additional benefit in developing SK-NIR applications has been to demonstrate the value in sorting grain infected with a fungus or mycotoxins such as deoxynivalenol, fumonisins and aflatoxins. However, there is still a need to develop cost-effective technologies for high-speed sorting which can be used for small grain samples such as those from breeding programmes or commercial sorting; capable of sorting tonnes per hour. Development of SK-NIR technologies also includes standardisation of SK reference methods to analyse single kernels. For protein content, the use of the Dumas method would require minimal standardisation; for starch or oil content, considerable development would be required. SK-NIR, including the use of hyperspectral imaging, will improve our understanding of grain quality and the inherent variation in the range of a trait. In the area of food safety, this technology will benefit farmers, industry and consumers if it enables contaminated grain to be removed from the human food chain.
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Affiliation(s)
- Glen Fox
- Queensland Alliance for Agriculture & Food Innovation, Centre for Nutrition & Food Science, The University of Queensland, P.O. Box 2282, Toowoomba, Qld, 4350, Australia; Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, (Stellenbosch), 7602, South Africa
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Cheng JH, Sun DW, Zeng XA, Pu HB. Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging. INNOV FOOD SCI EMERG 2014. [DOI: 10.1016/j.ifset.2013.10.013] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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25
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Foca G, Salvo D, Cino A, Ferrari C, Lo Fiego DP, Minelli G, Ulrici A. Classification of pig fat samples from different subcutaneous layers by means of fast and non-destructive analytical techniques. Food Res Int 2013. [DOI: 10.1016/j.foodres.2013.03.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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26
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Growth characteristics of three Fusarium species evaluated by near-infrared hyperspectral imaging and multivariate image analysis. Appl Microbiol Biotechnol 2012; 96:803-13. [DOI: 10.1007/s00253-012-4380-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2012] [Revised: 08/10/2012] [Accepted: 08/13/2012] [Indexed: 10/27/2022]
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